diff --git a/Source Code/1. BM Constraint Example.ipynb b/Source Code/1. BM Constraint Example.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..adfad4d5e9d7a1ebf2ae4b8cf462b7975f27b446 --- /dev/null +++ b/Source Code/1. BM Constraint Example.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOPIZihqPUIJGu63F2Cqqh6"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vDOZ4yhQ785H","executionInfo":{"status":"ok","timestamp":1734494682014,"user_tz":-420,"elapsed":611,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"37fa8875-565a-44a2-bafc-2097f0128538"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 101 cocok dengan bundle: [1]\n","User 102 cocok dengan bundle: [1, 2, 3, 4]\n"]}],"source":["import ast # Untuk parsing string seperti list di CSV\n","\n","# Dataset bundle (contoh)\n","bundles = [\n"," {\"Bundle_ID\": 1, \"Item_IDs\": [1, 2], \"Total_Price\": 65, \"Average_Rating\": 4.15, \"Categories\": [\"Electronics\", \"Books\"]},\n"," {\"Bundle_ID\": 2, \"Item_IDs\": [3, 4], \"Total_Price\": 100, \"Average_Rating\": 4.5, \"Categories\": [\"Clothing\", \"Electronics\"]},\n"," {\"Bundle_ID\": 3, \"Item_IDs\": [2, 5], \"Total_Price\": 25, \"Average_Rating\": 3.9, \"Categories\": [\"Books\", \"Books\"]},\n"," {\"Bundle_ID\": 4, \"Item_IDs\": [1, 3, 5], \"Total_Price\": 90, \"Average_Rating\": 4.2, \"Categories\": [\"Electronics\", \"Clothing\", \"Books\"]},\n","]\n","\n","# Dataset user (contoh)\n","users = [\n"," {\"User_ID\": 101, \"Preferred_Budget\": 80, \"Minimum_Rating\": 4.0, \"Preferred_Categories\": [\"Electronics\", \"Books\"]},\n"," {\"User_ID\": 102, \"Preferred_Budget\": 120, \"Minimum_Rating\": 3.8, \"Preferred_Categories\": [\"Clothing\", \"Books\"]},\n","]\n","\n","# Fungsi untuk mencocokkan constraints\n","def match_bundles(user, bundles):\n"," matched_bundles = []\n"," for bundle in bundles:\n"," # Cek constraints\n"," within_budget = bundle[\"Total_Price\"] <= user[\"Preferred_Budget\"]\n"," meets_rating = bundle[\"Average_Rating\"] >= user[\"Minimum_Rating\"]\n"," matches_category = any(cat in user[\"Preferred_Categories\"] for cat in bundle[\"Categories\"])\n","\n"," if within_budget and meets_rating and matches_category:\n"," matched_bundles.append(bundle[\"Bundle_ID\"])\n","\n"," return matched_bundles\n","\n","# Matching untuk setiap user\n","for user in users:\n"," matched = match_bundles(user, bundles)\n"," print(f\"User {user['User_ID']} cocok dengan bundle: {matched}\")"]}]} \ No newline at end of file diff --git a/Source Code/2. Preview Data.ipynb b/Source Code/2. Preview Data.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..770e362a36a48bb16546368aa6a89a0571dfaa82 --- /dev/null +++ b/Source Code/2. Preview Data.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1-JP87V3k7Nkh2fwsit6eOTMcKcsQVTgb","authorship_tag":"ABX9TyPHsc4nFyZrhbr3OHU2jX2b"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## User Tags and Specs Data"],"metadata":{"id":"2d9sa3NhKcQq"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O-nesBDAFiw0","executionInfo":{"status":"ok","timestamp":1735285832271,"user_tz":-420,"elapsed":21449,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d6cdb02a-037f-4bc2-bcb6-ca4588e6ae01"},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["for user_id, user_bundle in data.items():\n"," print(f\"User ID: {user_id}\")\n"," print(f\"Bundle: {user_bundle}\")\n"," break"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vbZRtIvtGLfj","executionInfo":{"status":"ok","timestamp":1735285918488,"user_tz":-420,"elapsed":349,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"fef24705-e54b-4774-e5f0-96cad0abe3f6"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["User ID: 76561197970982479\n","Bundle: {'232': {'frequent_distinct_tags': {'Fighting': 0, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 0, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 1, '2D': 1, 'Colorful': 0, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 0, 'Dystopian': 1, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 1, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 0, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 1, 'Destruction': 0, 'Magic': 0, 'Funny': 1, 'Assassin': 0, 'Parkour': 0, 'Anime': 0, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 1, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 0, 'Team-Based': 1, 'Gambling': 0, 'Fantasy': 1, 'RTS': 0, 'Loot': 0, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 0, 'CRPG': 0, 'Short': 1, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 1, 'Walking Simulator': 0, 'Space': 1, 'Local Multiplayer': 0, 'Strategy': 1, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 1, 'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 0, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 1, 'Simulation': 1, 'PvP': 0, 'Nudity': 0, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 1, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 0, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 1, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 1, \"Shoot 'Em Up\": 0, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 1, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 1, 'Fast-Paced': 0, 'Classic': 1, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}, 'frequent_distinct_specs': {'Steam Workshop': 0, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}, 'label': 1}, '383': {'frequent_distinct_tags': {'Fighting': 0, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 0, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 1, '2D': 1, 'Colorful': 0, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 0, 'Dystopian': 1, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 1, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 0, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 1, 'Destruction': 0, 'Magic': 0, 'Funny': 1, 'Assassin': 0, 'Parkour': 0, 'Anime': 0, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 1, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 0, 'Team-Based': 1, 'Gambling': 0, 'Fantasy': 1, 'RTS': 0, 'Loot': 0, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 0, 'CRPG': 0, 'Short': 1, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 1, 'Walking Simulator': 0, 'Space': 1, 'Local Multiplayer': 0, 'Strategy': 1, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 1, 'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 0, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 1, 'Simulation': 1, 'PvP': 0, 'Nudity': 0, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 1, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 0, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 1, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 1, \"Shoot 'Em Up\": 0, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 1, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 1, 'Fast-Paced': 0, 'Classic': 1, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}, 'frequent_distinct_specs': {'Steam Workshop': 0, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}, 'label': 0}, '231': {'frequent_distinct_tags': {'Fighting': 0, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 0, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 1, '2D': 1, 'Colorful': 0, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 0, 'Dystopian': 1, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 1, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 0, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 1, 'Destruction': 0, 'Magic': 0, 'Funny': 1, 'Assassin': 0, 'Parkour': 0, 'Anime': 0, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 1, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 0, 'Team-Based': 1, 'Gambling': 0, 'Fantasy': 1, 'RTS': 0, 'Loot': 0, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 0, 'CRPG': 0, 'Short': 1, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 1, 'Walking Simulator': 0, 'Space': 1, 'Local Multiplayer': 0, 'Strategy': 1, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 1, 'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 0, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 1, 'Simulation': 1, 'PvP': 0, 'Nudity': 0, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 1, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 0, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 1, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 1, \"Shoot 'Em Up\": 0, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 1, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 1, 'Fast-Paced': 0, 'Classic': 1, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}, 'frequent_distinct_specs': {'Steam Workshop': 0, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}, 'label': 1}, '362': {'frequent_distinct_tags': {'Fighting': 0, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 0, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 1, '2D': 1, 'Colorful': 0, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 0, 'Dystopian': 1, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 1, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 0, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 1, 'Destruction': 0, 'Magic': 0, 'Funny': 1, 'Assassin': 0, 'Parkour': 0, 'Anime': 0, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 1, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 0, 'Team-Based': 1, 'Gambling': 0, 'Fantasy': 1, 'RTS': 0, 'Loot': 0, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 0, 'CRPG': 0, 'Short': 1, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 1, 'Walking Simulator': 0, 'Space': 1, 'Local Multiplayer': 0, 'Strategy': 1, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 1, 'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 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'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 0, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 1, 'Simulation': 1, 'PvP': 0, 'Nudity': 0, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 1, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 0, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 1, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 1, \"Shoot 'Em Up\": 0, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 1, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 1, 'Fast-Paced': 0, 'Classic': 1, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}, 'frequent_distinct_specs': {'Steam Workshop': 0, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}, 'label': 0}, '448': {'frequent_distinct_tags': {'Fighting': 0, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 0, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 1, '2D': 1, 'Colorful': 0, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 0, 'Dystopian': 1, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 1, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 0, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 1, 'Destruction': 0, 'Magic': 0, 'Funny': 1, 'Assassin': 0, 'Parkour': 0, 'Anime': 0, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 1, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 0, 'Team-Based': 1, 'Gambling': 0, 'Fantasy': 1, 'RTS': 0, 'Loot': 0, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 0, 'CRPG': 0, 'Short': 1, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 1, 'Walking Simulator': 0, 'Space': 1, 'Local Multiplayer': 0, 'Strategy': 1, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 1, 'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 0, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 1, 'Simulation': 1, 'PvP': 0, 'Nudity': 0, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 1, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 0, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 1, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 1, \"Shoot 'Em Up\": 0, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 1, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 1, 'Fast-Paced': 0, 'Classic': 1, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}, 'frequent_distinct_specs': {'Steam Workshop': 0, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}, 'label': 1}}\n"]}]},{"cell_type":"code","source":["user_bundle[\"76561197970982479\"]['']"],"metadata":{"id":"YSHNRqSjJZd8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt:\n","\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import classification_report\n","import numpy as np\n","\n","# Assuming 'new_bundle_recommendations' is defined as in the previous code\n","\n","# Prepare the data for SVM\n","dataset = { \"features\": [], \"labels\": [] }\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," print(recommendation['frequent_distinct_tags'].values())\n"," print(recommendation['frequent_distinct_specs'].values())\n"," features = list(recommendation['frequent_distinct_tags'].values()) + list(recommendation['frequent_distinct_specs'].values())\n"," label = recommendation['label']\n"," dataset[\"features\"].append(features)\n"," dataset[\"labels\"].append(label)\n"," print(recommendation)\n"," break\n"," break\n","\n","# X = np.array(data[\"features\"])\n","# y = np.array(data[\"labels\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tEGsBHRxHUlC","executionInfo":{"status":"ok","timestamp":1735286804255,"user_tz":-420,"elapsed":305,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"91b02a06-0933-403d-c1b0-cdb5d7b91adb"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["dict_values([0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0])\n","dict_values([0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1])\n","{'frequent_distinct_tags': {'Fighting': 0, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 0, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 1, '2D': 1, 'Colorful': 0, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 0, 'Dystopian': 1, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 1, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 0, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 1, 'Destruction': 0, 'Magic': 0, 'Funny': 1, 'Assassin': 0, 'Parkour': 0, 'Anime': 0, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 1, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 0, 'Team-Based': 1, 'Gambling': 0, 'Fantasy': 1, 'RTS': 0, 'Loot': 0, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 0, 'CRPG': 0, 'Short': 1, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 1, 'Walking Simulator': 0, 'Space': 1, 'Local Multiplayer': 0, 'Strategy': 1, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 1, 'Turn-Based': 0, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 0, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 1, 'Simulation': 1, 'PvP': 0, 'Nudity': 0, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 1, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 0, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 1, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 1, \"Shoot 'Em Up\": 0, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 1, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 1, 'Fast-Paced': 0, 'Classic': 1, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}, 'frequent_distinct_specs': {'Steam Workshop': 0, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}, 'label': 1}\n"]}]},{"cell_type":"code","source":["# prompt:\n","\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import classification_report\n","import numpy as np\n","\n","# Assuming 'new_bundle_recommendations' is defined as in the previous code\n","\n","# Prepare the data for SVM\n","dataset = { \"features\": [], \"labels\": [] }\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," features = list(recommendation['frequent_distinct_tags'].values()) + list(recommendation['frequent_distinct_specs'].values())\n"," label = recommendation['label']\n"," dataset[\"features\"].append(features)\n"," dataset[\"labels\"].append(label)\n","\n","X = np.array(dataset[\"features\"])\n","y = np.array(dataset[\"labels\"])"],"metadata":{"id":"fXNujLDAJ5e3","executionInfo":{"status":"ok","timestamp":1735286857441,"user_tz":-420,"elapsed":4767,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy', X)\n","np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features_v2.npy', X)\n","print(\"X saved successfully!\")\n","\n","# np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy', y)\n","np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_labels_v2.npy', y)\n","print(\"y saved successfully!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6Yr8VPzFKDy5","executionInfo":{"status":"ok","timestamp":1735286914086,"user_tz":-420,"elapsed":3362,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0e593265-c200-4650-c76e-14d3035b39e1"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["X saved successfully!\n","y saved successfully!\n"]}]},{"cell_type":"code","source":["# Calculate and print the distribution of feature vector lengths\n","lengths = [len(feature_vector) for feature_vector in X]\n","print(\"Distribution of feature vector lengths:\")\n","print(np.unique(lengths, return_counts=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dMnZEhE2L-yd","executionInfo":{"status":"ok","timestamp":1735287455486,"user_tz":-420,"elapsed":309,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"387c2139-5546-4c25-e91d-b977261fd07e"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Distribution of feature vector lengths:\n","(array([225]), array([183958]))\n"]}]},{"cell_type":"markdown","source":["## User Price Patten"],"metadata":{"id":"3SumhZmIKgOQ"}},{"cell_type":"code","source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_result_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_WhJFFWoKjFJ","executionInfo":{"status":"ok","timestamp":1735287124667,"user_tz":-420,"elapsed":38358,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"da4b5fdb-d3b4-40f3-d877-8059b61060b0"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Error decoding JSON: Expecting property name enclosed in double quotes: line 73220572 column 7 (char 2083358412)\n"]}]},{"cell_type":"code","source":["for user_id, user_bundle in data.items():\n"," print(f\"User ID: {user_id}\")\n"," print(f\"Bundle: {user_bundle}\")\n"," break"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3S--F39yKs9u","executionInfo":{"status":"ok","timestamp":1735287129267,"user_tz":-420,"elapsed":390,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"84362455-2087-4155-9122-9336072d6c53"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["User ID: 76561197970982479\n","Bundle: {'avg_price': 14.88830434782609, 'price_ranges': {'0-25': {'start': 0.0, 'end': 9.99, 'count': 109}, '51-75': {'start': 14.99, 'end': 19.99, 'count': 61}, '26-50': {'start': 9.99, 'end': 14.99, 'count': 32}, '76-100': {'start': 19.99, 'end': 59.99, 'count': 28}}}\n"]}]}]} \ No newline at end of file diff --git a/Source Code/2. Process Data.ipynb b/Source Code/2. Process Data.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0a4f114c1d094991c333b634afa70df9a2e79264 --- /dev/null +++ b/Source Code/2. Process Data.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5160,"status":"ok","timestamp":1736139804723,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"AEp-GngME9rE","outputId":"7611629e-2a50-4d0b-8b39-0d7df2e829bd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/114.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.3.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1736139807341,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import json\n","import gzip\n","import ast"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1736139811683,"user_tz":-420,"elapsed":1048,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"]},{"cell_type":"markdown","metadata":{"id":"_T8fPo2P6CFY"},"source":["### Load Data"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7174,"status":"ok","timestamp":1736139822277,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"t_HB8JApYOZr","outputId":"c42891cb-f2bb-4393-af4d-9d5f1c1be5cd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 25799\n","Number of reviews 59305\n","{'user_id': 'LydiaMorley', 'user_url': 'http://steamcommunity.com/id/LydiaMorley', 'reviews': [{'funny': '1 person found this review funny', 'posted': 'Posted July 3.', 'last_edited': '', 'item_id': '273110', 'helpful': '1 of 2 people (50%) found this review helpful', 'recommend': True, 'review': 'had so much fun plaing this and collecting resources xD we won on my first try and killed final boss!'}, {'funny': '', 'posted': 'Posted July 20.', 'last_edited': '', 'item_id': '730', 'helpful': 'No ratings yet', 'recommend': True, 'review': ':D'}, {'funny': '', 'posted': 'Posted July 2.', 'last_edited': '', 'item_id': '440', 'helpful': 'No ratings yet', 'recommend': True, 'review': 'so much fun :D'}]}\n"]}],"source":["# Open the gzipped file\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"reviews\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[0]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"reviews\"] += len(data_dict[\"reviews\"])\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n"," count[\"users\"] += 1\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of reviews {count['reviews']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"FfPC2AIt6HbT"},"source":["#### User Item Interactions"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":41904,"status":"ok","timestamp":1736139864179,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"-N_F4Z2JZVgZ","outputId":"d1876272-80cd-44fe-8543-819c3023bcf7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. Winterbottom', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40990', 'item_name': 'Mafia', 'playtime_forever': 21, 'playtime_2weeks': 0}, {'item_id': '50130', 'item_name': 'Mafia II', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '50310', 'item_name': 'MLB 2K11', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '57900', 'item_name': 'Duke Nukem Forever', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '65950', 'item_name': 'NBA 2K11', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '201020', 'item_name': 'NBA 2K12', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409710', 'item_name': 'BioShock Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409720', 'item_name': 'BioShock 2 Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '67370', 'item_name': 'The Darkness II', 'playtime_forever': 146, 'playtime_2weeks': 0}, {'item_id': '207610', 'item_name': 'The Walking Dead', 'playtime_forever': 457, 'playtime_2weeks': 0}, {'item_id': '97330', 'item_name': 'Magic: The Gathering - Duels of the Planeswalkers 2013', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '65800', 'item_name': 'Dungeon Defenders', 'playtime_forever': 4376, 'playtime_2weeks': 0}, {'item_id': '211420', 'item_name': 'Dark Souls: Prepare to Die Edition', 'playtime_forever': 71, 'playtime_2weeks': 0}, {'item_id': '730', 'item_name': 'Counter-Strike: Global Offensive', 'playtime_forever': 1900, 'playtime_2weeks': 0}, {'item_id': '204300', 'item_name': 'Awesomenauts', 'playtime_forever': 584, 'playtime_2weeks': 0}, {'item_id': '200170', 'item_name': 'Worms Revolution', 'playtime_forever': 423, 'playtime_2weeks': 0}, {'item_id': '217200', 'item_name': 'Worms Armageddon', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '204360', 'item_name': 'Castle Crashers', 'playtime_forever': 614, 'playtime_2weeks': 0}, {'item_id': '49520', 'item_name': 'Borderlands 2', 'playtime_forever': 3393, 'playtime_2weeks': 0}, {'item_id': '216890', 'item_name': 'Blood Bowl: Chaos Edition', 'playtime_forever': 846, 'playtime_2weeks': 0}, {'item_id': '200510', 'item_name': 'XCOM: Enemy Unknown', 'playtime_forever': 715, 'playtime_2weeks': 0}, {'item_id': '4920', 'item_name': 'Natural Selection 2', 'playtime_forever': 10, 'playtime_2weeks': 0}, {'item_id': '220240', 'item_name': 'Far Cry® 3', 'playtime_forever': 1336, 'playtime_2weeks': 0}, {'item_id': '219740', 'item_name': \"Don't Starve\", 'playtime_forever': 4806, 'playtime_2weeks': 0}, {'item_id': '322330', 'item_name': \"Don't Starve Together\", 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '220440', 'item_name': 'DmC Devil May Cry', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '226700', 'item_name': 'Infestation: Survivor Stories Classic', 'playtime_forever': 22, 'playtime_2weeks': 0}, {'item_id': '4540', 'item_name': 'Titan Quest', 'playtime_forever': 40, 'playtime_2weeks': 0}, 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'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = []\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n"," user_item_interactions.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"dpwRuG_JLs1R","executionInfo":{"status":"ok","timestamp":1736139956629,"user_tz":-420,"elapsed":291,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["user_item_interactions_dict = {}\n","for user in user_item_interactions:\n"," user_item_interactions_dict[user[\"user_id\"]] = user"]},{"cell_type":"markdown","metadata":{"id":"i9xjn3xt6KnA"},"source":["#### Bundle Data"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":991,"status":"ok","timestamp":1736139962233,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"lymblLpNcrfu","outputId":"37be6571-65e6-4e5a-d2e3-dc56988b0320"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}],"source":["# Open the gzipped file\n","bundle_data = []\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n"," bundle_data.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"_qeUSVyE4mNL","executionInfo":{"status":"ok","timestamp":1736139964129,"user_tz":-420,"elapsed":306,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["bundle_data_dict = {}\n","for bundle in bundle_data:\n"," bundle_data_dict[bundle[\"bundle_id\"]] = bundle"]},{"cell_type":"markdown","metadata":{"id":"gwgqwSLF6Mdr"},"source":["#### Item Dictionary"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5876,"status":"ok","timestamp":1736139971228,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x1DG03kNdCEm","outputId":"1345cd79-fcc5-4e34-dae5-6b99ac0227ad"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1736139971228,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"jp2NZYvQojiv","outputId":"df1d8be7-d360-42ab-aba9-4dfa59b36389"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'app_name': 'Maze Run VR',\n"," 'sentiment': 'Positive',\n"," 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'],\n"," 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/',\n"," 'price': 4.99,\n"," 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1',\n"," 'id': '681550',\n"," 'early_access': True,\n"," 'specs': ['Single-player',\n"," 'Stats',\n"," 'Steam Leaderboards',\n"," 'HTC Vive',\n"," 'Oculus Rift',\n"," 'Tracked Motion Controllers',\n"," 'Standing',\n"," 'Room-Scale']}"]},"metadata":{},"execution_count":11}],"source":["data_dict"]},{"cell_type":"markdown","metadata":{"id":"9gZBAta66SW4"},"source":["#### Bundle Item IDS"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"MC9PXLOSZAOj","executionInfo":{"status":"ok","timestamp":1736139971701,"user_tz":-420,"elapsed":475,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Give me list of item_id for each bundles with pairs like this {bundle_id: (item_id1, item_id2, ....)} dimana (item_id1, item_id2, ....) tipenya set\n","bundle_item_ids = {}\n","for bundle in bundle_data:\n"," set_item_id = []\n"," for item in bundle[\"items\"]:\n"," set_item_id.append(item[\"item_id\"])\n"," bundle_item_ids[bundle[\"bundle_id\"]] = set(set_item_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1736139864181,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UfDaMcYRaNfM"},"outputs":[],"source":["bundle_item_ids['594']"]},{"cell_type":"markdown","metadata":{"id":"ZPirDD6r6Uqc"},"source":["#### User Item Interactions Item IDS"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"I9xpS5bcahNI","executionInfo":{"status":"ok","timestamp":1736139976130,"user_tz":-420,"elapsed":605,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Give me list of item_id for each user_item_interactions with pairs like this {user_id: (item_id1, item_id2, ....)} dimana (item_id1, item_id2, ....) tipenya set\n","user_item_interactions_item_ids = {}\n","for user_id, user_data in user_item_interactions_dict.items():\n"," user_item_interactions_item_ids[user_id] = set(item[\"item_id\"] for item in user_data[\"items\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1736139864182,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"_DI-wC9Ya3xK"},"outputs":[],"source":["user_item_interactions_item_ids[\"76561198030000787\"]"]},{"cell_type":"markdown","metadata":{"id":"4C0l9VqZ6YO9"},"source":["#### Matching Bundles"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":4808,"status":"ok","timestamp":1736139983015,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"rKHK4Lm8a-1C","outputId":"ec16cb60-2b78-4f19-951b-fd1781d7af9c"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 76561197970982479:\n"," Bundle 1482: ['38720', '38700']\n"," Bundle 1468: ['11450']\n"," Bundle 279: ['303390']\n"," Bundle 355: ['55140']\n"," Bundle 1418: ['3590']\n"," Bundle 803: ['263980']\n"," Bundle 396: ['263980']\n"," Bundle 1350: ['337000']\n"," Bundle 1351: ['337000']\n"," Bundle 1352: ['337000']\n"," Bundle 1100: ['222880']\n"," Bundle 1246: ['24980', '17460']\n"," Bundle 813: ['263980', '295690', '461640']\n"," Bundle 1243: ['24960']\n"," Bundle 951: ['47700']\n"," Bundle 1009: ['24740']\n"," Bundle 1229: ['11450']\n"," Bundle 384: ['35460']\n"," Bundle 1158: ['379720']\n"," Bundle 364: ['295690']\n"," Bundle 362: ['203160', '8190', '6910', '238010']\n"," Bundle 983: ['237930', '107100']\n"," Bundle 575: ['8930']\n"," Bundle 823: ['41070']\n"," Bundle 957: ['316790', '3830', '232790']\n"," Bundle 804: ['295690']\n"," Bundle 398: ['461640']\n"," Bundle 403: ['461640']\n"," Bundle 380: ['257350', '228280']\n"," Bundle 760: ['260230']\n"," Bundle 757: ['242050']\n"," Bundle 383: ['31230', '207610', '261030', '31270', '31240', '250320', '31250', '31220', '31280', '31260']\n"," Bundle 727: ['20920', '20900']\n"," Bundle 379: ['257350', '228280']\n"," Bundle 690: ['35700']\n"," Bundle 712: ['8980', '49520']\n"," Bundle 721: ['35700']\n"," Bundle 572: ['7670', '8870', '8850']\n"," Bundle 574: ['8930']\n"," Bundle 635: ['72850']\n"," Bundle 612: ['1250']\n"," Bundle 616: ['35460', '1250', '232090']\n"," Bundle 139: ['250900']\n"," Bundle 145: ['232790']\n"," Bundle 157: ['41070']\n"," Bundle 231: ['20', '220', '420', '50', '70', '130']\n"," Bundle 232: ['20', '550', '220', '70', '240', '500', '730', '620', '60', '420', '400', '300', '40', '50', '130', '30']\n"," Bundle 233: ['500', '550']\n"," Bundle 234: ['620', '400']\n"," Bundle 235: ['60', '30', '40']\n"," Bundle 236: ['730', '240']\n"," Bundle 237: ['20', '50', '70', '130']\n"," Bundle 240: ['300', '240', '320']\n"," Bundle 250: ['204300']\n"," Bundle 257: ['8980', '49520']\n"," Bundle 292: ['289130']\n"," Bundle 307: ['42910']\n"," Bundle 312: ['322330', '219740']\n"," Bundle 335: ['237990']\n"," Bundle 382: ['222730']\n"," Bundle 448: ['289130']\n"]}],"source":["# prompt: Tolong di cek saya ingin melihat list of user_item_interaction_item_ids dan bundle_item_ids berikan saya intersection untuk kombinasi bundle mana saja yang memiliki item_id yang sama pada user_item_interaction\n","\n","# Find the intersection of bundle_item_ids and user_item_interactions_item_ids\n","matching_bundles = {}\n","for user_id, user_items in user_item_interactions_item_ids.items():\n"," for bundle_id, bundle_items in bundle_item_ids.items():\n"," intersection = user_items.intersection(bundle_items)\n"," if intersection:\n"," if user_id not in matching_bundles:\n"," matching_bundles[user_id] = {}\n"," matching_bundles[user_id][bundle_id] = list(intersection)\n","\n","# Print the results\n","for user_id, bundles in matching_bundles.items():\n"," print(f\"User {user_id}:\")\n"," for bundle_id, common_items in bundles.items():\n"," print(f\" Bundle {bundle_id}: {common_items}\")\n"," break"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":501,"status":"ok","timestamp":1736139983514,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"4ryLWGRucnjo","outputId":"696fb061-aa82-4c76-b8d4-5a1feecfd380"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 76561197970982479:\n"," Bundle 232: ['20', '550', '220', '70', '240', '500', '730', '620', '60', '420', '400', '300', '40', '50', '130', '30']\n"," Bundle 383: ['31230', '207610', '261030', '31270', '31240', '250320', '31250', '31220', '31280', '31260']\n"," Bundle 231: ['20', '220', '420', '50', '70', '130']\n"," Bundle 362: ['203160', '8190', '6910', '238010']\n"," Bundle 237: ['20', '50', '70', '130']\n"," Bundle 813: ['263980', '295690', '461640']\n"," Bundle 957: ['316790', '3830', '232790']\n"," Bundle 572: ['7670', '8870', '8850']\n"," Bundle 616: ['35460', '1250', '232090']\n"," Bundle 235: ['60', '30', '40']\n"," Bundle 240: ['300', '240', '320']\n"," Bundle 1482: ['38720', '38700']\n"," Bundle 1246: ['24980', '17460']\n"," Bundle 983: ['237930', '107100']\n"," Bundle 380: ['257350', '228280']\n"," Bundle 727: ['20920', '20900']\n"," Bundle 379: ['257350', '228280']\n"," Bundle 712: ['8980', '49520']\n"," Bundle 233: ['500', '550']\n"," Bundle 234: ['620', '400']\n"," Bundle 236: ['730', '240']\n"," Bundle 257: ['8980', '49520']\n"," Bundle 312: ['322330', '219740']\n"," Bundle 1468: ['11450']\n"," Bundle 279: ['303390']\n"," Bundle 355: ['55140']\n"," Bundle 1418: ['3590']\n"," Bundle 803: ['263980']\n"," Bundle 396: ['263980']\n"," Bundle 1350: ['337000']\n"," Bundle 1351: ['337000']\n"," Bundle 1352: ['337000']\n"," Bundle 1100: ['222880']\n"," Bundle 1243: ['24960']\n"," Bundle 951: ['47700']\n"," Bundle 1009: ['24740']\n"," Bundle 1229: ['11450']\n"," Bundle 384: ['35460']\n"," Bundle 1158: ['379720']\n"," Bundle 364: ['295690']\n"," Bundle 575: ['8930']\n"," Bundle 823: ['41070']\n"," Bundle 804: ['295690']\n"," Bundle 398: ['461640']\n"," Bundle 403: ['461640']\n"," Bundle 760: ['260230']\n"," Bundle 757: ['242050']\n"," Bundle 690: ['35700']\n"," Bundle 721: ['35700']\n"," Bundle 574: ['8930']\n"," Bundle 635: ['72850']\n"," Bundle 612: ['1250']\n"," Bundle 139: ['250900']\n"," Bundle 145: ['232790']\n"," Bundle 157: ['41070']\n"," Bundle 250: ['204300']\n"," Bundle 292: ['289130']\n"," Bundle 307: ['42910']\n"," Bundle 335: ['237990']\n"," Bundle 382: ['222730']\n"," Bundle 448: ['289130']\n"]}],"source":["# prompt: Sekarang sorting bundle yang memiliki jumlah intersection terbanyak pada matching_bundles untuk setiap user\n","\n","# Sort the bundles based on the number of intersections for each user\n","sorted_matching_bundles = {}\n","for user_id, bundles in matching_bundles.items():\n"," sorted_bundles = dict(sorted(bundles.items(), key=lambda item: len(item[1]), reverse=True))\n"," sorted_matching_bundles[user_id] = sorted_bundles\n","\n","# Print the sorted results\n","for user_id, bundles in sorted_matching_bundles.items():\n"," print(f\"User {user_id}:\")\n"," for bundle_id, common_items in bundles.items():\n"," print(f\" Bundle {bundle_id}: {common_items}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"hwKhM8yzYnsW"},"source":["### Utilities"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"XgcEyRkMYpj-","executionInfo":{"status":"ok","timestamp":1736139983515,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["def get_real_price(price_str):\n"," if \"free\" in f\"{price_str}\".lower() or \"install\" in f\"{price_str}\".lower() or \"use\" in f\"{price_str}\".lower() or \"play\" in f\"{price_str}\".lower() or \"third\" in f\"{price_str}\".lower():\n"," price = 0.0\n"," else:\n"," price = float(price_str)\n"," return price"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"ZhsHNsBjY3jE","executionInfo":{"status":"ok","timestamp":1736139983839,"user_tz":-420,"elapsed":1,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"7npa4n706w4K"},"source":["#### Bundle Count"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"Q162du8fie9w","executionInfo":{"status":"ok","timestamp":1736140031501,"user_tz":-420,"elapsed":638,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Give me function that count how many number of items in each bundle on bundle_item_ids and sorted_matching_bundles for each users\n","\n","def count_bundle_items(bundle_item_ids, sorted_matching_bundles):\n"," \"\"\"\n"," Counts the number of items in each bundle for each user.\n","\n"," Args:\n"," bundle_item_ids (dict): A dictionary where keys are bundle IDs and values are sets of item IDs in the bundle.\n"," sorted_matching_bundles (dict): A dictionary where keys are user IDs and values are dictionaries of matching bundles.\n"," The inner dictionaries have bundle IDs as keys and lists of common item IDs as values.\n","\n"," Returns:\n"," dict: A dictionary where keys are user IDs, and values are dictionaries.\n"," The inner dictionaries have bundle IDs as keys and the number of items in that bundle as values.\n"," \"\"\"\n"," bundle_item_counts = {}\n"," for user_id, bundles in sorted_matching_bundles.items():\n"," bundle_item_counts[user_id] = {}\n"," for bundle_id, common_items in bundles.items():\n"," bundle_item_counts[user_id][bundle_id] = {\n"," 'intersection_item_count': len(common_items),\n"," 'original_item_count': len(bundle_item_ids.get(bundle_id, [])),\n"," 'item_ids': common_items\n"," }\n"," return bundle_item_counts\n","\n","# Example usage (assuming you have bundle_item_ids and sorted_matching_bundles from your previous code):\n","bundle_counts = count_bundle_items(bundle_item_ids, sorted_matching_bundles)"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":282,"status":"ok","timestamp":1736140033827,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"1GhAKkqSkOPF","outputId":"fb4f6757-fc65-409c-b0fc-c0e63b42b646"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 76561197970982479:\n"," Bundle 232: {'intersection_item_count': 16, 'original_item_count': 19, 'item_ids': ['20', '550', '220', '70', '240', '500', '730', '620', '60', '420', '400', '300', '40', '50', '130', '30']} items\n"," Bundle 383: {'intersection_item_count': 10, 'original_item_count': 48, 'item_ids': ['31230', '207610', '261030', '31270', '31240', '250320', '31250', '31220', '31280', '31260']} items\n"," Bundle 231: {'intersection_item_count': 6, 'original_item_count': 8, 'item_ids': ['20', '220', '420', '50', '70', '130']} items\n"," Bundle 362: {'intersection_item_count': 4, 'original_item_count': 89, 'item_ids': ['203160', '8190', '6910', '238010']} items\n"," Bundle 237: {'intersection_item_count': 4, 'original_item_count': 4, 'item_ids': ['20', '50', '70', '130']} items\n"," Bundle 813: {'intersection_item_count': 3, 'original_item_count': 24, 'item_ids': ['263980', '295690', '461640']} items\n"," Bundle 957: {'intersection_item_count': 3, 'original_item_count': 17, 'item_ids': ['316790', '3830', '232790']} items\n"," Bundle 572: {'intersection_item_count': 3, 'original_item_count': 3, 'item_ids': ['7670', '8870', '8850']} items\n"," Bundle 616: {'intersection_item_count': 3, 'original_item_count': 27, 'item_ids': ['35460', '1250', '232090']} items\n"," Bundle 235: {'intersection_item_count': 3, 'original_item_count': 4, 'item_ids': ['60', '30', '40']} items\n"," Bundle 240: {'intersection_item_count': 3, 'original_item_count': 3, 'item_ids': ['300', '240', '320']} items\n"," Bundle 1482: {'intersection_item_count': 2, 'original_item_count': 4, 'item_ids': ['38720', '38700']} items\n"," Bundle 1246: {'intersection_item_count': 2, 'original_item_count': 4, 'item_ids': ['24980', '17460']} items\n"," Bundle 983: {'intersection_item_count': 2, 'original_item_count': 4, 'item_ids': ['237930', '107100']} items\n"," Bundle 380: {'intersection_item_count': 2, 'original_item_count': 8, 'item_ids': ['257350', '228280']} items\n"," Bundle 727: {'intersection_item_count': 2, 'original_item_count': 3, 'item_ids': ['20920', '20900']} items\n"," Bundle 379: {'intersection_item_count': 2, 'original_item_count': 3, 'item_ids': ['257350', '228280']} items\n"," Bundle 712: {'intersection_item_count': 2, 'original_item_count': 8, 'item_ids': ['8980', '49520']} items\n"," Bundle 233: {'intersection_item_count': 2, 'original_item_count': 2, 'item_ids': ['500', '550']} items\n"," Bundle 234: {'intersection_item_count': 2, 'original_item_count': 2, 'item_ids': ['620', '400']} items\n"," Bundle 236: {'intersection_item_count': 2, 'original_item_count': 3, 'item_ids': ['730', '240']} items\n"," Bundle 257: {'intersection_item_count': 2, 'original_item_count': 7, 'item_ids': ['8980', '49520']} items\n"," Bundle 312: {'intersection_item_count': 2, 'original_item_count': 4, 'item_ids': ['322330', '219740']} items\n"," Bundle 1468: {'intersection_item_count': 1, 'original_item_count': 56, 'item_ids': ['11450']} items\n"," Bundle 279: {'intersection_item_count': 1, 'original_item_count': 6, 'item_ids': ['303390']} items\n"," Bundle 355: {'intersection_item_count': 1, 'original_item_count': 36, 'item_ids': ['55140']} items\n"," Bundle 1418: {'intersection_item_count': 1, 'original_item_count': 23, 'item_ids': ['3590']} items\n"," Bundle 803: {'intersection_item_count': 1, 'original_item_count': 9, 'item_ids': ['263980']} items\n"," Bundle 396: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['263980']} items\n"," Bundle 1350: {'intersection_item_count': 1, 'original_item_count': 2, 'item_ids': ['337000']} items\n"," Bundle 1351: {'intersection_item_count': 1, 'original_item_count': 2, 'item_ids': ['337000']} items\n"," Bundle 1352: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['337000']} items\n"," Bundle 1100: {'intersection_item_count': 1, 'original_item_count': 2, 'item_ids': ['222880']} items\n"," Bundle 1243: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['24960']} items\n"," Bundle 951: {'intersection_item_count': 1, 'original_item_count': 5, 'item_ids': ['47700']} items\n"," Bundle 1009: {'intersection_item_count': 1, 'original_item_count': 5, 'item_ids': ['24740']} items\n"," Bundle 1229: {'intersection_item_count': 1, 'original_item_count': 4, 'item_ids': ['11450']} items\n"," Bundle 384: {'intersection_item_count': 1, 'original_item_count': 2, 'item_ids': ['35460']} items\n"," Bundle 1158: {'intersection_item_count': 1, 'original_item_count': 2, 'item_ids': ['379720']} items\n"," Bundle 364: {'intersection_item_count': 1, 'original_item_count': 4, 'item_ids': ['295690']} items\n"," Bundle 575: {'intersection_item_count': 1, 'original_item_count': 16, 'item_ids': ['8930']} items\n"," Bundle 823: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['41070']} items\n"," Bundle 804: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['295690']} items\n"," Bundle 398: {'intersection_item_count': 1, 'original_item_count': 6, 'item_ids': ['461640']} items\n"," Bundle 403: {'intersection_item_count': 1, 'original_item_count': 9, 'item_ids': ['461640']} items\n"," Bundle 760: {'intersection_item_count': 1, 'original_item_count': 7, 'item_ids': ['260230']} items\n"," Bundle 757: {'intersection_item_count': 1, 'original_item_count': 4, 'item_ids': ['242050']} items\n"," Bundle 690: {'intersection_item_count': 1, 'original_item_count': 6, 'item_ids': ['35700']} items\n"," Bundle 721: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['35700']} items\n"," Bundle 574: {'intersection_item_count': 1, 'original_item_count': 5, 'item_ids': ['8930']} items\n"," Bundle 635: {'intersection_item_count': 1, 'original_item_count': 4, 'item_ids': ['72850']} items\n"," Bundle 612: {'intersection_item_count': 1, 'original_item_count': 22, 'item_ids': ['1250']} items\n"," Bundle 139: {'intersection_item_count': 1, 'original_item_count': 2, 'item_ids': ['250900']} items\n"," Bundle 145: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['232790']} items\n"," Bundle 157: {'intersection_item_count': 1, 'original_item_count': 6, 'item_ids': ['41070']} items\n"," Bundle 250: {'intersection_item_count': 1, 'original_item_count': 68, 'item_ids': ['204300']} items\n"," Bundle 292: {'intersection_item_count': 1, 'original_item_count': 12, 'item_ids': ['289130']} items\n"," Bundle 307: {'intersection_item_count': 1, 'original_item_count': 28, 'item_ids': ['42910']} items\n"," Bundle 335: {'intersection_item_count': 1, 'original_item_count': 4, 'item_ids': ['237990']} items\n"," Bundle 382: {'intersection_item_count': 1, 'original_item_count': 3, 'item_ids': ['222730']} items\n"," Bundle 448: {'intersection_item_count': 1, 'original_item_count': 7, 'item_ids': ['289130']} items\n"]}],"source":["# Print the results\n","for user_id, bundle_counts_per_user in bundle_counts.items():\n"," print(f\"User {user_id}:\")\n"," for bundle_id, item_count in bundle_counts_per_user.items():\n"," print(f\" Bundle {bundle_id}: {item_count} items\")\n"," break"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"executionInfo":{"elapsed":2738,"status":"ok","timestamp":1736140060020,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"ytZ2X9cDkVmz","outputId":"03493d0d-d249-4bff-a384-f437a21362fd"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# prompt: Sekarang berikan saya distribusi dari intersection_item_count dan original_item_count dalam satu chart\n","\n","import matplotlib.pyplot as plt\n","\n","# Extract intersection and original item counts\n","intersection_counts = []\n","original_counts = []\n","\n","for user_id, bundle_counts_per_user in bundle_counts.items():\n"," for bundle_id, item_count in bundle_counts_per_user.items():\n"," intersection_counts.append(item_count['intersection_item_count'])\n"," original_counts.append(item_count['original_item_count'])\n","\n","# Create the plot\n","plt.figure(figsize=(10, 6))\n","plt.hist(intersection_counts, bins=20, alpha=0.5, label='Intersection Item Count')\n","plt.hist(original_counts, bins=20, alpha=0.5, label='Original Item Count')\n","plt.xlabel('Number of Items')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Intersection and Original Item Counts')\n","plt.legend(loc='upper right')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"5sFp1ocn63tC"},"source":["#### Generate Persona"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13639,"status":"ok","timestamp":1735296046250,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"c6uTIFQkk58e","outputId":"e28a2c0e-1052-4ff9-a777-3d00ec186a94"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tag Frequencies:\n"," user_id tag frequency\n","808797 phrostb Indie 831\n","784206 mayshowganmore Indie 691\n","808678 phrostb Action 688\n","808681 phrostb Adventure 683\n","808903 phrostb Singleplayer 681\n","... ... ... ...\n","679256 crispymemesmantheyremyfavourite Dungeon Crawler 1\n","318953 76561198077199736 Dark 1\n","318952 76561198077199736 Cyberpunk 1\n","679259 crispymemesmantheyremyfavourite Exploration 1\n","275606 76561198068985504 God Game 1\n","\n","[874351 rows x 3 columns]\n","\n","Spec Frequencies:\n"]},{"output_type":"execute_result","data":{"text/plain":[" user_id spec frequency\n","163862 phrostb Single-player 1222\n","137678 chidvd Single-player 967\n","163894 piepai Single-player 964\n","159080 mayshowganmore Single-player 933\n","163868 phrostb Steam Trading Cards 863\n","... ... ... ...\n","24937 76561198041362390 Seated 1\n","24940 76561198041362390 Standing 1\n","24947 76561198041362390 Tracked Motion Controllers 1\n","137892 chocodude Oculus Rift 1\n","64482 76561198076069088 Single-player 1\n","\n","[176110 rows x 3 columns]"],"text/html":["\n","
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user_idspecfrequency
163862phrostbSingle-player1222
137678chidvdSingle-player967
163894piepaiSingle-player964
159080mayshowganmoreSingle-player933
163868phrostbSteam Trading Cards863
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2494076561198041362390Standing1
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176110 rows × 3 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"spec_freq_df"}},"metadata":{},"execution_count":22}],"source":["# prompt: Dari sorted_matching_bundles hitung frequency tags dan specs dari setiap item pada bundle yang dimiliki user dan urutkan jumlah frequency tag dan spec secara desc sehingga saya bisa mengetahui persona pengguna terhadap tags dan specs tertentu pada bundle, catatan tag dan spec jangan digabung frekuensinya bedakan storage\n","\n","import pandas as pd\n","\n","def analyze_user_persona(sorted_matching_bundles, items_dictionary):\n"," \"\"\"\n"," Analyzes user personas based on the frequency of tags and specs in their matching bundles.\n","\n"," Args:\n"," sorted_matching_bundles (dict): A dictionary of sorted matching bundles for each user.\n"," items_dictionary (dict): A dictionary of items with their respective tags and specs.\n","\n"," Returns:\n"," tuple: Two pandas DataFrames, one for tag frequencies and one for spec frequencies.\n"," Each DataFrame has columns for 'user_id', 'tag/spec', and 'frequency'.\n"," \"\"\"\n","\n"," user_tag_frequencies = []\n"," user_spec_frequencies = []\n","\n"," for user_id, bundles in sorted_matching_bundles.items():\n"," for bundle_id, item_ids in bundles.items():\n"," for item_id in item_ids:\n"," if item_id in items_dictionary:\n"," item_data = items_dictionary[item_id]\n"," if 'tags' in item_data:\n"," for tag in item_data['tags']:\n"," user_tag_frequencies.append({'user_id': user_id, 'tag': tag, 'bundle_id': bundle_id})\n"," if 'specs' in item_data:\n"," for spec in item_data['specs']:\n"," user_spec_frequencies.append({'user_id': user_id, 'spec': spec, 'bundle_id': bundle_id})\n","\n"," tag_df = pd.DataFrame(user_tag_frequencies)\n"," spec_df = pd.DataFrame(user_spec_frequencies)\n","\n"," # Aggregate and sort frequencies\n"," tag_freq_df = tag_df.groupby(['user_id', 'tag']).size().reset_index(name='frequency').sort_values(by='frequency', ascending=False)\n"," spec_freq_df = spec_df.groupby(['user_id', 'spec']).size().reset_index(name='frequency').sort_values(by='frequency', ascending=False)\n","\n"," return tag_freq_df, spec_freq_df\n","\n","# Example usage\n","tag_freq_df, spec_freq_df = analyze_user_persona(sorted_matching_bundles, items_dictionary)\n","print(\"Tag Frequencies:\")\n","print(tag_freq_df)\n","\n","print(\"\\nSpec Frequencies:\")\n","spec_freq_df"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47047,"status":"ok","timestamp":1735296093292,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JWxKpld6swCx","outputId":"e1247dce-00e4-4f9f-d961-82212a60e154"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 76561197970982479:\n"," Bundle 232:\n"," Max playtime item: 23532\n"," Average playtime: 3419.1\n"," 25th Quartile: 370.5\n"," 75th Quartile: 1758.25\n"," Bundle 383:\n"," Max playtime item: 798\n"," Average playtime: 428.4\n"," 25th Quartile: 206.0\n"," 75th Quartile: 567.0\n"," Bundle 231:\n"," Max playtime item: 696\n"," Average playtime: 509.5\n"," 25th Quartile: 416.25\n"," 75th Quartile: 602.75\n"," Bundle 362:\n"," Max playtime item: 3083\n"," Average playtime: 2648.0\n"," 25th Quartile: 2430.5\n"," 75th Quartile: 2884.0\n"," Bundle 957:\n"," Max playtime item: 333\n"," Average playtime: 197.0\n"," 25th Quartile: 129.0\n"," 75th Quartile: 274.5\n"," Bundle 572:\n"," Max playtime item: 2084\n"," Average playtime: 1407.0\n"," 25th Quartile: 1068.5\n"," 75th Quartile: 1794.0\n"," Bundle 616:\n"," Max playtime item: 10006\n"," Average playtime: 8250.0\n"," 25th Quartile: 7372.0\n"," 75th Quartile: 9128.0\n"," Bundle 235:\n"," Max playtime item: 7\n"," Average playtime: 7.0\n"," 25th Quartile: 7.0\n"," 75th Quartile: 7.0\n"," Bundle 240:\n"," Max playtime item: 4733\n"," Average playtime: 3293.0\n"," 25th Quartile: 2573.0\n"," 75th Quartile: 4013.0\n"," Bundle 1246:\n"," Max playtime item: 5001\n"," Average playtime: 3307.0\n"," 25th Quartile: 2460.0\n"," 75th Quartile: 4154.0\n"," Bundle 983:\n"," Max playtime item: 640\n"," Average playtime: 397.0\n"," 25th Quartile: 275.5\n"," 75th Quartile: 518.5\n"," Bundle 380:\n"," Max playtime item: 905\n"," Average playtime: 905.0\n"," 25th Quartile: 905.0\n"," 75th Quartile: 905.0\n"," Bundle 727:\n"," Max playtime item: 1343\n"," Average playtime: 741.0\n"," 25th Quartile: 440.0\n"," 75th Quartile: 1042.0\n"," Bundle 379:\n"," Max playtime item: 905\n"," Average playtime: 905.0\n"," 25th Quartile: 905.0\n"," 75th Quartile: 905.0\n"," Bundle 712:\n"," Max playtime item: 3061\n"," Average playtime: 2182.0\n"," 25th Quartile: 1742.5\n"," 75th Quartile: 2621.5\n"," Bundle 233:\n"," Max playtime item: 1474\n"," Average playtime: 993.5\n"," 25th Quartile: 753.25\n"," 75th Quartile: 1233.75\n"," Bundle 234:\n"," Max playtime item: 887\n"," Average playtime: 530.0\n"," 25th Quartile: 351.5\n"," 75th Quartile: 708.5\n"," Bundle 236:\n"," Max playtime item: 23532\n"," Average playtime: 12692.5\n"," 25th Quartile: 7272.75\n"," 75th Quartile: 18112.25\n"," Bundle 257:\n"," Max playtime item: 3061\n"," Average playtime: 2182.0\n"," 25th Quartile: 1742.5\n"," 75th Quartile: 2621.5\n"," Bundle 312:\n"," Max playtime item: 139\n"," Average playtime: 139.0\n"," 25th Quartile: 139.0\n"," 75th Quartile: 139.0\n"," Bundle 1418:\n"," Max playtime item: 4413\n"," Average playtime: 4413.0\n"," 25th Quartile: 4413.0\n"," 75th Quartile: 4413.0\n"," Bundle 1350:\n"," Max playtime item: 1159\n"," Average playtime: 1159.0\n"," 25th Quartile: 1159.0\n"," 75th Quartile: 1159.0\n"," Bundle 1351:\n"," Max playtime item: 1159\n"," Average playtime: 1159.0\n"," 25th Quartile: 1159.0\n"," 75th Quartile: 1159.0\n"," Bundle 1352:\n"," Max playtime item: 1159\n"," Average playtime: 1159.0\n"," 25th Quartile: 1159.0\n"," 75th Quartile: 1159.0\n"," Bundle 1100:\n"," Max playtime item: 3031\n"," Average playtime: 3031.0\n"," 25th Quartile: 3031.0\n"," 75th Quartile: 3031.0\n"," Bundle 1243:\n"," Max playtime item: 5716\n"," Average playtime: 5716.0\n"," 25th Quartile: 5716.0\n"," 75th Quartile: 5716.0\n"," Bundle 951:\n"," Max playtime item: 85\n"," Average playtime: 85.0\n"," 25th Quartile: 85.0\n"," 75th Quartile: 85.0\n"," Bundle 1158:\n"," Max playtime item: 793\n"," Average playtime: 793.0\n"," 25th Quartile: 793.0\n"," 75th Quartile: 793.0\n"," Bundle 575:\n"," Max playtime item: 10345\n"," Average playtime: 10345.0\n"," 25th Quartile: 10345.0\n"," 75th Quartile: 10345.0\n"," Bundle 823:\n"," Max playtime item: 716\n"," Average playtime: 716.0\n"," 25th Quartile: 716.0\n"," 75th Quartile: 716.0\n"," Bundle 760:\n"," Max playtime item: 467\n"," Average playtime: 467.0\n"," 25th Quartile: 467.0\n"," 75th Quartile: 467.0\n"," Bundle 757:\n"," Max playtime item: 1377\n"," Average playtime: 1377.0\n"," 25th Quartile: 1377.0\n"," 75th Quartile: 1377.0\n"," Bundle 690:\n"," Max playtime item: 199\n"," Average playtime: 199.0\n"," 25th Quartile: 199.0\n"," 75th Quartile: 199.0\n"," Bundle 721:\n"," Max playtime item: 199\n"," Average playtime: 199.0\n"," 25th Quartile: 199.0\n"," 75th Quartile: 199.0\n"," Bundle 574:\n"," Max playtime item: 10345\n"," Average playtime: 10345.0\n"," 25th Quartile: 10345.0\n"," 75th Quartile: 10345.0\n"," Bundle 635:\n"," Max playtime item: 2512\n"," Average playtime: 2512.0\n"," 25th Quartile: 2512.0\n"," 75th Quartile: 2512.0\n"," Bundle 612:\n"," Max playtime item: 10006\n"," Average playtime: 10006.0\n"," 25th Quartile: 10006.0\n"," 75th Quartile: 10006.0\n"," Bundle 139:\n"," Max playtime item: 329\n"," Average playtime: 329.0\n"," 25th Quartile: 329.0\n"," 75th Quartile: 329.0\n"," Bundle 145:\n"," Max playtime item: 216\n"," Average playtime: 216.0\n"," 25th Quartile: 216.0\n"," 75th Quartile: 216.0\n"," Bundle 157:\n"," Max playtime item: 716\n"," Average playtime: 716.0\n"," 25th Quartile: 716.0\n"," 75th Quartile: 716.0\n"," Bundle 292:\n"," Max playtime item: 593\n"," Average playtime: 593.0\n"," 25th Quartile: 593.0\n"," 75th Quartile: 593.0\n"," Bundle 307:\n"," Max playtime item: 588\n"," Average playtime: 588.0\n"," 25th Quartile: 588.0\n"," 75th Quartile: 588.0\n"," Bundle 335:\n"," Max playtime item: 19\n"," Average playtime: 19.0\n"," 25th Quartile: 19.0\n"," 75th Quartile: 19.0\n"," Bundle 382:\n"," Max playtime item: 319\n"," Average playtime: 319.0\n"," 25th Quartile: 319.0\n"," 75th Quartile: 319.0\n"," Bundle 448:\n"," Max playtime item: 593\n"," Average playtime: 593.0\n"," 25th Quartile: 593.0\n"," 75th Quartile: 593.0\n"]}],"source":["# prompt: Dari sorted_matching_bundles hitung distribusi playtime_forever dengan memanfaatkan user_item_interactions_dict saya ingin tau item mana untuk setiap user dari sorted_matching_bundles user mainkan terlama, berapa rata - rata waktu mainnya, dan apa batas quartile 25 dan 75 nya\n","\n","def analyze_playtime(sorted_matching_bundles, user_item_interactions_dict):\n"," \"\"\"\n"," Analyzes playtime for items in matching bundles.\n","\n"," Args:\n"," sorted_matching_bundles (dict): Dictionary of sorted matching bundles.\n"," user_item_interactions_dict (dict): Dictionary of user-item interactions.\n","\n"," Returns:\n"," dict: A dictionary containing playtime statistics for each user and bundle.\n"," \"\"\"\n"," playtime_stats = {}\n","\n"," for user_id, bundles in sorted_matching_bundles.items():\n"," playtime_stats[user_id] = {}\n"," if user_id in user_item_interactions_dict:\n"," user_items = user_item_interactions_dict[user_id]['items']\n"," user_items_dict = {item['item_id']: item['playtime_forever'] for item in user_items}\n"," for bundle_id, item_ids in bundles.items():\n"," playtimes = []\n"," for item_id in item_ids:\n"," if item_id in user_items_dict:\n"," if user_items_dict[item_id] > 0.0:\n"," playtimes.append(user_items_dict[item_id])\n","\n"," if len(playtimes) > 0:\n"," playtime_stats[user_id][bundle_id] = {\n"," 'max_playtime_item': max(playtimes) if playtimes else None,\n"," 'avg_playtime': np.mean(playtimes),\n"," 'quartile_25': np.percentile(playtimes, 25),\n"," 'quartile_75': np.percentile(playtimes, 75),\n"," }\n","\n"," return playtime_stats\n","\n","# Example usage\n","playtime_data = analyze_playtime(sorted_matching_bundles, user_item_interactions_dict)\n","\n","# Print or process the playtime_data as needed\n","for user_id, bundles in playtime_data.items():\n"," print(f\"User {user_id}:\")\n"," for bundle_id, stats in bundles.items():\n"," print(f\" Bundle {bundle_id}:\")\n"," print(f\" Max playtime item: {stats['max_playtime_item']}\")\n"," print(f\" Average playtime: {stats['avg_playtime']}\")\n"," print(f\" 25th Quartile: {stats['quartile_25']}\")\n"," print(f\" 75th Quartile: {stats['quartile_75']}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2066,"status":"ok","timestamp":1735296095356,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"33K-pwRb0BPd","outputId":"f83fc3fd-e576-4c7f-bb92-e394e6cc6137"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id bundle_id avg_item_price original_bundle_price \\\n","0 76561197970982479 232 10.802500 90.45 \n","1 76561197970982479 383 19.990000 253.07 \n","2 76561197970982479 231 7.156667 36.52 \n","3 76561197970982479 362 15.490000 224.61 \n","4 76561197970982479 237 6.240000 13.71 \n","... ... ... ... ... \n","171924 76561198030000787 233 19.990000 29.98 \n","171925 76561198030000787 236 14.990000 29.22 \n","171926 76561198030000787 264 39.990000 98.98 \n","171927 76561198030000787 265 39.990000 135.98 \n","171928 76561198030000787 292 11.990000 107.88 \n","\n"," original_avg_bundle_price preferer_bundle_price \n","0 4.760526 8.373062 \n","1 5.164694 12.659830 \n","2 4.565000 5.102934 \n","3 2.523708 14.500323 \n","4 3.427500 2.197115 \n","... ... ... \n","171924 14.990000 1.499750 \n","171925 9.740000 1.949300 \n","171926 49.490000 2.475119 \n","171927 67.990000 3.400350 \n","171928 8.990000 8.997498 \n","\n","[171929 rows x 6 columns]"],"text/html":["\n","
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user_idbundle_idavg_item_priceoriginal_bundle_priceoriginal_avg_bundle_pricepreferer_bundle_price
07656119797098247923210.80250090.454.7605268.373062
17656119797098247938319.990000253.075.16469412.659830
2765611979709824792317.15666736.524.5650005.102934
37656119797098247936215.490000224.612.52370814.500323
4765611979709824792376.24000013.713.4275002.197115
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1719247656119803000078723319.99000029.9814.9900001.499750
1719257656119803000078723614.99000029.229.7400001.949300
1719267656119803000078726439.99000098.9849.4900002.475119
1719277656119803000078726539.990000135.9867.9900003.400350
1719287656119803000078729211.990000107.888.9900008.997498
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171929 rows × 6 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"price_preference_df"}},"metadata":{},"execution_count":24}],"source":["# prompt: Dari sorted_matching_bundles hitung persona pengguna terhadap harga item, buatkan distribusi untuk tiap user mengenai average prefer item price, prefer bundle prices (original bundle price / sorted_matching_bundles price) based on bundle_id\n","\n","import pandas as pd\n","\n","def analyze_price_preference(sorted_matching_bundles, bundle_data, items_dictionary):\n"," \"\"\"\n"," Analyzes user price preferences based on sorted matching bundles.\n","\n"," Args:\n"," sorted_matching_bundles (dict): Sorted matching bundles for each user.\n"," bundle_data (list): List of bundle data.\n"," items_dictionary (dict): Dictionary of item data.\n","\n"," Returns:\n"," pd.DataFrame: DataFrame with user price preferences.\n"," \"\"\"\n","\n"," user_price_preferences = []\n"," for user_id, bundles in sorted_matching_bundles.items():\n"," for bundle_id, item_ids in bundles.items():\n"," bundle_raw_data = bundle_data_dict.get(bundle_id)\n"," original_bundle_price = 0.0\n"," if bundle_raw_data:\n"," original_bundle_price = remove_currency_symbol(bundle_raw_data['bundle_final_price'])\n","\n"," temp_bundle_item_price = []\n"," avg_item_price = 0.0\n"," for item_id in item_ids:\n"," if item_id in items_dictionary:\n"," item_info = items_dictionary[item_id]\n"," if item_info and 'price' in item_info:\n"," temp_bundle_item_price.append(get_real_price(item_info['price']))\n","\n"," if len(temp_bundle_item_price) > 0:\n"," avg_item_price = sum(temp_bundle_item_price) / len(temp_bundle_item_price)\n","\n"," if original_bundle_price == 0.0 or sum(temp_bundle_item_price) == 0.0:\n"," continue\n","\n"," user_price_preferences.append({\n"," 'user_id': user_id,\n"," 'bundle_id': bundle_id,\n"," 'avg_item_price': avg_item_price,\n"," 'original_bundle_price': original_bundle_price,\n"," 'original_avg_bundle_price': original_bundle_price / len(bundle_raw_data[\"items\"]),\n"," 'preferer_bundle_price': original_bundle_price / avg_item_price\n"," })\n","\n"," return pd.DataFrame(user_price_preferences)\n","\n","\n","# Example usage:\n","price_preference_df = analyze_price_preference(sorted_matching_bundles, bundle_data, items_dictionary)\n","price_preference_df\n","\n","#Further analysis or visualization can be performed on price_preference_df\n","#For example, group by 'user_id' and get the average 'avg_item_price' for each user\n","#Or create a histogram to see the distribution of prices."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_uwyWZ7mwXjh"},"outputs":[],"source":["# prompt: Convert tag_freq_df to dictionary based group by user_id\n","\n","# Convert tag_freq_df to a dictionary based on groupby user_id\n","tag_freq_dict = {}\n","for user_id, group_df in tag_freq_df.groupby('user_id'):\n"," tag_freq_dict[user_id] = group_df.set_index('tag')['frequency'].to_dict()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sawaM081xKQC"},"outputs":[],"source":["# prompt: Convert spec_freq_df to dictionary based group by user_id\n","\n","# Convert spec_freq_df to a dictionary based on groupby user_id\n","spec_freq_dict = {}\n","for user_id, group_df in spec_freq_df.groupby('user_id'):\n"," spec_freq_dict[user_id] = group_df.set_index('spec')['frequency'].to_dict()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"058DDLeYyAz4"},"outputs":[],"source":["# prompt: Convert price_preference_df to dictionary based group by user_id\n","\n","# Convert price_preference_df to a dictionary based on groupby user_id\n","price_preference_dict = {}\n","for user_id, group_df in price_preference_df.groupby('user_id'):\n"," price_preference_dict[user_id] = group_df.to_dict(orient='records')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1735296116407,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"P-DchoQbwpCX","outputId":"cf8a89ca-1271-429e-a1bc-c434f08580a6"},"outputs":[{"output_type":"stream","name":"stdout","text":["The average tag frequency for user 76561198030000787 is: 8.189655172413794\n","The max tag frequency for user 76561198030000787 is: 71\n","The median tag frequency for user 76561198030000787 is: 3.0\n","[71, 64, 61, 52, 49, 42, 36, 36, 33, 32, 32, 30, 29, 24, 22, 22, 21, 21, 20, 20, 19, 19, 19, 18, 18, 17, 17, 17, 15, 15, 14, 14, 13, 12, 12, 11, 11, 11, 11, 10, 10, 10, 9, 9, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n","['Singleplayer', 'Action', 'Adventure', 'Atmospheric', 'Multiplayer', 'Co-op', 'Great Soundtrack', 'RPG', 'Shooter', 'Online Co-Op', 'Third Person', 'Open World', 'Story Rich', 'Funny', 'Horror', 'Comedy', 'Sci-fi', 'Gore', 'Strategy', 'Survival', 'Classic', 'First-Person', 'FPS', 'Zombies', 'Fantasy', 'Third-Person Shooter', 'Survival Horror', 'Female Protagonist', 'Action RPG', 'Moddable', 'Sandbox', 'Local Co-Op', 'Difficult', 'Controller', '2D', 'Simulation', 'Mature', 'Indie', 'Racing', 'Replay Value', 'Remake', 'Post-apocalyptic', 'Tactical', 'Casual', 'Quick-Time Events', 'Team-Based', 'Choices Matter', 'Platformer', 'Isometric', 'Hack and Slash', 'Driving', 'Turn-Based', 'Real-Time with Pause', 'Character Customization', 'CRPG', \"Beat 'em up\", 'Arcade', 'Magic', 'Fast-Paced', 'Loot', 'Exploration', 'Stylized', 'Turn-Based Strategy', 'Touch-Friendly', 'War', 'Space', 'Party-Based RPG', 'Medieval', 'Physics', 'Anime', 'Local Multiplayer', 'Episodic', 'Romance', 'Realistic', 'Spectacle fighter', '3D Vision', 'Character Action Game', 'Dark', 'Choose Your Own Adventure', 'Dark Fantasy', 'Nudity', 'Memes', 'Dark Humor', 'Point & Click', 'Historical', 'Puzzle', 'Rogue-like', 'Side Scroller', 'Split Screen', 'Sports', 'Steampunk', 'Multiple Endings', '4X', 'Based On A Novel', 'Cinematic', 'Co-op Campaign', 'Comic Book', 'Competitive', 'Crime', 'Dystopian', 'Grand Strategy', 'Military', 'Underwater', 'e-sports', 'Pixel Graphics', 'Political', 'PvP', 'Retro', 'Rogue-lite', 'Trading', 'Music', 'Cyberpunk', 'Demons', 'Crafting', 'Destruction', 'Diplomacy', 'Class-Based', '2.5D', 'Action-Adventure', 'Alternate History', 'Dragons', 'Futuristic', 'Hex Grid', 'Inventory Management', 'Linear', 'Massively Multiplayer', 'Free to Play', 'Drama', 'Dungeon Crawler', 'Economy', 'Education', \"1990's\", '2D Fighter', '4 Player Local', '1980s', 'Blood', 'Assassin', 'Aliens', 'Mechs', 'Metroidvania', 'Match 3', 'Kickstarter', 'Interactive Fiction', 'Management', 'MOBA', 'Noir', 'Nonlinear', 'Mystery', 'Narration', 'Naval', 'Ninja', 'Colorful', 'Cartoony', 'Cartoon', 'Detective', 'Fighting', 'Gothic', 'Dynamic Narration', 'Family Friendly', 'Resource Management', 'Relaxing', 'RTS', 'Procedural Generation', 'Twin Stick Shooter', 'Turn-Based Tactics', 'Tower Defense', 'Time Travel', 'Stealth', 'Perma Death', 'Parkour', 'Offroad', 'Philisophical', 'Pirates', 'Visual Novel']\n"]}],"source":["# prompt: Calculate average tag_freq_dict[\"76561198030000787\"]\n","\n","user_id = \"76561198030000787\"\n","\n","if user_id in tag_freq_dict:\n"," frequencies = list(tag_freq_dict[user_id].values())\n"," frequencies_keys = list(tag_freq_dict[user_id].keys())\n"," average_frequency = sum(frequencies) / len(frequencies) if frequencies else 0\n"," print(f\"The average tag frequency for user {user_id} is: {average_frequency}\")\n"," print(f\"The max tag frequency for user {user_id} is: {max(frequencies)}\")\n"," print(f\"The median tag frequency for user {user_id} is: {np.percentile(frequencies, 50)}\")\n"," print(frequencies)\n"," print(frequencies_keys)\n","else:\n"," print(f\"No tag frequency data found for user {user_id}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1735296116407,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"B0izaDFjB2g-","outputId":"9556606c-e6b1-419e-fc23-f46490c8e0b9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Frequencies above average (8.189655172413794): [71, 64, 61, 52, 49, 42, 36, 36, 33, 32, 32, 30, 29, 24, 22, 22, 21, 21, 20, 20, 19, 19, 19, 18, 18, 17, 17, 17, 15, 15, 14, 14, 13, 12, 12, 11, 11, 11, 11, 10, 10, 10, 9, 9]\n"]}],"source":["# prompt: Give me frequencies that more than average_frequency\n","\n","# Assuming 'frequencies' list is already defined as in the provided code.\n","# Calculate the average frequency\n","if user_id in tag_freq_dict:\n"," frequencies = list(tag_freq_dict[user_id].values())\n"," average_frequency = sum(frequencies) / len(frequencies) if frequencies else 0\n","\n"," # Find frequencies greater than the average\n"," above_average_frequencies = [freq for freq in frequencies if freq > average_frequency]\n","\n"," print(f\"Frequencies above average ({average_frequency}): {above_average_frequencies}\")\n","else:\n"," print(f\"No tag frequency data found for user {user_id}\")"]},{"cell_type":"markdown","metadata":{"id":"tZyju0KqA_eK"},"source":["#### Get the most liked tags and specifications"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BnrfrY5OA-aN"},"outputs":[],"source":["# Calculate the average tag frequency for the specified user ID\n","temp_tag_freq_dict = {}\n","for user_id, tag_freq in tag_freq_dict.items():\n"," frequencies = list(tag_freq.values())\n"," keys = list(tag_freq.keys())\n"," average_frequency = sum(frequencies) / len(frequencies) if frequencies else 0\n","\n"," # Find frequencies greater than the average\n"," above_average_frequencies = {}\n"," for i in range(len(frequencies)):\n"," if frequencies[i] > average_frequency:\n"," above_average_frequencies[keys[i]] = frequencies[i]\n","\n"," if len(above_average_frequencies) > 0:\n"," temp_tag_freq_dict[user_id] = above_average_frequencies\n","\n","tag_freq_dict = temp_tag_freq_dict"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1735296116925,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"0wjwKjdYBKTk","outputId":"9546288c-266a-4cd3-fa5d-83174ad5dfb0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Singleplayer': 71,\n"," 'Action': 64,\n"," 'Adventure': 61,\n"," 'Atmospheric': 52,\n"," 'Multiplayer': 49,\n"," 'Co-op': 42,\n"," 'Great Soundtrack': 36,\n"," 'RPG': 36,\n"," 'Shooter': 33,\n"," 'Online Co-Op': 32,\n"," 'Third Person': 32,\n"," 'Open World': 30,\n"," 'Story Rich': 29,\n"," 'Funny': 24,\n"," 'Horror': 22,\n"," 'Comedy': 22,\n"," 'Sci-fi': 21,\n"," 'Gore': 21,\n"," 'Strategy': 20,\n"," 'Survival': 20,\n"," 'Classic': 19,\n"," 'First-Person': 19,\n"," 'FPS': 19,\n"," 'Zombies': 18,\n"," 'Fantasy': 18,\n"," 'Third-Person Shooter': 17,\n"," 'Survival Horror': 17,\n"," 'Female Protagonist': 17,\n"," 'Action RPG': 15,\n"," 'Moddable': 15,\n"," 'Sandbox': 14,\n"," 'Local Co-Op': 14,\n"," 'Difficult': 13,\n"," 'Controller': 12,\n"," '2D': 12,\n"," 'Simulation': 11,\n"," 'Mature': 11,\n"," 'Indie': 11,\n"," 'Racing': 11,\n"," 'Replay Value': 10,\n"," 'Remake': 10,\n"," 'Post-apocalyptic': 10,\n"," 'Tactical': 9,\n"," 'Casual': 9}"]},"metadata":{},"execution_count":31}],"source":["tag_freq_dict[\"76561198030000787\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1735296116925,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"xicuDlbyE2Yf","outputId":"421055b9-5568-406b-8ada-a9d39190a305"},"outputs":[{"output_type":"stream","name":"stdout","text":["['Stealth', 'Shooter', '3D Vision', 'Relaxing', 'Software']\n"]}],"source":["# prompt: Berikan saya daftar tag yang unik dari semua data pada tag_freq_dict tanpa adanya duplikasi tag\n","\n","unique_tags = set()\n","for user_id, tag_freq in tag_freq_dict.items():\n"," unique_tags.update(tag_freq.keys())\n","print(list(unique_tags)[:5])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7lDfoTQ4BnTO"},"outputs":[],"source":["# Calculate the average specs frequency for the specified user ID\n","temp_spec_freq_dict = {}\n","for user_id, spec_freq in spec_freq_dict.items():\n"," frequencies = list(spec_freq.values())\n"," keys = list(spec_freq.keys())\n"," average_frequency = sum(frequencies) / len(frequencies) if frequencies else 0\n","\n"," # Find frequencies greater than the average\n"," above_average_frequencies = {}\n"," for i in range(len(frequencies)):\n"," if frequencies[i] > average_frequency:\n"," above_average_frequencies[keys[i]] = frequencies[i]\n","\n"," if len(above_average_frequencies) > 0:\n"," temp_spec_freq_dict[user_id] = above_average_frequencies\n","\n","spec_freq_dict = temp_spec_freq_dict"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1735296116925,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"b1eIO4WeB-Ly","outputId":"bed0fbf9-edfa-455c-b243-fd7bb4ef6623"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Single-player': 75,\n"," 'Steam Achievements': 61,\n"," 'Steam Trading Cards': 50,\n"," 'Steam Cloud': 44,\n"," 'Multi-player': 40,\n"," 'Full controller support': 39,\n"," 'Co-op': 31}"]},"metadata":{},"execution_count":34}],"source":["spec_freq_dict[\"76561198030000787\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1735296116925,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"XTzZvEhcFMUa","outputId":"57a7e913-269b-4b97-823e-efe1f4681edf"},"outputs":[{"output_type":"stream","name":"stdout","text":["['Steam Cloud', 'Includes level editor', 'Cross-Platform Multiplayer', 'Local Multi-Player', 'Steam Workshop']\n"]}],"source":["# prompt: Berikan saya daftar specifications yang unik dari semua data pada spec_freq_dict tanpa adanya duplikasi specifications\n","\n","unique_specs = set()\n","for user_id, spec_freq in spec_freq_dict.items():\n"," unique_specs.update(spec_freq.keys())\n","print(list(unique_specs)[:5])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1735296116925,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LlWGSfiUyQrF","outputId":"93f70669-d28a-4520-f81b-8ca20c3f23de"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '232',\n"," 'avg_item_price': 13.823333333333332,\n"," 'original_bundle_price': 90.45,\n"," 'original_avg_bundle_price': 4.760526315789474,\n"," 'preferer_bundle_price': 6.543284301904992},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '362',\n"," 'avg_item_price': 11.99,\n"," 'original_bundle_price': 224.61,\n"," 'original_avg_bundle_price': 2.5237078651685394,\n"," 'preferer_bundle_price': 18.733110925771477},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '231',\n"," 'avg_item_price': 8.99,\n"," 'original_bundle_price': 36.52,\n"," 'original_avg_bundle_price': 4.565,\n"," 'preferer_bundle_price': 4.0622914349276975},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '234',\n"," 'avg_item_price': 14.989999999999998,\n"," 'original_bundle_price': 22.48,\n"," 'original_avg_bundle_price': 11.24,\n"," 'preferer_bundle_price': 1.4996664442961976},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '312',\n"," 'avg_item_price': 14.99,\n"," 'original_bundle_price': 26.76,\n"," 'original_avg_bundle_price': 6.69,\n"," 'preferer_bundle_price': 1.7851901267511676},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '1100',\n"," 'avg_item_price': 9.99,\n"," 'original_bundle_price': 21.23,\n"," 'original_avg_bundle_price': 10.615,\n"," 'preferer_bundle_price': 2.125125125125125},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '575',\n"," 'avg_item_price': 29.99,\n"," 'original_bundle_price': 49.33,\n"," 'original_avg_bundle_price': 3.083125,\n"," 'preferer_bundle_price': 1.6448816272090696},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '383',\n"," 'avg_item_price': 4.99,\n"," 'original_bundle_price': 253.07,\n"," 'original_avg_bundle_price': 5.16469387755102,\n"," 'preferer_bundle_price': 50.71543086172344},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '572',\n"," 'avg_item_price': 29.99,\n"," 'original_bundle_price': 69.97,\n"," 'original_avg_bundle_price': 23.323333333333334,\n"," 'preferer_bundle_price': 2.3331110370123374},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '574',\n"," 'avg_item_price': 29.99,\n"," 'original_bundle_price': 134.95,\n"," 'original_avg_bundle_price': 26.99,\n"," 'preferer_bundle_price': 4.499833277759253},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '233',\n"," 'avg_item_price': 19.99,\n"," 'original_bundle_price': 29.98,\n"," 'original_avg_bundle_price': 14.99,\n"," 'preferer_bundle_price': 1.499749874937469},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '236',\n"," 'avg_item_price': 14.99,\n"," 'original_bundle_price': 29.22,\n"," 'original_avg_bundle_price': 9.74,\n"," 'preferer_bundle_price': 1.9492995330220146},\n"," {'user_id': 'zyxwvutsrqponm',\n"," 'bundle_id': '240',\n"," 'avg_item_price': 4.99,\n"," 'original_bundle_price': 29.72,\n"," 'original_avg_bundle_price': 9.906666666666666,\n"," 'preferer_bundle_price': 5.955911823647294}]"]},"metadata":{},"execution_count":36}],"source":["price_preference_dict[user_id]"]},{"cell_type":"markdown","metadata":{"id":"-s3p5tlhopND"},"source":["## Recommendation"]},{"cell_type":"markdown","metadata":{"id":"lXW9_XiY4d4S"},"source":["#### With tags and specifications"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3Juq5nY-7xGH"},"outputs":[],"source":["# prompt: Dari for user_id, bundle_counts_per_user in bundle_counts.items(): buatkan dictionary baru dengan keys adalah user_id dan valuesnya adalah bundle -bundle bundle_counts_per_user namun dengan tambahan label 0 / 1 dimana 0 berarti tidak direkomendasikan karena jumlah intersection_item_count kurang dari 3/4 dari original_item_count\n","\n","# Matrix Feature Tag\n","default_matrix_features_tags = {}\n","for tag in list(unique_tags):\n"," default_matrix_features_tags[tag] = 0\n","\n","# Matrix Feature Spec\n","default_matrix_features_specs = {}\n","for spec in list(unique_specs):\n"," default_matrix_features_specs[spec] = 0\n","\n","# Bundles Recommendation\n","new_bundle_recommendations = {}\n","for user_id, bundle_counts_per_user in bundle_counts.items():\n"," new_bundle_recommendations[user_id] = []\n"," for bundle_id, item_count in bundle_counts_per_user.items():\n"," # Tags feature matrix\n"," matrix_features_tags = default_matrix_features_tags.copy()\n"," matrix_features_specs = default_matrix_features_specs.copy()\n","\n"," label = 1 # Default to recommended\n"," if item_count['intersection_item_count'] < (0.75 * item_count['original_item_count']):\n"," label = 0\n","\n"," for item_id in item_count['item_ids']:\n"," if item_id in items_dictionary:\n"," item_info = items_dictionary[item_id]\n"," if 'tags' in item_info:\n"," for tag in item_info['tags']:\n"," if tag in matrix_features_tags:\n"," matrix_features_tags[tag] = 1\n"," if 'specs' in item_info:\n"," for spec in item_info['specs']:\n"," if spec in matrix_features_specs:\n"," matrix_features_specs[spec] = 1\n","\n"," number_of_tags_1 = sum(1 for value in matrix_features_tags.values() if value == 1)\n"," number_of_specs_1 = sum(1 for value in matrix_features_specs.values() if value == 1)\n"," if user_id in tag_freq_dict:\n"," if number_of_tags_1 >= len(tag_freq_dict[user_id]):\n"," label = 1\n"," if user_id in spec_freq_dict:\n"," if number_of_specs_1 >= len(spec_freq_dict[user_id]):\n"," label = 1\n"," else:\n"," for item_id in item_count['item_ids']:\n"," if item_id in items_dictionary:\n"," item_info = items_dictionary[item_id]\n"," if 'tags' in item_info:\n"," for tag in item_info['tags']:\n"," if tag in matrix_features_tags:\n"," matrix_features_tags[tag] = 1\n"," if 'specs' in item_info:\n"," for spec in item_info['specs']:\n"," if spec in matrix_features_specs:\n"," matrix_features_specs[spec] = 1\n","\n"," new_bundle_recommendations[user_id].append({\n"," 'bundle_id': bundle_id,\n"," 'matrix_features_tags': matrix_features_tags,\n"," 'matrix_features_specs': matrix_features_specs,\n"," 'label': label\n"," })"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1734844966645,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"zTSz18rJ9Cmr","outputId":"8cc56b2b-809a-4e64-cca3-e8d59fa1486e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Label Counts: {1: 90782, 0: 93176}\n"]}],"source":["# prompt: Perlihatkan ke saya berapa jumlah label == 1 dan 0 pada new_bundle_recommendations\n","\n","label_counts = {}\n","for user_id, recommendations in new_bundle_recommendations.items():\n"," for recommendation in recommendations:\n"," label = recommendation['label']\n"," label_counts[label] = label_counts.get(label, 0) + 1\n","\n","print(f\"Label Counts: {label_counts}\")"]},{"cell_type":"code","source":["# prompt: get distinct tags and specs from new_bundle_recommendations\n","\n","def get_distinct_tags_specs(new_bundle_recommendations):\n"," \"\"\"\n"," Extracts distinct tags and specs from the new_bundle_recommendations dictionary.\n","\n"," Args:\n"," new_bundle_recommendations (dict): The dictionary containing bundle recommendations.\n","\n"," Returns:\n"," tuple: A tuple containing two sets: distinct_tags and distinct_specs.\n"," \"\"\"\n"," distinct_tags = set()\n"," distinct_specs = set()\n","\n"," for user_id, recommendations in new_bundle_recommendations.items():\n"," for recommendation in recommendations:\n"," distinct_tags.update(recommendation['matrix_features_tags'].keys())\n"," distinct_specs.update(recommendation['matrix_features_specs'].keys())\n","\n"," return distinct_tags, distinct_specs\n","\n","distinct_tags, distinct_specs = get_distinct_tags_specs(new_bundle_recommendations)"],"metadata":{"id":"4AJDH3rI2WvL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["distinct_tags"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2BabVTYO23Sg","executionInfo":{"status":"ok","timestamp":1735281836333,"user_tz":-420,"elapsed":289,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4fd1e769-2275-4165-fa9b-06ecc3f41639"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{\"1990's\",\n"," '2D',\n"," '3D Vision',\n"," '4 Player Local',\n"," '4X',\n"," 'Abstract',\n"," 'Action',\n"," 'Action RPG',\n"," 'Action-Adventure',\n"," 'Adventure',\n"," 'Aliens',\n"," 'Alternate History',\n"," 'Animation & Modeling',\n"," 'Anime',\n"," 'Arcade',\n"," 'Assassin',\n"," 'Atmospheric',\n"," 'Base Building',\n"," 'Based On A Novel',\n"," \"Beat 'em up\",\n"," 'Bullet Hell',\n"," 'CRPG',\n"," 'Card Game',\n"," 'Casual',\n"," 'Character Customization',\n"," 'Choices Matter',\n"," 'Choose Your Own Adventure',\n"," 'Cinematic',\n"," 'City Builder',\n"," 'Class-Based',\n"," 'Classic',\n"," 'Co-op',\n"," 'Co-op Campaign',\n"," 'Colorful',\n"," 'Comedy',\n"," 'Comic Book',\n"," 'Competitive',\n"," 'Controller',\n"," 'Crafting',\n"," 'Crime',\n"," 'Cult Classic',\n"," 'Cute',\n"," 'Cyberpunk',\n"," 'Dark',\n"," 'Dark Fantasy',\n"," 'Dark Humor',\n"," 'Demons',\n"," 'Design & Illustration',\n"," 'Destruction',\n"," 'Detective',\n"," 'Difficult',\n"," 'Diplomacy',\n"," 'Dragons',\n"," 'Drama',\n"," 'Driving',\n"," 'Dungeon Crawler',\n"," 'Dystopian',\n"," 'Early Access',\n"," 'Economy',\n"," 'Education',\n"," 'Episodic',\n"," 'Exploration',\n"," 'FMV',\n"," 'FPS',\n"," 'Family Friendly',\n"," 'Fantasy',\n"," 'Fast-Paced',\n"," 'Female Protagonist',\n"," 'Fighting',\n"," 'First-Person',\n"," 'Free to Play',\n"," 'Funny',\n"," 'Futuristic',\n"," 'Gambling',\n"," 'Game Development',\n"," 'GameMaker',\n"," 'God Game',\n"," 'Gore',\n"," 'Grand Strategy',\n"," 'Great Soundtrack',\n"," 'Hack and Slash',\n"," 'Hand-drawn',\n"," 'Hex Grid',\n"," 'Hidden Object',\n"," 'Historical',\n"," 'Horror',\n"," 'Indie',\n"," 'Interactive Fiction',\n"," 'Inventory Management',\n"," 'Isometric',\n"," 'JRPG',\n"," 'Kickstarter',\n"," 'Lara Croft',\n"," 'Level Editor',\n"," 'Linear',\n"," 'Local Co-Op',\n"," 'Local Multiplayer',\n"," 'Loot',\n"," 'Magic',\n"," 'Management',\n"," 'Massively Multiplayer',\n"," 'Match 3',\n"," 'Mature',\n"," 'Mechs',\n"," 'Medieval',\n"," 'Memes',\n"," 'Metroidvania',\n"," 'Military',\n"," 'Moddable',\n"," 'Multiplayer',\n"," 'Multiple Endings',\n"," 'Mystery',\n"," 'Narration',\n"," 'Nudity',\n"," 'Online Co-Op',\n"," 'Open World',\n"," 'Parkour',\n"," 'Party-Based RPG',\n"," 'Physics',\n"," 'Pinball',\n"," 'Pirates',\n"," 'Pixel Graphics',\n"," 'Platformer',\n"," 'Point & Click',\n"," 'Political',\n"," 'Post-apocalyptic',\n"," 'Procedural Generation',\n"," 'Programming',\n"," 'Psychological Horror',\n"," 'Puzzle',\n"," 'Puzzle-Platformer',\n"," 'PvP',\n"," 'Quick-Time Events',\n"," 'RPG',\n"," 'RPGMaker',\n"," 'RTS',\n"," 'Racing',\n"," 'Real Time Tactics',\n"," 'Real-Time with Pause',\n"," 'Realistic',\n"," 'Relaxing',\n"," 'Remake',\n"," 'Replay Value',\n"," 'Resource Management',\n"," 'Retro',\n"," 'Robots',\n"," 'Rogue-like',\n"," 'Rogue-lite',\n"," 'Romance',\n"," 'Sandbox',\n"," 'Sci-fi',\n"," 'Science',\n"," 'Sexual Content',\n"," \"Shoot 'Em Up\",\n"," 'Shooter',\n"," 'Short',\n"," 'Side Scroller',\n"," 'Silent Protagonist',\n"," 'Simulation',\n"," 'Singleplayer',\n"," 'Sniper',\n"," 'Software',\n"," 'Software Training',\n"," 'Space',\n"," 'Spectacle fighter',\n"," 'Split Screen',\n"," 'Sports',\n"," 'Stealth',\n"," 'Steampunk',\n"," 'Story Rich',\n"," 'Strategy',\n"," 'Stylized',\n"," 'Superhero',\n"," 'Survival',\n"," 'Survival Horror',\n"," 'Tactical',\n"," 'Team-Based',\n"," 'Text-Based',\n"," 'Third Person',\n"," 'Third-Person Shooter',\n"," 'Time Travel',\n"," 'Top-Down',\n"," 'Top-Down Shooter',\n"," 'Touch-Friendly',\n"," 'Tower Defense',\n"," 'Trading',\n"," 'Transhumanism',\n"," 'Turn-Based',\n"," 'Turn-Based Combat',\n"," 'Turn-Based Strategy',\n"," 'Twin Stick Shooter',\n"," 'Underwater',\n"," 'Utilities',\n"," 'VR',\n"," 'Vampire',\n"," 'Villain Protagonist',\n"," 'Visual Novel',\n"," 'Voxel',\n"," 'Walking Simulator',\n"," 'War',\n"," 'Web Publishing',\n"," 'World War II',\n"," 'Zombies',\n"," 'e-sports'}"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["distinct_specs"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XMDVD8k129FP","executionInfo":{"status":"ok","timestamp":1735281858973,"user_tz":-420,"elapsed":342,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"db7d4d20-4434-46ce-988b-bdceb40edcbc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Captions available',\n"," 'Co-op',\n"," 'Commentary available',\n"," 'Cross-Platform Multiplayer',\n"," 'Full controller support',\n"," 'In-App Purchases',\n"," 'Includes Source SDK',\n"," 'Includes level editor',\n"," 'Local Multi-Player',\n"," 'Multi-player',\n"," 'Online Multi-Player',\n"," 'Partial Controller Support',\n"," 'Shared/Split Screen',\n"," 'Single-player',\n"," 'Stats',\n"," 'Steam Achievements',\n"," 'Steam Cloud',\n"," 'Steam Leaderboards',\n"," 'Steam Trading Cards',\n"," 'Steam Workshop',\n"," 'Valve Anti-Cheat enabled'}"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["print(f\"Size of tags {len(distinct_tags)}\")\n","print(f\"Size of specs {len(distinct_specs)}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V3yN-QtD3LJT","executionInfo":{"status":"ok","timestamp":1735282014875,"user_tz":-420,"elapsed":299,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9b772e16-02e7-4b0d-be3d-ce2651c6bfa5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Size of tags 204\n","Size of specs 21\n"]}]},{"cell_type":"code","source":["# prompt: Please check for each bundle in users from new_bundle_recommendations please set frequestion distinct_tags from tags based on bundle\n","\n","def set_frequent_distinct_tags_and_specs(new_bundle_recommendations, tag_freq_dict, spec_freq_dict):\n"," \"\"\"\n"," Sets the frequent distinct tags for each bundle in the recommendations.\n","\n"," Args:\n"," new_bundle_recommendations (dict): The bundle recommendations.\n"," tag_freq_dict (dict): User tag frequencies.\n"," \"\"\"\n"," default_frequent_distinct_tags = {}\n"," for tag in list(distinct_tags):\n"," default_frequent_distinct_tags[tag] = 0\n","\n"," default_frequent_distinct_specs = {}\n"," for spec in list(distinct_specs):\n"," default_frequent_distinct_specs[spec] = 0\n","\n"," user_bundle_data = {}\n"," for user_id, recommendations in new_bundle_recommendations.items():\n"," user_bundle_data[user_id] = {}\n"," for recommendation in recommendations:\n"," bundle_id = recommendation['bundle_id']\n"," matrix_features_tags = recommendation['matrix_features_tags']\n"," frequent_distinct_tags = default_frequent_distinct_tags.copy()\n","\n"," if bundle_id not in user_bundle_data[user_id]:\n"," user_bundle_data[user_id][bundle_id] = {}\n","\n"," if user_id in tag_freq_dict:\n"," for tag, freq in tag_freq_dict[user_id].items():\n","\n"," if tag not in frequent_distinct_tags:\n"," frequent_distinct_tags[tag] = 0\n","\n"," if tag in matrix_features_tags:\n"," frequent_distinct_tags[tag] += freq\n","\n"," user_bundle_data[user_id][bundle_id]['frequent_distinct_tags'] = frequent_distinct_tags\n","\n"," matrix_features_specs = recommendation['matrix_features_specs']\n"," frequent_distinct_specs = default_frequent_distinct_specs.copy()\n","\n"," if user_id in spec_freq_dict:\n"," for spec, freq in spec_freq_dict[user_id].items():\n","\n"," if spec not in frequent_distinct_specs:\n"," frequent_distinct_specs[spec] = 0\n","\n"," if spec in matrix_features_specs:\n"," frequent_distinct_specs[spec] += freq\n","\n"," user_bundle_data[user_id][bundle_id]['frequent_distinct_specs'] = frequent_distinct_specs\n"," user_bundle_data[user_id][bundle_id]['label'] = recommendation['label']\n","\n"," return user_bundle_data\n","\n","user_bundle_data = set_frequent_distinct_tags_and_specs(new_bundle_recommendations, tag_freq_dict, spec_freq_dict)\n","#Example Access\n","#print(new_bundle_recommendations['76561198030000787'][0]['frequent_distinct_tags'])"],"metadata":{"id":"KwvjOumd4xyg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Apakah ada frequency tags yang lebih dari 1 nilainya? user_bundle_data\n","\n","def check_frequency_tags(user_bundle_data):\n"," \"\"\"\n"," Checks if there are any frequency tags with a value greater than 1 in the user_bundle_data.\n","\n"," Args:\n"," user_bundle_data (dict): A dictionary containing user bundle data.\n","\n"," Returns:\n"," bool: True if any frequency tag has a value greater than 1, False otherwise.\n"," list: A list of user IDs with frequency tags greater than 1.\n"," \"\"\"\n"," users_with_high_frequency_tags = []\n"," for user_id, bundle_data in user_bundle_data.items():\n"," for bundle_id, data in bundle_data.items():\n"," for tag, freq in data['frequent_distinct_tags'].items():\n"," if freq > 1:\n"," users_with_high_frequency_tags.append(user_id)\n"," break # Exit inner loop once a frequency tag > 1 is found for a bundle\n"," else:\n"," continue # Continue to next bundle if no frequency tag > 1 is found\n"," break #Exit the bundle loop once we've found a frequency > 1 for the bundle\n"," return len(users_with_high_frequency_tags) > 0, users_with_high_frequency_tags\n","\n","has_high_frequency, users = check_frequency_tags(user_bundle_data)\n","\n","if has_high_frequency:\n"," print(\"Yes, there are frequency tags with values greater than 1.\")\n"," print(\"Users with frequency tags greater than 1:\", users)\n","else:\n"," print(\"No frequency tags with values greater than 1 found.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"id":"HZiTzq7a8i_X","executionInfo":{"status":"error","timestamp":1735286147969,"user_tz":-420,"elapsed":369,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b6164a87-6127-4243-94d9-f0d9f40df491"},"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'user_bundle_data' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musers_with_high_frequency_tags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musers_with_high_frequency_tags\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mhas_high_frequency\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_frequency_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_bundle_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhas_high_frequency\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'user_bundle_data' is not defined"]}]},{"cell_type":"code","source":["# prompt: Check freeuncy of label in user_bundle_data\n","\n","def analyze_label_frequency(user_bundle_data):\n"," \"\"\"\n"," Analyzes the frequency of labels in the user_bundle_data.\n","\n"," Args:\n"," user_bundle_data (dict): A dictionary containing user data, bundles, and labels.\n","\n"," Returns:\n"," dict: A dictionary with label frequencies.\n"," \"\"\"\n"," label_counts = {}\n"," for user_id, bundles in user_bundle_data.items():\n"," for bundle_id, bundle_info in bundles.items():\n"," label = bundle_info['label']\n"," label_counts[label] = label_counts.get(label, 0) + 1\n"," return label_counts\n","\n","label_frequencies = analyze_label_frequency(user_bundle_data)\n","label_frequencies"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qViaXjunARLm","executionInfo":{"status":"ok","timestamp":1735284321101,"user_tz":-420,"elapsed":312,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"26f09e48-7059-4d53-92c2-1fc320069511"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{1: 90782, 0: 93176}"]},"metadata":{},"execution_count":60}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hnRTUGdExdM8"},"outputs":[],"source":["# prompt:\n","\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import classification_report\n","\n","# Assuming 'new_bundle_recommendations' is defined as in the previous code\n","\n","# Prepare the data for SVM\n","data = { \"features\": [], \"label\": [] }\n","for user_id, recommendations in user_bundle_data.items():\n"," for recommendation in recommendations:\n"," features = list(recommendation['matrix_features_tags'].values()) + list(recommendation['matrix_features_specs'].values())\n"," label = recommendation['label']\n"," data[\"features\"].append(features)\n"," data[\"label\"].append(label)\n","\n","X = np.array(data[\"features\"])\n","y = np.array(data[\"label\"])\n","\n","# Split data into training and testing sets\n","# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"]},{"cell_type":"markdown","metadata":{"id":"BvNcVTvH4y1J"},"source":["#### With price pattern"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7917,"status":"ok","timestamp":1735296132810,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"hO7GCvhT4y1K","outputId":"b1d85671-4983-4c2e-f982-fb7bcceaf895"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 76561197970982479:\n"," Average Price: 14.88830434782609\n"," Price Range 0-25: Start = 0.0, End = 9.99, Count = 109\n"," Price Range 26-50: Start = 9.99, End = 14.99, Count = 32\n"," Price Range 51-75: Start = 14.99, End = 19.99, Count = 61\n"," Price Range 76-100: Start = 19.99, End = 59.99, Count = 28\n"]}],"source":["# prompt: Dari user user_item_interactions_dict, hitung price rata - rata item yang di beli oleh pengguna, kemudian kelompokkan user interaction berdasarkan range price (sorting dahulu, lalu gunakan quartile 0-25, 26-50, 51-75, 76-100) namun selain quartile berapa kasih juga daftar harga start dan end untuk masing - masing quartile\n","# , lalu Hitung frekuensi interaction pengguna terhadap range price\n","\n","import pandas as pd\n","import numpy as np\n","\n","def analyze_price_interaction(user_item_interactions_dict):\n"," \"\"\"\n"," Analyzes user price interaction based on user_item_interactions_dict.\n","\n"," Args:\n"," user_item_interactions_dict (dict): Dictionary of user-item interactions.\n","\n"," Returns:\n"," dict: A dictionary containing user price interaction data.\n"," \"\"\"\n","\n"," user_price_interactions = {}\n"," for user_id, user_data in user_item_interactions_dict.items():\n"," prices = []\n"," for item in user_data['items']:\n"," if item[\"item_id\"] in items_dictionary and 'price' in items_dictionary[item[\"item_id\"]]:\n"," prices.append(get_real_price(f\"{items_dictionary[item['item_id']]['price']}\"))\n","\n"," if not prices:\n"," continue\n","\n"," avg_price = np.mean(prices)\n"," sorted_prices = np.sort(prices)\n","\n"," quartiles = np.percentile(sorted_prices, [0, 25, 50, 75, 100])\n","\n"," price_ranges = {\n"," '0-25': {'start': quartiles[0], 'end': quartiles[1], 'count': 0},\n"," '26-50': {'start': quartiles[1], 'end': quartiles[2], 'count': 0},\n"," '51-75': {'start': quartiles[2], 'end': quartiles[3], 'count': 0},\n"," '76-100': {'start': quartiles[3], 'end': quartiles[4], 'count': 0}\n"," }\n","\n"," for price in sorted_prices:\n"," if quartiles[0] <= price <= quartiles[1]:\n"," price_ranges['0-25']['count'] += 1\n"," elif quartiles[1] < price <= quartiles[2]:\n"," price_ranges['26-50']['count'] += 1\n"," elif quartiles[2] < price <= quartiles[3]:\n"," price_ranges['51-75']['count'] += 1\n"," elif quartiles[3] < price <= quartiles[4]:\n"," price_ranges['76-100']['count'] += 1\n","\n"," user_price_interactions[user_id] = {\n"," 'avg_price': avg_price,\n"," 'price_ranges': price_ranges,\n"," }\n","\n"," return user_price_interactions\n","\n","# Example usage\n","user_price_data = analyze_price_interaction(user_item_interactions_dict)\n","\n","#Print the result\n","for user_id, user_data in user_price_data.items():\n"," print(f\"User {user_id}:\")\n"," print(f\" Average Price: {user_data['avg_price']}\")\n"," for range_name, range_data in user_data['price_ranges'].items():\n"," print(f\" Price Range {range_name}: Start = {range_data['start']}, End = {range_data['end']}, Count = {range_data['count']}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1735296132810,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"_4tajHfz_R03","outputId":"f1c85f37-3273-4474-a621-86bb69c3388c"},"outputs":[{"output_type":"stream","name":"stdout","text":["User 76561197970982479:\n"," Average Price: 14.88830434782609\n"," Price Range 0-25: Start = 0.0, End = 9.99, Count = 109\n"," Price Range 51-75: Start = 14.99, End = 19.99, Count = 61\n"," Price Range 26-50: Start = 9.99, End = 14.99, Count = 32\n"," Price Range 76-100: Start = 19.99, End = 59.99, Count = 28\n"]}],"source":["# prompt: now sort user_price_data based on their count in price range and do not change the dictionary format\n","\n","# Sort price ranges within each user's data based on count\n","for user_id, user_data in user_price_data.items():\n"," sorted_price_ranges = dict(sorted(user_data['price_ranges'].items(), key=lambda item: item[1]['count'], reverse=True))\n"," user_data['price_ranges'] = sorted_price_ranges\n","\n","#Print the result\n","for user_id, user_data in user_price_data.items():\n"," print(f\"User {user_id}:\")\n"," print(f\" Average Price: {user_data['avg_price']}\")\n"," for range_name, range_data in user_data['price_ranges'].items():\n"," print(f\" Price Range {range_name}: Start = {range_data['start']}, End = {range_data['end']}, Count = {range_data['count']}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IqypixNcCB7x"},"outputs":[],"source":["# prompt: - Untuk setiap user pada user_price_data lakukan loop terhadap bundle_data\n","# - Untuk setiap bundle pada bundle_data lakukan loop pada items\n","# - Hitung average price pada items bundle data\n","# - Bandingkan average price pada bundle items terhadap average price pada user_price_data\n","# - Semua bundle dengan average price item dibawah atau sama dengan user_price_data maka di labelkan 1\n","# - Selain itu maka labelnya 0\n","# - Pada section else yang berlabel 0 maka lakukan :\n","# - pengecekkan berdasarkan range price\n","# - Kelompokkan item pada bundle berdasarkan range price pada user_price_data\n","# - Hitung frekuensi item pada bundle untuk setiap range price\n","# - Lakukan perbandingan jika terdapat 2 range price lebih yang frekuensinya melebihi threshold range price user_price_data maka labelkan jadi 1\n","# - Kemudian simpan data dalam bentuk dictionary dengan isi berikut\n","# \t- user_id\n","# \t- bundle_id\n","# \t- average_price_interaction\n","# \t- average_price_bundle\n","# \t- i_price_range_q1\n","# \t- i_price_range_q2\n","# \t- i_price_range_q3\n","# \t- i_price_range_q4\n","# \t- b_price_range_q1\n","# \t- b_price_range_q2\n","# \t- b_price_range_q3\n","# \t- b_price_range_q4\n","# \t- label\n","# Jangan dalam function\n","\n","# Create an empty list to store the results\n","result_data = {}\n","\n","# Loop through each user in user_price_data\n","for user_id, user_data in user_price_data.items():\n"," # Loop through each bundle in bundle_data\n"," for bundle_id, bundle_data in bundle_data_dict.items(): # Assuming bundle_data_dict exists\n"," # Extract items from the bundle\n"," items = bundle_data.get(\"items\", [])\n","\n"," # Calculate average price for items in the bundle\n"," item_prices = []\n"," for item in items:\n"," if item['item_id'] in items_dictionary and 'price' in items_dictionary[item['item_id']]:\n"," item_prices.append(get_real_price(items_dictionary[item['item_id']]['price']))\n"," avg_bundle_price = np.mean(item_prices) if item_prices else 0\n","\n","\n"," # Compare average bundle price with user's average price\n","\n"," # Group items by price range and count frequencies\n"," bundle_price_ranges = {\n"," '0-25': 0,\n"," '26-50': 0,\n"," '51-75': 0,\n"," '76-100': 0,\n"," }\n"," for price in item_prices:\n"," if user_data['price_ranges']['0-25']['start'] <= price <= user_data['price_ranges']['0-25']['end']:\n"," bundle_price_ranges['0-25'] += 1\n"," elif user_data['price_ranges']['26-50']['start'] < price <= user_data['price_ranges']['26-50']['end']:\n"," bundle_price_ranges['26-50'] += 1\n"," elif user_data['price_ranges']['51-75']['start'] < price <= user_data['price_ranges']['51-75']['end']:\n"," bundle_price_ranges['51-75'] += 1\n"," elif user_data['price_ranges']['76-100']['start'] < price <= user_data['price_ranges']['76-100']['end']:\n"," bundle_price_ranges['76-100'] += 1\n","\n"," # if avg_bundle_price <= user_data['avg_price']:\n"," # label = 1\n"," # else:\n"," label = 0\n","\n"," # Check if two or more price ranges exceed the threshold\n"," ranges_above_threshold = 0\n"," for range_name, count in bundle_price_ranges.items():\n"," if count > (0.15 * user_data['price_ranges'][range_name]['count']):\n"," ranges_above_threshold +=1\n","\n"," if ranges_above_threshold >= 1:\n"," label = 1\n","\n"," # Append the results\n"," if user_id not in result_data:\n"," result_data[user_id] = {}\n","\n"," result_data[user_id][bundle_id] = {\n"," 'user_id': user_id,\n"," 'bundle_id': bundle_id,\n"," 'average_price_interaction': user_data['avg_price'],\n"," 'average_price_bundle': avg_bundle_price,\n"," 'i_price_range_q1': user_data['price_ranges']['0-25']['count'],\n"," 'i_price_range_q2': user_data['price_ranges']['26-50']['count'],\n"," 'i_price_range_q3': user_data['price_ranges']['51-75']['count'],\n"," 'i_price_range_q4': user_data['price_ranges']['76-100']['count'],\n"," 'b_price_range_q1': bundle_price_ranges['0-25'],\n"," 'b_price_range_q2': bundle_price_ranges['26-50'],\n"," 'b_price_range_q3': bundle_price_ranges['51-75'],\n"," 'b_price_range_q4': bundle_price_ranges['76-100'],\n"," 'label': label\n"," }"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3769,"status":"ok","timestamp":1735296464673,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"hg5fhBdx4y1L","outputId":"f25941aa-772d-4943-9622-31731eea16b9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Label Counts in result_data: {0: 3010764, 1: 3062976}\n"]}],"source":["# prompt: Perlihatkan ke saya berapa jumlah label == 1 dan 0 pada result_data\n","\n","label_counts = {}\n","for user_id, user_recommendations in result_data.items():\n"," for bundle_id, recommendation_data in user_recommendations.items():\n"," label = recommendation_data['label']\n"," label_counts[label] = label_counts.get(label, 0) + 1\n","\n","print(f\"Label Counts in result_data: {label_counts}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"executionInfo":{"elapsed":5165,"status":"ok","timestamp":1735296469833,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"yzuLFSOZcgdb","outputId":"73dae906-dc33-4a2c-ac12-0fecf02f05a7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# prompt: Perlihatkan saya gambar distribusi dari jumlah label == 1 dan 0 pada result_data\n","\n","import matplotlib.pyplot as plt\n","\n","label_counts = {}\n","for user_id, user_recommendations in result_data.items():\n"," for bundle_id, recommendation_data in user_recommendations.items():\n"," label = recommendation_data['label']\n"," label_counts[label] = label_counts.get(label, 0) + 1\n","\n","labels, counts = zip(*label_counts.items())\n","\n","plt.figure(figsize=(8, 6))\n","plt.bar(labels, counts, color=['skyblue', 'lightcoral'])\n","plt.xlabel(\"Labels (0 or 1)\")\n","plt.ylabel(\"Number of Occurrences\")\n","plt.title(\"Distribution of Labels in result_data\")\n","plt.xticks(labels)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u8-EKvH_4y1M"},"outputs":[],"source":["# prompt: Ok sekarang dari result_data terapkan algoritma stocastic gradient descent (SGD) dan gunakan cross validation dan hyperparameter tuning, dan jangan menggunakan data = [] tapi gunakan dictionary agar tidak terlalu berat\n","\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.linear_model import SGDClassifier\n","from sklearn.metrics import classification_report, accuracy_score\n","import numpy as np\n","\n","# Prepare data for SGD\n","data = { \"features\": [], \"label\": [], \"identity\": [] }\n","for user_id, user_recommendations in result_data.items():\n"," for bundle_id, recommendation_data in user_recommendations.items():\n"," identity = [\n"," recommendation_data['user_id'],\n"," recommendation_data['bundle_id'],\n"," ]\n"," # features = [\n"," # recommendation_data['average_price_interaction'],\n"," # recommendation_data['average_price_bundle'],\n"," # recommendation_data['i_price_range_q1'],\n"," # recommendation_data['i_price_range_q2'],\n"," # recommendation_data['i_price_range_q3'],\n"," # recommendation_data['i_price_range_q4'],\n"," # recommendation_data['b_price_range_q1'],\n"," # recommendation_data['b_price_range_q2'],\n"," # recommendation_data['b_price_range_q3'],\n"," # recommendation_data['b_price_range_q4'],\n"," # ]\n"," # label = recommendation_data['label']\n"," # data[\"features\"].append(features)\n"," # data[\"label\"].append(label)\n"," data[\"identity\"].append(identity)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yZfekYqTjlCQ"},"outputs":[],"source":["# X = np.array(data[\"features\"])\n","# y = np.array(data[\"label\"])\n","z = np.array(data[\"identity\"])"]},{"cell_type":"code","source":["len(z[0])"],"metadata":{"id":"nmkVoEF1vXzY","executionInfo":{"status":"ok","timestamp":1735296656849,"user_tz":-420,"elapsed":316,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b1a4825a-93df-4ded-95aa-8fe77993cb09","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cjoDcRZvjpz8"},"outputs":[],"source":["# Split data\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","metadata":{"id":"pAnI6hP91049"},"source":["#### With playtime"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6190,"status":"ok","timestamp":1734846124392,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"Uxjy-FMz15MD","outputId":"ccdc4d96-731c-47d6-bda4-72a1ef881afb"},"outputs":[{"name":"stdout","output_type":"stream","text":["User 76561197970982479:\n"," Average Playtime: 792.1293103448276\n"," Play Time Range 0-25: Start = 0.0, End = 0.0, Count = 61\n"," Play Time Range 26-50: Start = 0.0, End = 126.5, Count = 55\n"," Play Time Range 51-75: Start = 126.5, End = 607.5, Count = 58\n"," Play Time Range 76-100: Start = 607.5, End = 23532.0, Count = 58\n"]}],"source":["# prompt: create hal yang sama analyze_price_interaction function yang fokusnya pada playtime_forever\n","\n","def analyze_playtime_interaction(user_item_interactions_dict):\n"," \"\"\"\n"," Analyzes user playtime interaction based on user_item_interactions_dict.\n","\n"," Args:\n"," user_item_interactions_dict (dict): Dictionary of user-item interactions.\n","\n"," Returns:\n"," dict: A dictionary containing user playtime interaction data.\n"," \"\"\"\n","\n"," user_playtime_interactions = {}\n"," for user_id, user_data in user_item_interactions_dict.items():\n"," playtimes = []\n"," for item in user_data['items']:\n"," if item[\"item_id\"] in items_dictionary and 'playtime_forever' in item:\n"," playtimes.append(item['playtime_forever'])\n","\n"," if not playtimes:\n"," continue\n","\n"," avg_playtime = np.mean(playtimes)\n"," sorted_playtimes = np.sort(playtimes)\n","\n"," quartiles = np.percentile(sorted_playtimes, [0, 25, 50, 75, 100])\n","\n"," playtime_ranges = {\n"," '0-25': {'start': quartiles[0], 'end': quartiles[1], 'count': 0},\n"," '26-50': {'start': quartiles[1], 'end': quartiles[2], 'count': 0},\n"," '51-75': {'start': quartiles[2], 'end': quartiles[3], 'count': 0},\n"," '76-100': {'start': quartiles[3], 'end': quartiles[4], 'count': 0}\n"," }\n","\n"," for playtime in sorted_playtimes:\n"," if quartiles[0] <= playtime <= quartiles[1]:\n"," playtime_ranges['0-25']['count'] += 1\n"," elif quartiles[1] < playtime <= quartiles[2]:\n"," playtime_ranges['26-50']['count'] += 1\n"," elif quartiles[2] < playtime <= quartiles[3]:\n"," playtime_ranges['51-75']['count'] += 1\n"," elif quartiles[3] < playtime <= quartiles[4]:\n"," playtime_ranges['76-100']['count'] += 1\n","\n"," user_playtime_interactions[user_id] = {\n"," 'avg_playtime': avg_playtime,\n"," 'playtime_ranges': playtime_ranges,\n"," }\n","\n"," return user_playtime_interactions\n","\n","# Example usage\n","user_playtime_data = analyze_playtime_interaction(user_item_interactions_dict)\n","\n","#Print the result\n","for user_id, user_data in user_playtime_data.items():\n"," print(f\"User {user_id}:\")\n"," print(f\" Average Playtime: {user_data['avg_playtime']}\")\n"," for range_name, range_data in user_data['playtime_ranges'].items():\n"," print(f\" Play Time Range {range_name}: Start = {range_data['start']}, End = {range_data['end']}, Count = {range_data['count']}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X3H-gqEN5X1P"},"outputs":[],"source":["# prompt: now sort user_playtime_data based on their count in playtime_ranges and do not change the dictionary format\n","\n","# Sort playtime ranges within each user's data based on count\n","for user_id, user_data in user_playtime_data.items():\n"," sorted_playtime_ranges = dict(sorted(user_data['playtime_ranges'].items(), key=lambda item: item[1]['count'], reverse=True))\n"," user_data['playtime_ranges'] = sorted_playtime_ranges"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":594,"status":"ok","timestamp":1734848085971,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"sjuHzHQI_9Yw","outputId":"dd35ac32-e8ae-443e-b0a9-18a1c891b939"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479, Item ID: 10, Item Info: {'item_id': '10', 'item_name': 'Counter-Strike', 'playtime_forever': 6, 'playtime_2weeks': 0}\n"]}],"source":["# prompt: buat items pada user_item_interactions_dict menjadi dictionary dengan menggunakan item_id sebagai keynya\n","\n","def transform_user_item_interactions(user_item_interactions_dict):\n"," \"\"\"Transforms the 'items' list in user_item_interactions_dict to a dictionary.\n","\n"," Args:\n"," user_item_interactions_dict (dict): The input dictionary.\n","\n"," Returns:\n"," dict: A new dictionary with 'items' as a dictionary keyed by 'item_id'.\n"," \"\"\"\n"," new_dict = {}\n"," for user_id, user_data in user_item_interactions_dict.items():\n"," new_items = {}\n"," for item in user_data['items']:\n"," new_items[item['item_id']] = item # Use item_id as the key\n"," new_dict[user_id] = {'items': new_items}\n"," return new_dict\n","\n","# Example usage (assuming user_item_interactions_dict is defined)\n","transformed_interactions = transform_user_item_interactions(user_item_interactions_dict)\n","\n","# Accessing item data using the new dictionary\n","for user_id, data in transformed_interactions.items():\n"," for item_id, item_info in data['items'].items():\n"," print(f\"User: {user_id}, Item ID: {item_id}, Item Info: {item_info}\")\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":233079,"status":"ok","timestamp":1734848579395,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"lS3H6d8n8w7A","outputId":"2c38d0b5-68aa-4909-db80-d87f6e6e71e0","collapsed":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","count: 27132 of 32132\n","count: 27133 of 32132\n","count: 27134 of 32132\n","count: 27135 of 32132\n","count: 27136 of 32132\n","count: 27137 of 32132\n","count: 27138 of 32132\n","count: 27139 of 32132\n","count: 27140 of 32132\n","count: 27141 of 32132\n","count: 27142 of 32132\n","count: 27143 of 32132\n","count: 27144 of 32132\n","count: 27145 of 32132\n","count: 27146 of 32132\n","count: 27147 of 32132\n","count: 27148 of 32132\n","count: 27149 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32132\n","count: 32014 of 32132\n","count: 32015 of 32132\n","count: 32016 of 32132\n","count: 32017 of 32132\n","count: 32018 of 32132\n","count: 32019 of 32132\n","count: 32020 of 32132\n","count: 32021 of 32132\n","count: 32022 of 32132\n","count: 32023 of 32132\n","count: 32024 of 32132\n","count: 32025 of 32132\n","count: 32026 of 32132\n","count: 32027 of 32132\n","count: 32028 of 32132\n","count: 32029 of 32132\n","count: 32030 of 32132\n","count: 32031 of 32132\n","count: 32032 of 32132\n","count: 32033 of 32132\n","count: 32034 of 32132\n","count: 32035 of 32132\n","count: 32036 of 32132\n","count: 32037 of 32132\n","count: 32038 of 32132\n","count: 32039 of 32132\n","count: 32040 of 32132\n","count: 32041 of 32132\n","count: 32042 of 32132\n","count: 32043 of 32132\n","count: 32044 of 32132\n","count: 32045 of 32132\n","count: 32046 of 32132\n","count: 32047 of 32132\n","count: 32048 of 32132\n","count: 32049 of 32132\n","count: 32050 of 32132\n","count: 32051 of 32132\n","count: 32052 of 32132\n","count: 32053 of 32132\n","count: 32054 of 32132\n","count: 32055 of 32132\n","count: 32056 of 32132\n","count: 32057 of 32132\n","count: 32058 of 32132\n","count: 32059 of 32132\n","count: 32060 of 32132\n","count: 32061 of 32132\n","count: 32062 of 32132\n","count: 32063 of 32132\n","count: 32064 of 32132\n","count: 32065 of 32132\n","count: 32066 of 32132\n","count: 32067 of 32132\n","count: 32068 of 32132\n","count: 32069 of 32132\n","count: 32070 of 32132\n","count: 32071 of 32132\n","count: 32072 of 32132\n","count: 32073 of 32132\n","count: 32074 of 32132\n","count: 32075 of 32132\n","count: 32076 of 32132\n","count: 32077 of 32132\n","count: 32078 of 32132\n","count: 32079 of 32132\n","count: 32080 of 32132\n","count: 32081 of 32132\n","count: 32082 of 32132\n","count: 32083 of 32132\n","count: 32084 of 32132\n","count: 32085 of 32132\n","count: 32086 of 32132\n","count: 32087 of 32132\n","count: 32088 of 32132\n","count: 32089 of 32132\n","count: 32090 of 32132\n","count: 32091 of 32132\n","count: 32092 of 32132\n","count: 32093 of 32132\n","count: 32094 of 32132\n","count: 32095 of 32132\n","count: 32096 of 32132\n","count: 32097 of 32132\n","count: 32098 of 32132\n","count: 32099 of 32132\n","count: 32100 of 32132\n","count: 32101 of 32132\n","count: 32102 of 32132\n","count: 32103 of 32132\n","count: 32104 of 32132\n","count: 32105 of 32132\n","count: 32106 of 32132\n","count: 32107 of 32132\n","count: 32108 of 32132\n","count: 32109 of 32132\n","count: 32110 of 32132\n","count: 32111 of 32132\n","count: 32112 of 32132\n","count: 32113 of 32132\n","count: 32114 of 32132\n","count: 32115 of 32132\n","count: 32116 of 32132\n","count: 32117 of 32132\n","count: 32118 of 32132\n","count: 32119 of 32132\n","count: 32120 of 32132\n","count: 32121 of 32132\n","count: 32122 of 32132\n","count: 32123 of 32132\n","count: 32124 of 32132\n","count: 32125 of 32132\n","count: 32126 of 32132\n","count: 32127 of 32132\n","count: 32128 of 32132\n","count: 32129 of 32132\n","count: 32130 of 32132\n","count: 32131 of 32132\n"]}],"source":["# prompt: Untuk setiap item pada items_dictionary lakukan loop dan tampung playtime_forever dari transformed_interactions dalam dictionary dengan nama key user_play_times yang terikat pada current items_dictionary dengan format { user_id: playtime_forever }, jika item tidak ada pada transformed_interactions maka set user_play_times dengan {} dan simpan items_dictionary yang sudah mengandung playtime_forever dengan variable items_with_playtime_dictionary\n","\n","items_with_playtime_dictionary = {}\n","count = 0\n","for item_id, item_info in items_dictionary.items():\n"," items_with_playtime_dictionary[item_id] = item_info.copy() # Create a copy to avoid modifying the original\n"," user_play_times = {}\n"," for user_id, user_data in transformed_interactions.items():\n"," if item_id in user_data['items']:\n"," playtime_forever = user_data['items'][item_id].get('playtime_forever', 0)\n"," user_play_times[user_id] = playtime_forever\n"," items_with_playtime_dictionary[item_id]['user_play_times'] = user_play_times\n"," print(f\"count: {count} of {len(items_dictionary)}\")\n"," count += 1"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zm_f3jdgCQq9","executionInfo":{"status":"ok","timestamp":1734851907402,"user_tz":-420,"elapsed":343,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"20a5374e-c588-4855-b9e4-f49f7fdceb73"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items with user_play_times equal to 0: 23602 / 32132\n"]}],"source":["# prompt: How many items that user_play_times is 0\n","\n","zero_playtime_count = 0\n","for item_id, item_data in items_with_playtime_dictionary.items():\n"," if len(item_data['user_play_times']) == 0:\n"," zero_playtime_count += 1\n","\n","print(f\"Number of items with user_play_times equal to 0: {zero_playtime_count} / {len(items_with_playtime_dictionary)}\")"]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":560},"id":"idS0pAKKSzCc","executionInfo":{"status":"ok","timestamp":1734853020608,"user_tz":-420,"elapsed":1078,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0ea7b69d-e6c4-4f47-a020-f3a8f3f0cbdd"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"otOFRFHU0-ql","executionInfo":{"status":"ok","timestamp":1735281372817,"user_tz":-420,"elapsed":13973,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"67290359-d6f0-4456-e5f9-8cb668320417"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","metadata":{"id":"h9g0gPIn7smF"},"source":["### Save"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZBttusthosDF"},"outputs":[],"source":["# prompt: save user_price_data to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data as .json.gz\n","\n","import json\n","import gzip\n","\n","# Assuming user_price_data is defined as in the provided code\n","\n","def save_to_json_gz(data, filepath):\n"," with gzip.open(filepath, 'wt', encoding='utf-8') as f:\n"," json.dump(data, f, ensure_ascii=False, indent=2)\n","\n","# Save user_price_data to the specified file path\n","# save_to_json_gz(user_price_data, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_data.json.gz')\n","save_to_json_gz(user_bundle_data, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14432,"status":"ok","timestamp":1735296580430,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"CDyd0HguqlSL","outputId":"26074a9e-e964-45e9-d346-98074bf062a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["X saved successfully!\n","y saved successfully!\n"]}],"source":["# prompt: save X to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data\n","\n","# Assuming 'X' is defined as in the previous code\n","\n","# Save the variable X to the specified path in Google Drive\n","# Assuming X is a numpy array or a pandas DataFrame\n","# np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy', X)\n","# np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy', X)\n","# joblib.dump(X, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.pkl')\n","print(\"X saved successfully!\")\n","\n","# np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy', y)\n","# np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy', y)\n","# joblib.dump(y, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.pkl')\n","\n","np.save('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy', z)\n","print(\"y saved successfully!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fBcSzZRrtyAH"},"outputs":[],"source":["# prompt: save X using np.save as user_price_features.npy\n","\n"]}],"metadata":{"colab":{"collapsed_sections":["_T8fPo2P6CFY"],"provenance":[],"mount_file_id":"1-DKrF0h0aW_9nsChNzSvMWlAAdwSxelL","authorship_tag":"ABX9TyNCj1S++YT7IJi3ur9TtvLp"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation ANN.ipynb b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation ANN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8e1b7b9571a3d4b5c415ef132e2507e8e8c05581 --- /dev/null +++ b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation ANN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["_T8fPo2P6CFY"],"mount_file_id":"1SCVehPExmPWiDTqE0iizmY4LAf2YxdAB","authorship_tag":"ABX9TyOh0SZUiX1rMI7kJQw4FoMi"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1734855315693,"user_tz":-420,"elapsed":4116,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a1857a9c-4c1a-4185-ccb5-83d533f3827d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/114.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m112.6/114.5 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.3.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"eFg-8moUb52J"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"Egw06Nzgb8g1","executionInfo":{"status":"ok","timestamp":1735287282640,"user_tz":-420,"elapsed":8755,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# prompt: Distribution on len user_tags_and_specs_features\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming user_tags_and_specs_features is already loaded as a NumPy array\n","\n","lengths = [len(x) for x in user_tags_and_specs_features]\n","\n","plt.hist(lengths, bins=50) # Adjust the number of bins as needed\n","plt.xlabel('Length of user_tags_and_specs_features')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Lengths')\n","plt.show()\n","print(f\"The average length is {np.mean(lengths)}\")\n","print(f\"The median length is {np.median(lengths)}\")\n","print(f\"The standard deviation of length is {np.std(lengths)}\")\n","print(f\"The maximum length is {np.max(lengths)}\")\n","print(f\"The minimum length is {np.min(lengths)}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":559},"id":"yJpD_1HALjO_","executionInfo":{"status":"ok","timestamp":1735287312801,"user_tz":-420,"elapsed":1396,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"013d14e0-23b4-45d5-e0de-da2a8b0a36c9"},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["The average length is 225.0\n","The median length is 225.0\n","The standard deviation of length is 0.0\n","The maximum length is 225\n","The minimum length is 225\n"]}]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma neural networks tf.keras.Sequential([\n","# tf.keras.layers.Dense(128, activation=\"relu\"),\n","# tf.keras.layers.Dense(256, activation=\"relu\"),\n","# tf.keras.layers.Dense(128, activation=\"relu\"),\n","# tf.keras.layers.Dense(1, activation=\"sigmoid\")\n","# ]) dan gunakan cross validation dan hyperparameter tuning\n","\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.metrics import accuracy_score, classification_report\n","import numpy as np\n","\n","# Assuming 'new_bundle_recommendations' is defined as in your original code\n","# Define the neural network model\n","def create_model():\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(user_tags_and_specs_features.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n"," return model\n","\n","\n","# Hyperparameter tuning (example using a simple grid search)\n","best_accuracy = 0\n","best_params = {}\n","param_grid = {'epochs': [10, 20], 'batch_size': [32, 64]}\n","\n","for epochs in param_grid['epochs']:\n"," for batch_size in param_grid['batch_size']:\n"," kf = KFold(n_splits=5, shuffle=True, random_state=42) # 5-fold cross validation\n"," accuracies = []\n"," for train_index, val_index in kf.split(user_tags_and_specs_features):\n"," X_train, X_val = user_tags_and_specs_features[train_index], user_tags_and_specs_features[val_index]\n"," y_train, y_val = user_tags_and_specs_label[train_index], user_tags_and_specs_label[val_index]\n","\n"," model = create_model()\n"," model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1) # Train the model\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\") #make predictions\n"," accuracy = accuracy_score(y_val, y_pred)\n"," accuracies.append(accuracy)\n","\n"," avg_accuracy = np.mean(accuracies)\n"," print(f\"Epochs: {epochs}, Batch Size: {batch_size}, Average Accuracy: {avg_accuracy}\")\n","\n"," if avg_accuracy > best_accuracy:\n"," best_accuracy = avg_accuracy\n"," best_params = {'epochs': epochs, 'batch_size': batch_size}\n","\n","print(f\"Best hyperparameters: {best_params}, Best accuracy: {best_accuracy}\")\n","\n","# Train final model with best hyperparameters\n","final_model = create_model()\n","final_model.fit(user_tags_and_specs_features, user_tags_and_specs_label, epochs=best_params['epochs'], batch_size=best_params['batch_size'])\n","\n","# Example prediction (replace with your actual data)\n","example_features = user_tags_and_specs_features[0] # Example datapoint\n","prediction = (final_model.predict(np.array([example_features])) > 0.5).astype(\"int32\")\n","print(f\"Prediction for the example datapoint: {prediction}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qpcxK_8thvGV","executionInfo":{"status":"ok","timestamp":1734783414623,"user_tz":-420,"elapsed":4674115,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c33e9ca4-f6f9-4d52-e9e4-8aca885b48fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8544 - loss: 0.3322\n","Epoch 2/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2ms/step - accuracy: 0.8695 - loss: 0.2989\n","Epoch 3/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8718 - loss: 0.2929\n","Epoch 4/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8734 - loss: 0.2884\n","Epoch 5/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8730 - loss: 0.2884\n","Epoch 6/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.2893\n","Epoch 7/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2869\n","Epoch 8/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8752 - loss: 0.2841\n","Epoch 9/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8749 - loss: 0.2840\n","Epoch 10/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8730 - loss: 0.2875\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 3ms/step - accuracy: 0.8569 - loss: 0.3298\n","Epoch 2/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8701 - loss: 0.2972\n","Epoch 3/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8728 - loss: 0.2905\n","Epoch 4/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8731 - loss: 0.2886\n","Epoch 5/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8719 - loss: 0.2897\n","Epoch 6/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.2881\n","Epoch 7/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8757 - loss: 0.2815\n","Epoch 8/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8758 - loss: 0.2830\n","Epoch 9/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8766 - loss: 0.2812\n","Epoch 10/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8758 - loss: 0.2829\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - accuracy: 0.8572 - loss: 0.3305\n","Epoch 2/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8709 - loss: 0.2959\n","Epoch 3/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8725 - loss: 0.2928\n","Epoch 4/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8732 - loss: 0.2885\n","Epoch 5/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8737 - loss: 0.2878\n","Epoch 6/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.2841\n","Epoch 7/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8741 - loss: 0.2872\n","Epoch 8/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8761 - loss: 0.2843\n","Epoch 9/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2840\n","Epoch 10/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8760 - loss: 0.2816\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8550 - loss: 0.3311\n","Epoch 2/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8692 - loss: 0.2974\n","Epoch 3/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8700 - loss: 0.2936\n","Epoch 4/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8724 - loss: 0.2924\n","Epoch 5/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8736 - loss: 0.2879\n","Epoch 6/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8734 - loss: 0.2876\n","Epoch 7/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.2898\n","Epoch 8/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8740 - loss: 0.2861\n","Epoch 9/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8749 - loss: 0.2856\n","Epoch 10/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8727 - loss: 0.2862\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - accuracy: 0.8577 - loss: 0.3307\n","Epoch 2/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.2969\n","Epoch 3/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.2912\n","Epoch 4/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8713 - loss: 0.2923\n","Epoch 5/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8757 - loss: 0.2864\n","Epoch 6/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2ms/step - accuracy: 0.8739 - loss: 0.2884\n","Epoch 7/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8767 - loss: 0.2832\n","Epoch 8/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8767 - loss: 0.2823\n","Epoch 9/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - accuracy: 0.8750 - loss: 0.2865\n","Epoch 10/10\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 3ms/step - accuracy: 0.8766 - loss: 0.2824\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n","Epochs: 10, Batch Size: 32, Average Accuracy: 0.8736178851964874\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8529 - loss: 0.3358\n","Epoch 2/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8699 - loss: 0.2995\n","Epoch 3/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8713 - loss: 0.2916\n","Epoch 4/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8723 - loss: 0.2911\n","Epoch 5/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.2867\n","Epoch 6/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8727 - loss: 0.2892\n","Epoch 7/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8736 - loss: 0.2856\n","Epoch 8/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8769 - loss: 0.2829\n","Epoch 9/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8740 - loss: 0.2866\n","Epoch 10/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8744 - loss: 0.2847\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8533 - loss: 0.3329\n","Epoch 2/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8698 - loss: 0.2978\n","Epoch 3/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8721 - loss: 0.2917\n","Epoch 4/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 4ms/step - accuracy: 0.8732 - loss: 0.2890\n","Epoch 5/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8725 - loss: 0.2893\n","Epoch 6/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.8741 - loss: 0.2856\n","Epoch 7/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8752 - loss: 0.2837\n","Epoch 8/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8744 - loss: 0.2845\n","Epoch 9/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8747 - loss: 0.2835\n","Epoch 10/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8757 - loss: 0.2819\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8546 - loss: 0.3359\n","Epoch 2/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8704 - loss: 0.2957\n","Epoch 3/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8738 - loss: 0.2887\n","Epoch 4/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8747 - loss: 0.2862\n","Epoch 5/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8730 - loss: 0.2876\n","Epoch 6/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8751 - loss: 0.2860\n","Epoch 7/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8758 - loss: 0.2837\n","Epoch 8/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 4ms/step - accuracy: 0.8751 - loss: 0.2853\n","Epoch 9/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8763 - loss: 0.2825\n","Epoch 10/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8744 - loss: 0.2826\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8520 - loss: 0.3341\n","Epoch 2/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8702 - loss: 0.2976\n","Epoch 3/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8712 - loss: 0.2939\n","Epoch 4/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.8720 - loss: 0.2907\n","Epoch 5/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8735 - loss: 0.2875\n","Epoch 6/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8721 - loss: 0.2890\n","Epoch 7/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 3ms/step - accuracy: 0.8746 - loss: 0.2869\n","Epoch 8/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8737 - loss: 0.2846\n","Epoch 9/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8746 - loss: 0.2841\n","Epoch 10/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 4ms/step - accuracy: 0.8757 - loss: 0.2843\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 5ms/step - accuracy: 0.8539 - loss: 0.3344\n","Epoch 2/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8718 - loss: 0.2960\n","Epoch 3/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8719 - loss: 0.2898\n","Epoch 4/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8731 - loss: 0.2891\n","Epoch 5/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8732 - loss: 0.2889\n","Epoch 6/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8751 - loss: 0.2848\n","Epoch 7/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8747 - loss: 0.2844\n","Epoch 8/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8750 - loss: 0.2835\n","Epoch 9/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8750 - loss: 0.2846\n","Epoch 10/10\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8766 - loss: 0.2824\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n","Epochs: 10, Batch Size: 64, Average Accuracy: 0.8733243372120212\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8553 - loss: 0.3299\n","Epoch 2/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8699 - loss: 0.2992\n","Epoch 3/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2945\n","Epoch 4/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8730 - loss: 0.2902\n","Epoch 5/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.2907\n","Epoch 6/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8730 - loss: 0.2881\n","Epoch 7/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8741 - loss: 0.2847\n","Epoch 8/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8749 - loss: 0.2866\n","Epoch 9/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2841\n","Epoch 10/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8737 - loss: 0.2860\n","Epoch 11/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8755 - loss: 0.2833\n","Epoch 12/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8731 - loss: 0.2873\n","Epoch 13/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8747 - loss: 0.2829\n","Epoch 14/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 3ms/step - accuracy: 0.8752 - loss: 0.2839\n","Epoch 15/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2852\n","Epoch 16/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8740 - loss: 0.2852\n","Epoch 17/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8766 - loss: 0.2817\n","Epoch 18/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8753 - loss: 0.2821\n","Epoch 19/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8757 - loss: 0.2816\n","Epoch 20/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8761 - loss: 0.2795\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - accuracy: 0.8565 - loss: 0.3302\n","Epoch 2/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8706 - loss: 0.2967\n","Epoch 3/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8722 - loss: 0.2895\n","Epoch 4/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8735 - loss: 0.2883\n","Epoch 5/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8731 - loss: 0.2870\n","Epoch 6/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8726 - loss: 0.2886\n","Epoch 7/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8750 - loss: 0.2853\n","Epoch 8/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8738 - loss: 0.2855\n","Epoch 9/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 2ms/step - accuracy: 0.8747 - loss: 0.2836\n","Epoch 10/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8742 - loss: 0.2851\n","Epoch 11/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8729 - loss: 0.2861\n","Epoch 12/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8761 - loss: 0.2834\n","Epoch 13/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8769 - loss: 0.2810\n","Epoch 14/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 2ms/step - accuracy: 0.8766 - loss: 0.2824\n","Epoch 15/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8755 - loss: 0.2817\n","Epoch 16/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8753 - loss: 0.2831\n","Epoch 17/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8766 - loss: 0.2806\n","Epoch 18/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8762 - loss: 0.2814\n","Epoch 19/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8774 - loss: 0.2778\n","Epoch 20/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - accuracy: 0.8765 - loss: 0.2821\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - accuracy: 0.8542 - loss: 0.3311\n","Epoch 2/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8691 - loss: 0.2981\n","Epoch 3/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - accuracy: 0.8732 - loss: 0.2921\n","Epoch 4/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8740 - loss: 0.2888\n","Epoch 5/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8738 - loss: 0.2863\n","Epoch 6/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 3ms/step - accuracy: 0.8744 - loss: 0.2854\n","Epoch 7/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8737 - loss: 0.2874\n","Epoch 8/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8739 - loss: 0.2857\n","Epoch 9/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8742 - loss: 0.2851\n","Epoch 10/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8749 - loss: 0.2848\n","Epoch 11/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - accuracy: 0.8767 - loss: 0.2806\n","Epoch 12/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8759 - loss: 0.2812\n","Epoch 13/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8765 - loss: 0.2817\n","Epoch 14/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8755 - loss: 0.2813\n","Epoch 15/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8760 - loss: 0.2825\n","Epoch 16/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8754 - loss: 0.2814\n","Epoch 17/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8750 - loss: 0.2829\n","Epoch 18/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 3ms/step - accuracy: 0.8772 - loss: 0.2809\n","Epoch 19/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8765 - loss: 0.2809\n","Epoch 20/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8745 - loss: 0.2826\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8565 - loss: 0.3306\n","Epoch 2/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.2959\n","Epoch 3/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - accuracy: 0.8707 - loss: 0.2955\n","Epoch 4/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.2907\n","Epoch 5/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8719 - loss: 0.2907\n","Epoch 6/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8750 - loss: 0.2883\n","Epoch 7/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2873\n","Epoch 8/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 3ms/step - accuracy: 0.8736 - loss: 0.2878\n","Epoch 9/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2853\n","Epoch 10/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8751 - loss: 0.2851\n","Epoch 11/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 2ms/step - accuracy: 0.8737 - loss: 0.2860\n","Epoch 12/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8752 - loss: 0.2836\n","Epoch 13/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.2839\n","Epoch 14/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8753 - loss: 0.2833\n","Epoch 15/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8744 - loss: 0.2847\n","Epoch 16/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8754 - loss: 0.2825\n","Epoch 17/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8775 - loss: 0.2798\n","Epoch 18/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 3ms/step - accuracy: 0.8759 - loss: 0.2826\n","Epoch 19/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - accuracy: 0.8749 - loss: 0.2838\n","Epoch 20/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.2842\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8568 - loss: 0.3298\n","Epoch 2/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8704 - loss: 0.2983\n","Epoch 3/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8723 - loss: 0.2925\n","Epoch 4/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8730 - loss: 0.2897\n","Epoch 5/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8744 - loss: 0.2865\n","Epoch 6/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3ms/step - accuracy: 0.8752 - loss: 0.2850\n","Epoch 7/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 3ms/step - accuracy: 0.8757 - loss: 0.2850\n","Epoch 8/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - accuracy: 0.8750 - loss: 0.2837\n","Epoch 9/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8779 - loss: 0.2808\n","Epoch 10/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8763 - loss: 0.2821\n","Epoch 11/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8759 - loss: 0.2824\n","Epoch 12/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8746 - loss: 0.2843\n","Epoch 13/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8772 - loss: 0.2802\n","Epoch 14/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8763 - loss: 0.2829\n","Epoch 15/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8763 - loss: 0.2807\n","Epoch 16/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2ms/step - accuracy: 0.8756 - loss: 0.2826\n","Epoch 17/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - accuracy: 0.8775 - loss: 0.2802\n","Epoch 18/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8750 - loss: 0.2817\n","Epoch 19/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3ms/step - accuracy: 0.8749 - loss: 0.2833\n","Epoch 20/20\n","\u001b[1m4599/4599\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - accuracy: 0.8753 - loss: 0.2836\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n","Epochs: 20, Batch Size: 32, Average Accuracy: 0.8735907099551163\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 5ms/step - accuracy: 0.8546 - loss: 0.3339\n","Epoch 2/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8686 - loss: 0.2971\n","Epoch 3/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8704 - loss: 0.2948\n","Epoch 4/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8720 - loss: 0.2914\n","Epoch 5/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8720 - loss: 0.2900\n","Epoch 6/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8732 - loss: 0.2895\n","Epoch 7/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8748 - loss: 0.2858\n","Epoch 8/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8743 - loss: 0.2858\n","Epoch 9/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 6ms/step - accuracy: 0.8749 - loss: 0.2842\n","Epoch 10/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.2863\n","Epoch 11/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8744 - loss: 0.2856\n","Epoch 12/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8765 - loss: 0.2819\n","Epoch 13/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8746 - loss: 0.2829\n","Epoch 14/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8754 - loss: 0.2829\n","Epoch 15/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8742 - loss: 0.2835\n","Epoch 16/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8764 - loss: 0.2820\n","Epoch 17/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8764 - loss: 0.2802\n","Epoch 18/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 5ms/step - accuracy: 0.8751 - loss: 0.2834\n","Epoch 19/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8753 - loss: 0.2821\n","Epoch 20/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 5ms/step - accuracy: 0.8768 - loss: 0.2801\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8530 - loss: 0.3339\n","Epoch 2/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 5ms/step - accuracy: 0.8702 - loss: 0.2976\n","Epoch 3/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8723 - loss: 0.2926\n","Epoch 4/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 4ms/step - accuracy: 0.8717 - loss: 0.2897\n","Epoch 5/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8741 - loss: 0.2858\n","Epoch 6/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8735 - loss: 0.2867\n","Epoch 7/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8737 - loss: 0.2861\n","Epoch 8/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8726 - loss: 0.2880\n","Epoch 9/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8752 - loss: 0.2832\n","Epoch 10/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.2841\n","Epoch 11/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8748 - loss: 0.2814\n","Epoch 12/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8752 - loss: 0.2815\n","Epoch 13/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8762 - loss: 0.2811\n","Epoch 14/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8770 - loss: 0.2800\n","Epoch 15/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8739 - loss: 0.2834\n","Epoch 16/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.2810\n","Epoch 17/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8762 - loss: 0.2787\n","Epoch 18/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8746 - loss: 0.2833\n","Epoch 19/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.2810\n","Epoch 20/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 5ms/step - accuracy: 0.8770 - loss: 0.2803\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8534 - loss: 0.3331\n","Epoch 2/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8708 - loss: 0.2971\n","Epoch 3/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 4ms/step - accuracy: 0.8692 - loss: 0.2936\n","Epoch 4/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8747 - loss: 0.2866\n","Epoch 5/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8743 - loss: 0.2889\n","Epoch 6/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8746 - loss: 0.2844\n","Epoch 7/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 5ms/step - accuracy: 0.8741 - loss: 0.2856\n","Epoch 8/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 5ms/step - accuracy: 0.8741 - loss: 0.2855\n","Epoch 9/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8747 - loss: 0.2839\n","Epoch 10/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8745 - loss: 0.2826\n","Epoch 11/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8756 - loss: 0.2828\n","Epoch 12/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8768 - loss: 0.2806\n","Epoch 13/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.2811\n","Epoch 14/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8752 - loss: 0.2811\n","Epoch 15/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8764 - loss: 0.2805\n","Epoch 16/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8774 - loss: 0.2776\n","Epoch 17/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 4ms/step - accuracy: 0.8773 - loss: 0.2809\n","Epoch 18/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8766 - loss: 0.2795\n","Epoch 19/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 5ms/step - accuracy: 0.8759 - loss: 0.2807\n","Epoch 20/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 4ms/step - accuracy: 0.8757 - loss: 0.2793\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8562 - loss: 0.3318\n","Epoch 2/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8693 - loss: 0.2991\n","Epoch 3/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8706 - loss: 0.2930\n","Epoch 4/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8731 - loss: 0.2886\n","Epoch 5/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8730 - loss: 0.2894\n","Epoch 6/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8741 - loss: 0.2866\n","Epoch 7/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.2867\n","Epoch 8/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8763 - loss: 0.2836\n","Epoch 9/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8756 - loss: 0.2825\n","Epoch 10/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8747 - loss: 0.2849\n","Epoch 11/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8751 - loss: 0.2825\n","Epoch 12/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.2808\n","Epoch 13/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8743 - loss: 0.2836\n","Epoch 14/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.2854\n","Epoch 15/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8757 - loss: 0.2832\n","Epoch 16/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8755 - loss: 0.2824\n","Epoch 17/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8753 - loss: 0.2826\n","Epoch 18/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.2825\n","Epoch 19/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8756 - loss: 0.2828\n","Epoch 20/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8756 - loss: 0.2816\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8521 - loss: 0.3338\n","Epoch 2/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8705 - loss: 0.2961\n","Epoch 3/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8714 - loss: 0.2935\n","Epoch 4/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8741 - loss: 0.2884\n","Epoch 5/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 5ms/step - accuracy: 0.8756 - loss: 0.2863\n","Epoch 6/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8742 - loss: 0.2869\n","Epoch 7/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8735 - loss: 0.2871\n","Epoch 8/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8744 - loss: 0.2849\n","Epoch 9/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8752 - loss: 0.2822\n","Epoch 10/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8760 - loss: 0.2819\n","Epoch 11/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.8768 - loss: 0.2810\n","Epoch 12/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.8764 - loss: 0.2823\n","Epoch 13/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.8774 - loss: 0.2793\n","Epoch 14/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8772 - loss: 0.2810\n","Epoch 15/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8765 - loss: 0.2821\n","Epoch 16/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 5ms/step - accuracy: 0.8770 - loss: 0.2781\n","Epoch 17/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8779 - loss: 0.2800\n","Epoch 18/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 5ms/step - accuracy: 0.8791 - loss: 0.2769\n","Epoch 19/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8748 - loss: 0.2823\n","Epoch 20/20\n","\u001b[1m2300/2300\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.8762 - loss: 0.2789\n","\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n","Epochs: 20, Batch Size: 64, Average Accuracy: 0.8737972767477917\n","Best hyperparameters: {'epochs': 20, 'batch_size': 64}, Best accuracy: 0.8737972767477917\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 4ms/step - accuracy: 0.8564 - loss: 0.3291\n","Epoch 2/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8713 - loss: 0.2943\n","Epoch 3/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 4ms/step - accuracy: 0.8717 - loss: 0.2933\n","Epoch 4/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 4ms/step - accuracy: 0.8730 - loss: 0.2894\n","Epoch 5/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8727 - loss: 0.2900\n","Epoch 6/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 4ms/step - accuracy: 0.8743 - loss: 0.2855\n","Epoch 7/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8737 - loss: 0.2855\n","Epoch 8/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.2848\n","Epoch 9/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 5ms/step - accuracy: 0.8761 - loss: 0.2815\n","Epoch 10/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 4ms/step - accuracy: 0.8754 - loss: 0.2844\n","Epoch 11/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 4ms/step - accuracy: 0.8741 - loss: 0.2857\n","Epoch 12/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 5ms/step - accuracy: 0.8750 - loss: 0.2835\n","Epoch 13/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.2825\n","Epoch 14/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 4ms/step - accuracy: 0.8759 - loss: 0.2814\n","Epoch 15/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 4ms/step - accuracy: 0.8754 - loss: 0.2812\n","Epoch 16/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 5ms/step - accuracy: 0.8755 - loss: 0.2830\n","Epoch 17/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.8763 - loss: 0.2803\n","Epoch 18/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 4ms/step - accuracy: 0.8762 - loss: 0.2810\n","Epoch 19/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 4ms/step - accuracy: 0.8759 - loss: 0.2804\n","Epoch 20/20\n","\u001b[1m2875/2875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 5ms/step - accuracy: 0.8753 - loss: 0.2821\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step\n","Prediction for the example datapoint: [[1]]\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/neural_networks_tags_specifications.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cdFS0AWac8Q4","executionInfo":{"status":"ok","timestamp":1734855843779,"user_tz":-420,"elapsed":6440,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"db40b6ea-e2b3-4c19-9d57-d9936ea19682"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step\n"," precision recall f1-score support\n","\n"," 0 0.88 0.44 0.59 18645\n"," 1 0.62 0.94 0.75 18147\n","\n"," accuracy 0.69 36792\n"," macro avg 0.75 0.69 0.67 36792\n","weighted avg 0.75 0.69 0.67 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"FZMag36kctY2"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')"],"metadata":{"id":"nY0oy9x2cy8P"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma neural networks tf.keras.Sequential([\n","# tf.keras.layers.Dense(128, activation=\"relu\"),\n","# tf.keras.layers.Dense(256, activation=\"relu\"),\n","# tf.keras.layers.Dense(128, activation=\"relu\"),\n","# tf.keras.layers.Dense(1, activation=\"sigmoid\")\n","# ]) dan gunakan cross validation dan hyperparameter tuning\n","# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma neural networks tf.keras.Sequential([\n","# tf.keras.layers.Dense(128, activation=\"relu\"),\n","# tf.keras.layers.Dense(256, activation=\"relu\"),\n","# tf.keras.layers.Dense(128, activation=\"relu\"),\n","# tf.keras.layers.Dense(1, activation=\"sigmoid\")\n","# ]) dan gunakan cross validation dan hyperparameter tuning\n","\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.metrics import accuracy_score, classification_report\n","import numpy as np\n","\n","# Assuming 'new_bundle_recommendations' is defined as in your original code\n","# Define the neural network model\n","def create_model():\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(user_price_features.shape[1],)), # Use user_price_features shape\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n"," return model\n","\n","\n","# Hyperparameter tuning (example using a simple grid search)\n","best_accuracy = 0\n","best_params = {}\n","param_grid = {'epochs': [10, 20], 'batch_size': [32, 64]}\n","\n","for epochs in param_grid['epochs']:\n"," for batch_size in param_grid['batch_size']:\n"," kf = KFold(n_splits=5, shuffle=True, random_state=42) # 5-fold cross validation\n"," accuracies = []\n"," for train_index, val_index in kf.split(user_price_features): # Use user_price_features for splitting\n"," # Use user_price_features for X_train and X_val\n"," X_train, X_val = user_price_features[train_index], user_price_features[val_index]\n"," # Use user_price_label for y_train and y_val\n"," y_train, y_val = user_price_label[train_index], user_price_label[val_index]\n","\n"," model = create_model()\n"," model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1) # Train the model\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\") #make predictions\n"," accuracy = accuracy_score(y_val, y_pred)\n"," accuracies.append(accuracy)\n","\n"," avg_accuracy = np.mean(accuracies)\n"," print(f\"Epochs: {epochs}, Batch Size: {batch_size}, Average Accuracy: {avg_accuracy}\")\n","\n"," if avg_accuracy > best_accuracy:\n"," best_accuracy = avg_accuracy\n"," best_params = {'epochs': epochs, 'batch_size': batch_size}\n","\n","print(f\"Best hyperparameters: {best_params}, Best accuracy: {best_accuracy}\")\n","\n","# Train final model with best hyperparameters\n","final_model = create_model()\n","final_model.fit(user_tags_and_specs_features, user_tags_and_specs_label, epochs=best_params['epochs'], batch_size=best_params['batch_size'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"twU-lTQPcvo8","executionInfo":{"status":"error","timestamp":1734887316731,"user_tz":-420,"elapsed":5035727,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"49935711-c475-4659-8275-4f44383e5402"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m465s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0398\n","Epoch 2/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m460s\u001b[0m 3ms/step - accuracy: 0.9956 - loss: 0.0125\n","Epoch 3/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m506s\u001b[0m 3ms/step - accuracy: 0.9966 - loss: 0.0099\n","Epoch 4/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m503s\u001b[0m 3ms/step - accuracy: 0.9969 - loss: 0.0090\n","Epoch 5/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m503s\u001b[0m 3ms/step - accuracy: 0.9975 - loss: 0.0076\n","Epoch 6/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m466s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0071\n","Epoch 7/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m501s\u001b[0m 3ms/step - accuracy: 0.9979 - loss: 0.0068\n","Epoch 8/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m460s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0060\n","Epoch 9/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m461s\u001b[0m 3ms/step - accuracy: 0.9983 - loss: 0.0056\n","Epoch 10/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m458s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0059\n","\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m453s\u001b[0m 3ms/step - accuracy: 0.9841 - loss: 0.0398\n","Epoch 2/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m566s\u001b[0m 3ms/step - accuracy: 0.9958 - loss: 0.0121\n","Epoch 3/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m449s\u001b[0m 3ms/step - accuracy: 0.9968 - loss: 0.0093\n","Epoch 4/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m455s\u001b[0m 3ms/step - accuracy: 0.9973 - loss: 0.0080\n","Epoch 5/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m449s\u001b[0m 3ms/step - accuracy: 0.9975 - loss: 0.0074\n","Epoch 6/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m448s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0067\n","Epoch 7/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m503s\u001b[0m 3ms/step - accuracy: 0.9979 - loss: 0.0064\n","Epoch 8/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m448s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0060\n","Epoch 9/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m506s\u001b[0m 3ms/step - accuracy: 0.9981 - loss: 0.0058\n","Epoch 10/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m450s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0055\n","\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m457s\u001b[0m 3ms/step - accuracy: 0.9839 - loss: 0.0402\n","Epoch 2/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m459s\u001b[0m 3ms/step - accuracy: 0.9952 - loss: 0.0137\n","Epoch 3/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m454s\u001b[0m 3ms/step - accuracy: 0.9965 - loss: 0.0102\n","Epoch 4/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m453s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0092\n","Epoch 5/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m502s\u001b[0m 3ms/step - accuracy: 0.9974 - loss: 0.0078\n","Epoch 6/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m461s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0070\n","Epoch 7/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m463s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0062\n","Epoch 8/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m458s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0057\n","Epoch 9/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m507s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0056\n","Epoch 10/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m497s\u001b[0m 3ms/step - accuracy: 0.9984 - loss: 0.0051\n","\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m464s\u001b[0m 3ms/step - accuracy: 0.9838 - loss: 0.0406\n","Epoch 2/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m502s\u001b[0m 3ms/step - accuracy: 0.9953 - loss: 0.0131\n","Epoch 3/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m460s\u001b[0m 3ms/step - accuracy: 0.9967 - loss: 0.0100\n","Epoch 4/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m502s\u001b[0m 3ms/step - accuracy: 0.9973 - loss: 0.0084\n","Epoch 5/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m465s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0075\n","Epoch 6/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m505s\u001b[0m 3ms/step - accuracy: 0.9979 - loss: 0.0068\n","Epoch 7/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m501s\u001b[0m 3ms/step - accuracy: 0.9979 - loss: 0.0065\n","Epoch 8/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m508s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0064\n","Epoch 9/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m497s\u001b[0m 3ms/step - accuracy: 0.9978 - loss: 0.0068\n","Epoch 10/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m472s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0064\n","\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m484s\u001b[0m 3ms/step - accuracy: 0.9844 - loss: 0.0391\n","Epoch 2/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m480s\u001b[0m 3ms/step - accuracy: 0.9955 - loss: 0.0126\n","Epoch 3/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m487s\u001b[0m 3ms/step - accuracy: 0.9966 - loss: 0.0099\n","Epoch 4/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m494s\u001b[0m 3ms/step - accuracy: 0.9973 - loss: 0.0084\n","Epoch 5/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m504s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0074\n","Epoch 6/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m510s\u001b[0m 3ms/step - accuracy: 0.9979 - loss: 0.0067\n","Epoch 7/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m481s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0061\n","Epoch 8/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m480s\u001b[0m 3ms/step - accuracy: 0.9982 - loss: 0.0062\n","Epoch 9/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m488s\u001b[0m 3ms/step - accuracy: 0.9984 - loss: 0.0055\n","Epoch 10/10\n","\u001b[1m151844/151844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m494s\u001b[0m 3ms/step - accuracy: 0.9984 - loss: 0.0056\n","\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2ms/step\n","Epochs: 10, Batch Size: 32, Average Accuracy: 0.9985613477033919\n","Best hyperparameters: {'epochs': 10, 'batch_size': 32}, Best accuracy: 0.9985613477033919\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n"]},{"output_type":"error","ename":"ValueError","evalue":"Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense_28\" is incompatible with the layer: expected axis -1 of input shape to have value 10, but received input with shape (None, 225)\u001b[0m\n\nArguments received by Sequential.call():\n • inputs=tf.Tensor(shape=(None, 225), dtype=int64)\n • training=True\n • mask=None","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;31m# Train final model with best hyperparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mfinal_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mfinal_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_tags_and_specs_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_tags_and_specs_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbest_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'epochs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbest_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'batch_size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;31m# Example prediction (replace with your actual data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/layers/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m }:\n\u001b[0;32m--> 227\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 228\u001b[0m \u001b[0;34mf'Input {input_index} of layer \"{layer_name}\" is '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[0;34mf\"incompatible with the layer: expected axis {axis} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Exception encountered when calling Sequential.call().\n\n\u001b[1mInput 0 of layer \"dense_28\" is incompatible with the layer: expected axis -1 of input shape to have value 10, but received input with shape (None, 225)\u001b[0m\n\nArguments received by Sequential.call():\n • inputs=tf.Tensor(shape=(None, 225), dtype=int64)\n • training=True\n • mask=None"]}]},{"cell_type":"code","source":["\n","# Example prediction (replace with your actual data)\n","example_features = user_price_features[0] # Example datapoint\n","prediction = (model.predict(np.array([example_features])) > 0.5).astype(\"int32\")\n","print(f\"Prediction for the example datapoint: {prediction}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FXgmBUNTWiOJ","executionInfo":{"status":"ok","timestamp":1734887544151,"user_tz":-420,"elapsed":379,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"28a5c81c-e433-4a69-ef99-509a40fc8fe6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n","Prediction for the example datapoint: [[0]]\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FMRD6RqjGNbK","executionInfo":{"status":"ok","timestamp":1735051114521,"user_tz":-420,"elapsed":91190,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"386467ef-11cc-48e9-dee6-d7ee94ca95d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2ms/step\n"," precision recall f1-score support\n","\n"," 0 1.00 1.00 1.00 602313\n"," 1 1.00 1.00 1.00 612435\n","\n"," accuracy 1.00 1214748\n"," macro avg 1.00 1.00 1.00 1214748\n","weighted avg 1.00 1.00 1.00 1214748\n","\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XfpLJxOdF79c","executionInfo":{"status":"ok","timestamp":1735050941077,"user_tz":-420,"elapsed":19421,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"804fb55e-d0c2-4955-acf1-949a0e3e6594"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: save model best_mlp_classifier\n","\n","import joblib\n","\n","# Assuming neural_networks is defined as in your provided code.\n","# Assuming 'neural_networks' is your trained model\n","joblib.dump(model, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl')\n","print(\"Model saved to neural_networks.pkl\")"],"metadata":{"id":"FoYSaGaJR1wo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1734887466457,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3bf0e99e-5628-471d-cc09-85a630a08376"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to neural_networks.pkl\n"]}]},{"cell_type":"markdown","source":["### Predic Bundle"],"metadata":{"id":"unvcsZe_xUw-"}},{"cell_type":"code","source":[],"metadata":{"id":"6EVDa1hXxYv6"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Decision Tree.ipynb b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Decision Tree.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..12e1268cfe6c235584bb92d00cda1416cbdb1608 --- /dev/null +++ b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Decision Tree.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1734854038703,"user_tz":-420,"elapsed":4472,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"61ddaca1-1eff-40fc-d11a-525d3123253c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/114.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.3.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"iRZwe19LYEPm"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"u4jFU5HCYDmw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma decisition tree dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Hyperparameter Tuning using GridSearchCV\n","param_grid = {\n"," 'max_depth': [None, 10, 20, 30],\n"," 'min_samples_split': [2, 5, 10],\n"," 'min_samples_leaf': [1, 2, 4]\n","}\n","\n","dt_classifier = DecisionTreeClassifier(random_state=42)\n","grid_search = GridSearchCV(estimator=dt_classifier, param_grid=param_grid, cv=5, scoring='accuracy', verbose=True, n_jobs=-1) # Use 5-fold cross-validation\n","grid_search.fit(X_train, y_train)\n","\n","best_dt_classifier = grid_search.best_estimator_\n","print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n","\n","# Evaluate the best model using cross-validation\n","cv_scores = cross_val_score(best_dt_classifier, user_tags_and_specs_features, user_tags_and_specs_label, cv=5, scoring='accuracy', verbose=True, n_jobs=-1) # Use 5-fold cross-validation\n","print(f\"Cross-Validation Scores: {cv_scores}\")\n","print(f\"Mean Cross-Validation Score: {np.mean(cv_scores)}\")\n","\n","# Predict on the test set and evaluate the accuracy\n","y_pred = best_dt_classifier.predict(X_test)\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Test Accuracy: {accuracy}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZNJX8CrOYc_d","executionInfo":{"status":"ok","timestamp":1734855233433,"user_tz":-420,"elapsed":732251,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4eba866d-96e4-4055-f529-e05bbca417ef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 36 candidates, totalling 180 fits\n","Best Hyperparameters: {'max_depth': 30, 'min_samples_leaf': 1, 'min_samples_split': 5}\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"]},{"output_type":"stream","name":"stdout","text":["Cross-Validation Scores: [0.87084149 0.86749837 0.87478256 0.87477916 0.87630127]\n","Mean Cross-Validation Score: 0.8728405690390698\n","Test Accuracy: 0.8748912807131985\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 27.2s finished\n"]}]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred))"],"metadata":{"id":"INQEDf_Qbupl","executionInfo":{"status":"ok","timestamp":1734855292859,"user_tz":-420,"elapsed":316,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"63142595-1bf6-4f67-800d-b563a8c2c881","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," 0 0.86 0.90 0.88 18645\n"," 1 0.89 0.85 0.87 18147\n","\n"," accuracy 0.87 36792\n"," macro avg 0.88 0.87 0.87 36792\n","weighted avg 0.88 0.87 0.87 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"-uHARtrfYAse"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')"],"metadata":{"id":"MA0bSAHwsT-0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma decisition tree dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Hyperparameter Tuning using GridSearchCV\n","param_grid = {\n"," 'max_depth': [None, 10, 20, 30],\n"," 'min_samples_split': [2, 5, 10],\n"," 'min_samples_leaf': [1, 2, 4]\n","}\n","\n","dt_classifier = DecisionTreeClassifier(random_state=42)\n","grid_search = GridSearchCV(estimator=dt_classifier, param_grid=param_grid, cv=5, scoring='accuracy', verbose=True, n_jobs=-1) # Use 5-fold cross-validation\n","grid_search.fit(X_train, y_train)\n","\n","best_dt_classifier = grid_search.best_estimator_\n","print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n","\n","# Evaluate the best model using cross-validation\n","cv_scores = cross_val_score(best_dt_classifier, user_price_features, user_price_label, cv=5, scoring='accuracy', verbose=True, n_jobs=-1) # Use 5-fold cross-validation\n","print(f\"Cross-Validation Scores: {cv_scores}\")\n","print(f\"Mean Cross-Validation Score: {np.mean(cv_scores)}\")\n","\n","# Predict on the test set and evaluate the accuracy\n","y_pred = best_dt_classifier.predict(X_test)\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Test Accuracy: {accuracy}\")"],"metadata":{"id":"PyQaMKU0LLqx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1734848350797,"user_tz":-420,"elapsed":4423815,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3bcf502c-22f2-4092-c069-e076eb83f138"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 36 candidates, totalling 180 fits\n","Best Hyperparameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n","[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 2.0min finished\n"]},{"output_type":"stream","name":"stdout","text":["Cross-Validation Scores: [0.99977362 0.9997234 0.99971105 0.99978432 0.99965837]\n","Mean Cross-Validation Score: 0.999730149792385\n","Test Accuracy: 0.9998526443344627\n"]}]},{"cell_type":"code","source":["# prompt: Load /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/decision_tree_price_patterns.pkl\n","\n","import joblib\n","import pandas as pd\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/decision_tree_price_patterns.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8nxqXfTGWgzK","executionInfo":{"status":"ok","timestamp":1734854225883,"user_tz":-420,"elapsed":4367,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b7e80999-db6a-4244-cb18-f92873f3ef14"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," 0 1.00 1.00 1.00 602313\n"," 1 1.00 1.00 1.00 612435\n","\n"," accuracy 1.00 1214748\n"," macro avg 1.00 1.00 1.00 1214748\n","weighted avg 1.00 1.00 1.00 1214748\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model\n","\n","import joblib\n","\n","# Assuming 'best_dt_classifier' is your trained model\n","# joblib.dump(best_dt_classifier, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/decision_tree_model.pkl')\n","joblib.dump(best_dt_classifier, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/decision_tree_price_patterns.pkl')\n","print(\"Model saved to decision_tree_model.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SnVH0_fBnE-H","executionInfo":{"status":"ok","timestamp":1734848876693,"user_tz":-420,"elapsed":656,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"21994a41-b1f1-4bbc-aa53-59ace3667c04"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to decision_tree_model.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation KNN.ipynb b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation KNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5c3aac51f690e0259eaf16e0d76aedfdce225184 --- /dev/null +++ b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation KNN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1734864555770,"user_tz":-420,"elapsed":3886,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a69efc42-10ce-4657-b85a-f03a75f225da"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/114.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.3.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dengan menggunakan user_tags_and_specs_features sebagai X dan user_tags_and_specs_label sebagai y untuk kasus binary classification terapkan algoritma fuzzy logic yang ringan dan gunakan cross validation dan hyperparameter tuning\n","\n","import numpy as np\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.metrics import classification_report\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.neighbors import KNeighborsClassifier\n","\n","# Load data (assuming it's already loaded as in your previous code)\n","\n","# Normalize the features\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(user_tags_and_specs_features)\n","y = user_tags_and_specs_label\n","\n","\n","# Split the data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","\n","# Use KNN (a lightweight fuzzy-like approach for this case)\n","knn = KNeighborsClassifier()\n","\n","# Define hyperparameters for tuning\n","param_grid = {\n"," 'n_neighbors': [3, 5], # Try different numbers of neighbors\n"," 'weights': ['distance'] # Consider different weighting methods\n","}\n","\n","# Use GridSearchCV for hyperparameter tuning with 5-fold cross validation\n","grid_search = GridSearchCV(knn, param_grid, cv=5)\n","grid_search.fit(X_train, y_train)\n","\n","# Evaluate the model\n","y_pred = grid_search.predict(X_test)\n","\n","# Print classification report\n","print(classification_report(y_test, y_pred))\n","\n","# Print best hyperparameters\n","print(\"Best Hyperparameters:\", grid_search.best_params_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1734866533555,"user_tz":-420,"elapsed":929694,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"f00295d6-4d16-438d-a523-2c4705ff5384"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," 0 0.87 0.85 0.86 18645\n"," 1 0.85 0.86 0.86 18147\n","\n"," accuracy 0.86 36792\n"," macro avg 0.86 0.86 0.86 36792\n","weighted avg 0.86 0.86 0.86 36792\n","\n","Best Hyperparameters: {'n_neighbors': 5, 'weights': 'distance'}\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dengan menggunakan user_tags_and_specs_features sebagai X dan user_tags_and_specs_label sebagai y untuk kasus binary classification terapkan algoritma fuzzy logic yang ringan dan gunakan cross validation dan hyperparameter tuning\n","\n","import numpy as np\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.metrics import classification_report\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.neighbors import KNeighborsClassifier\n","\n","# Load data (assuming it's already loaded as in your previous code)\n","\n","# Normalize the features\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(user_price_features)\n","y = user_price_label\n","\n","\n","# Split the data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","\n","# Use KNN (a lightweight fuzzy-like approach for this case)\n","knn = KNeighborsClassifier()\n","\n","# Define hyperparameters for tuning\n","param_grid = {\n"," 'n_neighbors': [3, 5], # Try different numbers of neighbors\n"," 'weights': ['distance'] # Consider different weighting methods\n","}\n","\n","# Use GridSearchCV for hyperparameter tuning with 5-fold cross validation\n","grid_search = GridSearchCV(knn, param_grid, cv=5)\n","grid_search.fit(X_train, y_train)\n","\n","# Evaluate the model\n","y_pred = grid_search.predict(X_test)\n","\n","# Print classification report\n","print(classification_report(y_test, y_pred))\n","\n","# Print best hyperparameters\n","print(\"Best Hyperparameters:\", grid_search.best_params_)"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1734869555969,"user_tz":-420,"elapsed":2907907,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6df27a62-d894-45dd-d394-ae004e0f74a9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," 0 0.99 0.99 0.99 602313\n"," 1 0.99 0.99 0.99 612435\n","\n"," accuracy 0.99 1214748\n"," macro avg 0.99 0.99 0.99 1214748\n","weighted avg 0.99 0.99 0.99 1214748\n","\n","Best Hyperparameters: {'n_neighbors': 5, 'weights': 'distance'}\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model best_logreg_classifier\n","import joblib\n","\n","# Assuming 'best_logreg_classifier' is your trained model\n","# joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/knn_tags_specifications.pkl')\n","joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/knn_price_patterns.pkl')\n","print(\"Model saved to best_logreg_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1734869567483,"user_tz":-420,"elapsed":11523,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b5e5fa97-9fbe-4063-f4c7-43d12fd24f8b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_logreg_classifier.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Logistic Regression.ipynb b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Logistic Regression.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6b765890f78173d1856301ecc4982915d794927f --- /dev/null +++ b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Logistic Regression.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1734849151899,"user_tz":-420,"elapsed":4163,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0f6fceb3-2d1d-4601-e912-11cbf41e2032"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/114.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.3.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma logistic regression dan gunakan cross validation dan hyperparameter tuning\n","\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import accuracy_score, classification_report\n","import pandas as pd\n","import numpy as np\n","\n","# Prepare the data for logistic regression\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Hyperparameter tuning with GridSearchCV\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1], # Inverse of regularization strength\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear', 'newton-cg'] # Solvers that support both 'l1' and 'l2' penalties\n","}\n","\n","logreg_classifier = LogisticRegression(max_iter=1000, random_state=42) # Increase max_iter if needed\n","\n","grid_search = GridSearchCV(estimator=logreg_classifier, param_grid=param_grid, cv=5, scoring='accuracy', verbose=True, n_jobs=-1)\n","grid_search.fit(X_train, y_train)\n","\n","best_logreg_classifier = grid_search.best_estimator_\n","print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n","\n","# Evaluate the best model using cross-validation\n","cv_scores = cross_val_score(best_logreg_classifier, user_tags_and_specs_features, user_tags_and_specs_label, cv=5, scoring='accuracy', verbose=True, n_jobs=-1)\n","print(f\"Cross-Validation Scores: {cv_scores}\")\n","print(f\"Mean Cross-Validation Score: {np.mean(cv_scores)}\")\n","\n","# Predict on the test set and evaluate\n","y_pred = best_logreg_classifier.predict(X_test)\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Test Accuracy: {accuracy}\")\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1734854798049,"user_tz":-420,"elapsed":169114,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"45ee404b-4fb1-43ff-9624-27fc962c1a48"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 9 candidates, totalling 45 fits\n","Best Hyperparameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"]},{"output_type":"stream","name":"stdout","text":["Cross-Validation Scores: [0.86078495 0.85934442 0.86562296 0.8650757 0.87048463]\n","Mean Cross-Validation Score: 0.8642625329437228\n","Test Accuracy: 0.8667373342030876\n"," precision recall f1-score support\n","\n"," 0 0.85 0.90 0.87 18645\n"," 1 0.89 0.83 0.86 18147\n","\n"," accuracy 0.87 36792\n"," macro avg 0.87 0.87 0.87 36792\n","weighted avg 0.87 0.87 0.87 36792\n","\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 21.6s finished\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma logistic regression dan gunakan cross validation dan hyperparameter tuning\n","\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import accuracy_score, classification_report\n","import pandas as pd\n","import numpy as np\n","\n","# Prepare the data for logistic regression\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Hyperparameter tuning with GridSearchCV\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1], # Inverse of regularization strength\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear', 'newton-cg'] # Solvers that support both 'l1' and 'l2' penalties\n","}\n","\n","logreg_classifier = LogisticRegression(max_iter=1000, random_state=42) # Increase max_iter if needed\n","\n","grid_search = GridSearchCV(estimator=logreg_classifier, param_grid=param_grid, cv=5, scoring='accuracy', verbose=True, n_jobs=-1)\n","grid_search.fit(X_train, y_train)\n","\n","best_logreg_classifier = grid_search.best_estimator_\n","print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n","\n","# Evaluate the best model using cross-validation\n","cv_scores = cross_val_score(best_logreg_classifier, user_price_features, user_price_label, cv=5, scoring='accuracy', verbose=True, n_jobs=-1)\n","print(f\"Cross-Validation Scores: {cv_scores}\")\n","print(f\"Mean Cross-Validation Score: {np.mean(cv_scores)}\")\n","\n","# Predict on the test set and evaluate\n","y_pred = best_logreg_classifier.predict(X_test)\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Test Accuracy: {accuracy}\")\n","print(classification_report(y_test, y_pred))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1734850572283,"user_tz":-420,"elapsed":1218620,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c9c8ff42-f039-4aca-cbda-4ccab92484b8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 9 candidates, totalling 45 fits\n","Best Hyperparameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n","[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 2.8min finished\n"]},{"output_type":"stream","name":"stdout","text":["Cross-Validation Scores: [0.90297494 0.90737914 0.90538778 0.90579692 0.90958536]\n","Mean Cross-Validation Score: 0.9062248301705373\n","Test Accuracy: 0.9069197891249873\n"," precision recall f1-score support\n","\n"," 0 0.91 0.90 0.91 602313\n"," 1 0.91 0.91 0.91 612435\n","\n"," accuracy 0.91 1214748\n"," macro avg 0.91 0.91 0.91 1214748\n","weighted avg 0.91 0.91 0.91 1214748\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model best_logreg_classifier\n","import joblib\n","\n","# Assuming 'best_logreg_classifier' is your trained model\n","# joblib.dump(best_logreg_classifier, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/best_logreg_classifier.pkl')\n","joblib.dump(best_logreg_classifier, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/best_logreg_classifier_price_patterns.pkl')\n","print(\"Model saved to best_logreg_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1734853416272,"user_tz":-420,"elapsed":1433,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"55642a95-e88e-4e9a-bdf3-001440d194d6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_logreg_classifier.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Naive Bayes.ipynb b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Naive Bayes.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3a39ec6432f6b77cc9b04d0c5a6cb3be0ab04a2e --- /dev/null +++ b/Source Code/3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation Naive Bayes.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1734863516974,"user_tz":-420,"elapsed":3556,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7ac8993c-29fe-443f-87d1-be3316661631"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: ijson in /usr/local/lib/python3.10/dist-packages (3.3.0)\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dengan menggunakan user_tags_and_specs_features sebagai X dan user_tags_and_specs_label sebagai y untuk kasus binary classification terapkan algoritma naive bayes yang ringan dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Load data (assuming it's already loaded as numpy arrays)\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","\n","# Normalize features using MinMaxScaler\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Define the parameter grid for hyperparameter tuning\n","param_grid = {\n"," 'alpha': [0.1, 0.5, 1.0, 2.0, 5.0], # Laplace smoothing parameter\n"," 'binarize': [0.0, 0.5, 1.0] # Threshold for binarizing input features\n","}\n","\n","\n","# Initialize the Bernoulli Naive Bayes classifier\n","bnb = BernoulliNB()\n","\n","# Perform GridSearchCV for hyperparameter tuning\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=True) #n_jobs=-1 to use all cores\n","grid_search.fit(X_train, y_train)\n","\n","# Get the best model and its hyperparameters\n","best_bnb = grid_search.best_estimator_\n","best_params = grid_search.best_params_\n","print(f\"Best hyperparameters: {best_params}\")\n","\n","# Evaluate the best model on the test set\n","y_pred = best_bnb.predict(X_test)\n","\n","# Print classification report and accuracy\n","print(classification_report(y_test, y_pred))\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Accuracy: {accuracy}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1734863640035,"user_tz":-420,"elapsed":38357,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"05c2712b-d299-409f-810d-02f8cc5694f6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","Best hyperparameters: {'alpha': 1.0, 'binarize': 0.0}\n"," precision recall f1-score support\n","\n"," 0 0.81 0.85 0.83 18645\n"," 1 0.84 0.79 0.82 18147\n","\n"," accuracy 0.82 36792\n"," macro avg 0.83 0.82 0.82 36792\n","weighted avg 0.83 0.82 0.82 36792\n","\n","Accuracy: 0.8246086105675147\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dengan menggunakan user_tags_and_specs_features sebagai X dan user_tags_and_specs_label sebagai y untuk kasus binary classification terapkan algoritma naive bayes yang ringan dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Load data (assuming it's already loaded as numpy arrays)\n","X = user_price_features\n","y = user_price_label\n","\n","# Normalize features using MinMaxScaler\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Define the parameter grid for hyperparameter tuning\n","param_grid = {\n"," 'alpha': [0.1, 0.5, 1.0, 2.0, 5.0], # Laplace smoothing parameter\n"," 'binarize': [0.0, 0.5, 1.0] # Threshold for binarizing input features\n","}\n","\n","\n","# Initialize the Bernoulli Naive Bayes classifier\n","bnb = BernoulliNB()\n","\n","# Perform GridSearchCV for hyperparameter tuning\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=True) #n_jobs=-1 to use all cores\n","grid_search.fit(X_train, y_train)\n","\n","# Get the best model and its hyperparameters\n","best_bnb = grid_search.best_estimator_\n","best_params = grid_search.best_params_\n","print(f\"Best hyperparameters: {best_params}\")\n","\n","# Evaluate the best model on the test set\n","y_pred = best_bnb.predict(X_test)\n","\n","# Print classification report and accuracy\n","print(classification_report(y_test, y_pred))\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Accuracy: {accuracy}\")"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1734863822534,"user_tz":-420,"elapsed":124848,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3ec09add-4e79-49ad-b27d-749db3de66a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","Best hyperparameters: {'alpha': 0.1, 'binarize': 0.0}\n"," precision recall f1-score support\n","\n"," 0 0.70 0.60 0.65 602313\n"," 1 0.66 0.75 0.70 612435\n","\n"," accuracy 0.68 1214748\n"," macro avg 0.68 0.68 0.67 1214748\n","weighted avg 0.68 0.68 0.67 1214748\n","\n","Accuracy: 0.6756710033685999\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model best_logreg_classifier\n","import joblib\n","\n","# Assuming 'best_logreg_classifier' is your trained model\n","# joblib.dump(best_bnb, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/best_bnb_tags_specifications.pkl')\n","joblib.dump(best_bnb, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/best_bnb_price_patterns.pkl')\n","print(\"Model saved to best_logreg_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1734863856699,"user_tz":-420,"elapsed":313,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"93e13fdf-4a14-4169-aaf1-df24c3129d93"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_logreg_classifier.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/4. Judge Bundle For Each Users DT.ipynb b/Source Code/4. Judge Bundle For Each Users DT.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..682f0a8b8998eb1eae981cbbde616ef00254141d --- /dev/null +++ b/Source Code/4. Judge Bundle For Each Users DT.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16607,"status":"ok","timestamp":1735781615069,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"TqtPuipCMpzT","outputId":"78849c72-039d-4bfc-e1b9-1c2b1b8d8c15"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded model: decision_tree_price_patterns\n","Loaded model: best_logreg_classifier_price_patterns\n","Loaded model: best_sgd_classifier_price_patterns\n","Loaded model: best_bnb_price_patterns\n","Loaded model: knn_price_patterns\n","Loaded model: neural_networks_price_patterns\n","Loaded 6 models.\n"]}],"source":["# prompt: Load all models from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications using joblib\n","\n","import joblib\n","import os\n","\n","# models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications\"\n","models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns\"\n","loaded_models = {}\n","\n","for filename in os.listdir(models_path):\n"," if filename.endswith(\".pkl\"):\n"," model_path = os.path.join(models_path, filename)\n"," model_name = filename[:-4] # Remove \".joblib\" extension\n"," try:\n"," loaded_models[model_name] = joblib.load(model_path)\n"," print(f\"Loaded model: {model_name}\")\n"," except Exception as e:\n"," print(f\"Error loading model {model_name}: {e}\")\n","\n","print(f\"Loaded {len(loaded_models)} models.\")\n"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1735781054946,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LXdGNRK8RcwY"},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"cs-PpqN8TbRM"},"source":["## Bundle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1735303253454,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"oBdZF6YtO3Ef","outputId":"9bf13c93-1555-4e6b-a0f0-040196dd9051"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1735303253456,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JQDVAG3bRiAH","outputId":"e686321a-8c30-4baa-df71-49ab65d86046"},"outputs":[{"data":{"text/plain":["{'bundle_id': '450',\n"," 'bundle_price': 73.86,\n"," 'bundle_name': 'Dharker Studio 2015 Complete',\n"," 'items': {'326950',\n"," '331490',\n"," '331491',\n"," '331492',\n"," '348540',\n"," '352010',\n"," '387110',\n"," '387120',\n"," '390470',\n"," '394050',\n"," '396610',\n"," '396620',\n"," '398100',\n"," '408770'}}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["bundle_data[\"450\"]"]},{"cell_type":"markdown","metadata":{"id":"SY4SrF4sTdmZ"},"source":["## Items Dictionary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3800,"status":"ok","timestamp":1735303258466,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"pXudpCiWTffK","outputId":"c450dfec-0cfa-473c-fe6c-ccb71184a906"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"PAxnBn8zUwfT"},"source":["## User Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":43890,"status":"ok","timestamp":1735303302353,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"RWWQ3GPvUy1a","outputId":"551041c0-2c5d-4589-c662-9ea0ef6e77c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. 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SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"IInSMlBIUVNv"},"source":["## Create Input For Model Tags and Specs"]},{"cell_type":"markdown","metadata":{"id":"LwqMdBsgbrC_"},"source":["#### 1. Load Hot Persona Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24858,"status":"ok","timestamp":1735303327208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x4BKOF2sWkLC","outputId":"e7e4123f-9849-44ab-8ad8-63bda828cf70"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXPeELdRXNhn"},"outputs":[],"source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10,"status":"ok","timestamp":1735303327209,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"SHGaOHecX37a","outputId":"b356d01f-5e28-4f71-96c1-380e8066b968"},"outputs":[{"data":{"text/plain":["{'Fighting': 0,\n"," 'Action RPG': 0,\n"," 'Retro': 0,\n"," 'Bullet Hell': 0,\n"," 'Base Building': 0,\n"," 'Turn-Based Strategy': 0,\n"," 'Early Access': 0,\n"," 'Hex Grid': 0,\n"," 'Kickstarter': 0,\n"," 'Resource Management': 0,\n"," 'Abstract': 0,\n"," '4 Player Local': 0,\n"," 'Sniper': 0,\n"," 'Transhumanism': 0,\n"," 'Metroidvania': 0,\n"," 'Episodic': 0,\n"," 'e-sports': 0,\n"," 'Based On A Novel': 0,\n"," 'Crime': 0,\n"," 'Turn-Based Combat': 0,\n"," 'Vampire': 0,\n"," 'Level Editor': 0,\n"," 'Programming': 0,\n"," 'Gore': 0,\n"," '2D': 0,\n"," 'Colorful': 0,\n"," 'Co-op': 0,\n"," 'Puzzle': 0,\n"," 'Hack and Slash': 0,\n"," 'Dystopian': 0,\n"," '3D Vision': 0,\n"," 'Futuristic': 0,\n"," 'Hidden Object': 0,\n"," 'Post-apocalyptic': 0,\n"," 'Comic Book': 0,\n"," 'Linear': 0,\n"," 'Online Co-Op': 0,\n"," 'Stylized': 0,\n"," 'Dragons': 0,\n"," 'Rogue-lite': 0,\n"," 'Utilities': 0,\n"," 'Dark Humor': 0,\n"," 'Romance': 0,\n"," 'Economy': 0,\n"," 'Rogue-like': 0,\n"," 'Isometric': 0,\n"," \"1990's\": 0,\n"," 'Destruction': 0,\n"," 'Magic': 0,\n"," 'Funny': 0,\n"," 'Assassin': 0,\n"," 'Parkour': 0,\n"," 'Anime': 0,\n"," 'Multiple Endings': 0,\n"," 'Quick-Time Events': 0,\n"," 'Mystery': 0,\n"," 'Survival Horror': 0,\n"," 'Real-Time with Pause': 0,\n"," 'Psychological Horror': 0,\n"," 'Dark Fantasy': 0,\n"," 'Memes': 0,\n"," 'Cinematic': 0,\n"," 'Party-Based RPG': 0,\n"," 'Free to Play': 0,\n"," 'Tower Defense': 0,\n"," 'War': 0,\n"," 'Co-op Campaign': 0,\n"," 'Family Friendly': 0,\n"," 'Card Game': 0,\n"," 'Management': 0,\n"," 'Narration': 0,\n"," 'Procedural Generation': 0,\n"," 'Inventory Management': 0,\n"," 'Animation & Modeling': 0,\n"," 'Lara Croft': 0,\n"," 'Sexual Content': 0,\n"," 'Team-Based': 0,\n"," 'Gambling': 0,\n"," 'Fantasy': 0,\n"," 'RTS': 0,\n"," 'Loot': 0,\n"," 'Sci-fi': 0,\n"," 'Casual': 0,\n"," 'Web Publishing': 0,\n"," 'World War II': 0,\n"," \"Beat 'em up\": 0,\n"," 'RPG': 0,\n"," 'Local Co-Op': 0,\n"," 'Pirates': 0,\n"," 'CRPG': 0,\n"," 'Short': 0,\n"," 'Dungeon Crawler': 0,\n"," 'Character Customization': 0,\n"," 'Pinball': 0,\n"," 'VR': 0,\n"," 'Twin Stick Shooter': 0,\n"," 'Third-Person Shooter': 0,\n"," 'GameMaker': 0,\n"," 'Detective': 0,\n"," 'Stealth': 0,\n"," 'Platformer': 0,\n"," 'Walking Simulator': 0,\n"," 'Space': 0,\n"," 'Local Multiplayer': 0,\n"," 'Strategy': 0,\n"," 'Physics': 0,\n"," 'Steampunk': 0,\n"," 'Text-Based': 0,\n"," 'Action': 0,\n"," 'Mechs': 0,\n"," 'Side Scroller': 0,\n"," 'Multiplayer': 0,\n"," 'Turn-Based': 0,\n"," 'FPS': 0,\n"," 'Story Rich': 0,\n"," 'Sandbox': 0,\n"," 'Interactive Fiction': 0,\n"," 'Medieval': 0,\n"," 'Aliens': 0,\n"," 'Atmospheric': 0,\n"," 'Moddable': 0,\n"," 'Open World': 0,\n"," 'Cult Classic': 0,\n"," 'Time Travel': 0,\n"," 'Realistic': 0,\n"," 'Split Screen': 0,\n"," 'Choose Your Own Adventure': 0,\n"," 'Science': 0,\n"," 'Underwater': 0,\n"," 'Female Protagonist': 0,\n"," 'Competitive': 0,\n"," 'Shooter': 0,\n"," 'Software': 0,\n"," 'Software Training': 0,\n"," 'Mature': 0,\n"," 'Demons': 0,\n"," 'Cyberpunk': 0,\n"," '4X': 0,\n"," 'Zombies': 0,\n"," 'Simulation': 0,\n"," 'PvP': 0,\n"," 'Nudity': 0,\n"," 'Horror': 0,\n"," 'Survival': 0,\n"," 'Massively Multiplayer': 0,\n"," 'Voxel': 0,\n"," 'Tactical': 0,\n"," 'FMV': 0,\n"," 'First-Person': 0,\n"," 'Exploration': 0,\n"," 'Dark': 0,\n"," 'Adventure': 0,\n"," 'Silent Protagonist': 0,\n"," 'Controller': 0,\n"," 'Remake': 0,\n"," 'Diplomacy': 0,\n"," 'Military': 0,\n"," 'Spectacle fighter': 0,\n"," 'Sports': 0,\n"," 'Visual Novel': 0,\n"," 'Grand Strategy': 0,\n"," 'Relaxing': 0,\n"," 'Real Time Tactics': 0,\n"," 'Top-Down Shooter': 0,\n"," 'Alternate History': 0,\n"," 'Comedy': 0,\n"," 'Villain Protagonist': 0,\n"," 'Singleplayer': 0,\n"," 'Puzzle-Platformer': 0,\n"," 'Class-Based': 0,\n"," 'Touch-Friendly': 0,\n"," 'Trading': 0,\n"," 'City Builder': 0,\n"," 'Difficult': 0,\n"," \"Shoot 'Em Up\": 0,\n"," 'RPGMaker': 0,\n"," 'Replay Value': 0,\n"," 'Design & Illustration': 0,\n"," 'Cute': 0,\n"," 'God Game': 0,\n"," 'Point & Click': 0,\n"," 'Political': 0,\n"," 'Third Person': 0,\n"," 'Pixel Graphics': 0,\n"," 'Choices Matter': 0,\n"," 'Match 3': 0,\n"," 'Crafting': 0,\n"," 'Game Development': 0,\n"," 'Top-Down': 0,\n"," 'Education': 0,\n"," 'Indie': 0,\n"," 'Driving': 0,\n"," 'Hand-drawn': 0,\n"," 'Arcade': 0,\n"," 'Drama': 0,\n"," 'Robots': 0,\n"," 'Great Soundtrack': 0,\n"," 'Fast-Paced': 0,\n"," 'Classic': 0,\n"," 'Racing': 0,\n"," 'Historical': 0,\n"," 'Action-Adventure': 0,\n"," 'JRPG': 0,\n"," 'Superhero': 0}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["input_default_tags"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":9,"status":"ok","timestamp":1735303327210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"GbhHKsFvX5Vb","outputId":"4b02a1c8-284c-408f-92b6-c7a193f8012c"},"outputs":[{"data":{"text/plain":["{'Steam Workshop': 0,\n"," 'Multi-player': 0,\n"," 'Valve Anti-Cheat enabled': 0,\n"," 'Captions available': 0,\n"," 'Local Multi-Player': 0,\n"," 'Steam Trading Cards': 0,\n"," 'Steam Leaderboards': 0,\n"," 'Stats': 0,\n"," 'Shared/Split Screen': 0,\n"," 'Co-op': 0,\n"," 'Single-player': 0,\n"," 'Partial Controller Support': 0,\n"," 'Full controller support': 0,\n"," 'Steam Achievements': 0,\n"," 'Commentary available': 0,\n"," 'Includes level editor': 0,\n"," 'Online Multi-Player': 0,\n"," 'Cross-Platform Multiplayer': 0,\n"," 'Includes Source SDK': 0,\n"," 'In-App Purchases': 0,\n"," 'Steam Cloud': 0}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["input_default_specs"]},{"cell_type":"markdown","metadata":{"id":"E8Y-taqrb1g_"},"source":["#### 2. Check Hot Persona Tags and Specs For Each User Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5761,"status":"ok","timestamp":1735303332964,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UDTwrBJqUcjY","outputId":"a7ffbd3e-b3a4-4df2-d2d8-dfef49865c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Tags: {'Fighting': 1, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 1, 'Base Building': 1, 'Turn-Based Strategy': 1, 'Early Access': 1, 'Hex Grid': 1, 'Kickstarter': 1, 'Resource Management': 1, 'Abstract': 1, '4 Player Local': 1, 'Sniper': 0, 'Transhumanism': 1, 'Metroidvania': 1, 'Episodic': 1, 'e-sports': 1, 'Based On A Novel': 1, 'Crime': 1, 'Turn-Based Combat': 1, 'Vampire': 0, 'Level Editor': 1, 'Programming': 1, 'Gore': 1, '2D': 1, 'Colorful': 1, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 1, 'Dystopian': 1, '3D Vision': 1, 'Futuristic': 1, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 1, 'Linear': 1, 'Online Co-Op': 1, 'Stylized': 1, 'Dragons': 1, 'Rogue-lite': 1, 'Utilities': 0, 'Dark Humor': 1, 'Romance': 1, 'Economy': 1, 'Rogue-like': 1, 'Isometric': 1, \"1990's\": 1, 'Destruction': 1, 'Magic': 1, 'Funny': 1, 'Assassin': 1, 'Parkour': 1, 'Anime': 1, 'Multiple Endings': 1, 'Quick-Time Events': 1, 'Mystery': 1, 'Survival Horror': 1, 'Real-Time with Pause': 1, 'Psychological Horror': 1, 'Dark Fantasy': 1, 'Memes': 1, 'Cinematic': 1, 'Party-Based RPG': 1, 'Free to Play': 1, 'Tower Defense': 1, 'War': 1, 'Co-op Campaign': 1, 'Family Friendly': 1, 'Card Game': 1, 'Management': 1, 'Narration': 1, 'Procedural Generation': 1, 'Inventory Management': 1, 'Animation & Modeling': 0, 'Lara Croft': 1, 'Sexual Content': 1, 'Team-Based': 1, 'Gambling': 1, 'Fantasy': 1, 'RTS': 1, 'Loot': 1, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 1, \"Beat 'em up\": 1, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 1, 'CRPG': 1, 'Short': 1, 'Dungeon Crawler': 1, 'Character Customization': 1, 'Pinball': 0, 'VR': 1, 'Twin Stick Shooter': 1, 'Third-Person Shooter': 1, 'GameMaker': 0, 'Detective': 1, 'Stealth': 1, 'Platformer': 1, 'Walking Simulator': 1, 'Space': 1, 'Local Multiplayer': 1, 'Strategy': 1, 'Physics': 1, 'Steampunk': 1, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 1, 'Multiplayer': 1, 'Turn-Based': 1, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 1, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 1, 'Time Travel': 1, 'Realistic': 1, 'Split Screen': 1, 'Choose Your Own Adventure': 1, 'Science': 1, 'Underwater': 1, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 1, 'Cyberpunk': 1, '4X': 1, 'Zombies': 1, 'Simulation': 1, 'PvP': 1, 'Nudity': 1, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 1, 'Voxel': 1, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 1, 'Dark': 1, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 1, 'Remake': 1, 'Diplomacy': 1, 'Military': 1, 'Spectacle fighter': 1, 'Sports': 1, 'Visual Novel': 1, 'Grand Strategy': 1, 'Relaxing': 1, 'Real Time Tactics': 1, 'Top-Down Shooter': 1, 'Alternate History': 1, 'Comedy': 1, 'Villain Protagonist': 1, 'Singleplayer': 1, 'Puzzle-Platformer': 1, 'Class-Based': 1, 'Touch-Friendly': 1, 'Trading': 1, 'City Builder': 1, 'Difficult': 1, \"Shoot 'Em Up\": 1, 'RPGMaker': 1, 'Replay Value': 1, 'Design & Illustration': 0, 'Cute': 1, 'God Game': 1, 'Point & Click': 1, 'Political': 1, 'Third Person': 1, 'Pixel Graphics': 1, 'Choices Matter': 1, 'Match 3': 0, 'Crafting': 1, 'Game Development': 0, 'Top-Down': 1, 'Education': 1, 'Indie': 1, 'Driving': 1, 'Hand-drawn': 1, 'Arcade': 1, 'Drama': 1, 'Robots': 1, 'Great Soundtrack': 1, 'Fast-Paced': 1, 'Classic': 1, 'Racing': 1, 'Historical': 1, 'Action-Adventure': 1, 'JRPG': 1, 'Superhero': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","user_item_tags = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_tags[user_id] = input_default_tags.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," user_item_tags[user_id][tag] = 1\n","\n","# Example usage (you can print or process user_item_tags as needed):\n","for user_id, item_tags in user_item_tags.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3306,"status":"ok","timestamp":1735303336267,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LxfFCIj9Zkrc","outputId":"a6a0993a-4155-4ebb-b569-f7ce8fc03610"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Specs: {'Steam Workshop': 1, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 1, 'Local Multi-Player': 1, 'Steam Trading Cards': 1, 'Steam Leaderboards': 1, 'Stats': 1, 'Shared/Split Screen': 1, 'Co-op': 1, 'Single-player': 1, 'Partial Controller Support': 1, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 1, 'Includes level editor': 1, 'Online Multi-Player': 1, 'Cross-Platform Multiplayer': 1, 'Includes Source SDK': 1, 'In-App Purchases': 1, 'Steam Cloud': 1}\n"]}],"source":["user_item_specs = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_specs[user_id] = input_default_specs.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," user_item_specs[user_id][spec] = 1\n","\n","# Example usage (you can print or process user_item_specs as needed):\n","for user_id, item_specs in user_item_specs.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"lziPgL49cJXd"},"source":["#### 2. Check Hot Persona Tags and Specs For Each Bundle Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"7DQdfGE0aZn8","outputId":"2b59b46a-2354-4f70-a08e-17f6feb5e6ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["Bundle: 450\n","Tags: {'Fighting': 0, 'Action RPG': 0, 'Retro': 0, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 1, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 0, '2D': 0, 'Colorful': 0, 'Co-op': 0, 'Puzzle': 0, 'Hack and Slash': 0, 'Dystopian': 0, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 0, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 0, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 1, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 0, 'Destruction': 0, 'Magic': 0, 'Funny': 0, 'Assassin': 0, 'Parkour': 0, 'Anime': 1, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 0, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 1, 'Team-Based': 0, 'Gambling': 0, 'Fantasy': 0, 'RTS': 0, 'Loot': 0, 'Sci-fi': 0, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 0, 'Local Co-Op': 0, 'Pirates': 0, 'CRPG': 0, 'Short': 0, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 0, 'Walking Simulator': 0, 'Space': 0, 'Local Multiplayer': 0, 'Strategy': 0, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 0, 'Turn-Based': 0, 'FPS': 0, 'Story Rich': 0, 'Sandbox': 0, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 0, 'Atmospheric': 0, 'Moddable': 0, 'Open World': 0, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 0, 'Competitive': 0, 'Shooter': 0, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 0, 'Simulation': 0, 'PvP': 0, 'Nudity': 1, 'Horror': 0, 'Survival': 0, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 0, 'FMV': 0, 'First-Person': 0, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 0, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 0, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 1, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 0, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 0, \"Shoot 'Em Up\": 1, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 0, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 0, 'Fast-Paced': 0, 'Classic': 0, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}\n","Bundle: 450\n","Specs: {'Steam Workshop': 0, 'Multi-player': 0, 'Valve Anti-Cheat enabled': 0, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 0, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","bundle_item_tags = {}\n","bundle_item_specs = {}\n","\n","for bundle_id, bundle_items in bundle_data.items():\n"," bundle_item_tags[bundle_id] = input_default_tags.copy()\n"," bundle_item_specs[bundle_id] = input_default_specs.copy()\n","\n"," for item_id in bundle_items[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," bundle_item_tags[bundle_id][tag] = 1\n","\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," bundle_item_specs[bundle_id][spec] = 1\n","\n","# Example usage (you can print or process bundle_item_tags as needed):\n","for bundle_id, item_tags in bundle_item_tags.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break\n","\n","# Example usage (you can print or process bundle_item_specs as needed):\n","for bundle_id, item_specs in bundle_item_specs.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"_qoICCA4cPyU"},"source":["#### 3. Get Intersection Between Persona User Item Interactions and Persona Bundle Items Based On Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"EFAWQsJgcb3G","outputId":"24288744-33ad-4a78-bd2f-d77696610862"},"outputs":[{"name":"stdout","output_type":"stream","text":["Intersection for user 76561197970982479 and bundle 450:\n","Tags: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","Specs: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"]}],"source":["# prompt: Get Intersection Between Persona User Item Interactions and Persona Bundle Items By Tags and Specs That Have Value == 1\n","\n","def get_intersection(user_id, bundle_id):\n"," intersection_tags = {}\n"," intersection_specs = {}\n","\n"," for tag, value in user_item_tags[user_id].items():\n"," if value == 1 and bundle_item_tags[bundle_id][tag] == 1:\n"," intersection_tags[tag] = 1\n"," else:\n"," intersection_tags[tag] = 0\n","\n"," for spec, value in user_item_specs[user_id].items():\n"," if value == 1 and bundle_item_specs[bundle_id][spec] == 1:\n"," intersection_specs[spec] = 1\n"," else:\n"," intersection_specs[spec] = 0\n","\n"," return intersection_tags, intersection_specs\n","\n","def get_intersection_as_list(user_id, bundle_id):\n"," user_tags = user_item_tags[user_id]\n"," bundle_tags = bundle_item_tags[bundle_id]\n"," user_specs = user_item_specs[user_id]\n"," bundle_specs = bundle_item_specs[bundle_id]\n","\n"," # Using list comprehensions to find common tags and specs\n"," common_tags = [1 if user_tags[tag] == 1 and bundle_tags[tag] == 1 else 0 for tag in user_tags]\n"," common_specs = [1 if user_specs[spec] == 1 and bundle_specs[spec] == 1 else 0 for spec in user_specs]\n","\n"," return common_tags, common_specs\n","\n","# Example usage:\n","user_id = list(user_item_interactions.keys())[0] # Get the first user ID\n","bundle_id = list(bundle_data.keys())[0] # Get the first bundle ID\n","\n","intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n","\n","print(f\"Intersection for user {user_id} and bundle {bundle_id}:\")\n","print(f\"Tags: {intersection_tags}\")\n","print(f\"Specs: {intersection_specs}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":309041,"status":"ok","timestamp":1735305675372,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"BuFxKyM6cyCv","outputId":"fad7d31b-9e29-4644-80b1-456508338d8c","collapsed":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 / 10.000\n","4898 / 10.000\n","4899 / 10.000\n","4900 / 10.000\n","4901 / 10.000\n","4902 / 10.000\n","4903 / 10.000\n","4904 / 10.000\n","4905 / 10.000\n","4906 / 10.000\n","4907 / 10.000\n","4908 / 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intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n"," data_to_write[user_id][bundle_id] = {\n"," \"tags\": intersection_tags,\n"," \"specs\": intersection_specs\n"," }\n","\n"," json.dump(data_to_write, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," print(f\"{i} / 10.000\")"]},{"cell_type":"markdown","metadata":{"id":"45XFujQeHeG-"},"source":["#### Do Recommendation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XzQCZ1fmHh5D","outputId":"5b85258a-7111-4cb4-d49c-b74806f220bb","executionInfo":{"status":"ok","timestamp":1735350261826,"user_tz":-420,"elapsed":2192160,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"collapsed":true},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 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'/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_dt.json.gz'\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","data_dict_out = None\n","\n","file_path_read = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_dt.json.gz\"\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_read, 'rt', encoding='utf-8') as infile, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," data_dict_out = {}\n"," for user_id, bundle_data in data_dict.items():\n"," for bundle_id, recommendation in bundle_data.items():\n"," data_dict_out[\"user_id\"] = user_id\n"," data_dict_out[\"bundle_id\"] = bundle_id\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['decision_tree_model_tags_specifications'].predict([recommendation['tags'] + recommendation['specs']])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," # break\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # break\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":322,"status":"ok","timestamp":1735348014563,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"MnRRI8KSaOgO","outputId":"eb39c0d7-9945-476a-9865-81173a73e948"},"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_dt.json.gz'\n","Count: 215\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_dt.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"]},{"cell_type":"markdown","metadata":{"id":"na7Ok_umxQnJ"},"source":["## Create Input For Model Price Patterns"]},{"cell_type":"markdown","source":["#### 1. Load Data"],"metadata":{"id":"p6y7SbgJmBRo"}},{"cell_type":"code","execution_count":5,"metadata":{"id":"pmSwZ6yPxdN5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735779833843,"user_tz":-420,"elapsed":356,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8c848c26-3751-4b2c-80a3-162ed9f10b10"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":5}],"source":["# prompt: Load file /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy\n","\n","import numpy as np\n","\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')\n","\n","user_price_label[:5]"]},{"cell_type":"code","source":["# prompt: Calculate how many 0 and 1 in user_price_label[:615]\n","\n","zeros = np.count_nonzero(user_price_label[:615] == 0)\n","ones = np.count_nonzero(user_price_label[:615] == 1)\n","\n","print(f\"Number of 0s: {zeros}\")\n","print(f\"Number of 1s: {ones}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBye6gkth3l6","executionInfo":{"status":"ok","timestamp":1735779703855,"user_tz":-420,"elapsed":329,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4ec05a48-bcfd-47ba-d5a6-9e13b12eae2d"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of 0s: 582\n","Number of 1s: 33\n"]}]},{"cell_type":"code","source":["user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')"],"metadata":{"id":"2LoxPq-8iYe3","executionInfo":{"status":"ok","timestamp":1735780718040,"user_tz":-420,"elapsed":6523,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["user_price_identity = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy')"],"metadata":{"id":"Jqtxa1XYl2sa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Find is that from user_price_identity user for all bundle theres no label 1 in user_price_label\n","\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","check_users = {}\n","\n","# Iterate through users and bundles (assuming bundle IDs are consecutive integers)\n","for user_index in range(num_users):\n"," user_id = user_price_identity[user_index]\n"," if user_id[0] not in check_users:\n"," check_users[user_id[0]] = 0\n","\n"," # Check if label is present for this user\n"," if user_price_label[user_index] is not None: # Check for None or NaN if necessary\n"," # Access the label directly instead of iterating\n"," if user_price_label[user_index] == 1:\n"," # Assuming bundle_id is the second element in user_price_identity\n"," check_users[user_id[0]] = 1\n"," else:\n"," print(f\"No labels found for user {user_id}\")"],"metadata":{"id":"g_NWI-KDiuIF","executionInfo":{"status":"ok","timestamp":1735780578611,"user_tz":-420,"elapsed":8898,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: From check_users check is there has 0 value?\n","\n","zeros = 0\n","for user_id, label in check_users.items():\n"," if label == 0:\n"," zeros += 1\n","\n","print(f\"Number of users with 0 value: {zeros}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qD-mnjmHlcqD","executionInfo":{"status":"ok","timestamp":1735780608038,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ddd2f1e-dea8-44b9-9f94-94adbc1bca4c"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of users with 0 value: 23\n"]}]},{"cell_type":"markdown","source":["#### 2. Do Recommendation"],"metadata":{"id":"TKiv8OUzmEF5"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict_out = {}\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_dt.json.gz\"\n","count = 0\n","\n","check_users = {}\n","\n","try:\n"," with gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for user_index in range(num_users):\n"," data_dict_out[\"user_id\"] = user_price_identity[user_index][0]\n"," data_dict_out[\"bundle_id\"] = user_price_identity[user_index][1]\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['decision_tree_price_patterns'].predict([user_price_features[user_index]])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," if user_price_identity[user_index][0] not in check_users:\n"," check_users[user_price_identity[user_index][0]] = 0\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # if count == 2:\n"," # break\n"," print(f\"Successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1nbQtXeoa1M","executionInfo":{"status":"ok","timestamp":1735784059857,"user_tz":-420,"elapsed":1920280,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"93e1c095-e348-435c-94f8-adc5b23b6c62"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 10.000\n","4879 / 10.000\n","4880 / 10.000\n","4881 / 10.000\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 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gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_write, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKv7TjHhq0YE","executionInfo":{"status":"ok","timestamp":1735782114480,"user_tz":-420,"elapsed":325,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9af4b1c5-761b-4961-fa29-32c09b7b0fd4"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["Count: 33\n"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"1OrfeLsuMTaVL7GS9uEdXvCv68A7Qxnxi","timestamp":1735347331860}],"mount_file_id":"1F6K5UXAIUXHv1lxOYMhC9yRKdjnZbsCX","authorship_tag":"ABX9TyN+2OlSFkfkv9AOKBoO9Fj3"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/4. Judge Bundle For Each Users KNN.ipynb b/Source Code/4. Judge Bundle For Each Users KNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5972b1fe888566e431d262090d25a5cf1920d5c6 --- /dev/null +++ b/Source Code/4. Judge Bundle For Each Users KNN.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28820,"status":"ok","timestamp":1735782625086,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"TqtPuipCMpzT","outputId":"6f02ed17-c4fd-4d00-b7d2-5e7326f977fe"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded model: decision_tree_price_patterns\n","Loaded model: best_logreg_classifier_price_patterns\n","Loaded model: best_sgd_classifier_price_patterns\n","Loaded model: best_bnb_price_patterns\n","Loaded model: knn_price_patterns\n","Loaded model: neural_networks_price_patterns\n","Loaded 6 models.\n"]}],"source":["# prompt: Load all models from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications using joblib\n","\n","import joblib\n","import os\n","\n","# models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications\"\n","models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns\"\n","loaded_models = {}\n","\n","for filename in os.listdir(models_path):\n"," if filename.endswith(\".pkl\"):\n"," model_path = os.path.join(models_path, filename)\n"," model_name = filename[:-4] # Remove \".joblib\" extension\n"," try:\n"," loaded_models[model_name] = joblib.load(model_path)\n"," print(f\"Loaded model: {model_name}\")\n"," except Exception as e:\n"," print(f\"Error loading model {model_name}: {e}\")\n","\n","print(f\"Loaded {len(loaded_models)} models.\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LXdGNRK8RcwY"},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"cs-PpqN8TbRM"},"source":["## Bundle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1735303253454,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"oBdZF6YtO3Ef","outputId":"9bf13c93-1555-4e6b-a0f0-040196dd9051"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1735303253456,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JQDVAG3bRiAH","outputId":"e686321a-8c30-4baa-df71-49ab65d86046"},"outputs":[{"data":{"text/plain":["{'bundle_id': '450',\n"," 'bundle_price': 73.86,\n"," 'bundle_name': 'Dharker Studio 2015 Complete',\n"," 'items': {'326950',\n"," '331490',\n"," '331491',\n"," '331492',\n"," '348540',\n"," '352010',\n"," '387110',\n"," '387120',\n"," '390470',\n"," '394050',\n"," '396610',\n"," '396620',\n"," '398100',\n"," '408770'}}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["bundle_data[\"450\"]"]},{"cell_type":"markdown","metadata":{"id":"SY4SrF4sTdmZ"},"source":["## Items Dictionary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3800,"status":"ok","timestamp":1735303258466,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"pXudpCiWTffK","outputId":"c450dfec-0cfa-473c-fe6c-ccb71184a906"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"PAxnBn8zUwfT"},"source":["## User Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":43890,"status":"ok","timestamp":1735303302353,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"RWWQ3GPvUy1a","outputId":"551041c0-2c5d-4589-c662-9ea0ef6e77c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. 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SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"IInSMlBIUVNv"},"source":["## Create Input For Model Tags and Specs"]},{"cell_type":"markdown","metadata":{"id":"LwqMdBsgbrC_"},"source":["#### 1. Load Hot Persona Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24858,"status":"ok","timestamp":1735303327208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x4BKOF2sWkLC","outputId":"e7e4123f-9849-44ab-8ad8-63bda828cf70"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXPeELdRXNhn"},"outputs":[],"source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10,"status":"ok","timestamp":1735303327209,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"SHGaOHecX37a","outputId":"b356d01f-5e28-4f71-96c1-380e8066b968"},"outputs":[{"data":{"text/plain":["{'Fighting': 0,\n"," 'Action RPG': 0,\n"," 'Retro': 0,\n"," 'Bullet Hell': 0,\n"," 'Base Building': 0,\n"," 'Turn-Based Strategy': 0,\n"," 'Early Access': 0,\n"," 'Hex Grid': 0,\n"," 'Kickstarter': 0,\n"," 'Resource Management': 0,\n"," 'Abstract': 0,\n"," '4 Player Local': 0,\n"," 'Sniper': 0,\n"," 'Transhumanism': 0,\n"," 'Metroidvania': 0,\n"," 'Episodic': 0,\n"," 'e-sports': 0,\n"," 'Based On A Novel': 0,\n"," 'Crime': 0,\n"," 'Turn-Based Combat': 0,\n"," 'Vampire': 0,\n"," 'Level Editor': 0,\n"," 'Programming': 0,\n"," 'Gore': 0,\n"," '2D': 0,\n"," 'Colorful': 0,\n"," 'Co-op': 0,\n"," 'Puzzle': 0,\n"," 'Hack and Slash': 0,\n"," 'Dystopian': 0,\n"," '3D Vision': 0,\n"," 'Futuristic': 0,\n"," 'Hidden Object': 0,\n"," 'Post-apocalyptic': 0,\n"," 'Comic Book': 0,\n"," 'Linear': 0,\n"," 'Online Co-Op': 0,\n"," 'Stylized': 0,\n"," 'Dragons': 0,\n"," 'Rogue-lite': 0,\n"," 'Utilities': 0,\n"," 'Dark Humor': 0,\n"," 'Romance': 0,\n"," 'Economy': 0,\n"," 'Rogue-like': 0,\n"," 'Isometric': 0,\n"," \"1990's\": 0,\n"," 'Destruction': 0,\n"," 'Magic': 0,\n"," 'Funny': 0,\n"," 'Assassin': 0,\n"," 'Parkour': 0,\n"," 'Anime': 0,\n"," 'Multiple Endings': 0,\n"," 'Quick-Time Events': 0,\n"," 'Mystery': 0,\n"," 'Survival Horror': 0,\n"," 'Real-Time with Pause': 0,\n"," 'Psychological Horror': 0,\n"," 'Dark Fantasy': 0,\n"," 'Memes': 0,\n"," 'Cinematic': 0,\n"," 'Party-Based RPG': 0,\n"," 'Free to Play': 0,\n"," 'Tower Defense': 0,\n"," 'War': 0,\n"," 'Co-op Campaign': 0,\n"," 'Family Friendly': 0,\n"," 'Card Game': 0,\n"," 'Management': 0,\n"," 'Narration': 0,\n"," 'Procedural Generation': 0,\n"," 'Inventory Management': 0,\n"," 'Animation & Modeling': 0,\n"," 'Lara Croft': 0,\n"," 'Sexual Content': 0,\n"," 'Team-Based': 0,\n"," 'Gambling': 0,\n"," 'Fantasy': 0,\n"," 'RTS': 0,\n"," 'Loot': 0,\n"," 'Sci-fi': 0,\n"," 'Casual': 0,\n"," 'Web Publishing': 0,\n"," 'World War II': 0,\n"," \"Beat 'em up\": 0,\n"," 'RPG': 0,\n"," 'Local Co-Op': 0,\n"," 'Pirates': 0,\n"," 'CRPG': 0,\n"," 'Short': 0,\n"," 'Dungeon Crawler': 0,\n"," 'Character Customization': 0,\n"," 'Pinball': 0,\n"," 'VR': 0,\n"," 'Twin Stick Shooter': 0,\n"," 'Third-Person Shooter': 0,\n"," 'GameMaker': 0,\n"," 'Detective': 0,\n"," 'Stealth': 0,\n"," 'Platformer': 0,\n"," 'Walking Simulator': 0,\n"," 'Space': 0,\n"," 'Local Multiplayer': 0,\n"," 'Strategy': 0,\n"," 'Physics': 0,\n"," 'Steampunk': 0,\n"," 'Text-Based': 0,\n"," 'Action': 0,\n"," 'Mechs': 0,\n"," 'Side Scroller': 0,\n"," 'Multiplayer': 0,\n"," 'Turn-Based': 0,\n"," 'FPS': 0,\n"," 'Story Rich': 0,\n"," 'Sandbox': 0,\n"," 'Interactive Fiction': 0,\n"," 'Medieval': 0,\n"," 'Aliens': 0,\n"," 'Atmospheric': 0,\n"," 'Moddable': 0,\n"," 'Open World': 0,\n"," 'Cult Classic': 0,\n"," 'Time Travel': 0,\n"," 'Realistic': 0,\n"," 'Split Screen': 0,\n"," 'Choose Your Own Adventure': 0,\n"," 'Science': 0,\n"," 'Underwater': 0,\n"," 'Female Protagonist': 0,\n"," 'Competitive': 0,\n"," 'Shooter': 0,\n"," 'Software': 0,\n"," 'Software Training': 0,\n"," 'Mature': 0,\n"," 'Demons': 0,\n"," 'Cyberpunk': 0,\n"," '4X': 0,\n"," 'Zombies': 0,\n"," 'Simulation': 0,\n"," 'PvP': 0,\n"," 'Nudity': 0,\n"," 'Horror': 0,\n"," 'Survival': 0,\n"," 'Massively Multiplayer': 0,\n"," 'Voxel': 0,\n"," 'Tactical': 0,\n"," 'FMV': 0,\n"," 'First-Person': 0,\n"," 'Exploration': 0,\n"," 'Dark': 0,\n"," 'Adventure': 0,\n"," 'Silent Protagonist': 0,\n"," 'Controller': 0,\n"," 'Remake': 0,\n"," 'Diplomacy': 0,\n"," 'Military': 0,\n"," 'Spectacle fighter': 0,\n"," 'Sports': 0,\n"," 'Visual Novel': 0,\n"," 'Grand Strategy': 0,\n"," 'Relaxing': 0,\n"," 'Real Time Tactics': 0,\n"," 'Top-Down Shooter': 0,\n"," 'Alternate History': 0,\n"," 'Comedy': 0,\n"," 'Villain Protagonist': 0,\n"," 'Singleplayer': 0,\n"," 'Puzzle-Platformer': 0,\n"," 'Class-Based': 0,\n"," 'Touch-Friendly': 0,\n"," 'Trading': 0,\n"," 'City Builder': 0,\n"," 'Difficult': 0,\n"," \"Shoot 'Em Up\": 0,\n"," 'RPGMaker': 0,\n"," 'Replay Value': 0,\n"," 'Design & Illustration': 0,\n"," 'Cute': 0,\n"," 'God Game': 0,\n"," 'Point & Click': 0,\n"," 'Political': 0,\n"," 'Third Person': 0,\n"," 'Pixel Graphics': 0,\n"," 'Choices Matter': 0,\n"," 'Match 3': 0,\n"," 'Crafting': 0,\n"," 'Game Development': 0,\n"," 'Top-Down': 0,\n"," 'Education': 0,\n"," 'Indie': 0,\n"," 'Driving': 0,\n"," 'Hand-drawn': 0,\n"," 'Arcade': 0,\n"," 'Drama': 0,\n"," 'Robots': 0,\n"," 'Great Soundtrack': 0,\n"," 'Fast-Paced': 0,\n"," 'Classic': 0,\n"," 'Racing': 0,\n"," 'Historical': 0,\n"," 'Action-Adventure': 0,\n"," 'JRPG': 0,\n"," 'Superhero': 0}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["input_default_tags"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":9,"status":"ok","timestamp":1735303327210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"GbhHKsFvX5Vb","outputId":"4b02a1c8-284c-408f-92b6-c7a193f8012c"},"outputs":[{"data":{"text/plain":["{'Steam Workshop': 0,\n"," 'Multi-player': 0,\n"," 'Valve Anti-Cheat enabled': 0,\n"," 'Captions available': 0,\n"," 'Local Multi-Player': 0,\n"," 'Steam Trading Cards': 0,\n"," 'Steam Leaderboards': 0,\n"," 'Stats': 0,\n"," 'Shared/Split Screen': 0,\n"," 'Co-op': 0,\n"," 'Single-player': 0,\n"," 'Partial Controller Support': 0,\n"," 'Full controller support': 0,\n"," 'Steam Achievements': 0,\n"," 'Commentary available': 0,\n"," 'Includes level editor': 0,\n"," 'Online Multi-Player': 0,\n"," 'Cross-Platform Multiplayer': 0,\n"," 'Includes Source SDK': 0,\n"," 'In-App Purchases': 0,\n"," 'Steam Cloud': 0}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["input_default_specs"]},{"cell_type":"markdown","metadata":{"id":"E8Y-taqrb1g_"},"source":["#### 2. Check Hot Persona Tags and Specs For Each User Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5761,"status":"ok","timestamp":1735303332964,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UDTwrBJqUcjY","outputId":"a7ffbd3e-b3a4-4df2-d2d8-dfef49865c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Tags: {'Fighting': 1, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 1, 'Base Building': 1, 'Turn-Based Strategy': 1, 'Early Access': 1, 'Hex Grid': 1, 'Kickstarter': 1, 'Resource Management': 1, 'Abstract': 1, '4 Player Local': 1, 'Sniper': 0, 'Transhumanism': 1, 'Metroidvania': 1, 'Episodic': 1, 'e-sports': 1, 'Based On A Novel': 1, 'Crime': 1, 'Turn-Based Combat': 1, 'Vampire': 0, 'Level Editor': 1, 'Programming': 1, 'Gore': 1, '2D': 1, 'Colorful': 1, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 1, 'Dystopian': 1, '3D Vision': 1, 'Futuristic': 1, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 1, 'Linear': 1, 'Online Co-Op': 1, 'Stylized': 1, 'Dragons': 1, 'Rogue-lite': 1, 'Utilities': 0, 'Dark Humor': 1, 'Romance': 1, 'Economy': 1, 'Rogue-like': 1, 'Isometric': 1, \"1990's\": 1, 'Destruction': 1, 'Magic': 1, 'Funny': 1, 'Assassin': 1, 'Parkour': 1, 'Anime': 1, 'Multiple Endings': 1, 'Quick-Time Events': 1, 'Mystery': 1, 'Survival Horror': 1, 'Real-Time with Pause': 1, 'Psychological Horror': 1, 'Dark Fantasy': 1, 'Memes': 1, 'Cinematic': 1, 'Party-Based RPG': 1, 'Free to Play': 1, 'Tower Defense': 1, 'War': 1, 'Co-op Campaign': 1, 'Family Friendly': 1, 'Card Game': 1, 'Management': 1, 'Narration': 1, 'Procedural Generation': 1, 'Inventory Management': 1, 'Animation & Modeling': 0, 'Lara Croft': 1, 'Sexual Content': 1, 'Team-Based': 1, 'Gambling': 1, 'Fantasy': 1, 'RTS': 1, 'Loot': 1, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 1, \"Beat 'em up\": 1, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 1, 'CRPG': 1, 'Short': 1, 'Dungeon Crawler': 1, 'Character Customization': 1, 'Pinball': 0, 'VR': 1, 'Twin Stick Shooter': 1, 'Third-Person Shooter': 1, 'GameMaker': 0, 'Detective': 1, 'Stealth': 1, 'Platformer': 1, 'Walking Simulator': 1, 'Space': 1, 'Local Multiplayer': 1, 'Strategy': 1, 'Physics': 1, 'Steampunk': 1, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 1, 'Multiplayer': 1, 'Turn-Based': 1, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 1, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 1, 'Time Travel': 1, 'Realistic': 1, 'Split Screen': 1, 'Choose Your Own Adventure': 1, 'Science': 1, 'Underwater': 1, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 1, 'Cyberpunk': 1, '4X': 1, 'Zombies': 1, 'Simulation': 1, 'PvP': 1, 'Nudity': 1, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 1, 'Voxel': 1, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 1, 'Dark': 1, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 1, 'Remake': 1, 'Diplomacy': 1, 'Military': 1, 'Spectacle fighter': 1, 'Sports': 1, 'Visual Novel': 1, 'Grand Strategy': 1, 'Relaxing': 1, 'Real Time Tactics': 1, 'Top-Down Shooter': 1, 'Alternate History': 1, 'Comedy': 1, 'Villain Protagonist': 1, 'Singleplayer': 1, 'Puzzle-Platformer': 1, 'Class-Based': 1, 'Touch-Friendly': 1, 'Trading': 1, 'City Builder': 1, 'Difficult': 1, \"Shoot 'Em Up\": 1, 'RPGMaker': 1, 'Replay Value': 1, 'Design & Illustration': 0, 'Cute': 1, 'God Game': 1, 'Point & Click': 1, 'Political': 1, 'Third Person': 1, 'Pixel Graphics': 1, 'Choices Matter': 1, 'Match 3': 0, 'Crafting': 1, 'Game Development': 0, 'Top-Down': 1, 'Education': 1, 'Indie': 1, 'Driving': 1, 'Hand-drawn': 1, 'Arcade': 1, 'Drama': 1, 'Robots': 1, 'Great Soundtrack': 1, 'Fast-Paced': 1, 'Classic': 1, 'Racing': 1, 'Historical': 1, 'Action-Adventure': 1, 'JRPG': 1, 'Superhero': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","user_item_tags = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_tags[user_id] = input_default_tags.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," user_item_tags[user_id][tag] = 1\n","\n","# Example usage (you can print or process user_item_tags as needed):\n","for user_id, item_tags in user_item_tags.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3306,"status":"ok","timestamp":1735303336267,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LxfFCIj9Zkrc","outputId":"a6a0993a-4155-4ebb-b569-f7ce8fc03610"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Specs: {'Steam Workshop': 1, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 1, 'Local Multi-Player': 1, 'Steam Trading Cards': 1, 'Steam Leaderboards': 1, 'Stats': 1, 'Shared/Split Screen': 1, 'Co-op': 1, 'Single-player': 1, 'Partial Controller Support': 1, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 1, 'Includes level editor': 1, 'Online Multi-Player': 1, 'Cross-Platform Multiplayer': 1, 'Includes Source SDK': 1, 'In-App Purchases': 1, 'Steam Cloud': 1}\n"]}],"source":["user_item_specs = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_specs[user_id] = input_default_specs.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," user_item_specs[user_id][spec] = 1\n","\n","# Example usage (you can print or process user_item_specs as needed):\n","for user_id, item_specs in user_item_specs.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"lziPgL49cJXd"},"source":["#### 2. Check Hot Persona Tags and Specs For Each Bundle Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"7DQdfGE0aZn8","outputId":"2b59b46a-2354-4f70-a08e-17f6feb5e6ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["Bundle: 450\n","Tags: {'Fighting': 0, 'Action RPG': 0, 'Retro': 0, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 1, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 0, '2D': 0, 'Colorful': 0, 'Co-op': 0, 'Puzzle': 0, 'Hack and Slash': 0, 'Dystopian': 0, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 0, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 0, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 1, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 0, 'Destruction': 0, 'Magic': 0, 'Funny': 0, 'Assassin': 0, 'Parkour': 0, 'Anime': 1, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 0, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 1, 'Team-Based': 0, 'Gambling': 0, 'Fantasy': 0, 'RTS': 0, 'Loot': 0, 'Sci-fi': 0, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 0, 'Local Co-Op': 0, 'Pirates': 0, 'CRPG': 0, 'Short': 0, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 0, 'Walking Simulator': 0, 'Space': 0, 'Local Multiplayer': 0, 'Strategy': 0, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 0, 'Turn-Based': 0, 'FPS': 0, 'Story Rich': 0, 'Sandbox': 0, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 0, 'Atmospheric': 0, 'Moddable': 0, 'Open World': 0, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 0, 'Competitive': 0, 'Shooter': 0, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 0, 'Simulation': 0, 'PvP': 0, 'Nudity': 1, 'Horror': 0, 'Survival': 0, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 0, 'FMV': 0, 'First-Person': 0, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 0, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 0, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 1, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 0, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 0, \"Shoot 'Em Up\": 1, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 0, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 0, 'Fast-Paced': 0, 'Classic': 0, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}\n","Bundle: 450\n","Specs: {'Steam Workshop': 0, 'Multi-player': 0, 'Valve Anti-Cheat enabled': 0, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 0, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","bundle_item_tags = {}\n","bundle_item_specs = {}\n","\n","for bundle_id, bundle_items in bundle_data.items():\n"," bundle_item_tags[bundle_id] = input_default_tags.copy()\n"," bundle_item_specs[bundle_id] = input_default_specs.copy()\n","\n"," for item_id in bundle_items[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," bundle_item_tags[bundle_id][tag] = 1\n","\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," bundle_item_specs[bundle_id][spec] = 1\n","\n","# Example usage (you can print or process bundle_item_tags as needed):\n","for bundle_id, item_tags in bundle_item_tags.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break\n","\n","# Example usage (you can print or process bundle_item_specs as needed):\n","for bundle_id, item_specs in bundle_item_specs.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"_qoICCA4cPyU"},"source":["#### 3. Get Intersection Between Persona User Item Interactions and Persona Bundle Items Based On Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"EFAWQsJgcb3G","outputId":"24288744-33ad-4a78-bd2f-d77696610862"},"outputs":[{"name":"stdout","output_type":"stream","text":["Intersection for user 76561197970982479 and bundle 450:\n","Tags: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","Specs: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"]}],"source":["# prompt: Get Intersection Between Persona User Item Interactions and Persona Bundle Items By Tags and Specs That Have Value == 1\n","\n","def get_intersection(user_id, bundle_id):\n"," intersection_tags = {}\n"," intersection_specs = {}\n","\n"," for tag, value in user_item_tags[user_id].items():\n"," if value == 1 and bundle_item_tags[bundle_id][tag] == 1:\n"," intersection_tags[tag] = 1\n"," else:\n"," intersection_tags[tag] = 0\n","\n"," for spec, value in user_item_specs[user_id].items():\n"," if value == 1 and bundle_item_specs[bundle_id][spec] == 1:\n"," intersection_specs[spec] = 1\n"," else:\n"," intersection_specs[spec] = 0\n","\n"," return intersection_tags, intersection_specs\n","\n","def get_intersection_as_list(user_id, bundle_id):\n"," user_tags = user_item_tags[user_id]\n"," bundle_tags = bundle_item_tags[bundle_id]\n"," user_specs = user_item_specs[user_id]\n"," bundle_specs = bundle_item_specs[bundle_id]\n","\n"," # Using list comprehensions to find common tags and specs\n"," common_tags = [1 if user_tags[tag] == 1 and bundle_tags[tag] == 1 else 0 for tag in user_tags]\n"," common_specs = [1 if user_specs[spec] == 1 and bundle_specs[spec] == 1 else 0 for spec in user_specs]\n","\n"," return common_tags, common_specs\n","\n","# Example usage:\n","user_id = list(user_item_interactions.keys())[0] # Get the first user ID\n","bundle_id = list(bundle_data.keys())[0] # Get the first bundle ID\n","\n","intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n","\n","print(f\"Intersection for user {user_id} and bundle {bundle_id}:\")\n","print(f\"Tags: {intersection_tags}\")\n","print(f\"Specs: {intersection_specs}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":309041,"status":"ok","timestamp":1735305675372,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"BuFxKyM6cyCv","outputId":"fad7d31b-9e29-4644-80b1-456508338d8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 / 10.000\n","4898 / 10.000\n","4899 / 10.000\n","4900 / 10.000\n","4901 / 10.000\n","4902 / 10.000\n","4903 / 10.000\n","4904 / 10.000\n","4905 / 10.000\n","4906 / 10.000\n","4907 / 10.000\n","4908 / 10.000\n","4909 / 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intersection_specs = get_intersection_as_list(user_id, bundle_id)\n"," data_to_write[user_id][bundle_id] = {\n"," \"tags\": intersection_tags,\n"," \"specs\": intersection_specs\n"," }\n","\n"," json.dump(data_to_write, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," print(f\"{i} / 10.000\")"]},{"cell_type":"markdown","metadata":{"id":"45XFujQeHeG-"},"source":["#### Do Recommendation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XzQCZ1fmHh5D","outputId":"e9dab3f2-04c6-42de-b2a7-6b74acbe213b","executionInfo":{"status":"ok","timestamp":1735399639026,"user_tz":-420,"elapsed":13920105,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["6000 / 10.000\n","6001 / 10.000\n","6002 / 10.000\n","6003 / 10.000\n","6004 / 10.000\n","6005 / 10.000\n","6006 / 10.000\n","6007 / 10.000\n","6008 / 10.000\n","6009 / 10.000\n","6010 / 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10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","9877 / 10.000\n","9878 / 10.000\n","9879 / 10.000\n","9880 / 10.000\n","9881 / 10.000\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_knn.json.gz'\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","data_dict_out = None\n","temp_bundle_out = None\n","\n","file_path_read = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_knn.json.gz\"\n","count = 0\n","index = 0\n","\n","try:\n"," with gzip.open(file_path_read, 'rt', encoding='utf-8') as infile, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," data_dict_out = {}\n"," temp_data_dict_out = {}\n"," temp_bundle_out = []\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," for user_id, bundle_data in data_dict.items():\n"," data_dict_out[user_id] = set()\n"," for bundle_id, recommendation in bundle_data.items():\n"," data_dict_out[user_id].add(bundle_id)\n"," temp_bundle_out.append(recommendation['tags'] + recommendation['specs'])\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," if (count % 1000 == 0) or count == 9881:\n"," predictions = loaded_models['knn_tags_specifications'].predict(temp_bundle_out, )\n"," index = 0\n"," for user_id, bundle_data in data_dict_out.items():\n"," for bundle_id in bundle_data:\n"," temp_data_dict_out[\"user_id\"] = user_id\n"," temp_data_dict_out[\"bundle_id\"] = bundle_id\n"," temp_data_dict_out[\"is_recommended\"] = f\"{predictions[index]}\"\n"," index += 1\n"," # data_dict_out[\"is_recommended\"] = f\"{loaded_models['knn_tags_specifications'].predict([recommendation['tags'] + recommendation['specs']])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(temp_data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," # break\n"," data_dict_out = {}\n"," temp_data_dict_out = {}\n"," temp_bundle_out = []\n"," # print(data_dict_out)\n"," # break\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":307,"status":"ok","timestamp":1735355760692,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"MnRRI8KSaOgO","outputId":"f95393f1-757f-4c1e-a844-5e7ba4a34ef4"},"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_knn.json.gz'\n","Count: 167\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_knn.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pmSwZ6yPxdN5"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"na7Ok_umxQnJ"},"source":["## Create Input For Model Price Patterns"]},{"cell_type":"markdown","source":["#### 1. Load Data"],"metadata":{"id":"p6y7SbgJmBRo"}},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735782689450,"user_tz":-420,"elapsed":591,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"471f8766-9489-4d9f-d7aa-c225084e6bda","id":"M4-evoXQtBLN"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":3}],"source":["# prompt: Load file /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy\n","\n","import numpy as np\n","\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')\n","\n","user_price_label[:5]"]},{"cell_type":"code","source":["# prompt: Calculate how many 0 and 1 in user_price_label[:615]\n","\n","zeros = np.count_nonzero(user_price_label[:615] == 0)\n","ones = np.count_nonzero(user_price_label[:615] == 1)\n","\n","print(f\"Number of 0s: {zeros}\")\n","print(f\"Number of 1s: {ones}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBye6gkth3l6","executionInfo":{"status":"ok","timestamp":1735782690470,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a84cc3b8-ca4e-4124-8a3c-ea088147dd1c"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of 0s: 582\n","Number of 1s: 33\n"]}]},{"cell_type":"code","source":["user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')"],"metadata":{"id":"2LoxPq-8iYe3","executionInfo":{"status":"ok","timestamp":1735782701451,"user_tz":-420,"elapsed":7206,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["user_price_identity = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy')"],"metadata":{"id":"Jqtxa1XYl2sa","executionInfo":{"status":"ok","timestamp":1735782721390,"user_tz":-420,"elapsed":19941,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Find is that from user_price_identity user for all bundle theres no label 1 in user_price_label\n","\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","check_users = {}\n","\n","# Iterate through users and bundles (assuming bundle IDs are consecutive integers)\n","for user_index in range(num_users):\n"," user_id = user_price_identity[user_index]\n"," if user_id[0] not in check_users:\n"," check_users[user_id[0]] = 0\n","\n"," # Check if label is present for this user\n"," if user_price_label[user_index] is not None: # Check for None or NaN if necessary\n"," # Access the label directly instead of iterating\n"," if user_price_label[user_index] == 1:\n"," # Assuming bundle_id is the second element in user_price_identity\n"," check_users[user_id[0]] = 1\n"," else:\n"," print(f\"No labels found for user {user_id}\")"],"metadata":{"id":"g_NWI-KDiuIF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: From check_users check is there has 0 value?\n","\n","zeros = 0\n","for user_id, label in check_users.items():\n"," if label == 0:\n"," zeros += 1\n","\n","print(f\"Number of users with 0 value: {zeros}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qD-mnjmHlcqD","executionInfo":{"status":"ok","timestamp":1735780608038,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ddd2f1e-dea8-44b9-9f94-94adbc1bca4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of users with 0 value: 23\n"]}]},{"cell_type":"markdown","source":["#### 2. Do Recommendation"],"metadata":{"id":"TKiv8OUzmEF5"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","num_users = len(user_price_identity)\n","data_dict_out = {}\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_knn.json.gz\"\n","count = 0\n","\n","check_users = {}\n","\n","try:\n"," with gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for user_index in range(num_users):\n"," data_dict_out[\"user_id\"] = user_price_identity[user_index][0]\n"," data_dict_out[\"bundle_id\"] = user_price_identity[user_index][1]\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['knn_price_patterns'].predict([user_price_features[user_index]])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," if user_price_identity[user_index][0] not in check_users:\n"," check_users[user_price_identity[user_index][0]] = 0\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # if count == 2:\n"," # break\n"," print(f\"Successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1nbQtXeoa1M","outputId":"bf7a0986-d2eb-4d79-c0c5-f8d9c6578ae7","executionInfo":{"status":"ok","timestamp":1735789607990,"user_tz":-420,"elapsed":6861905,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 10.000\n","4879 / 10.000\n","4880 / 10.000\n","4881 / 10.000\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 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10.000\n","9836 / 10.000\n","9837 / 10.000\n","9838 / 10.000\n","9839 / 10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","Successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_knn.json.gz'\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_write, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKv7TjHhq0YE","executionInfo":{"status":"ok","timestamp":1735782114480,"user_tz":-420,"elapsed":325,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9af4b1c5-761b-4961-fa29-32c09b7b0fd4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Count: 33\n"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"1F6K5UXAIUXHv1lxOYMhC9yRKdjnZbsCX","timestamp":1735348485863},{"file_id":"1OrfeLsuMTaVL7GS9uEdXvCv68A7Qxnxi","timestamp":1735347331860}],"mount_file_id":"1-exUF3ytZTEq1l5Qq3pewRtq-heTyUSB","authorship_tag":"ABX9TyO8dFcODo90Yzf0Rmvxe+vx"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/4. Judge Bundle For Each Users Log Reg .ipynb b/Source Code/4. Judge Bundle For Each Users Log Reg .ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0da286fba87c876ba491d5ad6a95b0b30b055de --- /dev/null +++ b/Source Code/4. Judge Bundle For Each Users Log Reg .ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28111,"status":"ok","timestamp":1735783079967,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"TqtPuipCMpzT","outputId":"9e3471cd-317d-488e-be91-663542f9af6c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded model: decision_tree_price_patterns\n","Loaded model: best_logreg_classifier_price_patterns\n","Loaded model: best_sgd_classifier_price_patterns\n","Loaded model: best_bnb_price_patterns\n","Loaded model: knn_price_patterns\n","Loaded model: neural_networks_price_patterns\n","Loaded 6 models.\n"]}],"source":["# prompt: Load all models from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications using joblib\n","\n","import joblib\n","import os\n","\n","# models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications\"\n","models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns\"\n","loaded_models = {}\n","\n","for filename in os.listdir(models_path):\n"," if filename.endswith(\".pkl\"):\n"," model_path = os.path.join(models_path, filename)\n"," model_name = filename[:-4] # Remove \".joblib\" extension\n"," try:\n"," loaded_models[model_name] = joblib.load(model_path)\n"," print(f\"Loaded model: {model_name}\")\n"," except Exception as e:\n"," print(f\"Error loading model {model_name}: {e}\")\n","\n","print(f\"Loaded {len(loaded_models)} models.\")\n"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1735783079967,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LXdGNRK8RcwY"},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"cs-PpqN8TbRM"},"source":["## Bundle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1735303253454,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"oBdZF6YtO3Ef","outputId":"9bf13c93-1555-4e6b-a0f0-040196dd9051"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1735303253456,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JQDVAG3bRiAH","outputId":"e686321a-8c30-4baa-df71-49ab65d86046"},"outputs":[{"data":{"text/plain":["{'bundle_id': '450',\n"," 'bundle_price': 73.86,\n"," 'bundle_name': 'Dharker Studio 2015 Complete',\n"," 'items': {'326950',\n"," '331490',\n"," '331491',\n"," '331492',\n"," '348540',\n"," '352010',\n"," '387110',\n"," '387120',\n"," '390470',\n"," '394050',\n"," '396610',\n"," '396620',\n"," '398100',\n"," '408770'}}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["bundle_data[\"450\"]"]},{"cell_type":"markdown","metadata":{"id":"SY4SrF4sTdmZ"},"source":["## Items Dictionary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3800,"status":"ok","timestamp":1735303258466,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"pXudpCiWTffK","outputId":"c450dfec-0cfa-473c-fe6c-ccb71184a906"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"PAxnBn8zUwfT"},"source":["## User Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":43890,"status":"ok","timestamp":1735303302353,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"RWWQ3GPvUy1a","outputId":"551041c0-2c5d-4589-c662-9ea0ef6e77c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 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A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"IInSMlBIUVNv"},"source":["## Create Input For Model Tags and Specs"]},{"cell_type":"markdown","metadata":{"id":"LwqMdBsgbrC_"},"source":["#### 1. Load Hot Persona Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24858,"status":"ok","timestamp":1735303327208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x4BKOF2sWkLC","outputId":"e7e4123f-9849-44ab-8ad8-63bda828cf70"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXPeELdRXNhn"},"outputs":[],"source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10,"status":"ok","timestamp":1735303327209,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"SHGaOHecX37a","outputId":"b356d01f-5e28-4f71-96c1-380e8066b968"},"outputs":[{"data":{"text/plain":["{'Fighting': 0,\n"," 'Action RPG': 0,\n"," 'Retro': 0,\n"," 'Bullet Hell': 0,\n"," 'Base Building': 0,\n"," 'Turn-Based Strategy': 0,\n"," 'Early Access': 0,\n"," 'Hex Grid': 0,\n"," 'Kickstarter': 0,\n"," 'Resource Management': 0,\n"," 'Abstract': 0,\n"," '4 Player Local': 0,\n"," 'Sniper': 0,\n"," 'Transhumanism': 0,\n"," 'Metroidvania': 0,\n"," 'Episodic': 0,\n"," 'e-sports': 0,\n"," 'Based On A Novel': 0,\n"," 'Crime': 0,\n"," 'Turn-Based Combat': 0,\n"," 'Vampire': 0,\n"," 'Level Editor': 0,\n"," 'Programming': 0,\n"," 'Gore': 0,\n"," '2D': 0,\n"," 'Colorful': 0,\n"," 'Co-op': 0,\n"," 'Puzzle': 0,\n"," 'Hack and Slash': 0,\n"," 'Dystopian': 0,\n"," '3D Vision': 0,\n"," 'Futuristic': 0,\n"," 'Hidden Object': 0,\n"," 'Post-apocalyptic': 0,\n"," 'Comic Book': 0,\n"," 'Linear': 0,\n"," 'Online Co-Op': 0,\n"," 'Stylized': 0,\n"," 'Dragons': 0,\n"," 'Rogue-lite': 0,\n"," 'Utilities': 0,\n"," 'Dark Humor': 0,\n"," 'Romance': 0,\n"," 'Economy': 0,\n"," 'Rogue-like': 0,\n"," 'Isometric': 0,\n"," \"1990's\": 0,\n"," 'Destruction': 0,\n"," 'Magic': 0,\n"," 'Funny': 0,\n"," 'Assassin': 0,\n"," 'Parkour': 0,\n"," 'Anime': 0,\n"," 'Multiple Endings': 0,\n"," 'Quick-Time Events': 0,\n"," 'Mystery': 0,\n"," 'Survival Horror': 0,\n"," 'Real-Time with Pause': 0,\n"," 'Psychological Horror': 0,\n"," 'Dark Fantasy': 0,\n"," 'Memes': 0,\n"," 'Cinematic': 0,\n"," 'Party-Based RPG': 0,\n"," 'Free to Play': 0,\n"," 'Tower Defense': 0,\n"," 'War': 0,\n"," 'Co-op Campaign': 0,\n"," 'Family Friendly': 0,\n"," 'Card Game': 0,\n"," 'Management': 0,\n"," 'Narration': 0,\n"," 'Procedural Generation': 0,\n"," 'Inventory Management': 0,\n"," 'Animation & Modeling': 0,\n"," 'Lara Croft': 0,\n"," 'Sexual Content': 0,\n"," 'Team-Based': 0,\n"," 'Gambling': 0,\n"," 'Fantasy': 0,\n"," 'RTS': 0,\n"," 'Loot': 0,\n"," 'Sci-fi': 0,\n"," 'Casual': 0,\n"," 'Web Publishing': 0,\n"," 'World War II': 0,\n"," \"Beat 'em up\": 0,\n"," 'RPG': 0,\n"," 'Local Co-Op': 0,\n"," 'Pirates': 0,\n"," 'CRPG': 0,\n"," 'Short': 0,\n"," 'Dungeon Crawler': 0,\n"," 'Character Customization': 0,\n"," 'Pinball': 0,\n"," 'VR': 0,\n"," 'Twin Stick Shooter': 0,\n"," 'Third-Person Shooter': 0,\n"," 'GameMaker': 0,\n"," 'Detective': 0,\n"," 'Stealth': 0,\n"," 'Platformer': 0,\n"," 'Walking Simulator': 0,\n"," 'Space': 0,\n"," 'Local Multiplayer': 0,\n"," 'Strategy': 0,\n"," 'Physics': 0,\n"," 'Steampunk': 0,\n"," 'Text-Based': 0,\n"," 'Action': 0,\n"," 'Mechs': 0,\n"," 'Side Scroller': 0,\n"," 'Multiplayer': 0,\n"," 'Turn-Based': 0,\n"," 'FPS': 0,\n"," 'Story Rich': 0,\n"," 'Sandbox': 0,\n"," 'Interactive Fiction': 0,\n"," 'Medieval': 0,\n"," 'Aliens': 0,\n"," 'Atmospheric': 0,\n"," 'Moddable': 0,\n"," 'Open World': 0,\n"," 'Cult Classic': 0,\n"," 'Time Travel': 0,\n"," 'Realistic': 0,\n"," 'Split Screen': 0,\n"," 'Choose Your Own Adventure': 0,\n"," 'Science': 0,\n"," 'Underwater': 0,\n"," 'Female Protagonist': 0,\n"," 'Competitive': 0,\n"," 'Shooter': 0,\n"," 'Software': 0,\n"," 'Software Training': 0,\n"," 'Mature': 0,\n"," 'Demons': 0,\n"," 'Cyberpunk': 0,\n"," '4X': 0,\n"," 'Zombies': 0,\n"," 'Simulation': 0,\n"," 'PvP': 0,\n"," 'Nudity': 0,\n"," 'Horror': 0,\n"," 'Survival': 0,\n"," 'Massively Multiplayer': 0,\n"," 'Voxel': 0,\n"," 'Tactical': 0,\n"," 'FMV': 0,\n"," 'First-Person': 0,\n"," 'Exploration': 0,\n"," 'Dark': 0,\n"," 'Adventure': 0,\n"," 'Silent Protagonist': 0,\n"," 'Controller': 0,\n"," 'Remake': 0,\n"," 'Diplomacy': 0,\n"," 'Military': 0,\n"," 'Spectacle fighter': 0,\n"," 'Sports': 0,\n"," 'Visual Novel': 0,\n"," 'Grand Strategy': 0,\n"," 'Relaxing': 0,\n"," 'Real Time Tactics': 0,\n"," 'Top-Down Shooter': 0,\n"," 'Alternate History': 0,\n"," 'Comedy': 0,\n"," 'Villain Protagonist': 0,\n"," 'Singleplayer': 0,\n"," 'Puzzle-Platformer': 0,\n"," 'Class-Based': 0,\n"," 'Touch-Friendly': 0,\n"," 'Trading': 0,\n"," 'City Builder': 0,\n"," 'Difficult': 0,\n"," \"Shoot 'Em Up\": 0,\n"," 'RPGMaker': 0,\n"," 'Replay Value': 0,\n"," 'Design & Illustration': 0,\n"," 'Cute': 0,\n"," 'God Game': 0,\n"," 'Point & Click': 0,\n"," 'Political': 0,\n"," 'Third Person': 0,\n"," 'Pixel Graphics': 0,\n"," 'Choices Matter': 0,\n"," 'Match 3': 0,\n"," 'Crafting': 0,\n"," 'Game Development': 0,\n"," 'Top-Down': 0,\n"," 'Education': 0,\n"," 'Indie': 0,\n"," 'Driving': 0,\n"," 'Hand-drawn': 0,\n"," 'Arcade': 0,\n"," 'Drama': 0,\n"," 'Robots': 0,\n"," 'Great Soundtrack': 0,\n"," 'Fast-Paced': 0,\n"," 'Classic': 0,\n"," 'Racing': 0,\n"," 'Historical': 0,\n"," 'Action-Adventure': 0,\n"," 'JRPG': 0,\n"," 'Superhero': 0}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["input_default_tags"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":9,"status":"ok","timestamp":1735303327210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"GbhHKsFvX5Vb","outputId":"4b02a1c8-284c-408f-92b6-c7a193f8012c"},"outputs":[{"data":{"text/plain":["{'Steam Workshop': 0,\n"," 'Multi-player': 0,\n"," 'Valve Anti-Cheat enabled': 0,\n"," 'Captions available': 0,\n"," 'Local Multi-Player': 0,\n"," 'Steam Trading Cards': 0,\n"," 'Steam Leaderboards': 0,\n"," 'Stats': 0,\n"," 'Shared/Split Screen': 0,\n"," 'Co-op': 0,\n"," 'Single-player': 0,\n"," 'Partial Controller Support': 0,\n"," 'Full controller support': 0,\n"," 'Steam Achievements': 0,\n"," 'Commentary available': 0,\n"," 'Includes level editor': 0,\n"," 'Online Multi-Player': 0,\n"," 'Cross-Platform Multiplayer': 0,\n"," 'Includes Source SDK': 0,\n"," 'In-App Purchases': 0,\n"," 'Steam Cloud': 0}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["input_default_specs"]},{"cell_type":"markdown","metadata":{"id":"E8Y-taqrb1g_"},"source":["#### 2. Check Hot Persona Tags and Specs For Each User Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5761,"status":"ok","timestamp":1735303332964,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UDTwrBJqUcjY","outputId":"a7ffbd3e-b3a4-4df2-d2d8-dfef49865c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Tags: {'Fighting': 1, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 1, 'Base Building': 1, 'Turn-Based Strategy': 1, 'Early Access': 1, 'Hex Grid': 1, 'Kickstarter': 1, 'Resource Management': 1, 'Abstract': 1, '4 Player Local': 1, 'Sniper': 0, 'Transhumanism': 1, 'Metroidvania': 1, 'Episodic': 1, 'e-sports': 1, 'Based On A Novel': 1, 'Crime': 1, 'Turn-Based Combat': 1, 'Vampire': 0, 'Level Editor': 1, 'Programming': 1, 'Gore': 1, '2D': 1, 'Colorful': 1, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 1, 'Dystopian': 1, '3D Vision': 1, 'Futuristic': 1, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 1, 'Linear': 1, 'Online Co-Op': 1, 'Stylized': 1, 'Dragons': 1, 'Rogue-lite': 1, 'Utilities': 0, 'Dark Humor': 1, 'Romance': 1, 'Economy': 1, 'Rogue-like': 1, 'Isometric': 1, \"1990's\": 1, 'Destruction': 1, 'Magic': 1, 'Funny': 1, 'Assassin': 1, 'Parkour': 1, 'Anime': 1, 'Multiple Endings': 1, 'Quick-Time Events': 1, 'Mystery': 1, 'Survival Horror': 1, 'Real-Time with Pause': 1, 'Psychological Horror': 1, 'Dark Fantasy': 1, 'Memes': 1, 'Cinematic': 1, 'Party-Based RPG': 1, 'Free to Play': 1, 'Tower Defense': 1, 'War': 1, 'Co-op Campaign': 1, 'Family Friendly': 1, 'Card Game': 1, 'Management': 1, 'Narration': 1, 'Procedural Generation': 1, 'Inventory Management': 1, 'Animation & Modeling': 0, 'Lara Croft': 1, 'Sexual Content': 1, 'Team-Based': 1, 'Gambling': 1, 'Fantasy': 1, 'RTS': 1, 'Loot': 1, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 1, \"Beat 'em up\": 1, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 1, 'CRPG': 1, 'Short': 1, 'Dungeon Crawler': 1, 'Character Customization': 1, 'Pinball': 0, 'VR': 1, 'Twin Stick Shooter': 1, 'Third-Person Shooter': 1, 'GameMaker': 0, 'Detective': 1, 'Stealth': 1, 'Platformer': 1, 'Walking Simulator': 1, 'Space': 1, 'Local Multiplayer': 1, 'Strategy': 1, 'Physics': 1, 'Steampunk': 1, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 1, 'Multiplayer': 1, 'Turn-Based': 1, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 1, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 1, 'Time Travel': 1, 'Realistic': 1, 'Split Screen': 1, 'Choose Your Own Adventure': 1, 'Science': 1, 'Underwater': 1, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 1, 'Cyberpunk': 1, '4X': 1, 'Zombies': 1, 'Simulation': 1, 'PvP': 1, 'Nudity': 1, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 1, 'Voxel': 1, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 1, 'Dark': 1, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 1, 'Remake': 1, 'Diplomacy': 1, 'Military': 1, 'Spectacle fighter': 1, 'Sports': 1, 'Visual Novel': 1, 'Grand Strategy': 1, 'Relaxing': 1, 'Real Time Tactics': 1, 'Top-Down Shooter': 1, 'Alternate History': 1, 'Comedy': 1, 'Villain Protagonist': 1, 'Singleplayer': 1, 'Puzzle-Platformer': 1, 'Class-Based': 1, 'Touch-Friendly': 1, 'Trading': 1, 'City Builder': 1, 'Difficult': 1, \"Shoot 'Em Up\": 1, 'RPGMaker': 1, 'Replay Value': 1, 'Design & Illustration': 0, 'Cute': 1, 'God Game': 1, 'Point & Click': 1, 'Political': 1, 'Third Person': 1, 'Pixel Graphics': 1, 'Choices Matter': 1, 'Match 3': 0, 'Crafting': 1, 'Game Development': 0, 'Top-Down': 1, 'Education': 1, 'Indie': 1, 'Driving': 1, 'Hand-drawn': 1, 'Arcade': 1, 'Drama': 1, 'Robots': 1, 'Great Soundtrack': 1, 'Fast-Paced': 1, 'Classic': 1, 'Racing': 1, 'Historical': 1, 'Action-Adventure': 1, 'JRPG': 1, 'Superhero': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","user_item_tags = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_tags[user_id] = input_default_tags.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," user_item_tags[user_id][tag] = 1\n","\n","# Example usage (you can print or process user_item_tags as needed):\n","for user_id, item_tags in user_item_tags.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3306,"status":"ok","timestamp":1735303336267,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LxfFCIj9Zkrc","outputId":"a6a0993a-4155-4ebb-b569-f7ce8fc03610"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Specs: {'Steam Workshop': 1, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 1, 'Local Multi-Player': 1, 'Steam Trading Cards': 1, 'Steam Leaderboards': 1, 'Stats': 1, 'Shared/Split Screen': 1, 'Co-op': 1, 'Single-player': 1, 'Partial Controller Support': 1, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 1, 'Includes level editor': 1, 'Online Multi-Player': 1, 'Cross-Platform Multiplayer': 1, 'Includes Source SDK': 1, 'In-App Purchases': 1, 'Steam Cloud': 1}\n"]}],"source":["user_item_specs = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_specs[user_id] = input_default_specs.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," user_item_specs[user_id][spec] = 1\n","\n","# Example usage (you can print or process user_item_specs as needed):\n","for user_id, item_specs in user_item_specs.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"lziPgL49cJXd"},"source":["#### 2. Check Hot Persona Tags and Specs For Each Bundle Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"7DQdfGE0aZn8","outputId":"2b59b46a-2354-4f70-a08e-17f6feb5e6ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["Bundle: 450\n","Tags: {'Fighting': 0, 'Action RPG': 0, 'Retro': 0, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 1, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 0, '2D': 0, 'Colorful': 0, 'Co-op': 0, 'Puzzle': 0, 'Hack and Slash': 0, 'Dystopian': 0, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 0, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 0, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 1, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 0, 'Destruction': 0, 'Magic': 0, 'Funny': 0, 'Assassin': 0, 'Parkour': 0, 'Anime': 1, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 0, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 1, 'Team-Based': 0, 'Gambling': 0, 'Fantasy': 0, 'RTS': 0, 'Loot': 0, 'Sci-fi': 0, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 0, 'Local Co-Op': 0, 'Pirates': 0, 'CRPG': 0, 'Short': 0, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 0, 'Walking Simulator': 0, 'Space': 0, 'Local Multiplayer': 0, 'Strategy': 0, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 0, 'Turn-Based': 0, 'FPS': 0, 'Story Rich': 0, 'Sandbox': 0, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 0, 'Atmospheric': 0, 'Moddable': 0, 'Open World': 0, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 0, 'Competitive': 0, 'Shooter': 0, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 0, 'Simulation': 0, 'PvP': 0, 'Nudity': 1, 'Horror': 0, 'Survival': 0, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 0, 'FMV': 0, 'First-Person': 0, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 0, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 0, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 1, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 0, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 0, \"Shoot 'Em Up\": 1, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 0, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 0, 'Fast-Paced': 0, 'Classic': 0, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}\n","Bundle: 450\n","Specs: {'Steam Workshop': 0, 'Multi-player': 0, 'Valve Anti-Cheat enabled': 0, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 0, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","bundle_item_tags = {}\n","bundle_item_specs = {}\n","\n","for bundle_id, bundle_items in bundle_data.items():\n"," bundle_item_tags[bundle_id] = input_default_tags.copy()\n"," bundle_item_specs[bundle_id] = input_default_specs.copy()\n","\n"," for item_id in bundle_items[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," bundle_item_tags[bundle_id][tag] = 1\n","\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," bundle_item_specs[bundle_id][spec] = 1\n","\n","# Example usage (you can print or process bundle_item_tags as needed):\n","for bundle_id, item_tags in bundle_item_tags.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break\n","\n","# Example usage (you can print or process bundle_item_specs as needed):\n","for bundle_id, item_specs in bundle_item_specs.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"_qoICCA4cPyU"},"source":["#### 3. Get Intersection Between Persona User Item Interactions and Persona Bundle Items Based On Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"EFAWQsJgcb3G","outputId":"24288744-33ad-4a78-bd2f-d77696610862"},"outputs":[{"name":"stdout","output_type":"stream","text":["Intersection for user 76561197970982479 and bundle 450:\n","Tags: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","Specs: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"]}],"source":["# prompt: Get Intersection Between Persona User Item Interactions and Persona Bundle Items By Tags and Specs That Have Value == 1\n","\n","def get_intersection(user_id, bundle_id):\n"," intersection_tags = {}\n"," intersection_specs = {}\n","\n"," for tag, value in user_item_tags[user_id].items():\n"," if value == 1 and bundle_item_tags[bundle_id][tag] == 1:\n"," intersection_tags[tag] = 1\n"," else:\n"," intersection_tags[tag] = 0\n","\n"," for spec, value in user_item_specs[user_id].items():\n"," if value == 1 and bundle_item_specs[bundle_id][spec] == 1:\n"," intersection_specs[spec] = 1\n"," else:\n"," intersection_specs[spec] = 0\n","\n"," return intersection_tags, intersection_specs\n","\n","def get_intersection_as_list(user_id, bundle_id):\n"," user_tags = user_item_tags[user_id]\n"," bundle_tags = bundle_item_tags[bundle_id]\n"," user_specs = user_item_specs[user_id]\n"," bundle_specs = bundle_item_specs[bundle_id]\n","\n"," # Using list comprehensions to find common tags and specs\n"," common_tags = [1 if user_tags[tag] == 1 and bundle_tags[tag] == 1 else 0 for tag in user_tags]\n"," common_specs = [1 if user_specs[spec] == 1 and bundle_specs[spec] == 1 else 0 for spec in user_specs]\n","\n"," return common_tags, common_specs\n","\n","# Example usage:\n","user_id = list(user_item_interactions.keys())[0] # Get the first user ID\n","bundle_id = list(bundle_data.keys())[0] # Get the first bundle ID\n","\n","intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n","\n","print(f\"Intersection for user {user_id} and bundle {bundle_id}:\")\n","print(f\"Tags: {intersection_tags}\")\n","print(f\"Specs: {intersection_specs}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":309041,"status":"ok","timestamp":1735305675372,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"BuFxKyM6cyCv","outputId":"fad7d31b-9e29-4644-80b1-456508338d8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 / 10.000\n","4898 / 10.000\n","4899 / 10.000\n","4900 / 10.000\n","4901 / 10.000\n","4902 / 10.000\n","4903 / 10.000\n","4904 / 10.000\n","4905 / 10.000\n","4906 / 10.000\n","4907 / 10.000\n","4908 / 10.000\n","4909 / 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intersection_specs = get_intersection_as_list(user_id, bundle_id)\n"," data_to_write[user_id][bundle_id] = {\n"," \"tags\": intersection_tags,\n"," \"specs\": intersection_specs\n"," }\n","\n"," json.dump(data_to_write, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," print(f\"{i} / 10.000\")"]},{"cell_type":"markdown","metadata":{"id":"45XFujQeHeG-"},"source":["#### Do Recommendation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XzQCZ1fmHh5D","outputId":"9ddfc0c3-8c81-4a8e-f314-6a6ac2fec9a2","executionInfo":{"status":"ok","timestamp":1735351521428,"user_tz":-420,"elapsed":1732361,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 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10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","9877 / 10.000\n","9878 / 10.000\n","9879 / 10.000\n","9880 / 10.000\n","9881 / 10.000\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_logreg.json.gz'\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","data_dict_out = None\n","\n","file_path_read = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_logreg.json.gz\"\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_read, 'rt', encoding='utf-8') as infile, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," data_dict_out = {}\n"," for user_id, bundle_data in data_dict.items():\n"," for bundle_id, recommendation in bundle_data.items():\n"," data_dict_out[\"user_id\"] = user_id\n"," data_dict_out[\"bundle_id\"] = bundle_id\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['best_logreg_classifier_tags_specifications'].predict([recommendation['tags'] + recommendation['specs']])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," # break\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # break\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":864,"status":"ok","timestamp":1735349770035,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"MnRRI8KSaOgO","outputId":"8e042675-cab4-44db-ca92-f95c0a0011a9"},"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_logreg.json.gz'\n","Count: 452\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_logreg.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pmSwZ6yPxdN5"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"na7Ok_umxQnJ"},"source":["## Create Input For Model Price Patterns"]},{"cell_type":"markdown","source":["#### 1. Load Data"],"metadata":{"id":"p6y7SbgJmBRo"}},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735783130863,"user_tz":-420,"elapsed":1397,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"430d4c32-45f3-4777-e144-4ba1b0a573a3","id":"HT2uTzBMvAry"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":3}],"source":["# prompt: Load file /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy\n","\n","import numpy as np\n","\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')\n","\n","user_price_label[:5]"]},{"cell_type":"code","source":["# prompt: Calculate how many 0 and 1 in user_price_label[:615]\n","\n","zeros = np.count_nonzero(user_price_label[:615] == 0)\n","ones = np.count_nonzero(user_price_label[:615] == 1)\n","\n","print(f\"Number of 0s: {zeros}\")\n","print(f\"Number of 1s: {ones}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBye6gkth3l6","executionInfo":{"status":"ok","timestamp":1735783130863,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1cf8ad9d-1a3d-4675-e7f2-2891205be9be"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of 0s: 582\n","Number of 1s: 33\n"]}]},{"cell_type":"code","source":["user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')"],"metadata":{"id":"2LoxPq-8iYe3","executionInfo":{"status":"ok","timestamp":1735783138975,"user_tz":-420,"elapsed":6843,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["user_price_identity = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy')"],"metadata":{"id":"Jqtxa1XYl2sa","executionInfo":{"status":"ok","timestamp":1735783158338,"user_tz":-420,"elapsed":19364,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Find is that from user_price_identity user for all bundle theres no label 1 in user_price_label\n","\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","check_users = {}\n","\n","# Iterate through users and bundles (assuming bundle IDs are consecutive integers)\n","for user_index in range(num_users):\n"," user_id = user_price_identity[user_index]\n"," if user_id[0] not in check_users:\n"," check_users[user_id[0]] = 0\n","\n"," # Check if label is present for this user\n"," if user_price_label[user_index] is not None: # Check for None or NaN if necessary\n"," # Access the label directly instead of iterating\n"," if user_price_label[user_index] == 1:\n"," # Assuming bundle_id is the second element in user_price_identity\n"," check_users[user_id[0]] = 1\n"," else:\n"," print(f\"No labels found for user {user_id}\")"],"metadata":{"id":"g_NWI-KDiuIF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: From check_users check is there has 0 value?\n","\n","zeros = 0\n","for user_id, label in check_users.items():\n"," if label == 0:\n"," zeros += 1\n","\n","print(f\"Number of users with 0 value: {zeros}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qD-mnjmHlcqD","executionInfo":{"status":"ok","timestamp":1735780608038,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ddd2f1e-dea8-44b9-9f94-94adbc1bca4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of users with 0 value: 23\n"]}]},{"cell_type":"markdown","source":["#### 2. Do Recommendation"],"metadata":{"id":"TKiv8OUzmEF5"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict_out = {}\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_logreg.json.gz\"\n","count = 0\n","\n","check_users = {}\n","\n","try:\n"," with gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for user_index in range(num_users):\n"," data_dict_out[\"user_id\"] = user_price_identity[user_index][0]\n"," data_dict_out[\"bundle_id\"] = user_price_identity[user_index][1]\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['best_logreg_classifier_price_patterns'].predict([user_price_features[user_index]])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," if user_price_identity[user_index][0] not in check_users:\n"," check_users[user_price_identity[user_index][0]] = 0\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # if count == 2:\n"," # break\n"," print(f\"Successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1nbQtXeoa1M","outputId":"487e837b-83ef-40a2-b0f1-4f8b15ccfc1d","executionInfo":{"status":"ok","timestamp":1735784738852,"user_tz":-420,"elapsed":1572576,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 10.000\n","4879 / 10.000\n","4880 / 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10.000\n","9831 / 10.000\n","9832 / 10.000\n","9833 / 10.000\n","9834 / 10.000\n","9835 / 10.000\n","9836 / 10.000\n","9837 / 10.000\n","9838 / 10.000\n","9839 / 10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","Successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_logreg.json.gz'\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_write, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKv7TjHhq0YE","executionInfo":{"status":"ok","timestamp":1735782114480,"user_tz":-420,"elapsed":325,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9af4b1c5-761b-4961-fa29-32c09b7b0fd4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Count: 33\n"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"1-exUF3ytZTEq1l5Qq3pewRtq-heTyUSB","timestamp":1735349262757},{"file_id":"1F6K5UXAIUXHv1lxOYMhC9yRKdjnZbsCX","timestamp":1735348485863},{"file_id":"1OrfeLsuMTaVL7GS9uEdXvCv68A7Qxnxi","timestamp":1735347331860}],"mount_file_id":"1hbEAWkFXV0hpOy1SN0srnqa5QCx8ogMq","authorship_tag":"ABX9TyNtgpBNU65ePnPlx3ybKbS1"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/4. Judge Bundle For Each Users Naive Bayes .ipynb b/Source Code/4. Judge Bundle For Each Users Naive Bayes .ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0bb31a115293199d6b924aed752964d87087e0fd --- /dev/null +++ b/Source Code/4. Judge Bundle For Each Users Naive Bayes .ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9765,"status":"ok","timestamp":1735783418648,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"TqtPuipCMpzT","outputId":"cd0f9f46-fa1c-4216-9005-c94ed19d1268"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded model: decision_tree_price_patterns\n","Loaded model: best_logreg_classifier_price_patterns\n","Loaded model: best_sgd_classifier_price_patterns\n","Loaded model: best_bnb_price_patterns\n","Loaded model: knn_price_patterns\n","Loaded model: neural_networks_price_patterns\n","Loaded 6 models.\n"]}],"source":["# prompt: Load all models from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications using joblib\n","\n","import joblib\n","import os\n","\n","# models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications\"\n","models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns\"\n","loaded_models = {}\n","\n","for filename in os.listdir(models_path):\n"," if filename.endswith(\".pkl\"):\n"," model_path = os.path.join(models_path, filename)\n"," model_name = filename[:-4] # Remove \".joblib\" extension\n"," try:\n"," loaded_models[model_name] = joblib.load(model_path)\n"," print(f\"Loaded model: {model_name}\")\n"," except Exception as e:\n"," print(f\"Error loading model {model_name}: {e}\")\n","\n","print(f\"Loaded {len(loaded_models)} models.\")\n"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":287,"status":"ok","timestamp":1735783449363,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LXdGNRK8RcwY"},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"cs-PpqN8TbRM"},"source":["## Bundle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1735303253454,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"oBdZF6YtO3Ef","outputId":"9bf13c93-1555-4e6b-a0f0-040196dd9051"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1735303253456,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JQDVAG3bRiAH","outputId":"e686321a-8c30-4baa-df71-49ab65d86046"},"outputs":[{"data":{"text/plain":["{'bundle_id': '450',\n"," 'bundle_price': 73.86,\n"," 'bundle_name': 'Dharker Studio 2015 Complete',\n"," 'items': {'326950',\n"," '331490',\n"," '331491',\n"," '331492',\n"," '348540',\n"," '352010',\n"," '387110',\n"," '387120',\n"," '390470',\n"," '394050',\n"," '396610',\n"," '396620',\n"," '398100',\n"," '408770'}}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["bundle_data[\"450\"]"]},{"cell_type":"markdown","metadata":{"id":"SY4SrF4sTdmZ"},"source":["## Items Dictionary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3800,"status":"ok","timestamp":1735303258466,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"pXudpCiWTffK","outputId":"c450dfec-0cfa-473c-fe6c-ccb71184a906"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"PAxnBn8zUwfT"},"source":["## User Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":43890,"status":"ok","timestamp":1735303302353,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"RWWQ3GPvUy1a","outputId":"551041c0-2c5d-4589-c662-9ea0ef6e77c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 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A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"IInSMlBIUVNv"},"source":["## Create Input For Model Tags and Specs"]},{"cell_type":"markdown","metadata":{"id":"LwqMdBsgbrC_"},"source":["#### 1. Load Hot Persona Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24858,"status":"ok","timestamp":1735303327208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x4BKOF2sWkLC","outputId":"e7e4123f-9849-44ab-8ad8-63bda828cf70"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXPeELdRXNhn"},"outputs":[],"source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10,"status":"ok","timestamp":1735303327209,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"SHGaOHecX37a","outputId":"b356d01f-5e28-4f71-96c1-380e8066b968"},"outputs":[{"data":{"text/plain":["{'Fighting': 0,\n"," 'Action RPG': 0,\n"," 'Retro': 0,\n"," 'Bullet Hell': 0,\n"," 'Base Building': 0,\n"," 'Turn-Based Strategy': 0,\n"," 'Early Access': 0,\n"," 'Hex Grid': 0,\n"," 'Kickstarter': 0,\n"," 'Resource Management': 0,\n"," 'Abstract': 0,\n"," '4 Player Local': 0,\n"," 'Sniper': 0,\n"," 'Transhumanism': 0,\n"," 'Metroidvania': 0,\n"," 'Episodic': 0,\n"," 'e-sports': 0,\n"," 'Based On A Novel': 0,\n"," 'Crime': 0,\n"," 'Turn-Based Combat': 0,\n"," 'Vampire': 0,\n"," 'Level Editor': 0,\n"," 'Programming': 0,\n"," 'Gore': 0,\n"," '2D': 0,\n"," 'Colorful': 0,\n"," 'Co-op': 0,\n"," 'Puzzle': 0,\n"," 'Hack and Slash': 0,\n"," 'Dystopian': 0,\n"," '3D Vision': 0,\n"," 'Futuristic': 0,\n"," 'Hidden Object': 0,\n"," 'Post-apocalyptic': 0,\n"," 'Comic Book': 0,\n"," 'Linear': 0,\n"," 'Online Co-Op': 0,\n"," 'Stylized': 0,\n"," 'Dragons': 0,\n"," 'Rogue-lite': 0,\n"," 'Utilities': 0,\n"," 'Dark Humor': 0,\n"," 'Romance': 0,\n"," 'Economy': 0,\n"," 'Rogue-like': 0,\n"," 'Isometric': 0,\n"," \"1990's\": 0,\n"," 'Destruction': 0,\n"," 'Magic': 0,\n"," 'Funny': 0,\n"," 'Assassin': 0,\n"," 'Parkour': 0,\n"," 'Anime': 0,\n"," 'Multiple Endings': 0,\n"," 'Quick-Time Events': 0,\n"," 'Mystery': 0,\n"," 'Survival Horror': 0,\n"," 'Real-Time with Pause': 0,\n"," 'Psychological Horror': 0,\n"," 'Dark Fantasy': 0,\n"," 'Memes': 0,\n"," 'Cinematic': 0,\n"," 'Party-Based RPG': 0,\n"," 'Free to Play': 0,\n"," 'Tower Defense': 0,\n"," 'War': 0,\n"," 'Co-op Campaign': 0,\n"," 'Family Friendly': 0,\n"," 'Card Game': 0,\n"," 'Management': 0,\n"," 'Narration': 0,\n"," 'Procedural Generation': 0,\n"," 'Inventory Management': 0,\n"," 'Animation & Modeling': 0,\n"," 'Lara Croft': 0,\n"," 'Sexual Content': 0,\n"," 'Team-Based': 0,\n"," 'Gambling': 0,\n"," 'Fantasy': 0,\n"," 'RTS': 0,\n"," 'Loot': 0,\n"," 'Sci-fi': 0,\n"," 'Casual': 0,\n"," 'Web Publishing': 0,\n"," 'World War II': 0,\n"," \"Beat 'em up\": 0,\n"," 'RPG': 0,\n"," 'Local Co-Op': 0,\n"," 'Pirates': 0,\n"," 'CRPG': 0,\n"," 'Short': 0,\n"," 'Dungeon Crawler': 0,\n"," 'Character Customization': 0,\n"," 'Pinball': 0,\n"," 'VR': 0,\n"," 'Twin Stick Shooter': 0,\n"," 'Third-Person Shooter': 0,\n"," 'GameMaker': 0,\n"," 'Detective': 0,\n"," 'Stealth': 0,\n"," 'Platformer': 0,\n"," 'Walking Simulator': 0,\n"," 'Space': 0,\n"," 'Local Multiplayer': 0,\n"," 'Strategy': 0,\n"," 'Physics': 0,\n"," 'Steampunk': 0,\n"," 'Text-Based': 0,\n"," 'Action': 0,\n"," 'Mechs': 0,\n"," 'Side Scroller': 0,\n"," 'Multiplayer': 0,\n"," 'Turn-Based': 0,\n"," 'FPS': 0,\n"," 'Story Rich': 0,\n"," 'Sandbox': 0,\n"," 'Interactive Fiction': 0,\n"," 'Medieval': 0,\n"," 'Aliens': 0,\n"," 'Atmospheric': 0,\n"," 'Moddable': 0,\n"," 'Open World': 0,\n"," 'Cult Classic': 0,\n"," 'Time Travel': 0,\n"," 'Realistic': 0,\n"," 'Split Screen': 0,\n"," 'Choose Your Own Adventure': 0,\n"," 'Science': 0,\n"," 'Underwater': 0,\n"," 'Female Protagonist': 0,\n"," 'Competitive': 0,\n"," 'Shooter': 0,\n"," 'Software': 0,\n"," 'Software Training': 0,\n"," 'Mature': 0,\n"," 'Demons': 0,\n"," 'Cyberpunk': 0,\n"," '4X': 0,\n"," 'Zombies': 0,\n"," 'Simulation': 0,\n"," 'PvP': 0,\n"," 'Nudity': 0,\n"," 'Horror': 0,\n"," 'Survival': 0,\n"," 'Massively Multiplayer': 0,\n"," 'Voxel': 0,\n"," 'Tactical': 0,\n"," 'FMV': 0,\n"," 'First-Person': 0,\n"," 'Exploration': 0,\n"," 'Dark': 0,\n"," 'Adventure': 0,\n"," 'Silent Protagonist': 0,\n"," 'Controller': 0,\n"," 'Remake': 0,\n"," 'Diplomacy': 0,\n"," 'Military': 0,\n"," 'Spectacle fighter': 0,\n"," 'Sports': 0,\n"," 'Visual Novel': 0,\n"," 'Grand Strategy': 0,\n"," 'Relaxing': 0,\n"," 'Real Time Tactics': 0,\n"," 'Top-Down Shooter': 0,\n"," 'Alternate History': 0,\n"," 'Comedy': 0,\n"," 'Villain Protagonist': 0,\n"," 'Singleplayer': 0,\n"," 'Puzzle-Platformer': 0,\n"," 'Class-Based': 0,\n"," 'Touch-Friendly': 0,\n"," 'Trading': 0,\n"," 'City Builder': 0,\n"," 'Difficult': 0,\n"," \"Shoot 'Em Up\": 0,\n"," 'RPGMaker': 0,\n"," 'Replay Value': 0,\n"," 'Design & Illustration': 0,\n"," 'Cute': 0,\n"," 'God Game': 0,\n"," 'Point & Click': 0,\n"," 'Political': 0,\n"," 'Third Person': 0,\n"," 'Pixel Graphics': 0,\n"," 'Choices Matter': 0,\n"," 'Match 3': 0,\n"," 'Crafting': 0,\n"," 'Game Development': 0,\n"," 'Top-Down': 0,\n"," 'Education': 0,\n"," 'Indie': 0,\n"," 'Driving': 0,\n"," 'Hand-drawn': 0,\n"," 'Arcade': 0,\n"," 'Drama': 0,\n"," 'Robots': 0,\n"," 'Great Soundtrack': 0,\n"," 'Fast-Paced': 0,\n"," 'Classic': 0,\n"," 'Racing': 0,\n"," 'Historical': 0,\n"," 'Action-Adventure': 0,\n"," 'JRPG': 0,\n"," 'Superhero': 0}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["input_default_tags"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":9,"status":"ok","timestamp":1735303327210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"GbhHKsFvX5Vb","outputId":"4b02a1c8-284c-408f-92b6-c7a193f8012c"},"outputs":[{"data":{"text/plain":["{'Steam Workshop': 0,\n"," 'Multi-player': 0,\n"," 'Valve Anti-Cheat enabled': 0,\n"," 'Captions available': 0,\n"," 'Local Multi-Player': 0,\n"," 'Steam Trading Cards': 0,\n"," 'Steam Leaderboards': 0,\n"," 'Stats': 0,\n"," 'Shared/Split Screen': 0,\n"," 'Co-op': 0,\n"," 'Single-player': 0,\n"," 'Partial Controller Support': 0,\n"," 'Full controller support': 0,\n"," 'Steam Achievements': 0,\n"," 'Commentary available': 0,\n"," 'Includes level editor': 0,\n"," 'Online Multi-Player': 0,\n"," 'Cross-Platform Multiplayer': 0,\n"," 'Includes Source SDK': 0,\n"," 'In-App Purchases': 0,\n"," 'Steam Cloud': 0}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["input_default_specs"]},{"cell_type":"markdown","metadata":{"id":"E8Y-taqrb1g_"},"source":["#### 2. Check Hot Persona Tags and Specs For Each User Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5761,"status":"ok","timestamp":1735303332964,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UDTwrBJqUcjY","outputId":"a7ffbd3e-b3a4-4df2-d2d8-dfef49865c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Tags: {'Fighting': 1, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 1, 'Base Building': 1, 'Turn-Based Strategy': 1, 'Early Access': 1, 'Hex Grid': 1, 'Kickstarter': 1, 'Resource Management': 1, 'Abstract': 1, '4 Player Local': 1, 'Sniper': 0, 'Transhumanism': 1, 'Metroidvania': 1, 'Episodic': 1, 'e-sports': 1, 'Based On A Novel': 1, 'Crime': 1, 'Turn-Based Combat': 1, 'Vampire': 0, 'Level Editor': 1, 'Programming': 1, 'Gore': 1, '2D': 1, 'Colorful': 1, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 1, 'Dystopian': 1, '3D Vision': 1, 'Futuristic': 1, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 1, 'Linear': 1, 'Online Co-Op': 1, 'Stylized': 1, 'Dragons': 1, 'Rogue-lite': 1, 'Utilities': 0, 'Dark Humor': 1, 'Romance': 1, 'Economy': 1, 'Rogue-like': 1, 'Isometric': 1, \"1990's\": 1, 'Destruction': 1, 'Magic': 1, 'Funny': 1, 'Assassin': 1, 'Parkour': 1, 'Anime': 1, 'Multiple Endings': 1, 'Quick-Time Events': 1, 'Mystery': 1, 'Survival Horror': 1, 'Real-Time with Pause': 1, 'Psychological Horror': 1, 'Dark Fantasy': 1, 'Memes': 1, 'Cinematic': 1, 'Party-Based RPG': 1, 'Free to Play': 1, 'Tower Defense': 1, 'War': 1, 'Co-op Campaign': 1, 'Family Friendly': 1, 'Card Game': 1, 'Management': 1, 'Narration': 1, 'Procedural Generation': 1, 'Inventory Management': 1, 'Animation & Modeling': 0, 'Lara Croft': 1, 'Sexual Content': 1, 'Team-Based': 1, 'Gambling': 1, 'Fantasy': 1, 'RTS': 1, 'Loot': 1, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 1, \"Beat 'em up\": 1, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 1, 'CRPG': 1, 'Short': 1, 'Dungeon Crawler': 1, 'Character Customization': 1, 'Pinball': 0, 'VR': 1, 'Twin Stick Shooter': 1, 'Third-Person Shooter': 1, 'GameMaker': 0, 'Detective': 1, 'Stealth': 1, 'Platformer': 1, 'Walking Simulator': 1, 'Space': 1, 'Local Multiplayer': 1, 'Strategy': 1, 'Physics': 1, 'Steampunk': 1, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 1, 'Multiplayer': 1, 'Turn-Based': 1, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 1, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 1, 'Time Travel': 1, 'Realistic': 1, 'Split Screen': 1, 'Choose Your Own Adventure': 1, 'Science': 1, 'Underwater': 1, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 1, 'Cyberpunk': 1, '4X': 1, 'Zombies': 1, 'Simulation': 1, 'PvP': 1, 'Nudity': 1, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 1, 'Voxel': 1, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 1, 'Dark': 1, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 1, 'Remake': 1, 'Diplomacy': 1, 'Military': 1, 'Spectacle fighter': 1, 'Sports': 1, 'Visual Novel': 1, 'Grand Strategy': 1, 'Relaxing': 1, 'Real Time Tactics': 1, 'Top-Down Shooter': 1, 'Alternate History': 1, 'Comedy': 1, 'Villain Protagonist': 1, 'Singleplayer': 1, 'Puzzle-Platformer': 1, 'Class-Based': 1, 'Touch-Friendly': 1, 'Trading': 1, 'City Builder': 1, 'Difficult': 1, \"Shoot 'Em Up\": 1, 'RPGMaker': 1, 'Replay Value': 1, 'Design & Illustration': 0, 'Cute': 1, 'God Game': 1, 'Point & Click': 1, 'Political': 1, 'Third Person': 1, 'Pixel Graphics': 1, 'Choices Matter': 1, 'Match 3': 0, 'Crafting': 1, 'Game Development': 0, 'Top-Down': 1, 'Education': 1, 'Indie': 1, 'Driving': 1, 'Hand-drawn': 1, 'Arcade': 1, 'Drama': 1, 'Robots': 1, 'Great Soundtrack': 1, 'Fast-Paced': 1, 'Classic': 1, 'Racing': 1, 'Historical': 1, 'Action-Adventure': 1, 'JRPG': 1, 'Superhero': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","user_item_tags = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_tags[user_id] = input_default_tags.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," user_item_tags[user_id][tag] = 1\n","\n","# Example usage (you can print or process user_item_tags as needed):\n","for user_id, item_tags in user_item_tags.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3306,"status":"ok","timestamp":1735303336267,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LxfFCIj9Zkrc","outputId":"a6a0993a-4155-4ebb-b569-f7ce8fc03610"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Specs: {'Steam Workshop': 1, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 1, 'Local Multi-Player': 1, 'Steam Trading Cards': 1, 'Steam Leaderboards': 1, 'Stats': 1, 'Shared/Split Screen': 1, 'Co-op': 1, 'Single-player': 1, 'Partial Controller Support': 1, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 1, 'Includes level editor': 1, 'Online Multi-Player': 1, 'Cross-Platform Multiplayer': 1, 'Includes Source SDK': 1, 'In-App Purchases': 1, 'Steam Cloud': 1}\n"]}],"source":["user_item_specs = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_specs[user_id] = input_default_specs.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," user_item_specs[user_id][spec] = 1\n","\n","# Example usage (you can print or process user_item_specs as needed):\n","for user_id, item_specs in user_item_specs.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"lziPgL49cJXd"},"source":["#### 2. Check Hot Persona Tags and Specs For Each Bundle Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"7DQdfGE0aZn8","outputId":"2b59b46a-2354-4f70-a08e-17f6feb5e6ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["Bundle: 450\n","Tags: {'Fighting': 0, 'Action RPG': 0, 'Retro': 0, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 1, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 0, '2D': 0, 'Colorful': 0, 'Co-op': 0, 'Puzzle': 0, 'Hack and Slash': 0, 'Dystopian': 0, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 0, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 0, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 1, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 0, 'Destruction': 0, 'Magic': 0, 'Funny': 0, 'Assassin': 0, 'Parkour': 0, 'Anime': 1, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 0, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 1, 'Team-Based': 0, 'Gambling': 0, 'Fantasy': 0, 'RTS': 0, 'Loot': 0, 'Sci-fi': 0, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 0, 'Local Co-Op': 0, 'Pirates': 0, 'CRPG': 0, 'Short': 0, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 0, 'Walking Simulator': 0, 'Space': 0, 'Local Multiplayer': 0, 'Strategy': 0, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 0, 'Turn-Based': 0, 'FPS': 0, 'Story Rich': 0, 'Sandbox': 0, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 0, 'Atmospheric': 0, 'Moddable': 0, 'Open World': 0, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 0, 'Competitive': 0, 'Shooter': 0, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 0, 'Simulation': 0, 'PvP': 0, 'Nudity': 1, 'Horror': 0, 'Survival': 0, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 0, 'FMV': 0, 'First-Person': 0, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 0, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 0, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 1, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 0, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 0, \"Shoot 'Em Up\": 1, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 0, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 0, 'Fast-Paced': 0, 'Classic': 0, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}\n","Bundle: 450\n","Specs: {'Steam Workshop': 0, 'Multi-player': 0, 'Valve Anti-Cheat enabled': 0, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 0, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","bundle_item_tags = {}\n","bundle_item_specs = {}\n","\n","for bundle_id, bundle_items in bundle_data.items():\n"," bundle_item_tags[bundle_id] = input_default_tags.copy()\n"," bundle_item_specs[bundle_id] = input_default_specs.copy()\n","\n"," for item_id in bundle_items[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," bundle_item_tags[bundle_id][tag] = 1\n","\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," bundle_item_specs[bundle_id][spec] = 1\n","\n","# Example usage (you can print or process bundle_item_tags as needed):\n","for bundle_id, item_tags in bundle_item_tags.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break\n","\n","# Example usage (you can print or process bundle_item_specs as needed):\n","for bundle_id, item_specs in bundle_item_specs.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"_qoICCA4cPyU"},"source":["#### 3. Get Intersection Between Persona User Item Interactions and Persona Bundle Items Based On Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"EFAWQsJgcb3G","outputId":"24288744-33ad-4a78-bd2f-d77696610862"},"outputs":[{"name":"stdout","output_type":"stream","text":["Intersection for user 76561197970982479 and bundle 450:\n","Tags: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","Specs: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"]}],"source":["# prompt: Get Intersection Between Persona User Item Interactions and Persona Bundle Items By Tags and Specs That Have Value == 1\n","\n","def get_intersection(user_id, bundle_id):\n"," intersection_tags = {}\n"," intersection_specs = {}\n","\n"," for tag, value in user_item_tags[user_id].items():\n"," if value == 1 and bundle_item_tags[bundle_id][tag] == 1:\n"," intersection_tags[tag] = 1\n"," else:\n"," intersection_tags[tag] = 0\n","\n"," for spec, value in user_item_specs[user_id].items():\n"," if value == 1 and bundle_item_specs[bundle_id][spec] == 1:\n"," intersection_specs[spec] = 1\n"," else:\n"," intersection_specs[spec] = 0\n","\n"," return intersection_tags, intersection_specs\n","\n","def get_intersection_as_list(user_id, bundle_id):\n"," user_tags = user_item_tags[user_id]\n"," bundle_tags = bundle_item_tags[bundle_id]\n"," user_specs = user_item_specs[user_id]\n"," bundle_specs = bundle_item_specs[bundle_id]\n","\n"," # Using list comprehensions to find common tags and specs\n"," common_tags = [1 if user_tags[tag] == 1 and bundle_tags[tag] == 1 else 0 for tag in user_tags]\n"," common_specs = [1 if user_specs[spec] == 1 and bundle_specs[spec] == 1 else 0 for spec in user_specs]\n","\n"," return common_tags, common_specs\n","\n","# Example usage:\n","user_id = list(user_item_interactions.keys())[0] # Get the first user ID\n","bundle_id = list(bundle_data.keys())[0] # Get the first bundle ID\n","\n","intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n","\n","print(f\"Intersection for user {user_id} and bundle {bundle_id}:\")\n","print(f\"Tags: {intersection_tags}\")\n","print(f\"Specs: {intersection_specs}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":309041,"status":"ok","timestamp":1735305675372,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"BuFxKyM6cyCv","outputId":"fad7d31b-9e29-4644-80b1-456508338d8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 / 10.000\n","4898 / 10.000\n","4899 / 10.000\n","4900 / 10.000\n","4901 / 10.000\n","4902 / 10.000\n","4903 / 10.000\n","4904 / 10.000\n","4905 / 10.000\n","4906 / 10.000\n","4907 / 10.000\n","4908 / 10.000\n","4909 / 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intersection_specs = get_intersection_as_list(user_id, bundle_id)\n"," data_to_write[user_id][bundle_id] = {\n"," \"tags\": intersection_tags,\n"," \"specs\": intersection_specs\n"," }\n","\n"," json.dump(data_to_write, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," print(f\"{i} / 10.000\")"]},{"cell_type":"markdown","metadata":{"id":"45XFujQeHeG-"},"source":["#### Do Recommendation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XzQCZ1fmHh5D","outputId":"cc229b5f-53a8-413e-a390-0669bb7cdd42","executionInfo":{"status":"ok","timestamp":1735354137249,"user_tz":-420,"elapsed":3806160,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 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10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","9877 / 10.000\n","9878 / 10.000\n","9879 / 10.000\n","9880 / 10.000\n","9881 / 10.000\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_bnb.json.gz'\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","data_dict_out = None\n","\n","file_path_read = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_bnb.json.gz\"\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_read, 'rt', encoding='utf-8') as infile, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," data_dict_out = {}\n"," for user_id, bundle_data in data_dict.items():\n"," for bundle_id, recommendation in bundle_data.items():\n"," data_dict_out[\"user_id\"] = user_id\n"," data_dict_out[\"bundle_id\"] = bundle_id\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['best_bnb_tags_specifications'].predict([recommendation['tags'] + recommendation['specs']])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," # break\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # break\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":345,"status":"ok","timestamp":1735350299411,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"MnRRI8KSaOgO","outputId":"70ea8b37-73b9-4ad8-f49b-f8ce84a46675"},"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_bnb.json.gz'\n","Count: 22\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_bnb.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pmSwZ6yPxdN5"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"na7Ok_umxQnJ"},"source":["## Create Input For Model Price Patterns"]},{"cell_type":"markdown","source":["#### 1. Load Data"],"metadata":{"id":"p6y7SbgJmBRo"}},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735783466109,"user_tz":-420,"elapsed":834,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"740178b8-26c6-4abf-995c-b864dbeb24bc","id":"HT2uTzBMvAry"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":4}],"source":["# prompt: Load file /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy\n","\n","import numpy as np\n","\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')\n","\n","user_price_label[:5]"]},{"cell_type":"code","source":["# prompt: Calculate how many 0 and 1 in user_price_label[:615]\n","\n","zeros = np.count_nonzero(user_price_label[:615] == 0)\n","ones = np.count_nonzero(user_price_label[:615] == 1)\n","\n","print(f\"Number of 0s: {zeros}\")\n","print(f\"Number of 1s: {ones}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBye6gkth3l6","executionInfo":{"status":"ok","timestamp":1735783466511,"user_tz":-420,"elapsed":1,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1b211baf-575e-4cb0-ae6a-b0a366392baa"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of 0s: 582\n","Number of 1s: 33\n"]}]},{"cell_type":"code","source":["user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')"],"metadata":{"id":"2LoxPq-8iYe3","executionInfo":{"status":"ok","timestamp":1735783474915,"user_tz":-420,"elapsed":7155,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["user_price_identity = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy')"],"metadata":{"id":"Jqtxa1XYl2sa","executionInfo":{"status":"ok","timestamp":1735783490307,"user_tz":-420,"elapsed":15394,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Find is that from user_price_identity user for all bundle theres no label 1 in user_price_label\n","\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","check_users = {}\n","\n","# Iterate through users and bundles (assuming bundle IDs are consecutive integers)\n","for user_index in range(num_users):\n"," user_id = user_price_identity[user_index]\n"," if user_id[0] not in check_users:\n"," check_users[user_id[0]] = 0\n","\n"," # Check if label is present for this user\n"," if user_price_label[user_index] is not None: # Check for None or NaN if necessary\n"," # Access the label directly instead of iterating\n"," if user_price_label[user_index] == 1:\n"," # Assuming bundle_id is the second element in user_price_identity\n"," check_users[user_id[0]] = 1\n"," else:\n"," print(f\"No labels found for user {user_id}\")"],"metadata":{"id":"g_NWI-KDiuIF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: From check_users check is there has 0 value?\n","\n","zeros = 0\n","for user_id, label in check_users.items():\n"," if label == 0:\n"," zeros += 1\n","\n","print(f\"Number of users with 0 value: {zeros}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qD-mnjmHlcqD","executionInfo":{"status":"ok","timestamp":1735780608038,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ddd2f1e-dea8-44b9-9f94-94adbc1bca4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of users with 0 value: 23\n"]}]},{"cell_type":"markdown","source":["#### 2. Do Recommendation"],"metadata":{"id":"TKiv8OUzmEF5"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict_out = {}\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_bnb.json.gz\"\n","count = 0\n","\n","check_users = {}\n","\n","try:\n"," with gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for user_index in range(num_users):\n"," data_dict_out[\"user_id\"] = user_price_identity[user_index][0]\n"," data_dict_out[\"bundle_id\"] = user_price_identity[user_index][1]\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['best_bnb_price_patterns'].predict([user_price_features[user_index]])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," if user_price_identity[user_index][0] not in check_users:\n"," check_users[user_price_identity[user_index][0]] = 0\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # if count == 2:\n"," # break\n"," print(f\"Successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1nbQtXeoa1M","outputId":"9fe6fed1-56db-4cb9-d0b5-ca105f196c81","executionInfo":{"status":"ok","timestamp":1735787222688,"user_tz":-420,"elapsed":3732383,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 10.000\n","4879 / 10.000\n","4880 / 10.000\n","4881 / 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10.000\n","9832 / 10.000\n","9833 / 10.000\n","9834 / 10.000\n","9835 / 10.000\n","9836 / 10.000\n","9837 / 10.000\n","9838 / 10.000\n","9839 / 10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","Successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_bnb.json.gz'\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_write, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKv7TjHhq0YE","executionInfo":{"status":"ok","timestamp":1735782114480,"user_tz":-420,"elapsed":325,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9af4b1c5-761b-4961-fa29-32c09b7b0fd4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Count: 33\n"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"1hbEAWkFXV0hpOy1SN0srnqa5QCx8ogMq","timestamp":1735350015004},{"file_id":"1-exUF3ytZTEq1l5Qq3pewRtq-heTyUSB","timestamp":1735349262757},{"file_id":"1F6K5UXAIUXHv1lxOYMhC9yRKdjnZbsCX","timestamp":1735348485863},{"file_id":"1OrfeLsuMTaVL7GS9uEdXvCv68A7Qxnxi","timestamp":1735347331860}],"mount_file_id":"1Ehm9A4ltHWn34AZ2BU-FRKs12FKFACr9","authorship_tag":"ABX9TyNC2nC2baW5bI2pWijvIkVh"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/4. Judge Bundle For Each Users SGD.ipynb b/Source Code/4. Judge Bundle For Each Users SGD.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..df2a151732386a52a4ca79faeccdd14a2ab5f5d8 --- /dev/null +++ b/Source Code/4. Judge Bundle For Each Users SGD.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21444,"status":"ok","timestamp":1735783649469,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"TqtPuipCMpzT","outputId":"d6dc3ea4-0545-42a1-f4e5-4c8fd8f8cf34"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded model: decision_tree_price_patterns\n","Loaded model: best_logreg_classifier_price_patterns\n","Loaded model: best_sgd_classifier_price_patterns\n","Loaded model: best_bnb_price_patterns\n","Loaded model: knn_price_patterns\n","Loaded model: neural_networks_price_patterns\n","Loaded 6 models.\n"]}],"source":["# prompt: Load all models from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications using joblib\n","\n","import joblib\n","import os\n","\n","# models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications\"\n","models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns\"\n","loaded_models = {}\n","\n","for filename in os.listdir(models_path):\n"," if filename.endswith(\".pkl\"):\n"," model_path = os.path.join(models_path, filename)\n"," model_name = filename[:-4] # Remove \".joblib\" extension\n"," try:\n"," loaded_models[model_name] = joblib.load(model_path)\n"," print(f\"Loaded model: {model_name}\")\n"," except Exception as e:\n"," print(f\"Error loading model {model_name}: {e}\")\n","\n","print(f\"Loaded {len(loaded_models)} models.\")\n"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1735783649470,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LXdGNRK8RcwY"},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"cs-PpqN8TbRM"},"source":["## Bundle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1735303253454,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"oBdZF6YtO3Ef","outputId":"9bf13c93-1555-4e6b-a0f0-040196dd9051"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1735303253456,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JQDVAG3bRiAH","outputId":"e686321a-8c30-4baa-df71-49ab65d86046"},"outputs":[{"data":{"text/plain":["{'bundle_id': '450',\n"," 'bundle_price': 73.86,\n"," 'bundle_name': 'Dharker Studio 2015 Complete',\n"," 'items': {'326950',\n"," '331490',\n"," '331491',\n"," '331492',\n"," '348540',\n"," '352010',\n"," '387110',\n"," '387120',\n"," '390470',\n"," '394050',\n"," '396610',\n"," '396620',\n"," '398100',\n"," '408770'}}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["bundle_data[\"450\"]"]},{"cell_type":"markdown","metadata":{"id":"SY4SrF4sTdmZ"},"source":["## Items Dictionary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3800,"status":"ok","timestamp":1735303258466,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"pXudpCiWTffK","outputId":"c450dfec-0cfa-473c-fe6c-ccb71184a906"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"PAxnBn8zUwfT"},"source":["## User Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":43890,"status":"ok","timestamp":1735303302353,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"RWWQ3GPvUy1a","outputId":"551041c0-2c5d-4589-c662-9ea0ef6e77c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 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A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"IInSMlBIUVNv"},"source":["## Create Input For Model Tags and Specs"]},{"cell_type":"markdown","metadata":{"id":"LwqMdBsgbrC_"},"source":["#### 1. Load Hot Persona Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24858,"status":"ok","timestamp":1735303327208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x4BKOF2sWkLC","outputId":"e7e4123f-9849-44ab-8ad8-63bda828cf70"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXPeELdRXNhn"},"outputs":[],"source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10,"status":"ok","timestamp":1735303327209,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"SHGaOHecX37a","outputId":"b356d01f-5e28-4f71-96c1-380e8066b968"},"outputs":[{"data":{"text/plain":["{'Fighting': 0,\n"," 'Action RPG': 0,\n"," 'Retro': 0,\n"," 'Bullet Hell': 0,\n"," 'Base Building': 0,\n"," 'Turn-Based Strategy': 0,\n"," 'Early Access': 0,\n"," 'Hex Grid': 0,\n"," 'Kickstarter': 0,\n"," 'Resource Management': 0,\n"," 'Abstract': 0,\n"," '4 Player Local': 0,\n"," 'Sniper': 0,\n"," 'Transhumanism': 0,\n"," 'Metroidvania': 0,\n"," 'Episodic': 0,\n"," 'e-sports': 0,\n"," 'Based On A Novel': 0,\n"," 'Crime': 0,\n"," 'Turn-Based Combat': 0,\n"," 'Vampire': 0,\n"," 'Level Editor': 0,\n"," 'Programming': 0,\n"," 'Gore': 0,\n"," '2D': 0,\n"," 'Colorful': 0,\n"," 'Co-op': 0,\n"," 'Puzzle': 0,\n"," 'Hack and Slash': 0,\n"," 'Dystopian': 0,\n"," '3D Vision': 0,\n"," 'Futuristic': 0,\n"," 'Hidden Object': 0,\n"," 'Post-apocalyptic': 0,\n"," 'Comic Book': 0,\n"," 'Linear': 0,\n"," 'Online Co-Op': 0,\n"," 'Stylized': 0,\n"," 'Dragons': 0,\n"," 'Rogue-lite': 0,\n"," 'Utilities': 0,\n"," 'Dark Humor': 0,\n"," 'Romance': 0,\n"," 'Economy': 0,\n"," 'Rogue-like': 0,\n"," 'Isometric': 0,\n"," \"1990's\": 0,\n"," 'Destruction': 0,\n"," 'Magic': 0,\n"," 'Funny': 0,\n"," 'Assassin': 0,\n"," 'Parkour': 0,\n"," 'Anime': 0,\n"," 'Multiple Endings': 0,\n"," 'Quick-Time Events': 0,\n"," 'Mystery': 0,\n"," 'Survival Horror': 0,\n"," 'Real-Time with Pause': 0,\n"," 'Psychological Horror': 0,\n"," 'Dark Fantasy': 0,\n"," 'Memes': 0,\n"," 'Cinematic': 0,\n"," 'Party-Based RPG': 0,\n"," 'Free to Play': 0,\n"," 'Tower Defense': 0,\n"," 'War': 0,\n"," 'Co-op Campaign': 0,\n"," 'Family Friendly': 0,\n"," 'Card Game': 0,\n"," 'Management': 0,\n"," 'Narration': 0,\n"," 'Procedural Generation': 0,\n"," 'Inventory Management': 0,\n"," 'Animation & Modeling': 0,\n"," 'Lara Croft': 0,\n"," 'Sexual Content': 0,\n"," 'Team-Based': 0,\n"," 'Gambling': 0,\n"," 'Fantasy': 0,\n"," 'RTS': 0,\n"," 'Loot': 0,\n"," 'Sci-fi': 0,\n"," 'Casual': 0,\n"," 'Web Publishing': 0,\n"," 'World War II': 0,\n"," \"Beat 'em up\": 0,\n"," 'RPG': 0,\n"," 'Local Co-Op': 0,\n"," 'Pirates': 0,\n"," 'CRPG': 0,\n"," 'Short': 0,\n"," 'Dungeon Crawler': 0,\n"," 'Character Customization': 0,\n"," 'Pinball': 0,\n"," 'VR': 0,\n"," 'Twin Stick Shooter': 0,\n"," 'Third-Person Shooter': 0,\n"," 'GameMaker': 0,\n"," 'Detective': 0,\n"," 'Stealth': 0,\n"," 'Platformer': 0,\n"," 'Walking Simulator': 0,\n"," 'Space': 0,\n"," 'Local Multiplayer': 0,\n"," 'Strategy': 0,\n"," 'Physics': 0,\n"," 'Steampunk': 0,\n"," 'Text-Based': 0,\n"," 'Action': 0,\n"," 'Mechs': 0,\n"," 'Side Scroller': 0,\n"," 'Multiplayer': 0,\n"," 'Turn-Based': 0,\n"," 'FPS': 0,\n"," 'Story Rich': 0,\n"," 'Sandbox': 0,\n"," 'Interactive Fiction': 0,\n"," 'Medieval': 0,\n"," 'Aliens': 0,\n"," 'Atmospheric': 0,\n"," 'Moddable': 0,\n"," 'Open World': 0,\n"," 'Cult Classic': 0,\n"," 'Time Travel': 0,\n"," 'Realistic': 0,\n"," 'Split Screen': 0,\n"," 'Choose Your Own Adventure': 0,\n"," 'Science': 0,\n"," 'Underwater': 0,\n"," 'Female Protagonist': 0,\n"," 'Competitive': 0,\n"," 'Shooter': 0,\n"," 'Software': 0,\n"," 'Software Training': 0,\n"," 'Mature': 0,\n"," 'Demons': 0,\n"," 'Cyberpunk': 0,\n"," '4X': 0,\n"," 'Zombies': 0,\n"," 'Simulation': 0,\n"," 'PvP': 0,\n"," 'Nudity': 0,\n"," 'Horror': 0,\n"," 'Survival': 0,\n"," 'Massively Multiplayer': 0,\n"," 'Voxel': 0,\n"," 'Tactical': 0,\n"," 'FMV': 0,\n"," 'First-Person': 0,\n"," 'Exploration': 0,\n"," 'Dark': 0,\n"," 'Adventure': 0,\n"," 'Silent Protagonist': 0,\n"," 'Controller': 0,\n"," 'Remake': 0,\n"," 'Diplomacy': 0,\n"," 'Military': 0,\n"," 'Spectacle fighter': 0,\n"," 'Sports': 0,\n"," 'Visual Novel': 0,\n"," 'Grand Strategy': 0,\n"," 'Relaxing': 0,\n"," 'Real Time Tactics': 0,\n"," 'Top-Down Shooter': 0,\n"," 'Alternate History': 0,\n"," 'Comedy': 0,\n"," 'Villain Protagonist': 0,\n"," 'Singleplayer': 0,\n"," 'Puzzle-Platformer': 0,\n"," 'Class-Based': 0,\n"," 'Touch-Friendly': 0,\n"," 'Trading': 0,\n"," 'City Builder': 0,\n"," 'Difficult': 0,\n"," \"Shoot 'Em Up\": 0,\n"," 'RPGMaker': 0,\n"," 'Replay Value': 0,\n"," 'Design & Illustration': 0,\n"," 'Cute': 0,\n"," 'God Game': 0,\n"," 'Point & Click': 0,\n"," 'Political': 0,\n"," 'Third Person': 0,\n"," 'Pixel Graphics': 0,\n"," 'Choices Matter': 0,\n"," 'Match 3': 0,\n"," 'Crafting': 0,\n"," 'Game Development': 0,\n"," 'Top-Down': 0,\n"," 'Education': 0,\n"," 'Indie': 0,\n"," 'Driving': 0,\n"," 'Hand-drawn': 0,\n"," 'Arcade': 0,\n"," 'Drama': 0,\n"," 'Robots': 0,\n"," 'Great Soundtrack': 0,\n"," 'Fast-Paced': 0,\n"," 'Classic': 0,\n"," 'Racing': 0,\n"," 'Historical': 0,\n"," 'Action-Adventure': 0,\n"," 'JRPG': 0,\n"," 'Superhero': 0}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["input_default_tags"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":9,"status":"ok","timestamp":1735303327210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"GbhHKsFvX5Vb","outputId":"4b02a1c8-284c-408f-92b6-c7a193f8012c"},"outputs":[{"data":{"text/plain":["{'Steam Workshop': 0,\n"," 'Multi-player': 0,\n"," 'Valve Anti-Cheat enabled': 0,\n"," 'Captions available': 0,\n"," 'Local Multi-Player': 0,\n"," 'Steam Trading Cards': 0,\n"," 'Steam Leaderboards': 0,\n"," 'Stats': 0,\n"," 'Shared/Split Screen': 0,\n"," 'Co-op': 0,\n"," 'Single-player': 0,\n"," 'Partial Controller Support': 0,\n"," 'Full controller support': 0,\n"," 'Steam Achievements': 0,\n"," 'Commentary available': 0,\n"," 'Includes level editor': 0,\n"," 'Online Multi-Player': 0,\n"," 'Cross-Platform Multiplayer': 0,\n"," 'Includes Source SDK': 0,\n"," 'In-App Purchases': 0,\n"," 'Steam Cloud': 0}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["input_default_specs"]},{"cell_type":"markdown","metadata":{"id":"E8Y-taqrb1g_"},"source":["#### 2. Check Hot Persona Tags and Specs For Each User Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5761,"status":"ok","timestamp":1735303332964,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UDTwrBJqUcjY","outputId":"a7ffbd3e-b3a4-4df2-d2d8-dfef49865c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Tags: {'Fighting': 1, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 1, 'Base Building': 1, 'Turn-Based Strategy': 1, 'Early Access': 1, 'Hex Grid': 1, 'Kickstarter': 1, 'Resource Management': 1, 'Abstract': 1, '4 Player Local': 1, 'Sniper': 0, 'Transhumanism': 1, 'Metroidvania': 1, 'Episodic': 1, 'e-sports': 1, 'Based On A Novel': 1, 'Crime': 1, 'Turn-Based Combat': 1, 'Vampire': 0, 'Level Editor': 1, 'Programming': 1, 'Gore': 1, '2D': 1, 'Colorful': 1, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 1, 'Dystopian': 1, '3D Vision': 1, 'Futuristic': 1, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 1, 'Linear': 1, 'Online Co-Op': 1, 'Stylized': 1, 'Dragons': 1, 'Rogue-lite': 1, 'Utilities': 0, 'Dark Humor': 1, 'Romance': 1, 'Economy': 1, 'Rogue-like': 1, 'Isometric': 1, \"1990's\": 1, 'Destruction': 1, 'Magic': 1, 'Funny': 1, 'Assassin': 1, 'Parkour': 1, 'Anime': 1, 'Multiple Endings': 1, 'Quick-Time Events': 1, 'Mystery': 1, 'Survival Horror': 1, 'Real-Time with Pause': 1, 'Psychological Horror': 1, 'Dark Fantasy': 1, 'Memes': 1, 'Cinematic': 1, 'Party-Based RPG': 1, 'Free to Play': 1, 'Tower Defense': 1, 'War': 1, 'Co-op Campaign': 1, 'Family Friendly': 1, 'Card Game': 1, 'Management': 1, 'Narration': 1, 'Procedural Generation': 1, 'Inventory Management': 1, 'Animation & Modeling': 0, 'Lara Croft': 1, 'Sexual Content': 1, 'Team-Based': 1, 'Gambling': 1, 'Fantasy': 1, 'RTS': 1, 'Loot': 1, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 1, \"Beat 'em up\": 1, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 1, 'CRPG': 1, 'Short': 1, 'Dungeon Crawler': 1, 'Character Customization': 1, 'Pinball': 0, 'VR': 1, 'Twin Stick Shooter': 1, 'Third-Person Shooter': 1, 'GameMaker': 0, 'Detective': 1, 'Stealth': 1, 'Platformer': 1, 'Walking Simulator': 1, 'Space': 1, 'Local Multiplayer': 1, 'Strategy': 1, 'Physics': 1, 'Steampunk': 1, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 1, 'Multiplayer': 1, 'Turn-Based': 1, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 1, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 1, 'Time Travel': 1, 'Realistic': 1, 'Split Screen': 1, 'Choose Your Own Adventure': 1, 'Science': 1, 'Underwater': 1, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 1, 'Cyberpunk': 1, '4X': 1, 'Zombies': 1, 'Simulation': 1, 'PvP': 1, 'Nudity': 1, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 1, 'Voxel': 1, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 1, 'Dark': 1, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 1, 'Remake': 1, 'Diplomacy': 1, 'Military': 1, 'Spectacle fighter': 1, 'Sports': 1, 'Visual Novel': 1, 'Grand Strategy': 1, 'Relaxing': 1, 'Real Time Tactics': 1, 'Top-Down Shooter': 1, 'Alternate History': 1, 'Comedy': 1, 'Villain Protagonist': 1, 'Singleplayer': 1, 'Puzzle-Platformer': 1, 'Class-Based': 1, 'Touch-Friendly': 1, 'Trading': 1, 'City Builder': 1, 'Difficult': 1, \"Shoot 'Em Up\": 1, 'RPGMaker': 1, 'Replay Value': 1, 'Design & Illustration': 0, 'Cute': 1, 'God Game': 1, 'Point & Click': 1, 'Political': 1, 'Third Person': 1, 'Pixel Graphics': 1, 'Choices Matter': 1, 'Match 3': 0, 'Crafting': 1, 'Game Development': 0, 'Top-Down': 1, 'Education': 1, 'Indie': 1, 'Driving': 1, 'Hand-drawn': 1, 'Arcade': 1, 'Drama': 1, 'Robots': 1, 'Great Soundtrack': 1, 'Fast-Paced': 1, 'Classic': 1, 'Racing': 1, 'Historical': 1, 'Action-Adventure': 1, 'JRPG': 1, 'Superhero': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","user_item_tags = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_tags[user_id] = input_default_tags.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," user_item_tags[user_id][tag] = 1\n","\n","# Example usage (you can print or process user_item_tags as needed):\n","for user_id, item_tags in user_item_tags.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3306,"status":"ok","timestamp":1735303336267,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LxfFCIj9Zkrc","outputId":"a6a0993a-4155-4ebb-b569-f7ce8fc03610"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Specs: {'Steam Workshop': 1, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 1, 'Local Multi-Player': 1, 'Steam Trading Cards': 1, 'Steam Leaderboards': 1, 'Stats': 1, 'Shared/Split Screen': 1, 'Co-op': 1, 'Single-player': 1, 'Partial Controller Support': 1, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 1, 'Includes level editor': 1, 'Online Multi-Player': 1, 'Cross-Platform Multiplayer': 1, 'Includes Source SDK': 1, 'In-App Purchases': 1, 'Steam Cloud': 1}\n"]}],"source":["user_item_specs = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_specs[user_id] = input_default_specs.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," user_item_specs[user_id][spec] = 1\n","\n","# Example usage (you can print or process user_item_specs as needed):\n","for user_id, item_specs in user_item_specs.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"lziPgL49cJXd"},"source":["#### 2. Check Hot Persona Tags and Specs For Each Bundle Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"7DQdfGE0aZn8","outputId":"2b59b46a-2354-4f70-a08e-17f6feb5e6ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["Bundle: 450\n","Tags: {'Fighting': 0, 'Action RPG': 0, 'Retro': 0, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 1, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 0, '2D': 0, 'Colorful': 0, 'Co-op': 0, 'Puzzle': 0, 'Hack and Slash': 0, 'Dystopian': 0, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 0, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 0, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 1, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 0, 'Destruction': 0, 'Magic': 0, 'Funny': 0, 'Assassin': 0, 'Parkour': 0, 'Anime': 1, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 0, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 1, 'Team-Based': 0, 'Gambling': 0, 'Fantasy': 0, 'RTS': 0, 'Loot': 0, 'Sci-fi': 0, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 0, 'Local Co-Op': 0, 'Pirates': 0, 'CRPG': 0, 'Short': 0, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 0, 'Walking Simulator': 0, 'Space': 0, 'Local Multiplayer': 0, 'Strategy': 0, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 0, 'Turn-Based': 0, 'FPS': 0, 'Story Rich': 0, 'Sandbox': 0, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 0, 'Atmospheric': 0, 'Moddable': 0, 'Open World': 0, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 0, 'Competitive': 0, 'Shooter': 0, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 0, 'Simulation': 0, 'PvP': 0, 'Nudity': 1, 'Horror': 0, 'Survival': 0, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 0, 'FMV': 0, 'First-Person': 0, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 0, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 0, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 1, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 0, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 0, \"Shoot 'Em Up\": 1, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 0, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 0, 'Fast-Paced': 0, 'Classic': 0, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}\n","Bundle: 450\n","Specs: {'Steam Workshop': 0, 'Multi-player': 0, 'Valve Anti-Cheat enabled': 0, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 0, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","bundle_item_tags = {}\n","bundle_item_specs = {}\n","\n","for bundle_id, bundle_items in bundle_data.items():\n"," bundle_item_tags[bundle_id] = input_default_tags.copy()\n"," bundle_item_specs[bundle_id] = input_default_specs.copy()\n","\n"," for item_id in bundle_items[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," bundle_item_tags[bundle_id][tag] = 1\n","\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," bundle_item_specs[bundle_id][spec] = 1\n","\n","# Example usage (you can print or process bundle_item_tags as needed):\n","for bundle_id, item_tags in bundle_item_tags.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break\n","\n","# Example usage (you can print or process bundle_item_specs as needed):\n","for bundle_id, item_specs in bundle_item_specs.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"_qoICCA4cPyU"},"source":["#### 3. Get Intersection Between Persona User Item Interactions and Persona Bundle Items Based On Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"EFAWQsJgcb3G","outputId":"24288744-33ad-4a78-bd2f-d77696610862"},"outputs":[{"name":"stdout","output_type":"stream","text":["Intersection for user 76561197970982479 and bundle 450:\n","Tags: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","Specs: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"]}],"source":["# prompt: Get Intersection Between Persona User Item Interactions and Persona Bundle Items By Tags and Specs That Have Value == 1\n","\n","def get_intersection(user_id, bundle_id):\n"," intersection_tags = {}\n"," intersection_specs = {}\n","\n"," for tag, value in user_item_tags[user_id].items():\n"," if value == 1 and bundle_item_tags[bundle_id][tag] == 1:\n"," intersection_tags[tag] = 1\n"," else:\n"," intersection_tags[tag] = 0\n","\n"," for spec, value in user_item_specs[user_id].items():\n"," if value == 1 and bundle_item_specs[bundle_id][spec] == 1:\n"," intersection_specs[spec] = 1\n"," else:\n"," intersection_specs[spec] = 0\n","\n"," return intersection_tags, intersection_specs\n","\n","def get_intersection_as_list(user_id, bundle_id):\n"," user_tags = user_item_tags[user_id]\n"," bundle_tags = bundle_item_tags[bundle_id]\n"," user_specs = user_item_specs[user_id]\n"," bundle_specs = bundle_item_specs[bundle_id]\n","\n"," # Using list comprehensions to find common tags and specs\n"," common_tags = [1 if user_tags[tag] == 1 and bundle_tags[tag] == 1 else 0 for tag in user_tags]\n"," common_specs = [1 if user_specs[spec] == 1 and bundle_specs[spec] == 1 else 0 for spec in user_specs]\n","\n"," return common_tags, common_specs\n","\n","# Example usage:\n","user_id = list(user_item_interactions.keys())[0] # Get the first user ID\n","bundle_id = list(bundle_data.keys())[0] # Get the first bundle ID\n","\n","intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n","\n","print(f\"Intersection for user {user_id} and bundle {bundle_id}:\")\n","print(f\"Tags: {intersection_tags}\")\n","print(f\"Specs: {intersection_specs}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":309041,"status":"ok","timestamp":1735305675372,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"BuFxKyM6cyCv","outputId":"fad7d31b-9e29-4644-80b1-456508338d8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 / 10.000\n","4898 / 10.000\n","4899 / 10.000\n","4900 / 10.000\n","4901 / 10.000\n","4902 / 10.000\n","4903 / 10.000\n","4904 / 10.000\n","4905 / 10.000\n","4906 / 10.000\n","4907 / 10.000\n","4908 / 10.000\n","4909 / 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intersection_specs = get_intersection_as_list(user_id, bundle_id)\n"," data_to_write[user_id][bundle_id] = {\n"," \"tags\": intersection_tags,\n"," \"specs\": intersection_specs\n"," }\n","\n"," json.dump(data_to_write, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," print(f\"{i} / 10.000\")"]},{"cell_type":"markdown","metadata":{"id":"45XFujQeHeG-"},"source":["#### Do Recommendation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XzQCZ1fmHh5D","outputId":"08fd261e-ccb1-4b8b-bcf3-e9fa9ba6218f","executionInfo":{"status":"ok","timestamp":1735352697261,"user_tz":-420,"elapsed":1883643,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 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10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","9877 / 10.000\n","9878 / 10.000\n","9879 / 10.000\n","9880 / 10.000\n","9881 / 10.000\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_sgd.json.gz'\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","data_dict_out = None\n","\n","file_path_read = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_sgd.json.gz\"\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_read, 'rt', encoding='utf-8') as infile, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," data_dict_out = {}\n"," for user_id, bundle_data in data_dict.items():\n"," for bundle_id, recommendation in bundle_data.items():\n"," data_dict_out[\"user_id\"] = user_id\n"," data_dict_out[\"bundle_id\"] = bundle_id\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['best_sgd_classifier_tags_specifications'].predict([recommendation['tags'] + recommendation['specs']])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," # break\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # break\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":307,"status":"ok","timestamp":1735350794863,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"MnRRI8KSaOgO","outputId":"5341cdcf-c926-4eaf-a8dc-0d8bab967416"},"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_sgd.json.gz'\n","Count: 223\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_sgd.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pmSwZ6yPxdN5"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"na7Ok_umxQnJ"},"source":["## Create Input For Model Price Patterns"]},{"cell_type":"markdown","source":["#### 1. Load Data"],"metadata":{"id":"p6y7SbgJmBRo"}},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735783663554,"user_tz":-420,"elapsed":902,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"22d5a25b-fecf-4fb8-88fe-c8e8d8ef41a1","id":"HT2uTzBMvAry"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":3}],"source":["# prompt: Load file /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy\n","\n","import numpy as np\n","\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')\n","\n","user_price_label[:5]"]},{"cell_type":"code","source":["# prompt: Calculate how many 0 and 1 in user_price_label[:615]\n","\n","zeros = np.count_nonzero(user_price_label[:615] == 0)\n","ones = np.count_nonzero(user_price_label[:615] == 1)\n","\n","print(f\"Number of 0s: {zeros}\")\n","print(f\"Number of 1s: {ones}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBye6gkth3l6","executionInfo":{"status":"ok","timestamp":1735783663922,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b7bf43fb-9800-448b-c6ed-c08f5ecad260"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of 0s: 582\n","Number of 1s: 33\n"]}]},{"cell_type":"code","source":["user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')"],"metadata":{"id":"2LoxPq-8iYe3","executionInfo":{"status":"ok","timestamp":1735783668962,"user_tz":-420,"elapsed":4035,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["user_price_identity = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy')"],"metadata":{"id":"Jqtxa1XYl2sa","executionInfo":{"status":"ok","timestamp":1735783686838,"user_tz":-420,"elapsed":17877,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Find is that from user_price_identity user for all bundle theres no label 1 in user_price_label\n","\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","check_users = {}\n","\n","# Iterate through users and bundles (assuming bundle IDs are consecutive integers)\n","for user_index in range(num_users):\n"," user_id = user_price_identity[user_index]\n"," if user_id[0] not in check_users:\n"," check_users[user_id[0]] = 0\n","\n"," # Check if label is present for this user\n"," if user_price_label[user_index] is not None: # Check for None or NaN if necessary\n"," # Access the label directly instead of iterating\n"," if user_price_label[user_index] == 1:\n"," # Assuming bundle_id is the second element in user_price_identity\n"," check_users[user_id[0]] = 1\n"," else:\n"," print(f\"No labels found for user {user_id}\")"],"metadata":{"id":"g_NWI-KDiuIF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: From check_users check is there has 0 value?\n","\n","zeros = 0\n","for user_id, label in check_users.items():\n"," if label == 0:\n"," zeros += 1\n","\n","print(f\"Number of users with 0 value: {zeros}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qD-mnjmHlcqD","executionInfo":{"status":"ok","timestamp":1735780608038,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ddd2f1e-dea8-44b9-9f94-94adbc1bca4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of users with 0 value: 23\n"]}]},{"cell_type":"markdown","source":["#### 2. Do Recommendation"],"metadata":{"id":"TKiv8OUzmEF5"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict_out = {}\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_sgd.json.gz\"\n","count = 0\n","\n","check_users = {}\n","\n","try:\n"," with gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for user_index in range(num_users):\n"," data_dict_out[\"user_id\"] = user_price_identity[user_index][0]\n"," data_dict_out[\"bundle_id\"] = user_price_identity[user_index][1]\n"," data_dict_out[\"is_recommended\"] = f\"{loaded_models['best_sgd_classifier_price_patterns'].predict([user_price_features[user_index]])[0]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," if user_price_identity[user_index][0] not in check_users:\n"," check_users[user_price_identity[user_index][0]] = 0\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # if count == 2:\n"," # break\n"," print(f\"Successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1nbQtXeoa1M","outputId":"f1232fd3-51ac-4fee-b561-ad385c6ccf99","executionInfo":{"status":"ok","timestamp":1735785526782,"user_tz":-420,"elapsed":1722476,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 10.000\n","4879 / 10.000\n","4880 / 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10.000\n","9831 / 10.000\n","9832 / 10.000\n","9833 / 10.000\n","9834 / 10.000\n","9835 / 10.000\n","9836 / 10.000\n","9837 / 10.000\n","9838 / 10.000\n","9839 / 10.000\n","9840 / 10.000\n","9841 / 10.000\n","9842 / 10.000\n","9843 / 10.000\n","9844 / 10.000\n","9845 / 10.000\n","9846 / 10.000\n","9847 / 10.000\n","9848 / 10.000\n","9849 / 10.000\n","9850 / 10.000\n","9851 / 10.000\n","9852 / 10.000\n","9853 / 10.000\n","9854 / 10.000\n","9855 / 10.000\n","9856 / 10.000\n","9857 / 10.000\n","9858 / 10.000\n","9859 / 10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","Successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_sgd.json.gz'\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","try:\n"," with gzip.open(file_path_write, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKv7TjHhq0YE","executionInfo":{"status":"ok","timestamp":1735782114480,"user_tz":-420,"elapsed":325,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9af4b1c5-761b-4961-fa29-32c09b7b0fd4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Count: 33\n"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"1F6K5UXAIUXHv1lxOYMhC9yRKdjnZbsCX","timestamp":1735350424942},{"file_id":"1OrfeLsuMTaVL7GS9uEdXvCv68A7Qxnxi","timestamp":1735347331860}],"mount_file_id":"1oTAV5K3BCgRnXaTEvP-D4OglFOBhS-GP","authorship_tag":"ABX9TyMjdPNBtRacL5EGhhydlaxL"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/4. Judge Bundle For Each Users.ipynb b/Source Code/4. Judge Bundle For Each Users.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..667928f3e8dc6352935f1c2b1eb1f42163ef50e9 --- /dev/null +++ b/Source Code/4. Judge Bundle For Each Users.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30001,"status":"ok","timestamp":1735784662166,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"TqtPuipCMpzT","outputId":"6f29c20b-3df7-4dcb-b7df-cd91713b5eac"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded model: decision_tree_price_patterns\n","Loaded model: best_logreg_classifier_price_patterns\n","Loaded model: best_sgd_classifier_price_patterns\n","Loaded model: best_bnb_price_patterns\n","Loaded model: knn_price_patterns\n","Loaded model: neural_networks_price_patterns\n","Loaded 6 models.\n"]}],"source":["# prompt: Load all models from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications using joblib\n","\n","import joblib\n","import os\n","\n","# models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications\"\n","models_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns\"\n","loaded_models = {}\n","\n","for filename in os.listdir(models_path):\n"," if filename.endswith(\".pkl\"):\n"," model_path = os.path.join(models_path, filename)\n"," model_name = filename[:-4] # Remove \".joblib\" extension\n"," try:\n"," loaded_models[model_name] = joblib.load(model_path)\n"," print(f\"Loaded model: {model_name}\")\n"," except Exception as e:\n"," print(f\"Error loading model {model_name}: {e}\")\n","\n","print(f\"Loaded {len(loaded_models)} models.\")\n"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1735784662167,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LXdGNRK8RcwY"},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"markdown","metadata":{"id":"cs-PpqN8TbRM"},"source":["## Bundle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":615,"status":"ok","timestamp":1735303253454,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"oBdZF6YtO3Ef","outputId":"9bf13c93-1555-4e6b-a0f0-040196dd9051"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1735303253456,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"JQDVAG3bRiAH","outputId":"e686321a-8c30-4baa-df71-49ab65d86046"},"outputs":[{"data":{"text/plain":["{'bundle_id': '450',\n"," 'bundle_price': 73.86,\n"," 'bundle_name': 'Dharker Studio 2015 Complete',\n"," 'items': {'326950',\n"," '331490',\n"," '331491',\n"," '331492',\n"," '348540',\n"," '352010',\n"," '387110',\n"," '387120',\n"," '390470',\n"," '394050',\n"," '396610',\n"," '396620',\n"," '398100',\n"," '408770'}}"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["bundle_data[\"450\"]"]},{"cell_type":"markdown","metadata":{"id":"SY4SrF4sTdmZ"},"source":["## Items Dictionary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3800,"status":"ok","timestamp":1735303258466,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"pXudpCiWTffK","outputId":"c450dfec-0cfa-473c-fe6c-ccb71184a906"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"PAxnBn8zUwfT"},"source":["## User Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":43890,"status":"ok","timestamp":1735303302353,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"RWWQ3GPvUy1a","outputId":"551041c0-2c5d-4589-c662-9ea0ef6e77c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 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A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"markdown","metadata":{"id":"IInSMlBIUVNv"},"source":["## Create Input For Model Tags and Specs"]},{"cell_type":"markdown","metadata":{"id":"LwqMdBsgbrC_"},"source":["#### 1. Load Hot Persona Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24858,"status":"ok","timestamp":1735303327208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"x4BKOF2sWkLC","outputId":"e7e4123f-9849-44ab-8ad8-63bda828cf70"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXPeELdRXNhn"},"outputs":[],"source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":10,"status":"ok","timestamp":1735303327209,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"SHGaOHecX37a","outputId":"b356d01f-5e28-4f71-96c1-380e8066b968"},"outputs":[{"data":{"text/plain":["{'Fighting': 0,\n"," 'Action RPG': 0,\n"," 'Retro': 0,\n"," 'Bullet Hell': 0,\n"," 'Base Building': 0,\n"," 'Turn-Based Strategy': 0,\n"," 'Early Access': 0,\n"," 'Hex Grid': 0,\n"," 'Kickstarter': 0,\n"," 'Resource Management': 0,\n"," 'Abstract': 0,\n"," '4 Player Local': 0,\n"," 'Sniper': 0,\n"," 'Transhumanism': 0,\n"," 'Metroidvania': 0,\n"," 'Episodic': 0,\n"," 'e-sports': 0,\n"," 'Based On A Novel': 0,\n"," 'Crime': 0,\n"," 'Turn-Based Combat': 0,\n"," 'Vampire': 0,\n"," 'Level Editor': 0,\n"," 'Programming': 0,\n"," 'Gore': 0,\n"," '2D': 0,\n"," 'Colorful': 0,\n"," 'Co-op': 0,\n"," 'Puzzle': 0,\n"," 'Hack and Slash': 0,\n"," 'Dystopian': 0,\n"," '3D Vision': 0,\n"," 'Futuristic': 0,\n"," 'Hidden Object': 0,\n"," 'Post-apocalyptic': 0,\n"," 'Comic Book': 0,\n"," 'Linear': 0,\n"," 'Online Co-Op': 0,\n"," 'Stylized': 0,\n"," 'Dragons': 0,\n"," 'Rogue-lite': 0,\n"," 'Utilities': 0,\n"," 'Dark Humor': 0,\n"," 'Romance': 0,\n"," 'Economy': 0,\n"," 'Rogue-like': 0,\n"," 'Isometric': 0,\n"," \"1990's\": 0,\n"," 'Destruction': 0,\n"," 'Magic': 0,\n"," 'Funny': 0,\n"," 'Assassin': 0,\n"," 'Parkour': 0,\n"," 'Anime': 0,\n"," 'Multiple Endings': 0,\n"," 'Quick-Time Events': 0,\n"," 'Mystery': 0,\n"," 'Survival Horror': 0,\n"," 'Real-Time with Pause': 0,\n"," 'Psychological Horror': 0,\n"," 'Dark Fantasy': 0,\n"," 'Memes': 0,\n"," 'Cinematic': 0,\n"," 'Party-Based RPG': 0,\n"," 'Free to Play': 0,\n"," 'Tower Defense': 0,\n"," 'War': 0,\n"," 'Co-op Campaign': 0,\n"," 'Family Friendly': 0,\n"," 'Card Game': 0,\n"," 'Management': 0,\n"," 'Narration': 0,\n"," 'Procedural Generation': 0,\n"," 'Inventory Management': 0,\n"," 'Animation & Modeling': 0,\n"," 'Lara Croft': 0,\n"," 'Sexual Content': 0,\n"," 'Team-Based': 0,\n"," 'Gambling': 0,\n"," 'Fantasy': 0,\n"," 'RTS': 0,\n"," 'Loot': 0,\n"," 'Sci-fi': 0,\n"," 'Casual': 0,\n"," 'Web Publishing': 0,\n"," 'World War II': 0,\n"," \"Beat 'em up\": 0,\n"," 'RPG': 0,\n"," 'Local Co-Op': 0,\n"," 'Pirates': 0,\n"," 'CRPG': 0,\n"," 'Short': 0,\n"," 'Dungeon Crawler': 0,\n"," 'Character Customization': 0,\n"," 'Pinball': 0,\n"," 'VR': 0,\n"," 'Twin Stick Shooter': 0,\n"," 'Third-Person Shooter': 0,\n"," 'GameMaker': 0,\n"," 'Detective': 0,\n"," 'Stealth': 0,\n"," 'Platformer': 0,\n"," 'Walking Simulator': 0,\n"," 'Space': 0,\n"," 'Local Multiplayer': 0,\n"," 'Strategy': 0,\n"," 'Physics': 0,\n"," 'Steampunk': 0,\n"," 'Text-Based': 0,\n"," 'Action': 0,\n"," 'Mechs': 0,\n"," 'Side Scroller': 0,\n"," 'Multiplayer': 0,\n"," 'Turn-Based': 0,\n"," 'FPS': 0,\n"," 'Story Rich': 0,\n"," 'Sandbox': 0,\n"," 'Interactive Fiction': 0,\n"," 'Medieval': 0,\n"," 'Aliens': 0,\n"," 'Atmospheric': 0,\n"," 'Moddable': 0,\n"," 'Open World': 0,\n"," 'Cult Classic': 0,\n"," 'Time Travel': 0,\n"," 'Realistic': 0,\n"," 'Split Screen': 0,\n"," 'Choose Your Own Adventure': 0,\n"," 'Science': 0,\n"," 'Underwater': 0,\n"," 'Female Protagonist': 0,\n"," 'Competitive': 0,\n"," 'Shooter': 0,\n"," 'Software': 0,\n"," 'Software Training': 0,\n"," 'Mature': 0,\n"," 'Demons': 0,\n"," 'Cyberpunk': 0,\n"," '4X': 0,\n"," 'Zombies': 0,\n"," 'Simulation': 0,\n"," 'PvP': 0,\n"," 'Nudity': 0,\n"," 'Horror': 0,\n"," 'Survival': 0,\n"," 'Massively Multiplayer': 0,\n"," 'Voxel': 0,\n"," 'Tactical': 0,\n"," 'FMV': 0,\n"," 'First-Person': 0,\n"," 'Exploration': 0,\n"," 'Dark': 0,\n"," 'Adventure': 0,\n"," 'Silent Protagonist': 0,\n"," 'Controller': 0,\n"," 'Remake': 0,\n"," 'Diplomacy': 0,\n"," 'Military': 0,\n"," 'Spectacle fighter': 0,\n"," 'Sports': 0,\n"," 'Visual Novel': 0,\n"," 'Grand Strategy': 0,\n"," 'Relaxing': 0,\n"," 'Real Time Tactics': 0,\n"," 'Top-Down Shooter': 0,\n"," 'Alternate History': 0,\n"," 'Comedy': 0,\n"," 'Villain Protagonist': 0,\n"," 'Singleplayer': 0,\n"," 'Puzzle-Platformer': 0,\n"," 'Class-Based': 0,\n"," 'Touch-Friendly': 0,\n"," 'Trading': 0,\n"," 'City Builder': 0,\n"," 'Difficult': 0,\n"," \"Shoot 'Em Up\": 0,\n"," 'RPGMaker': 0,\n"," 'Replay Value': 0,\n"," 'Design & Illustration': 0,\n"," 'Cute': 0,\n"," 'God Game': 0,\n"," 'Point & Click': 0,\n"," 'Political': 0,\n"," 'Third Person': 0,\n"," 'Pixel Graphics': 0,\n"," 'Choices Matter': 0,\n"," 'Match 3': 0,\n"," 'Crafting': 0,\n"," 'Game Development': 0,\n"," 'Top-Down': 0,\n"," 'Education': 0,\n"," 'Indie': 0,\n"," 'Driving': 0,\n"," 'Hand-drawn': 0,\n"," 'Arcade': 0,\n"," 'Drama': 0,\n"," 'Robots': 0,\n"," 'Great Soundtrack': 0,\n"," 'Fast-Paced': 0,\n"," 'Classic': 0,\n"," 'Racing': 0,\n"," 'Historical': 0,\n"," 'Action-Adventure': 0,\n"," 'JRPG': 0,\n"," 'Superhero': 0}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["input_default_tags"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":9,"status":"ok","timestamp":1735303327210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"GbhHKsFvX5Vb","outputId":"4b02a1c8-284c-408f-92b6-c7a193f8012c"},"outputs":[{"data":{"text/plain":["{'Steam Workshop': 0,\n"," 'Multi-player': 0,\n"," 'Valve Anti-Cheat enabled': 0,\n"," 'Captions available': 0,\n"," 'Local Multi-Player': 0,\n"," 'Steam Trading Cards': 0,\n"," 'Steam Leaderboards': 0,\n"," 'Stats': 0,\n"," 'Shared/Split Screen': 0,\n"," 'Co-op': 0,\n"," 'Single-player': 0,\n"," 'Partial Controller Support': 0,\n"," 'Full controller support': 0,\n"," 'Steam Achievements': 0,\n"," 'Commentary available': 0,\n"," 'Includes level editor': 0,\n"," 'Online Multi-Player': 0,\n"," 'Cross-Platform Multiplayer': 0,\n"," 'Includes Source SDK': 0,\n"," 'In-App Purchases': 0,\n"," 'Steam Cloud': 0}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["input_default_specs"]},{"cell_type":"markdown","metadata":{"id":"E8Y-taqrb1g_"},"source":["#### 2. Check Hot Persona Tags and Specs For Each User Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5761,"status":"ok","timestamp":1735303332964,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"UDTwrBJqUcjY","outputId":"a7ffbd3e-b3a4-4df2-d2d8-dfef49865c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Tags: {'Fighting': 1, 'Action RPG': 1, 'Retro': 1, 'Bullet Hell': 1, 'Base Building': 1, 'Turn-Based Strategy': 1, 'Early Access': 1, 'Hex Grid': 1, 'Kickstarter': 1, 'Resource Management': 1, 'Abstract': 1, '4 Player Local': 1, 'Sniper': 0, 'Transhumanism': 1, 'Metroidvania': 1, 'Episodic': 1, 'e-sports': 1, 'Based On A Novel': 1, 'Crime': 1, 'Turn-Based Combat': 1, 'Vampire': 0, 'Level Editor': 1, 'Programming': 1, 'Gore': 1, '2D': 1, 'Colorful': 1, 'Co-op': 1, 'Puzzle': 1, 'Hack and Slash': 1, 'Dystopian': 1, '3D Vision': 1, 'Futuristic': 1, 'Hidden Object': 0, 'Post-apocalyptic': 1, 'Comic Book': 1, 'Linear': 1, 'Online Co-Op': 1, 'Stylized': 1, 'Dragons': 1, 'Rogue-lite': 1, 'Utilities': 0, 'Dark Humor': 1, 'Romance': 1, 'Economy': 1, 'Rogue-like': 1, 'Isometric': 1, \"1990's\": 1, 'Destruction': 1, 'Magic': 1, 'Funny': 1, 'Assassin': 1, 'Parkour': 1, 'Anime': 1, 'Multiple Endings': 1, 'Quick-Time Events': 1, 'Mystery': 1, 'Survival Horror': 1, 'Real-Time with Pause': 1, 'Psychological Horror': 1, 'Dark Fantasy': 1, 'Memes': 1, 'Cinematic': 1, 'Party-Based RPG': 1, 'Free to Play': 1, 'Tower Defense': 1, 'War': 1, 'Co-op Campaign': 1, 'Family Friendly': 1, 'Card Game': 1, 'Management': 1, 'Narration': 1, 'Procedural Generation': 1, 'Inventory Management': 1, 'Animation & Modeling': 0, 'Lara Croft': 1, 'Sexual Content': 1, 'Team-Based': 1, 'Gambling': 1, 'Fantasy': 1, 'RTS': 1, 'Loot': 1, 'Sci-fi': 1, 'Casual': 1, 'Web Publishing': 0, 'World War II': 1, \"Beat 'em up\": 1, 'RPG': 1, 'Local Co-Op': 1, 'Pirates': 1, 'CRPG': 1, 'Short': 1, 'Dungeon Crawler': 1, 'Character Customization': 1, 'Pinball': 0, 'VR': 1, 'Twin Stick Shooter': 1, 'Third-Person Shooter': 1, 'GameMaker': 0, 'Detective': 1, 'Stealth': 1, 'Platformer': 1, 'Walking Simulator': 1, 'Space': 1, 'Local Multiplayer': 1, 'Strategy': 1, 'Physics': 1, 'Steampunk': 1, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 1, 'Multiplayer': 1, 'Turn-Based': 1, 'FPS': 1, 'Story Rich': 1, 'Sandbox': 1, 'Interactive Fiction': 0, 'Medieval': 1, 'Aliens': 1, 'Atmospheric': 1, 'Moddable': 1, 'Open World': 1, 'Cult Classic': 1, 'Time Travel': 1, 'Realistic': 1, 'Split Screen': 1, 'Choose Your Own Adventure': 1, 'Science': 1, 'Underwater': 1, 'Female Protagonist': 1, 'Competitive': 1, 'Shooter': 1, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 1, 'Cyberpunk': 1, '4X': 1, 'Zombies': 1, 'Simulation': 1, 'PvP': 1, 'Nudity': 1, 'Horror': 1, 'Survival': 1, 'Massively Multiplayer': 1, 'Voxel': 1, 'Tactical': 1, 'FMV': 0, 'First-Person': 1, 'Exploration': 1, 'Dark': 1, 'Adventure': 1, 'Silent Protagonist': 1, 'Controller': 1, 'Remake': 1, 'Diplomacy': 1, 'Military': 1, 'Spectacle fighter': 1, 'Sports': 1, 'Visual Novel': 1, 'Grand Strategy': 1, 'Relaxing': 1, 'Real Time Tactics': 1, 'Top-Down Shooter': 1, 'Alternate History': 1, 'Comedy': 1, 'Villain Protagonist': 1, 'Singleplayer': 1, 'Puzzle-Platformer': 1, 'Class-Based': 1, 'Touch-Friendly': 1, 'Trading': 1, 'City Builder': 1, 'Difficult': 1, \"Shoot 'Em Up\": 1, 'RPGMaker': 1, 'Replay Value': 1, 'Design & Illustration': 0, 'Cute': 1, 'God Game': 1, 'Point & Click': 1, 'Political': 1, 'Third Person': 1, 'Pixel Graphics': 1, 'Choices Matter': 1, 'Match 3': 0, 'Crafting': 1, 'Game Development': 0, 'Top-Down': 1, 'Education': 1, 'Indie': 1, 'Driving': 1, 'Hand-drawn': 1, 'Arcade': 1, 'Drama': 1, 'Robots': 1, 'Great Soundtrack': 1, 'Fast-Paced': 1, 'Classic': 1, 'Racing': 1, 'Historical': 1, 'Action-Adventure': 1, 'JRPG': 1, 'Superhero': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","user_item_tags = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_tags[user_id] = input_default_tags.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," user_item_tags[user_id][tag] = 1\n","\n","# Example usage (you can print or process user_item_tags as needed):\n","for user_id, item_tags in user_item_tags.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3306,"status":"ok","timestamp":1735303336267,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"LxfFCIj9Zkrc","outputId":"a6a0993a-4155-4ebb-b569-f7ce8fc03610"},"outputs":[{"name":"stdout","output_type":"stream","text":["User: 76561197970982479\n","Specs: {'Steam Workshop': 1, 'Multi-player': 1, 'Valve Anti-Cheat enabled': 1, 'Captions available': 1, 'Local Multi-Player': 1, 'Steam Trading Cards': 1, 'Steam Leaderboards': 1, 'Stats': 1, 'Shared/Split Screen': 1, 'Co-op': 1, 'Single-player': 1, 'Partial Controller Support': 1, 'Full controller support': 1, 'Steam Achievements': 1, 'Commentary available': 1, 'Includes level editor': 1, 'Online Multi-Player': 1, 'Cross-Platform Multiplayer': 1, 'Includes Source SDK': 1, 'In-App Purchases': 1, 'Steam Cloud': 1}\n"]}],"source":["user_item_specs = {}\n","\n","for user_id, user_data in user_item_interactions.items():\n"," user_item_specs[user_id] = input_default_specs.copy()\n"," for item_id in user_data[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," user_item_specs[user_id][spec] = 1\n","\n","# Example usage (you can print or process user_item_specs as needed):\n","for user_id, item_specs in user_item_specs.items():\n"," print(f\"User: {user_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"lziPgL49cJXd"},"source":["#### 2. Check Hot Persona Tags and Specs For Each Bundle Item Interactions"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"7DQdfGE0aZn8","outputId":"2b59b46a-2354-4f70-a08e-17f6feb5e6ea"},"outputs":[{"name":"stdout","output_type":"stream","text":["Bundle: 450\n","Tags: {'Fighting': 0, 'Action RPG': 0, 'Retro': 0, 'Bullet Hell': 0, 'Base Building': 0, 'Turn-Based Strategy': 0, 'Early Access': 0, 'Hex Grid': 0, 'Kickstarter': 0, 'Resource Management': 0, 'Abstract': 0, '4 Player Local': 0, 'Sniper': 0, 'Transhumanism': 0, 'Metroidvania': 0, 'Episodic': 1, 'e-sports': 0, 'Based On A Novel': 0, 'Crime': 0, 'Turn-Based Combat': 0, 'Vampire': 0, 'Level Editor': 0, 'Programming': 0, 'Gore': 0, '2D': 0, 'Colorful': 0, 'Co-op': 0, 'Puzzle': 0, 'Hack and Slash': 0, 'Dystopian': 0, '3D Vision': 0, 'Futuristic': 0, 'Hidden Object': 0, 'Post-apocalyptic': 0, 'Comic Book': 0, 'Linear': 0, 'Online Co-Op': 0, 'Stylized': 0, 'Dragons': 0, 'Rogue-lite': 0, 'Utilities': 0, 'Dark Humor': 0, 'Romance': 1, 'Economy': 0, 'Rogue-like': 0, 'Isometric': 0, \"1990's\": 0, 'Destruction': 0, 'Magic': 0, 'Funny': 0, 'Assassin': 0, 'Parkour': 0, 'Anime': 1, 'Multiple Endings': 0, 'Quick-Time Events': 0, 'Mystery': 0, 'Survival Horror': 0, 'Real-Time with Pause': 0, 'Psychological Horror': 0, 'Dark Fantasy': 0, 'Memes': 0, 'Cinematic': 0, 'Party-Based RPG': 0, 'Free to Play': 0, 'Tower Defense': 0, 'War': 0, 'Co-op Campaign': 0, 'Family Friendly': 0, 'Card Game': 0, 'Management': 0, 'Narration': 0, 'Procedural Generation': 0, 'Inventory Management': 0, 'Animation & Modeling': 0, 'Lara Croft': 0, 'Sexual Content': 1, 'Team-Based': 0, 'Gambling': 0, 'Fantasy': 0, 'RTS': 0, 'Loot': 0, 'Sci-fi': 0, 'Casual': 1, 'Web Publishing': 0, 'World War II': 0, \"Beat 'em up\": 0, 'RPG': 0, 'Local Co-Op': 0, 'Pirates': 0, 'CRPG': 0, 'Short': 0, 'Dungeon Crawler': 0, 'Character Customization': 0, 'Pinball': 0, 'VR': 0, 'Twin Stick Shooter': 0, 'Third-Person Shooter': 0, 'GameMaker': 0, 'Detective': 0, 'Stealth': 0, 'Platformer': 0, 'Walking Simulator': 0, 'Space': 0, 'Local Multiplayer': 0, 'Strategy': 0, 'Physics': 0, 'Steampunk': 0, 'Text-Based': 0, 'Action': 1, 'Mechs': 0, 'Side Scroller': 0, 'Multiplayer': 0, 'Turn-Based': 0, 'FPS': 0, 'Story Rich': 0, 'Sandbox': 0, 'Interactive Fiction': 0, 'Medieval': 0, 'Aliens': 0, 'Atmospheric': 0, 'Moddable': 0, 'Open World': 0, 'Cult Classic': 0, 'Time Travel': 0, 'Realistic': 0, 'Split Screen': 0, 'Choose Your Own Adventure': 0, 'Science': 0, 'Underwater': 0, 'Female Protagonist': 0, 'Competitive': 0, 'Shooter': 0, 'Software': 0, 'Software Training': 0, 'Mature': 1, 'Demons': 0, 'Cyberpunk': 0, '4X': 0, 'Zombies': 0, 'Simulation': 0, 'PvP': 0, 'Nudity': 1, 'Horror': 0, 'Survival': 0, 'Massively Multiplayer': 0, 'Voxel': 0, 'Tactical': 0, 'FMV': 0, 'First-Person': 0, 'Exploration': 0, 'Dark': 0, 'Adventure': 1, 'Silent Protagonist': 0, 'Controller': 0, 'Remake': 0, 'Diplomacy': 0, 'Military': 0, 'Spectacle fighter': 0, 'Sports': 0, 'Visual Novel': 1, 'Grand Strategy': 0, 'Relaxing': 0, 'Real Time Tactics': 0, 'Top-Down Shooter': 0, 'Alternate History': 0, 'Comedy': 0, 'Villain Protagonist': 0, 'Singleplayer': 1, 'Puzzle-Platformer': 0, 'Class-Based': 0, 'Touch-Friendly': 0, 'Trading': 0, 'City Builder': 0, 'Difficult': 0, \"Shoot 'Em Up\": 1, 'RPGMaker': 0, 'Replay Value': 0, 'Design & Illustration': 0, 'Cute': 0, 'God Game': 0, 'Point & Click': 0, 'Political': 0, 'Third Person': 0, 'Pixel Graphics': 0, 'Choices Matter': 0, 'Match 3': 0, 'Crafting': 0, 'Game Development': 0, 'Top-Down': 0, 'Education': 0, 'Indie': 1, 'Driving': 0, 'Hand-drawn': 0, 'Arcade': 0, 'Drama': 0, 'Robots': 0, 'Great Soundtrack': 0, 'Fast-Paced': 0, 'Classic': 0, 'Racing': 0, 'Historical': 0, 'Action-Adventure': 0, 'JRPG': 0, 'Superhero': 0}\n","Bundle: 450\n","Specs: {'Steam Workshop': 0, 'Multi-player': 0, 'Valve Anti-Cheat enabled': 0, 'Captions available': 0, 'Local Multi-Player': 0, 'Steam Trading Cards': 1, 'Steam Leaderboards': 0, 'Stats': 0, 'Shared/Split Screen': 0, 'Co-op': 0, 'Single-player': 1, 'Partial Controller Support': 0, 'Full controller support': 0, 'Steam Achievements': 1, 'Commentary available': 0, 'Includes level editor': 0, 'Online Multi-Player': 0, 'Cross-Platform Multiplayer': 0, 'Includes Source SDK': 0, 'In-App Purchases': 0, 'Steam Cloud': 1}\n"]}],"source":["# prompt: Untuk setiap user pada user_item_interactions, lakukan loop terhadap itemsnya, dan refer ke items_dictionary, kemudian cek tags terhadap input_default_tags untuk setiap user, jika ada berikan nilai 1\n","\n","bundle_item_tags = {}\n","bundle_item_specs = {}\n","\n","for bundle_id, bundle_items in bundle_data.items():\n"," bundle_item_tags[bundle_id] = input_default_tags.copy()\n"," bundle_item_specs[bundle_id] = input_default_specs.copy()\n","\n"," for item_id in bundle_items[\"items\"]:\n"," if item_id in items_dictionary:\n"," item_tags = items_dictionary[item_id].get(\"tags\", [])\n"," for tag in item_tags:\n"," if tag in input_default_tags:\n"," bundle_item_tags[bundle_id][tag] = 1\n","\n"," item_specs = items_dictionary[item_id].get(\"specs\", [])\n"," for spec in item_specs:\n"," if spec in input_default_specs:\n"," bundle_item_specs[bundle_id][spec] = 1\n","\n","# Example usage (you can print or process bundle_item_tags as needed):\n","for bundle_id, item_tags in bundle_item_tags.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Tags: {item_tags}\")\n"," break\n","\n","# Example usage (you can print or process bundle_item_specs as needed):\n","for bundle_id, item_specs in bundle_item_specs.items():\n"," print(f\"Bundle: {bundle_id}\")\n"," print(f\"Specs: {item_specs}\")\n"," break"]},{"cell_type":"markdown","metadata":{"id":"_qoICCA4cPyU"},"source":["#### 3. Get Intersection Between Persona User Item Interactions and Persona Bundle Items Based On Tags and Specs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1735303336268,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"EFAWQsJgcb3G","outputId":"24288744-33ad-4a78-bd2f-d77696610862"},"outputs":[{"name":"stdout","output_type":"stream","text":["Intersection for user 76561197970982479 and bundle 450:\n","Tags: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","Specs: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"]}],"source":["# prompt: Get Intersection Between Persona User Item Interactions and Persona Bundle Items By Tags and Specs That Have Value == 1\n","\n","def get_intersection(user_id, bundle_id):\n"," intersection_tags = {}\n"," intersection_specs = {}\n","\n"," for tag, value in user_item_tags[user_id].items():\n"," if value == 1 and bundle_item_tags[bundle_id][tag] == 1:\n"," intersection_tags[tag] = 1\n"," else:\n"," intersection_tags[tag] = 0\n","\n"," for spec, value in user_item_specs[user_id].items():\n"," if value == 1 and bundle_item_specs[bundle_id][spec] == 1:\n"," intersection_specs[spec] = 1\n"," else:\n"," intersection_specs[spec] = 0\n","\n"," return intersection_tags, intersection_specs\n","\n","def get_intersection_as_list(user_id, bundle_id):\n"," user_tags = user_item_tags[user_id]\n"," bundle_tags = bundle_item_tags[bundle_id]\n"," user_specs = user_item_specs[user_id]\n"," bundle_specs = bundle_item_specs[bundle_id]\n","\n"," # Using list comprehensions to find common tags and specs\n"," common_tags = [1 if user_tags[tag] == 1 and bundle_tags[tag] == 1 else 0 for tag in user_tags]\n"," common_specs = [1 if user_specs[spec] == 1 and bundle_specs[spec] == 1 else 0 for spec in user_specs]\n","\n"," return common_tags, common_specs\n","\n","# Example usage:\n","user_id = list(user_item_interactions.keys())[0] # Get the first user ID\n","bundle_id = list(bundle_data.keys())[0] # Get the first bundle ID\n","\n","intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n","\n","print(f\"Intersection for user {user_id} and bundle {bundle_id}:\")\n","print(f\"Tags: {intersection_tags}\")\n","print(f\"Specs: {intersection_specs}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":309041,"status":"ok","timestamp":1735305675372,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"BuFxKyM6cyCv","outputId":"fad7d31b-9e29-4644-80b1-456508338d8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 / 10.000\n","4898 / 10.000\n","4899 / 10.000\n","4900 / 10.000\n","4901 / 10.000\n","4902 / 10.000\n","4903 / 10.000\n","4904 / 10.000\n","4905 / 10.000\n","4906 / 10.000\n","4907 / 10.000\n","4908 / 10.000\n","4909 / 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10.000\n","9860 / 10.000\n","9861 / 10.000\n","9862 / 10.000\n","9863 / 10.000\n","9864 / 10.000\n","9865 / 10.000\n","9866 / 10.000\n","9867 / 10.000\n","9868 / 10.000\n","9869 / 10.000\n","9870 / 10.000\n","9871 / 10.000\n","9872 / 10.000\n","9873 / 10.000\n","9874 / 10.000\n","9875 / 10.000\n","9876 / 10.000\n","9877 / 10.000\n","9878 / 10.000\n","9879 / 10.000\n","9880 / 10.000\n","9881 / 10.000\n"]}],"source":["intersection_tags = {}\n","intersection_specs = {}\n","\n","user_ids = list(user_item_interactions.keys())\n","bundle_ids = list(bundle_data.keys())\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\" # Tentukan path file output\n","\n","with gzip.open(file_path, 'wt', encoding='utf-8') as outfile:\n"," i = 0\n"," for user_id in user_ids:\n"," i += 1\n"," data_to_write = {}\n"," data_to_write[user_id] = {}\n","\n"," for bundle_id in bundle_ids:\n"," intersection_tags, intersection_specs = get_intersection_as_list(user_id, bundle_id)\n"," data_to_write[user_id][bundle_id] = {\n"," \"tags\": intersection_tags,\n"," \"specs\": intersection_specs\n"," }\n","\n"," json.dump(data_to_write, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," print(f\"{i} / 10.000\")"]},{"cell_type":"markdown","metadata":{"id":"45XFujQeHeG-"},"source":["#### Do Recommendation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XzQCZ1fmHh5D"},"outputs":[],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","data_dict_out = None\n","temp_bundle_out = None\n","\n","file_path_read = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_native.json.gz\"\n","count = 0\n","index = 0\n","\n","try:\n"," with gzip.open(file_path_read, 'rt', encoding='utf-8') as infile, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," data_dict_out = {}\n"," temp_bundle_out = []\n"," for user_id, bundle_data in data_dict.items():\n"," for bundle_id, recommendation in bundle_data.items():\n"," temp_bundle_out.append(recommendation['tags'] + recommendation['specs'])\n","\n"," predictions = loaded_models['neural_networks_tags_specifications'].predict(np.array(temp_bundle_out), verbose=0)\n"," predictions = (predictions > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n"," predictions = predictions.flatten() # Flatten y_pred to a 1D array\n"," index = 0\n"," for user_id, bundle_data in data_dict.items():\n"," for bundle_id, recommendation in bundle_data.items():\n"," data_dict_out[\"user_id\"] = user_id\n"," data_dict_out[\"bundle_id\"] = bundle_id\n"," data_dict_out[\"is_recommended\"] = f\"{predictions[index]}\"\n"," index += 1\n","\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," # break\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # print(data_dict_out)\n"," # break\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VjV7oszXU3tS"},"outputs":[],"source":["data_dict['76561197970982479']['450']['tags']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":317,"status":"ok","timestamp":1735340498029,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"kgmFoKLhU3p9","outputId":"f878e87e-5b88-4528-afe1-bcf439f2a179"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n","Prediction for the example datapoint: [0]\n"]}],"source":["example_features = data_dict['76561197970982479']['450']['tags'] + data_dict['76561197970982479']['450']['specs']# Example datapoint\n","prediction = (loaded_models['neural_networks_tags_specifications'].predict(np.array([example_features])) > 0.5).astype(\"int32\").flatten()\n","print(f\"Prediction for the example datapoint: {prediction}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":300,"status":"ok","timestamp":1735356337204,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"MnRRI8KSaOgO","outputId":"c9d7122e-ec44-4ea1-ce58-06793ae72f41"},"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/v2_final_inputs_tags_and_specs_raw.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_native.json.gz'\n","Count: 419\n"]}],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_native.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{file_path_read}' successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pmSwZ6yPxdN5"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"na7Ok_umxQnJ"},"source":["## Create Input For Model Price Patterns"]},{"cell_type":"markdown","source":["#### 1. Load Data"],"metadata":{"id":"p6y7SbgJmBRo"}},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735784667918,"user_tz":-420,"elapsed":1073,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"722ddbf6-f6b5-4972-e51f-6af74aaf510a","id":"HT2uTzBMvAry"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0])"]},"metadata":{},"execution_count":3}],"source":["# prompt: Load file /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy\n","\n","import numpy as np\n","\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')\n","\n","user_price_label[:5]"]},{"cell_type":"code","source":["# prompt: Calculate how many 0 and 1 in user_price_label[:615]\n","\n","zeros = np.count_nonzero(user_price_label[:615] == 0)\n","ones = np.count_nonzero(user_price_label[:615] == 1)\n","\n","print(f\"Number of 0s: {zeros}\")\n","print(f\"Number of 1s: {ones}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBye6gkth3l6","executionInfo":{"status":"ok","timestamp":1735784668450,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ab99bdbd-65d6-4640-a868-e96b3f99e2e9"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of 0s: 582\n","Number of 1s: 33\n"]}]},{"cell_type":"code","source":["len(user_price_label)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_ORDJV_Q3XZR","executionInfo":{"status":"ok","timestamp":1735785286169,"user_tz":-420,"elapsed":533,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"cfca075a-26b6-4b54-ccab-fef25f3406d7"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6073740"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')"],"metadata":{"id":"2LoxPq-8iYe3","executionInfo":{"status":"ok","timestamp":1735784674926,"user_tz":-420,"elapsed":5606,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["user_price_identity = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_identity.npy')"],"metadata":{"id":"Jqtxa1XYl2sa","executionInfo":{"status":"ok","timestamp":1735784695412,"user_tz":-420,"elapsed":20488,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Find is that from user_price_identity user for all bundle theres no label 1 in user_price_label\n","\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","check_users = {}\n","\n","# Iterate through users and bundles (assuming bundle IDs are consecutive integers)\n","for user_index in range(num_users):\n"," user_id = user_price_identity[user_index]\n"," if user_id[0] not in check_users:\n"," check_users[user_id[0]] = 0\n","\n"," # Check if label is present for this user\n"," if user_price_label[user_index] is not None: # Check for None or NaN if necessary\n"," # Access the label directly instead of iterating\n"," if user_price_label[user_index] == 1:\n"," # Assuming bundle_id is the second element in user_price_identity\n"," check_users[user_id[0]] = 1\n"," else:\n"," print(f\"No labels found for user {user_id}\")"],"metadata":{"id":"g_NWI-KDiuIF","executionInfo":{"status":"ok","timestamp":1735785233948,"user_tz":-420,"elapsed":12099,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["# prompt: From check_users check is there has 0 value?\n","\n","zeros = 0\n","for user_id, label in check_users.items():\n"," if label == 0:\n"," zeros += 1\n","\n","print(f\"Number of users with 0 value: {zeros}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qD-mnjmHlcqD","executionInfo":{"status":"ok","timestamp":1735780608038,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ddd2f1e-dea8-44b9-9f94-94adbc1bca4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of users with 0 value: 23\n"]}]},{"cell_type":"markdown","source":["#### 2. Do Recommendation"],"metadata":{"id":"TKiv8OUzmEF5"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict_out = {}\n","temp_bundle_out_uid = []\n","temp_bundle_out_bid = []\n","temp_bundle_out_ftr = []\n","# Assuming user_price_identity and user_price_label have the same length\n","num_users = len(user_price_identity)\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_ann.json.gz\"\n","count = 0\n","index = 0\n","\n","check_users = {}\n","\n","try:\n"," with gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for user_index in range(num_users):\n"," temp_bundle_out_uid.append(user_price_identity[user_index][0])\n"," temp_bundle_out_bid.append(user_price_identity[user_index][1])\n"," temp_bundle_out_ftr.append(user_price_features[user_index])\n","\n"," if index % 1000 == 0 or index == num_users - 1:\n","\n"," predictions = loaded_models['neural_networks_price_patterns'].predict(np.array(temp_bundle_out_ftr), verbose=0)\n"," predictions = (predictions > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n"," predictions = predictions.flatten() # Flatten y_pred to a 1D array\n","\n"," for i in range(len(temp_bundle_out_uid)):\n"," data_dict_out[\"user_id\"] = temp_bundle_out_uid[i]\n"," data_dict_out[\"bundle_id\"] = temp_bundle_out_bid[i]\n"," data_dict_out[\"is_recommended\"] = f\"{predictions[i]}\"\n","\n"," # print(data_dict_out)\n"," json.dump(data_dict_out, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," if data_dict_out[\"user_id\"] not in check_users:\n"," check_users[data_dict_out[\"user_id\"]] = 0\n"," count += 1\n"," print(f\"{count} / 10.000\")\n"," # if count == 2:\n"," # break\n","\n"," temp_bundle_out_uid = []\n"," temp_bundle_out_bid = []\n"," temp_bundle_out_ftr = []\n"," index += 1\n"," print(f\"Successfully processed and saved to '{file_path_write}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_read}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1nbQtXeoa1M","outputId":"192edfcf-6c49-4b66-cf9e-b02a7e9e1370","executionInfo":{"status":"ok","timestamp":1735788153539,"user_tz":-420,"elapsed":1323899,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 10.000\n","4879 / 10.000\n","4880 / 10.000\n","4881 / 10.000\n","4882 / 10.000\n","4883 / 10.000\n","4884 / 10.000\n","4885 / 10.000\n","4886 / 10.000\n","4887 / 10.000\n","4888 / 10.000\n","4889 / 10.000\n","4890 / 10.000\n","4891 / 10.000\n","4892 / 10.000\n","4893 / 10.000\n","4894 / 10.000\n","4895 / 10.000\n","4896 / 10.000\n","4897 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json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," count += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path_write}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKv7TjHhq0YE","executionInfo":{"status":"ok","timestamp":1735786309114,"user_tz":-420,"elapsed":2766,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7776e229-98a9-4696-ad94-748172bae561"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Count: 263009\n"]}]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"1OrfeLsuMTaVL7GS9uEdXvCv68A7Qxnxi","authorship_tag":"ABX9TyN2WKH2KWfXwDWJfwTD/Pmu"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/5. Reranking Algorithm DT.ipynb b/Source Code/5. Reranking Algorithm DT.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cf785082fff933dbabafd74684bbc52fe7b47d9a --- /dev/null +++ b/Source Code/5. Reranking Algorithm DT.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1FG7ZKNWvY9NzttqgpQZ3lrCo4bBNQfa0","timestamp":1735482435351}],"mount_file_id":"1ZYogLNhD2G2-R5S9dh-e0xpAtnl2-x3H","authorship_tag":"ABX9TyOxUqeZfs+xd2l58pzxHt5F"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Reranking Applying"],"metadata":{"id":"TqUHSJ-AP8mE"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"],"metadata":{"id":"ndVraj2XhD0Y","executionInfo":{"status":"ok","timestamp":1735799545418,"user_tz":-420,"elapsed":453,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = {}\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items[item[\"item_id\"]] = item[\"playtime_forever\"]\n","\n"," user_item_interactions[data_dict['user_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yPu3n3l6hRLA","executionInfo":{"status":"ok","timestamp":1735799586829,"user_tz":-420,"elapsed":40825,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"6569b01a-3630-4511-eed2-0b4d3821cd30"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. Winterbottom', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40990', 'item_name': 'Mafia', 'playtime_forever': 21, 'playtime_2weeks': 0}, {'item_id': '50130', 'item_name': 'Mafia II', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '50310', 'item_name': 'MLB 2K11', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '57900', 'item_name': 'Duke Nukem Forever', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '65950', 'item_name': 'NBA 2K11', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '201020', 'item_name': 'NBA 2K12', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409710', 'item_name': 'BioShock Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409720', 'item_name': 'BioShock 2 Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '67370', 'item_name': 'The Darkness II', 'playtime_forever': 146, 'playtime_2weeks': 0}, {'item_id': '207610', 'item_name': 'The Walking Dead', 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'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive 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'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = set()\n","\n"," if len(data_dict[\"items\"]) > 0:\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZnLJTSvOm6tH","executionInfo":{"status":"ok","timestamp":1735799587309,"user_tz":-420,"elapsed":482,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4409cb81-51bd-40b5-e45f-2d8ebcb9370e"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KwK2r2ospM7p","executionInfo":{"status":"ok","timestamp":1735799591976,"user_tz":-420,"elapsed":4668,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"85dc454e-f04b-4e48-c486-303eeaeccb02"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"9HG4u_54plVH","executionInfo":{"status":"ok","timestamp":1735479680885,"user_tz":-420,"elapsed":730,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a732e27-e0bc-41f3-8417-703fc4de328b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: From data sentiment_counts i want to sorted from very positive to very negative\n","\n","import operator\n","\n","sorted_sentiment_counts = dict(sorted(sentiment_counts.items(), key=operator.itemgetter(1), reverse=True))\n","\n","sentiments, counts = zip(*sorted_sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary (Sorted)\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"sjR-gufgppmg","executionInfo":{"status":"ok","timestamp":1735479926795,"user_tz":-420,"elapsed":948,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e8acb122-9e3e-4b2d-fd64-15bfc992059f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["def generate_sentiment_value(sentimen_str):\n"," if sentimen_str == 'Overwhelmingly Positive':\n"," return 1.00\n"," elif sentimen_str == 'Very Positive':\n"," return 0.80\n"," elif sentimen_str == 'Positive':\n"," return 0.60\n"," elif sentimen_str == 'Mostly Positive':\n"," return 0.40\n"," elif sentimen_str == '9 user reviews':\n"," return 0.25\n"," elif sentimen_str == '8 user reviews':\n"," return 0.20\n"," elif sentimen_str == '7 user reviews':\n"," return 0.15\n"," elif sentimen_str == '6 user reviews':\n"," return 0.10\n"," elif sentimen_str == 'Mixed':\n"," return 0.5\n"," elif sentimen_str == '5 user reviews':\n"," return -0.5\n"," elif sentimen_str == '4 user reviews':\n"," return -0.10\n"," elif sentimen_str == '3 user reviews':\n"," return -0.15\n"," elif sentimen_str == '2 user reviews':\n"," return -0.20\n"," elif sentimen_str == '1 user review':\n"," return -0.25\n"," elif sentimen_str == 'Mostly Negative':\n"," return -0.40\n"," elif sentimen_str == 'Negative':\n"," return -0.60\n"," elif sentimen_str == 'Very Negative':\n"," return -0.85\n"," elif sentimen_str == 'Overwhelmingly Negative':\n"," return -1.00\n"," else:\n"," return 0.00"],"metadata":{"id":"Pd-DAKhQtwcR","executionInfo":{"status":"ok","timestamp":1735798201461,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n1KqBF9ifNWl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735482722617,"user_tz":-420,"elapsed":171584,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b641915e-45c2-4899-eec0-0bbab66c2551"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '1'}\n"]}],"source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_dt.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"]},{"cell_type":"code","source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_dt.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Js7tlRX-orop","executionInfo":{"status":"ok","timestamp":1735798436187,"user_tz":-420,"elapsed":191935,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8b071979-071c-479a-9324-ddc100ad8bd1"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," count = len(data_dict['rec_bundles'])\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nz3SqNvj-F7","executionInfo":{"status":"ok","timestamp":1735798501663,"user_tz":-420,"elapsed":1900,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"287403ed-60a3-44b4-d8e0-577b346911a4"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz'\n","Count: 137\n"]}]},{"cell_type":"markdown","source":["## Evaluate\n"],"metadata":{"id":"q1DFXWFOCsP0"}},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"],"metadata":{"id":"1M0JQWwPEAK5","executionInfo":{"status":"ok","timestamp":1735799591976,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit_ratios = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," hits = 0\n"," if user_id in user_item_interactions:\n"," user_items = set(user_item_interactions[user_id].keys())\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," hits += len(user_items.intersection(bundle_items))\n","\n"," hit_ratio = hits / top_n if top_n > 0 else 0 # Avoid division by zero\n"," hit_ratios.append({\"user_id\": user_id, \"top_n\": top_n, \"hit_ratio\": hit_ratio})\n"," return hit_ratios"],"metadata":{"id":"OBktUkbTEm5u","executionInfo":{"status":"ok","timestamp":1735799591976,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate diversity from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and diversity score.\n"," \"\"\"\n"," diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," total_items = 0\n"," unique_items = set()\n","\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," total_items += len(bundle_items)\n"," unique_items.update(bundle_items)\n","\n"," diversity = len(unique_items) / total_items if total_items > 0 else 0\n"," diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"diversity\": diversity})\n"," return diversity_scores"],"metadata":{"id":"Y9x5v5iNFzlZ","executionInfo":{"status":"ok","timestamp":1735799591976,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate inter_diversity from top_bundles_results by checkking how similar for each bundle\n","\n","import json\n","import gzip\n","import ast\n","import matplotlib.pyplot as plt\n","import operator\n","import pandas as pd\n","\n","# ... (Your existing code)\n","\n","def calculate_inter_diversity(top_bundles_results, bundle_data):\n"," \"\"\"\n"," Calculates the inter-diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and inter-diversity score.\n"," \"\"\"\n"," inter_diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," jaccard_similarities = []\n"," bundle_ids = list(top_bundles.keys())\n"," for i in range(len(bundle_ids)):\n"," for j in range(i + 1, len(bundle_ids)):\n"," bundle1_id = bundle_ids[i]\n"," bundle2_id = bundle_ids[j]\n"," if bundle1_id in bundle_data and bundle2_id in bundle_data:\n"," bundle1_items = bundle_data[bundle1_id]\n"," bundle2_items = bundle_data[bundle2_id]\n"," intersection = len(bundle1_items.intersection(bundle2_items))\n"," union = len(bundle1_items.union(bundle2_items))\n"," jaccard_similarity = intersection / union if union > 0 else 0\n"," jaccard_similarities.append(1 - jaccard_similarity) # 1-similarity to represent diversity\n","\n"," inter_diversity = sum(jaccard_similarities) / len(jaccard_similarities) if jaccard_similarities else 0\n"," inter_diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"inter_diversity\": inter_diversity})\n"," return inter_diversity_scores"],"metadata":{"id":"As-qpdgrGIDw","executionInfo":{"status":"ok","timestamp":1735799592460,"user_tz":-420,"elapsed":486,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"W5jfzwYKD21C","executionInfo":{"status":"ok","timestamp":1735520132696,"user_tz":-420,"elapsed":382,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7cede590-f529-4c0f-de13-ca0f7617eb5a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'user_id': 'js41637',\n"," 'rec_bundles': {'362': 45.94999999999998,\n"," '383': 21.400000000000002,\n"," '646': 20.200000000000006,\n"," '356': 19.70000000000001,\n"," '1468': 18.700000000000006,\n"," '1418': 15.5,\n"," '616': 14.299999999999999,\n"," '268': 13.650000000000002,\n"," '232': 13.600000000000003,\n"," '813': 12.700000000000003,\n"," '261': 11.749999999999998,\n"," '250': 11.049999999999997,\n"," '575': 10.899999999999999,\n"," '612': 10.699999999999998,\n"," 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'491': 0,\n"," '414': 0.0,\n"," '1295': 0,\n"," '492': 0.0,\n"," '409': 0.0,\n"," '1143': -0.04999999999999999,\n"," '540': -0.05000000000000002,\n"," '328': -0.15000000000000002,\n"," '1406': -0.2,\n"," '424': -0.2,\n"," '245': -0.2,\n"," '326': -0.25,\n"," '634': -0.2500000000000001,\n"," '254': -0.4,\n"," '536': -0.4,\n"," '1005': -0.4,\n"," '846': -0.4,\n"," '439': -0.5,\n"," '1437': -0.55,\n"," '440': -0.55,\n"," '570': -0.7500000000000001,\n"," '329': -0.7999999999999999,\n"," '539': -0.8,\n"," '701': -0.8,\n"," '436': -0.8,\n"," '955': -1.0,\n"," '327': -1.15,\n"," '956': -2.25}}"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jn67YIwICr93","executionInfo":{"status":"ok","timestamp":1735523516659,"user_tz":-420,"elapsed":25071,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"be6979ad-8ae4-4dad-d44e-eca4464b304a","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 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ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict[\"rec_bundles\"]) > 0:\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-PeX1qv8uD5p","executionInfo":{"status":"ok","timestamp":1735799751720,"user_tz":-420,"elapsed":32427,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2889c365-e6bf-4551-b8ba-c455b24a75cc"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 9881\n","4908 / 9881\n","4909 / 9881\n","4910 / 9881\n","4911 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9881\n","9872 / 9881\n","9873 / 9881\n","9874 / 9881\n","9875 / 9881\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz'\n","Count: 9875\n"]}]},{"cell_type":"code","source":["# prompt: Calculate average average_hit_ratio\n","\n","import pandas as pd\n","\n","def calculate_average_metrics(metrics_dict):\n"," \"\"\"Calculates the average of metrics across users.\n","\n"," Args:\n"," metrics_dict: A dictionary where keys are user IDs and values are lists of dictionaries,\n"," each containing a 'top_n' and a metric value.\n","\n"," Returns:\n"," A pandas DataFrame with the average metrics for each 'top_n'.\n"," \"\"\"\n","\n"," all_metrics = []\n"," for user_id, user_metrics in metrics_dict.items():\n"," all_metrics.extend(user_metrics)\n","\n"," df = pd.DataFrame(all_metrics)\n","\n"," numeric_cols = df.select_dtypes(include=['number']).columns\n"," return df.groupby('top_n')[numeric_cols].mean()\n","\n","# Calculate and print the average hit ratio\n","average_hit_ratio_df = calculate_average_metrics(average_hit_ratio)\n","print(\"Average Hit Ratio:\")\n","print(average_hit_ratio_df)\n","\n","# Calculate and print the average intra-list diversity\n","average_intra_diversity_df = calculate_average_metrics(average_intra_diversity)\n","print(\"\\nAverage Intra-list Diversity:\")\n","print(average_intra_diversity_df)\n","\n","# Calculate and print the average inter-list diversity\n","average_inter_diversity_df = calculate_average_metrics(average_inter_diversity)\n","print(\"\\nAverage Inter-list Diversity:\")\n","average_inter_diversity_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":501},"id":"r7-DgF7QKIJB","executionInfo":{"status":"ok","timestamp":1735799766931,"user_tz":-420,"elapsed":520,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2e850992-79e1-444e-984e-11536f801afd"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Hit Ratio:\n"," top_n hit_ratio\n","top_n \n","5 5.0 0.795724\n","10 10.0 0.895541\n","15 15.0 0.675429\n","20 20.0 0.542511\n","\n","Average Intra-list Diversity:\n"," top_n diversity\n","top_n \n","5 5.0 0.999928\n","10 10.0 0.999588\n","15 15.0 0.963657\n","20 20.0 0.965154\n","\n","Average Inter-list Diversity:\n"]},{"output_type":"execute_result","data":{"text/plain":[" top_n inter_diversity\n","top_n \n","5 5.0 0.998539\n","10 10.0 0.998394\n","15 15.0 0.990966\n","20 20.0 0.993914"],"text/html":["\n","
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top_ninter_diversity
top_n
55.00.998539
1010.00.998394
1515.00.990966
2020.00.993914
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"average_inter_diversity_df","repr_error":"cannot insert top_n, already exists"}},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# prompt: combine average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df and make it colomn like top_n | hit_ratio | intra_diversity | inter_diversity just create from dictionary do not using merge function of pandas\n","\n","import pandas as pd\n","\n","def combine_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in average_hit_ratio_df.index:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': average_hit_ratio_df.loc[top_n, 'hit_ratio'],\n"," 'intra_diversity': average_intra_diversity_df.loc[top_n, 'diversity'],\n"," 'inter_diversity': average_inter_diversity_df.loc[top_n, 'inter_diversity']\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)\n","\n","# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)\n","combined_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"cdolqR3XOM3R","executionInfo":{"status":"ok","timestamp":1735799777585,"user_tz":-420,"elapsed":1039,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8d16e426-636f-421b-9a31-90a345b82221"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio intra_diversity inter_diversity\n","0 5 0.795724 0.999928 0.998539\n","1 10 0.895541 0.999588 0.998394\n","2 15 0.675429 0.963657 0.990966\n","3 20 0.542511 0.965154 0.993914"],"text/html":["\n","
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top_nhit_ratiointra_diversityinter_diversity
050.7957240.9999280.998539
1100.8955410.9995880.998394
2150.6754290.9636570.990966
3200.5425110.9651540.993914
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.15256079195281103,\n \"min\": 0.5425111471422781,\n \"max\": 0.8955411430887719,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8955411430887719,\n 0.5425111471422781,\n 0.7957235508715038\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"intra_diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02042065090003041,\n \"min\": 0.96365671686874,\n \"max\": 0.9999284658279376,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9995880703606173,\n 0.9651539449074079,\n 0.9999284658279376\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"inter_diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0036823237500808754,\n \"min\": 0.9909655181662705,\n \"max\": 0.9985386640331919,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9983944600458974,\n 0.9939143246282781,\n 0.9985386640331919\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# prompt: save combined_df as /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_native.csv\n","\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_dt.csv', index=False)\n","combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns_dt.csv', index=False)"],"metadata":{"id":"lfky5tCoPDIx","executionInfo":{"status":"ok","timestamp":1735799798710,"user_tz":-420,"elapsed":431,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# prompt: generate bar chart for each average_hit_ratio_df, average_intra_diversity_df, and average_inter_diversity_df\n","# Make it dense space between bars, and change the color of bar for the best value\n","\n","# prompt: generate bar chart for each average_hit_ratio_df, average_intra_diversity_df, and average_inter_diversity_df\n","# Make it dense space between bars, and change the color of bar for the best value\n","\n","import matplotlib.pyplot as plt\n","\n","def plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df):\n"," \"\"\"Generates bar charts for average hit ratio, intra-diversity, and inter-diversity.\"\"\"\n","\n"," fig, axes = plt.subplots(3, 1, figsize=(8, 10)) # Adjust figsize as needed\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_hit_ratio_df['hit_ratio'].idxmax()\n"," top_n_values = average_hit_ratio_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Hit Ratio ---\n"," axes[0].bar(average_hit_ratio_df.index, average_hit_ratio_df['hit_ratio'], width=4.0, color=colors)\n"," axes[0].set_xlabel(\"Top N Recommendations\")\n"," axes[0].set_ylabel(\"Average Hit Ratio\")\n"," axes[0].set_title(\"Average Hit Ratio for Different Top N Values\")\n"," axes[0].set_xticks(average_hit_ratio_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_intra_diversity_df['diversity'].idxmax()\n"," top_n_values = average_intra_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Intra-list Diversity ---\n"," axes[1].bar(average_intra_diversity_df.index, average_intra_diversity_df['diversity'], width=4.0, color=colors)\n"," axes[1].set_xlabel(\"Top N Recommendations\")\n"," axes[1].set_ylabel(\"Average Intra-list Diversity\")\n"," axes[1].set_title(\"Average Intra-list Diversity for Different Top N Values\")\n"," axes[1].set_xticks(average_intra_diversity_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_inter_diversity_df['inter_diversity'].idxmax()\n"," top_n_values = average_inter_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Inter-list Diversity ---\n"," axes[2].bar(average_inter_diversity_df.index, average_inter_diversity_df['inter_diversity'], width=4.0, color=colors)\n"," axes[2].set_xlabel(\"Top N Recommendations\")\n"," axes[2].set_ylabel(\"Average Inter-list Diversity\")\n"," axes[2].set_title(\"Average Inter-list Diversity for Different Top N Values\")\n"," axes[2].set_xticks(average_inter_diversity_df.index)\n","\n"," plt.tight_layout() # Adjust layout to prevent overlapping\n"," plt.show()\n","\n","plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"H53yAPjpKxgM","executionInfo":{"status":"ok","timestamp":1735523562234,"user_tz":-420,"elapsed":1216,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"841f52d4-38d7-43d6-f327-325937fd8dee"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Source Code/5. Reranking Algorithm KNN.ipynb b/Source Code/5. Reranking Algorithm KNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e207eeab757dca9fdce22a42eb27d24dd1ec087f --- /dev/null +++ b/Source Code/5. Reranking Algorithm KNN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1ZYogLNhD2G2-R5S9dh-e0xpAtnl2-x3H","timestamp":1735482594838},{"file_id":"1FG7ZKNWvY9NzttqgpQZ3lrCo4bBNQfa0","timestamp":1735482435351}],"mount_file_id":"1VDzeZyw5dQEni3I1oSoABOBF7dee8oGv","authorship_tag":"ABX9TyNBkuigfq31ez8SKqrVRusb"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Reranking Applying\n","\n"],"metadata":{"id":"12RB1-6TSqjS"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"],"metadata":{"id":"ndVraj2XhD0Y","executionInfo":{"status":"ok","timestamp":1735799867719,"user_tz":-420,"elapsed":333,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = {}\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items[item[\"item_id\"]] = item[\"playtime_forever\"]\n","\n"," user_item_interactions[data_dict['user_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yPu3n3l6hRLA","executionInfo":{"status":"ok","timestamp":1735799911615,"user_tz":-420,"elapsed":43404,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"6af90f51-dc97-42ad-a259-265c9a388a62"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. Winterbottom', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40990', 'item_name': 'Mafia', 'playtime_forever': 21, 'playtime_2weeks': 0}, {'item_id': '50130', 'item_name': 'Mafia II', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '50310', 'item_name': 'MLB 2K11', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '57900', 'item_name': 'Duke Nukem Forever', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '65950', 'item_name': 'NBA 2K11', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '201020', 'item_name': 'NBA 2K12', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409710', 'item_name': 'BioShock Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409720', 'item_name': 'BioShock 2 Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '67370', 'item_name': 'The Darkness II', 'playtime_forever': 146, 'playtime_2weeks': 0}, {'item_id': '207610', 'item_name': 'The Walking Dead', 'playtime_forever': 457, 'playtime_2weeks': 0}, {'item_id': '97330', 'item_name': 'Magic: The Gathering - Duels of the Planeswalkers 2013', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '65800', 'item_name': 'Dungeon Defenders', 'playtime_forever': 4376, 'playtime_2weeks': 0}, {'item_id': '211420', 'item_name': 'Dark Souls: Prepare to Die Edition', 'playtime_forever': 71, 'playtime_2weeks': 0}, {'item_id': '730', 'item_name': 'Counter-Strike: Global Offensive', 'playtime_forever': 1900, 'playtime_2weeks': 0}, {'item_id': '204300', 'item_name': 'Awesomenauts', 'playtime_forever': 584, 'playtime_2weeks': 0}, {'item_id': '200170', 'item_name': 'Worms Revolution', 'playtime_forever': 423, 'playtime_2weeks': 0}, {'item_id': '217200', 'item_name': 'Worms Armageddon', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '204360', 'item_name': 'Castle Crashers', 'playtime_forever': 614, 'playtime_2weeks': 0}, {'item_id': '49520', 'item_name': 'Borderlands 2', 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'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive 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'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = set()\n","\n"," if len(data_dict[\"items\"]) > 0:\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZnLJTSvOm6tH","executionInfo":{"status":"ok","timestamp":1735799912095,"user_tz":-420,"elapsed":482,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7f9aad26-4af2-4f9e-d5b6-3925ce3d9dbb"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KwK2r2ospM7p","executionInfo":{"status":"ok","timestamp":1735799916743,"user_tz":-420,"elapsed":4650,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c7a1870a-7b0e-44e9-d7be-4af1daa44ba1"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"9HG4u_54plVH","executionInfo":{"status":"ok","timestamp":1735479680885,"user_tz":-420,"elapsed":730,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a732e27-e0bc-41f3-8417-703fc4de328b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: From data sentiment_counts i want to sorted from very positive to very negative\n","\n","import operator\n","\n","sorted_sentiment_counts = dict(sorted(sentiment_counts.items(), key=operator.itemgetter(1), reverse=True))\n","\n","sentiments, counts = zip(*sorted_sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary (Sorted)\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"sjR-gufgppmg","executionInfo":{"status":"ok","timestamp":1735479926795,"user_tz":-420,"elapsed":948,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e8acb122-9e3e-4b2d-fd64-15bfc992059f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["def generate_sentiment_value(sentimen_str):\n"," if sentimen_str == 'Overwhelmingly Positive':\n"," return 1.00\n"," elif sentimen_str == 'Very Positive':\n"," return 0.80\n"," elif sentimen_str == 'Positive':\n"," return 0.60\n"," elif sentimen_str == 'Mostly Positive':\n"," return 0.40\n"," elif sentimen_str == '9 user reviews':\n"," return 0.25\n"," elif sentimen_str == '8 user reviews':\n"," return 0.20\n"," elif sentimen_str == '7 user reviews':\n"," return 0.15\n"," elif sentimen_str == '6 user reviews':\n"," return 0.10\n"," elif sentimen_str == 'Mixed':\n"," return 0.5\n"," elif sentimen_str == '5 user reviews':\n"," return -0.5\n"," elif sentimen_str == '4 user reviews':\n"," return -0.10\n"," elif sentimen_str == '3 user reviews':\n"," return -0.15\n"," elif sentimen_str == '2 user reviews':\n"," return -0.20\n"," elif sentimen_str == '1 user review':\n"," return -0.25\n"," elif sentimen_str == 'Mostly Negative':\n"," return -0.40\n"," elif sentimen_str == 'Negative':\n"," return -0.60\n"," elif sentimen_str == 'Very Negative':\n"," return -0.85\n"," elif sentimen_str == 'Overwhelmingly Negative':\n"," return -1.00\n"," else:\n"," return 0.00"],"metadata":{"id":"Pd-DAKhQtwcR","executionInfo":{"status":"ok","timestamp":1735799166536,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n1KqBF9ifNWl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735482868636,"user_tz":-420,"elapsed":162397,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"f8d4b6ff-d602-481e-ebdc-3762bd5dda2c"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '1183', 'is_recommended': '0'}\n"]}],"source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_knn.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"]},{"cell_type":"code","source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_knn.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sYOVjEBOpk9b","executionInfo":{"status":"ok","timestamp":1735799315858,"user_tz":-420,"elapsed":149324,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"63cca272-2476-400e-c203-6ec8a43e52ba"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","count = 0\n","countall = 0\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," if len(data_dict['rec_bundles']) > 0:\n"," count += 1\n"," countall += 1\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count} / {countall}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nz3SqNvj-F7","executionInfo":{"status":"ok","timestamp":1735799452872,"user_tz":-420,"elapsed":614,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"df52527c-6371-47ec-d6d9-e14a4afa3fbb"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz'\n","Count: 5075 / 9875\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"FKMJ7w60S-QM"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Evaluate\n"],"metadata":{"id":"q1DFXWFOCsP0"}},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"],"metadata":{"id":"1M0JQWwPEAK5","executionInfo":{"status":"ok","timestamp":1735799916743,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit_ratios = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," hits = 0\n"," if user_id in user_item_interactions:\n"," user_items = set(user_item_interactions[user_id].keys())\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," hits += len(user_items.intersection(bundle_items))\n","\n"," hit_ratio = hits / top_n if top_n > 0 else 0 # Avoid division by zero\n"," hit_ratios.append({\"user_id\": user_id, \"top_n\": top_n, \"hit_ratio\": hit_ratio})\n"," return hit_ratios"],"metadata":{"id":"OBktUkbTEm5u","executionInfo":{"status":"ok","timestamp":1735799916743,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate diversity from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and diversity score.\n"," \"\"\"\n"," diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," total_items = 0\n"," unique_items = set()\n","\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," total_items += len(bundle_items)\n"," unique_items.update(bundle_items)\n","\n"," diversity = len(unique_items) / total_items if total_items > 0 else 0\n"," diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"diversity\": diversity})\n"," return diversity_scores"],"metadata":{"id":"Y9x5v5iNFzlZ","executionInfo":{"status":"ok","timestamp":1735799916744,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate inter_diversity from top_bundles_results by checkking how similar for each bundle\n","\n","import json\n","import gzip\n","import ast\n","import matplotlib.pyplot as plt\n","import operator\n","import pandas as pd\n","\n","# ... (Your existing code)\n","\n","def calculate_inter_diversity(top_bundles_results, bundle_data):\n"," \"\"\"\n"," Calculates the inter-diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and inter-diversity score.\n"," \"\"\"\n"," inter_diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," jaccard_similarities = []\n"," bundle_ids = list(top_bundles.keys())\n"," for i in range(len(bundle_ids)):\n"," for j in range(i + 1, len(bundle_ids)):\n"," bundle1_id = bundle_ids[i]\n"," bundle2_id = bundle_ids[j]\n"," if bundle1_id in bundle_data and bundle2_id in bundle_data:\n"," bundle1_items = bundle_data[bundle1_id]\n"," bundle2_items = bundle_data[bundle2_id]\n"," intersection = len(bundle1_items.intersection(bundle2_items))\n"," union = len(bundle1_items.union(bundle2_items))\n"," jaccard_similarity = intersection / union if union > 0 else 0\n"," jaccard_similarities.append(1 - jaccard_similarity) # 1-similarity to represent diversity\n","\n"," inter_diversity = sum(jaccard_similarities) / len(jaccard_similarities) if jaccard_similarities else 0\n"," inter_diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"inter_diversity\": inter_diversity})\n"," return inter_diversity_scores"],"metadata":{"id":"As-qpdgrGIDw","executionInfo":{"status":"ok","timestamp":1735799917177,"user_tz":-420,"elapsed":437,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"W5jfzwYKD21C","executionInfo":{"status":"ok","timestamp":1735524242289,"user_tz":-420,"elapsed":10,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9f30d4c0-ec78-4178-ff5c-fa5dd7ba7437"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'bundle_final_price': '$4.77',\n"," 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page',\n"," 'bundle_price': '$5.97',\n"," 'bundle_name': 'The Ninjahtic Series',\n"," 'bundle_id': '594',\n"," 'items': [{'genre': 'Action, Adventure, Indie',\n"," 'item_id': '385230',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/385230',\n"," 'item_name': 'Ninjahtic'},\n"," {'genre': 'Action, Adventure, Indie',\n"," 'item_id': '387880',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/387880',\n"," 'item_name': 'Ninjahtic Mind Tricks'},\n"," {'genre': 'Action, Indie',\n"," 'item_id': '454130',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/454130',\n"," 'item_name': 'Nil-Ninjahtic: Ronin'}],\n"," 'bundle_discount': '20%'}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jn67YIwICr93","executionInfo":{"status":"ok","timestamp":1735524265611,"user_tz":-420,"elapsed":23327,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1632d2a2-60c2-42f3-daa9-b8d648eadcdb","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 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Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict[\"rec_bundles\"]) > 0:\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"C7CEB5cmvFCZ","executionInfo":{"status":"ok","timestamp":1735799931133,"user_tz":-420,"elapsed":13957,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"5cff4db4-559d-48da-e214-4de34c1f10ab"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 9881\n","4908 / 9881\n","4909 / 9881\n","4910 / 9881\n","4911 / 9881\n","4912 / 9881\n","4913 / 9881\n","4914 / 9881\n","4915 / 9881\n","4916 / 9881\n","4917 / 9881\n","4918 / 9881\n","4919 / 9881\n","4920 / 9881\n","4921 / 9881\n","4922 / 9881\n","4923 / 9881\n","4924 / 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Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz'\n","Count: 9875\n"]}]},{"cell_type":"code","source":["# prompt: Calculate average average_hit_ratio\n","\n","import pandas as pd\n","\n","def calculate_average_metrics(metrics_dict):\n"," \"\"\"Calculates the average of metrics across users.\n","\n"," Args:\n"," metrics_dict: A dictionary where keys are user IDs and values are lists of dictionaries,\n"," each containing a 'top_n' and a metric value.\n","\n"," Returns:\n"," A pandas DataFrame with the average metrics for each 'top_n'.\n"," \"\"\"\n","\n"," all_metrics = []\n"," for user_id, user_metrics in metrics_dict.items():\n"," all_metrics.extend(user_metrics)\n","\n"," df = pd.DataFrame(all_metrics)\n","\n"," numeric_cols = df.select_dtypes(include=['number']).columns\n"," return df.groupby('top_n')[numeric_cols].mean()\n","\n","# Calculate and print the average hit ratio\n","average_hit_ratio_df = calculate_average_metrics(average_hit_ratio)\n","print(\"Average Hit Ratio:\")\n","print(average_hit_ratio_df)\n","\n","# Calculate and print the average intra-list diversity\n","average_intra_diversity_df = calculate_average_metrics(average_intra_diversity)\n","print(\"\\nAverage Intra-list Diversity:\")\n","print(average_intra_diversity_df)\n","\n","# Calculate and print the average inter-list diversity\n","average_inter_diversity_df = calculate_average_metrics(average_inter_diversity)\n","print(\"\\nAverage Inter-list Diversity:\")\n","average_inter_diversity_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":501},"id":"r7-DgF7QKIJB","executionInfo":{"status":"ok","timestamp":1735799931452,"user_tz":-420,"elapsed":322,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e6c230fb-3983-406a-8267-3f31fabd347e"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Hit Ratio:\n"," top_n hit_ratio\n","top_n \n","5 5.0 0.513340\n","10 10.0 0.343921\n","15 15.0 0.247396\n","20 20.0 0.192660\n","\n","Average Intra-list Diversity:\n"," top_n diversity\n","top_n \n","5 5.0 0.995296\n","10 10.0 0.987449\n","15 15.0 0.975292\n","20 20.0 0.969601\n","\n","Average Inter-list Diversity:\n"]},{"output_type":"execute_result","data":{"text/plain":[" top_n inter_diversity\n","top_n \n","5 5.0 0.776677\n","10 10.0 0.774081\n","15 15.0 0.771772\n","20 20.0 0.771809"],"text/html":["\n","
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top_ninter_diversity
top_n
55.00.776677
1010.00.774081
1515.00.771772
2020.00.771809
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top_nhit_ratiointra_diversityinter_diversity
050.5133400.9952960.776677
1100.3439210.9874490.774081
2150.2473960.9752920.771772
3200.1926600.9696010.771809
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Adjust figsize as needed\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_hit_ratio_df['hit_ratio'].idxmax()\n"," top_n_values = average_hit_ratio_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Hit Ratio ---\n"," axes[0].bar(average_hit_ratio_df.index, average_hit_ratio_df['hit_ratio'], width=4.0, color=colors)\n"," axes[0].set_xlabel(\"Top N Recommendations\")\n"," axes[0].set_ylabel(\"Average Hit Ratio\")\n"," axes[0].set_title(\"Average Hit Ratio for Different Top N Values\")\n"," axes[0].set_xticks(average_hit_ratio_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_intra_diversity_df['diversity'].idxmax()\n"," top_n_values = average_intra_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Intra-list Diversity ---\n"," axes[1].bar(average_intra_diversity_df.index, average_intra_diversity_df['diversity'], width=4.0, color=colors)\n"," axes[1].set_xlabel(\"Top N Recommendations\")\n"," axes[1].set_ylabel(\"Average Intra-list Diversity\")\n"," axes[1].set_title(\"Average Intra-list Diversity for Different Top N Values\")\n"," axes[1].set_xticks(average_intra_diversity_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_inter_diversity_df['inter_diversity'].idxmax()\n"," top_n_values = average_inter_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Inter-list Diversity ---\n"," axes[2].bar(average_inter_diversity_df.index, average_inter_diversity_df['inter_diversity'], width=4.0, color=colors)\n"," axes[2].set_xlabel(\"Top N Recommendations\")\n"," axes[2].set_ylabel(\"Average Inter-list Diversity\")\n"," axes[2].set_title(\"Average Inter-list Diversity for Different Top N Values\")\n"," axes[2].set_xticks(average_inter_diversity_df.index)\n","\n"," plt.tight_layout() # Adjust layout to prevent overlapping\n"," plt.show()\n","\n","plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"H53yAPjpKxgM","executionInfo":{"status":"ok","timestamp":1735524266965,"user_tz":-420,"elapsed":1039,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3d5723e3-b0be-4568-fdf5-05e6a9c60ba2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Source Code/5. Reranking Algorithm LogReg.ipynb b/Source Code/5. Reranking Algorithm LogReg.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9ac4843825b116668a4cbd954d421392f07a2626 --- /dev/null +++ b/Source Code/5. Reranking Algorithm LogReg.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1VDzeZyw5dQEni3I1oSoABOBF7dee8oGv","timestamp":1735482701187},{"file_id":"1ZYogLNhD2G2-R5S9dh-e0xpAtnl2-x3H","timestamp":1735482594838},{"file_id":"1FG7ZKNWvY9NzttqgpQZ3lrCo4bBNQfa0","timestamp":1735482435351}],"mount_file_id":"1e7RrFWyvPr-7xIhv-HwCgtg_buMpn5Ew","authorship_tag":"ABX9TyPVgMo9K0VjXoR5S9+gzWjf"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Reranking Applying\n","\n"],"metadata":{"id":"Ok2K9KTnSsUb"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"],"metadata":{"id":"ndVraj2XhD0Y","executionInfo":{"status":"ok","timestamp":1735799988374,"user_tz":-420,"elapsed":306,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = {}\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items[item[\"item_id\"]] = item[\"playtime_forever\"]\n","\n"," user_item_interactions[data_dict['user_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yPu3n3l6hRLA","executionInfo":{"status":"ok","timestamp":1735800052536,"user_tz":-420,"elapsed":63844,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8bd7e60e-26d9-47e0-e9d5-bca0c24b484b"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - 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{'item_id': '252490', 'item_name': 'Rust', 'playtime_forever': 3483, 'playtime_2weeks': 0}, {'item_id': '252950', 'item_name': 'Rocket League', 'playtime_forever': 915, 'playtime_2weeks': 0}, {'item_id': '241540', 'item_name': 'State of Decay', 'playtime_forever': 2099, 'playtime_2weeks': 0}, {'item_id': '237890', 'item_name': 'Agarest: Generations of War', 'playtime_forever': 135, 'playtime_2weeks': 0}, {'item_id': '242050', 'item_name': \"Assassin's Creed IV Black Flag\", 'playtime_forever': 6221, 'playtime_2weeks': 0}, {'item_id': '234530', 'item_name': 'War of the Vikings', 'playtime_forever': 2509, 'playtime_2weeks': 0}, {'item_id': '257350', 'item_name': \"Baldur's Gate II: Enhanced Edition\", 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '243470', 'item_name': 'Watch_Dogs', 'playtime_forever': 2043, 'playtime_2weeks': 0}, {'item_id': '247730', 'item_name': 'Nether', 'playtime_forever': 3114, 'playtime_2weeks': 0}, {'item_id': '260730', 'item_name': 'Desperados - Wanted Dead or Alive', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261030', 'item_name': 'The Walking Dead: Season Two', 'playtime_forever': 637, 'playtime_2weeks': 0}, {'item_id': '242700', 'item_name': 'Injustice: Gods Among Us Ultimate Edition', 'playtime_forever': 368, 'playtime_2weeks': 0}, {'item_id': '262060', 'item_name': 'Darkest Dungeon', 'playtime_forever': 332, 'playtime_2weeks': 0}, {'item_id': '263440', 'item_name': 'The Stomping Land', 'playtime_forever': 142, 'playtime_2weeks': 0}, {'item_id': '108600', 'item_name': 'Project Zomboid', 'playtime_forever': 160, 'playtime_2weeks': 0}, {'item_id': '211820', 'item_name': 'Starbound', 'playtime_forever': 137, 'playtime_2weeks': 0}, {'item_id': '367540', 'item_name': 'Starbound - Unstable', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '41900', 'item_name': \"The Bard's Tale\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '240760', 'item_name': 'Wasteland 2', 'playtime_forever': 710, 'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = set()\n","\n"," if len(data_dict[\"items\"]) > 0:\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZnLJTSvOm6tH","executionInfo":{"status":"ok","timestamp":1735800052537,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"cba8565b-368a-4417-a15f-188b93e1f66b"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"KwK2r2ospM7p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800058131,"user_tz":-420,"elapsed":5596,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"307a91cd-c88a-451a-e618-26bb16e52883"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"9HG4u_54plVH","executionInfo":{"status":"ok","timestamp":1735479680885,"user_tz":-420,"elapsed":730,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a732e27-e0bc-41f3-8417-703fc4de328b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: From data sentiment_counts i want to sorted from very positive to very negative\n","\n","import operator\n","\n","sorted_sentiment_counts = dict(sorted(sentiment_counts.items(), key=operator.itemgetter(1), reverse=True))\n","\n","sentiments, counts = zip(*sorted_sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary (Sorted)\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"sjR-gufgppmg","executionInfo":{"status":"ok","timestamp":1735479926795,"user_tz":-420,"elapsed":948,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e8acb122-9e3e-4b2d-fd64-15bfc992059f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["def generate_sentiment_value(sentimen_str):\n"," if sentimen_str == 'Overwhelmingly Positive':\n"," return 1.00\n"," elif sentimen_str == 'Very Positive':\n"," return 0.80\n"," elif sentimen_str == 'Positive':\n"," return 0.60\n"," elif sentimen_str == 'Mostly Positive':\n"," return 0.40\n"," elif sentimen_str == '9 user reviews':\n"," return 0.25\n"," elif sentimen_str == '8 user reviews':\n"," return 0.20\n"," elif sentimen_str == '7 user reviews':\n"," return 0.15\n"," elif sentimen_str == '6 user reviews':\n"," return 0.10\n"," elif sentimen_str == 'Mixed':\n"," return 0.5\n"," elif sentimen_str == '5 user reviews':\n"," return -0.5\n"," elif sentimen_str == '4 user reviews':\n"," return -0.10\n"," elif sentimen_str == '3 user reviews':\n"," return -0.15\n"," elif sentimen_str == '2 user reviews':\n"," return -0.20\n"," elif sentimen_str == '1 user review':\n"," return -0.25\n"," elif sentimen_str == 'Mostly Negative':\n"," return -0.40\n"," elif sentimen_str == 'Negative':\n"," return -0.60\n"," elif sentimen_str == 'Very Negative':\n"," return -0.85\n"," elif sentimen_str == 'Overwhelmingly Negative':\n"," return -1.00\n"," else:\n"," return 0.00"],"metadata":{"id":"Pd-DAKhQtwcR","executionInfo":{"status":"ok","timestamp":1735798610676,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n1KqBF9ifNWl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735483038727,"user_tz":-420,"elapsed":200148,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b3348ae6-1bbd-4ff3-8c76-ef6b88f42656"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '1'}\n"]}],"source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_logreg.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"]},{"cell_type":"code","source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_logreg.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z8x2uLIoqBQw","executionInfo":{"status":"ok","timestamp":1735798802171,"user_tz":-420,"elapsed":191497,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e2ac0637-5d33-479d-a5d5-cdc6f08258ca"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," count = len(data_dict['rec_bundles'])\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nz3SqNvj-F7","executionInfo":{"status":"ok","timestamp":1735798812899,"user_tz":-420,"elapsed":1844,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3cd28fde-55d6-4584-df5e-4b64e7074da1"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz'\n","Count: 138\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"KOOpGM97TFV8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Evaluate\n"],"metadata":{"id":"q1DFXWFOCsP0"}},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"],"metadata":{"id":"1M0JQWwPEAK5","executionInfo":{"status":"ok","timestamp":1735800058131,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit_ratios = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," hits = 0\n"," if user_id in user_item_interactions:\n"," user_items = set(user_item_interactions[user_id].keys())\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," hits += len(user_items.intersection(bundle_items))\n","\n"," hit_ratio = hits / top_n if top_n > 0 else 0 # Avoid division by zero\n"," hit_ratios.append({\"user_id\": user_id, \"top_n\": top_n, \"hit_ratio\": hit_ratio})\n"," return hit_ratios"],"metadata":{"id":"OBktUkbTEm5u","executionInfo":{"status":"ok","timestamp":1735800058131,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate diversity from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and diversity score.\n"," \"\"\"\n"," diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," total_items = 0\n"," unique_items = set()\n","\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," total_items += len(bundle_items)\n"," unique_items.update(bundle_items)\n","\n"," diversity = len(unique_items) / total_items if total_items > 0 else 0\n"," diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"diversity\": diversity})\n"," return diversity_scores"],"metadata":{"id":"Y9x5v5iNFzlZ","executionInfo":{"status":"ok","timestamp":1735800058131,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate inter_diversity from top_bundles_results by checkking how similar for each bundle\n","\n","import json\n","import gzip\n","import ast\n","import matplotlib.pyplot as plt\n","import operator\n","import pandas as pd\n","\n","# ... (Your existing code)\n","\n","def calculate_inter_diversity(top_bundles_results, bundle_data):\n"," \"\"\"\n"," Calculates the inter-diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and inter-diversity score.\n"," \"\"\"\n"," inter_diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," jaccard_similarities = []\n"," bundle_ids = list(top_bundles.keys())\n"," for i in range(len(bundle_ids)):\n"," for j in range(i + 1, len(bundle_ids)):\n"," bundle1_id = bundle_ids[i]\n"," bundle2_id = bundle_ids[j]\n"," if bundle1_id in bundle_data and bundle2_id in bundle_data:\n"," bundle1_items = bundle_data[bundle1_id]\n"," bundle2_items = bundle_data[bundle2_id]\n"," intersection = len(bundle1_items.intersection(bundle2_items))\n"," union = len(bundle1_items.union(bundle2_items))\n"," jaccard_similarity = intersection / union if union > 0 else 0\n"," jaccard_similarities.append(1 - jaccard_similarity) # 1-similarity to represent diversity\n","\n"," inter_diversity = sum(jaccard_similarities) / len(jaccard_similarities) if jaccard_similarities else 0\n"," inter_diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"inter_diversity\": inter_diversity})\n"," return inter_diversity_scores"],"metadata":{"id":"As-qpdgrGIDw","executionInfo":{"status":"ok","timestamp":1735800058534,"user_tz":-420,"elapsed":405,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"W5jfzwYKD21C","executionInfo":{"status":"ok","timestamp":1735524259175,"user_tz":-420,"elapsed":16,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"25ba9694-af66-435e-e371-d2fe8ae2fbc3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'bundle_final_price': '$4.77',\n"," 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page',\n"," 'bundle_price': '$5.97',\n"," 'bundle_name': 'The Ninjahtic Series',\n"," 'bundle_id': '594',\n"," 'items': [{'genre': 'Action, Adventure, Indie',\n"," 'item_id': '385230',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/385230',\n"," 'item_name': 'Ninjahtic'},\n"," {'genre': 'Action, Adventure, Indie',\n"," 'item_id': '387880',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/387880',\n"," 'item_name': 'Ninjahtic Mind Tricks'},\n"," {'genre': 'Action, Indie',\n"," 'item_id': '454130',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/454130',\n"," 'item_name': 'Nil-Ninjahtic: Ronin'}],\n"," 'bundle_discount': '20%'}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jn67YIwICr93","executionInfo":{"status":"ok","timestamp":1735524292845,"user_tz":-420,"elapsed":33679,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"495b86f9-fa0e-4c31-cc44-2b957ee6ae84","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 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Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict[\"rec_bundles\"]) > 0:\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_fqAPWp1viKC","executionInfo":{"status":"ok","timestamp":1735800091648,"user_tz":-420,"elapsed":33116,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a0dc3e4-465c-469c-f9ac-c5f0fa91fd79"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 9881\n","4908 / 9881\n","4909 / 9881\n","4910 / 9881\n","4911 / 9881\n","4912 / 9881\n","4913 / 9881\n","4914 / 9881\n","4915 / 9881\n","4916 / 9881\n","4917 / 9881\n","4918 / 9881\n","4919 / 9881\n","4920 / 9881\n","4921 / 9881\n","4922 / 9881\n","4923 / 9881\n","4924 / 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Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz'\n","Count: 9875\n"]}]},{"cell_type":"code","source":["# prompt: Calculate average average_hit_ratio\n","\n","import pandas as pd\n","\n","def calculate_average_metrics(metrics_dict):\n"," \"\"\"Calculates the average of metrics across users.\n","\n"," Args:\n"," metrics_dict: A dictionary where keys are user IDs and values are lists of dictionaries,\n"," each containing a 'top_n' and a metric value.\n","\n"," Returns:\n"," A pandas DataFrame with the average metrics for each 'top_n'.\n"," \"\"\"\n","\n"," all_metrics = []\n"," for user_id, user_metrics in metrics_dict.items():\n"," all_metrics.extend(user_metrics)\n","\n"," df = pd.DataFrame(all_metrics)\n","\n"," numeric_cols = df.select_dtypes(include=['number']).columns\n"," return df.groupby('top_n')[numeric_cols].mean()\n","\n","# Calculate and print the average hit ratio\n","average_hit_ratio_df = calculate_average_metrics(average_hit_ratio)\n","print(\"Average Hit Ratio:\")\n","print(average_hit_ratio_df)\n","\n","# Calculate and print the average intra-list diversity\n","average_intra_diversity_df = calculate_average_metrics(average_intra_diversity)\n","print(\"\\nAverage Intra-list Diversity:\")\n","print(average_intra_diversity_df)\n","\n","# Calculate and print the average inter-list diversity\n","average_inter_diversity_df = calculate_average_metrics(average_inter_diversity)\n","print(\"\\nAverage Inter-list Diversity:\")\n","average_inter_diversity_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":501},"id":"r7-DgF7QKIJB","executionInfo":{"status":"ok","timestamp":1735800092164,"user_tz":-420,"elapsed":519,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"504b7ef9-9510-415b-e855-d15f88ce5c83"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Hit Ratio:\n"," top_n hit_ratio\n","top_n \n","5 5.0 0.799575\n","10 10.0 0.935883\n","15 15.0 0.703434\n","20 20.0 0.564462\n","\n","Average Intra-list Diversity:\n"," top_n diversity\n","top_n \n","5 5.0 0.999972\n","10 10.0 0.999895\n","15 15.0 0.963327\n","20 20.0 0.967618\n","\n","Average Inter-list Diversity:\n"]},{"output_type":"execute_result","data":{"text/plain":[" top_n inter_diversity\n","top_n \n","5 5.0 0.998265\n","10 10.0 0.998226\n","15 15.0 0.990652\n","20 20.0 0.993865"],"text/html":["\n","
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top_ninter_diversity
top_n
55.00.998265
1010.00.998226
1515.00.990652
2020.00.993865
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top_nhit_ratiointra_diversityinter_diversity
050.7995750.9999720.998265
1100.9358830.9998950.998226
2150.7034340.9633270.990652
3200.5644620.9676180.993865
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Adjust figsize as needed\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_hit_ratio_df['hit_ratio'].idxmax()\n"," top_n_values = average_hit_ratio_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Hit Ratio ---\n"," axes[0].bar(average_hit_ratio_df.index, average_hit_ratio_df['hit_ratio'], width=4.0, color=colors)\n"," axes[0].set_xlabel(\"Top N Recommendations\")\n"," axes[0].set_ylabel(\"Average Hit Ratio\")\n"," axes[0].set_title(\"Average Hit Ratio for Different Top N Values\")\n"," axes[0].set_xticks(average_hit_ratio_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_intra_diversity_df['diversity'].idxmax()\n"," top_n_values = average_intra_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Intra-list Diversity ---\n"," axes[1].bar(average_intra_diversity_df.index, average_intra_diversity_df['diversity'], width=4.0, color=colors)\n"," axes[1].set_xlabel(\"Top N Recommendations\")\n"," axes[1].set_ylabel(\"Average Intra-list Diversity\")\n"," axes[1].set_title(\"Average Intra-list Diversity for Different Top N Values\")\n"," axes[1].set_xticks(average_intra_diversity_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_inter_diversity_df['inter_diversity'].idxmax()\n"," top_n_values = average_inter_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Inter-list Diversity ---\n"," axes[2].bar(average_inter_diversity_df.index, average_inter_diversity_df['inter_diversity'], width=4.0, color=colors)\n"," axes[2].set_xlabel(\"Top N Recommendations\")\n"," axes[2].set_ylabel(\"Average Inter-list Diversity\")\n"," axes[2].set_title(\"Average Inter-list Diversity for Different Top N Values\")\n"," axes[2].set_xticks(average_inter_diversity_df.index)\n","\n"," plt.tight_layout() # Adjust layout to prevent overlapping\n"," plt.show()\n","\n","plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H53yAPjpKxgM","executionInfo":{"status":"ok","timestamp":1735524294242,"user_tz":-420,"elapsed":842,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"62bcc23a-18fc-47bd-e1b5-989ed28bc02e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Source Code/5. Reranking Algorithm NB.ipynb b/Source Code/5. Reranking Algorithm NB.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d4ca29e55b5e81f0021fa97868a13574b98fd361 --- /dev/null +++ b/Source Code/5. Reranking Algorithm NB.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1hkVigVK6lDMH9vA4oXFeoKB25mlOmfPa","timestamp":1735482933567},{"file_id":"1e7RrFWyvPr-7xIhv-HwCgtg_buMpn5Ew","timestamp":1735482820148},{"file_id":"1VDzeZyw5dQEni3I1oSoABOBF7dee8oGv","timestamp":1735482701187},{"file_id":"1ZYogLNhD2G2-R5S9dh-e0xpAtnl2-x3H","timestamp":1735482594838},{"file_id":"1FG7ZKNWvY9NzttqgpQZ3lrCo4bBNQfa0","timestamp":1735482435351}],"mount_file_id":"1kMGmRq2b5xIY6XeC3qeMBuBUCw8ktv4E","authorship_tag":"ABX9TyOdKNKwNhNzpQk6bsYbt+Sf"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Reranking Applying\n","\n"],"metadata":{"id":"M5ZxhHaMSt4J"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"],"metadata":{"id":"ndVraj2XhD0Y","executionInfo":{"status":"ok","timestamp":1735800052192,"user_tz":-420,"elapsed":324,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = {}\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items[item[\"item_id\"]] = item[\"playtime_forever\"]\n","\n"," user_item_interactions[data_dict['user_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"yPu3n3l6hRLA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800096099,"user_tz":-420,"elapsed":43567,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"66a1603e-1944-4532-c96d-1ca7fd86b7a8"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - 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{'item_id': '252490', 'item_name': 'Rust', 'playtime_forever': 3483, 'playtime_2weeks': 0}, {'item_id': '252950', 'item_name': 'Rocket League', 'playtime_forever': 915, 'playtime_2weeks': 0}, {'item_id': '241540', 'item_name': 'State of Decay', 'playtime_forever': 2099, 'playtime_2weeks': 0}, {'item_id': '237890', 'item_name': 'Agarest: Generations of War', 'playtime_forever': 135, 'playtime_2weeks': 0}, {'item_id': '242050', 'item_name': \"Assassin's Creed IV Black Flag\", 'playtime_forever': 6221, 'playtime_2weeks': 0}, {'item_id': '234530', 'item_name': 'War of the Vikings', 'playtime_forever': 2509, 'playtime_2weeks': 0}, {'item_id': '257350', 'item_name': \"Baldur's Gate II: Enhanced Edition\", 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '243470', 'item_name': 'Watch_Dogs', 'playtime_forever': 2043, 'playtime_2weeks': 0}, {'item_id': '247730', 'item_name': 'Nether', 'playtime_forever': 3114, 'playtime_2weeks': 0}, {'item_id': '260730', 'item_name': 'Desperados - Wanted Dead or Alive', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261030', 'item_name': 'The Walking Dead: Season Two', 'playtime_forever': 637, 'playtime_2weeks': 0}, {'item_id': '242700', 'item_name': 'Injustice: Gods Among Us Ultimate Edition', 'playtime_forever': 368, 'playtime_2weeks': 0}, {'item_id': '262060', 'item_name': 'Darkest Dungeon', 'playtime_forever': 332, 'playtime_2weeks': 0}, {'item_id': '263440', 'item_name': 'The Stomping Land', 'playtime_forever': 142, 'playtime_2weeks': 0}, {'item_id': '108600', 'item_name': 'Project Zomboid', 'playtime_forever': 160, 'playtime_2weeks': 0}, {'item_id': '211820', 'item_name': 'Starbound', 'playtime_forever': 137, 'playtime_2weeks': 0}, {'item_id': '367540', 'item_name': 'Starbound - Unstable', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '41900', 'item_name': \"The Bard's Tale\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '240760', 'item_name': 'Wasteland 2', 'playtime_forever': 710, 'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = set()\n","\n"," if len(data_dict[\"items\"]) > 0:\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"ZnLJTSvOm6tH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800097267,"user_tz":-420,"elapsed":1170,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"415a142e-b87a-47df-a8fa-00e5d78010a5"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"KwK2r2ospM7p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800103908,"user_tz":-420,"elapsed":6643,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"fae14b67-53bd-48f7-9d92-4ad5ef68a1e7"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"9HG4u_54plVH","executionInfo":{"status":"ok","timestamp":1735479680885,"user_tz":-420,"elapsed":730,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a732e27-e0bc-41f3-8417-703fc4de328b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: From data sentiment_counts i want to sorted from very positive to very negative\n","\n","import operator\n","\n","sorted_sentiment_counts = dict(sorted(sentiment_counts.items(), key=operator.itemgetter(1), reverse=True))\n","\n","sentiments, counts = zip(*sorted_sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary (Sorted)\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"sjR-gufgppmg","executionInfo":{"status":"ok","timestamp":1735479926795,"user_tz":-420,"elapsed":948,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e8acb122-9e3e-4b2d-fd64-15bfc992059f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["def generate_sentiment_value(sentimen_str):\n"," if sentimen_str == 'Overwhelmingly Positive':\n"," return 1.00\n"," elif sentimen_str == 'Very Positive':\n"," return 0.80\n"," elif sentimen_str == 'Positive':\n"," return 0.60\n"," elif sentimen_str == 'Mostly Positive':\n"," return 0.40\n"," elif sentimen_str == '9 user reviews':\n"," return 0.25\n"," elif sentimen_str == '8 user reviews':\n"," return 0.20\n"," elif sentimen_str == '7 user reviews':\n"," return 0.15\n"," elif sentimen_str == '6 user reviews':\n"," return 0.10\n"," elif sentimen_str == 'Mixed':\n"," return 0.5\n"," elif sentimen_str == '5 user reviews':\n"," return -0.5\n"," elif sentimen_str == '4 user reviews':\n"," return -0.10\n"," elif sentimen_str == '3 user reviews':\n"," return -0.15\n"," elif sentimen_str == '2 user reviews':\n"," return -0.20\n"," elif sentimen_str == '1 user review':\n"," return -0.25\n"," elif sentimen_str == 'Mostly Negative':\n"," return -0.40\n"," elif sentimen_str == 'Negative':\n"," return -0.60\n"," elif sentimen_str == 'Very Negative':\n"," return -0.85\n"," elif sentimen_str == 'Overwhelmingly Negative':\n"," return -1.00\n"," else:\n"," return 0.00"],"metadata":{"id":"Pd-DAKhQtwcR","executionInfo":{"status":"ok","timestamp":1735798755921,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n1KqBF9ifNWl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735483194311,"user_tz":-420,"elapsed":144212,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"422e21b0-b64c-49c2-ecde-92bfba656974"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}],"source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_bnb.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"]},{"cell_type":"code","source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_bnb.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z4rNLk--qnaL","executionInfo":{"status":"ok","timestamp":1735798953857,"user_tz":-420,"elapsed":197939,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"cc72a2d8-560a-4a68-ca8c-3172f854628c"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," count = len(data_dict['rec_bundles'])\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nz3SqNvj-F7","executionInfo":{"status":"ok","timestamp":1735798970817,"user_tz":-420,"elapsed":1978,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"be15dad9-a17c-438b-dff6-ae1da1937d2f"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'\n","Count: 102\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"PIe5z_pHTHh4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Evaluate\n"],"metadata":{"id":"q1DFXWFOCsP0"}},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"],"metadata":{"id":"1M0JQWwPEAK5","executionInfo":{"status":"ok","timestamp":1735800103908,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit_ratios = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," hits = 0\n"," if user_id in user_item_interactions:\n"," user_items = set(user_item_interactions[user_id].keys())\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," hits += len(user_items.intersection(bundle_items))\n","\n"," hit_ratio = hits / top_n if top_n > 0 else 0 # Avoid division by zero\n"," hit_ratios.append({\"user_id\": user_id, \"top_n\": top_n, \"hit_ratio\": hit_ratio})\n"," return hit_ratios"],"metadata":{"id":"OBktUkbTEm5u","executionInfo":{"status":"ok","timestamp":1735800103908,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate diversity from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and diversity score.\n"," \"\"\"\n"," diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," total_items = 0\n"," unique_items = set()\n","\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," total_items += len(bundle_items)\n"," unique_items.update(bundle_items)\n","\n"," diversity = len(unique_items) / total_items if total_items > 0 else 0\n"," diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"diversity\": diversity})\n"," return diversity_scores"],"metadata":{"id":"Y9x5v5iNFzlZ","executionInfo":{"status":"ok","timestamp":1735800103908,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate inter_diversity from top_bundles_results by checkking how similar for each bundle\n","\n","import json\n","import gzip\n","import ast\n","import matplotlib.pyplot as plt\n","import operator\n","import pandas as pd\n","\n","# ... (Your existing code)\n","\n","def calculate_inter_diversity(top_bundles_results, bundle_data):\n"," \"\"\"\n"," Calculates the inter-diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and inter-diversity score.\n"," \"\"\"\n"," inter_diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," jaccard_similarities = []\n"," bundle_ids = list(top_bundles.keys())\n"," for i in range(len(bundle_ids)):\n"," for j in range(i + 1, len(bundle_ids)):\n"," bundle1_id = bundle_ids[i]\n"," bundle2_id = bundle_ids[j]\n"," if bundle1_id in bundle_data and bundle2_id in bundle_data:\n"," bundle1_items = bundle_data[bundle1_id]\n"," bundle2_items = bundle_data[bundle2_id]\n"," intersection = len(bundle1_items.intersection(bundle2_items))\n"," union = len(bundle1_items.union(bundle2_items))\n"," jaccard_similarity = intersection / union if union > 0 else 0\n"," jaccard_similarities.append(1 - jaccard_similarity) # 1-similarity to represent diversity\n","\n"," inter_diversity = sum(jaccard_similarities) / len(jaccard_similarities) if jaccard_similarities else 0\n"," inter_diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"inter_diversity\": inter_diversity})\n"," return inter_diversity_scores"],"metadata":{"id":"As-qpdgrGIDw","executionInfo":{"status":"ok","timestamp":1735800104199,"user_tz":-420,"elapsed":293,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"W5jfzwYKD21C","executionInfo":{"status":"ok","timestamp":1735524274408,"user_tz":-420,"elapsed":15,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"5ad695af-d8cd-44cc-dcd0-6dfb8579699a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'bundle_final_price': '$4.77',\n"," 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page',\n"," 'bundle_price': '$5.97',\n"," 'bundle_name': 'The Ninjahtic Series',\n"," 'bundle_id': '594',\n"," 'items': [{'genre': 'Action, Adventure, Indie',\n"," 'item_id': '385230',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/385230',\n"," 'item_name': 'Ninjahtic'},\n"," {'genre': 'Action, Adventure, Indie',\n"," 'item_id': '387880',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/387880',\n"," 'item_name': 'Ninjahtic Mind Tricks'},\n"," {'genre': 'Action, Indie',\n"," 'item_id': '454130',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/454130',\n"," 'item_name': 'Nil-Ninjahtic: Ronin'}],\n"," 'bundle_discount': '20%'}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jn67YIwICr93","executionInfo":{"status":"ok","timestamp":1735524293360,"user_tz":-420,"elapsed":18960,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9fb03db6-4987-4648-a5cb-36de975a695b","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 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Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict[\"rec_bundles\"]) > 0:\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3YsCJZHPvyVH","executionInfo":{"status":"ok","timestamp":1735800133970,"user_tz":-420,"elapsed":29772,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"fa9039dd-8ef9-415f-cc79-5581a1271a94"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 9881\n","4908 / 9881\n","4909 / 9881\n","4910 / 9881\n","4911 / 9881\n","4912 / 9881\n","4913 / 9881\n","4914 / 9881\n","4915 / 9881\n","4916 / 9881\n","4917 / 9881\n","4918 / 9881\n","4919 / 9881\n","4920 / 9881\n","4921 / 9881\n","4922 / 9881\n","4923 / 9881\n","4924 / 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Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'\n","Count: 9875\n"]}]},{"cell_type":"code","source":["# prompt: Calculate average average_hit_ratio\n","\n","import pandas as pd\n","\n","def calculate_average_metrics(metrics_dict):\n"," \"\"\"Calculates the average of metrics across users.\n","\n"," Args:\n"," metrics_dict: A dictionary where keys are user IDs and values are lists of dictionaries,\n"," each containing a 'top_n' and a metric value.\n","\n"," Returns:\n"," A pandas DataFrame with the average metrics for each 'top_n'.\n"," \"\"\"\n","\n"," all_metrics = []\n"," for user_id, user_metrics in metrics_dict.items():\n"," all_metrics.extend(user_metrics)\n","\n"," df = pd.DataFrame(all_metrics)\n","\n"," numeric_cols = df.select_dtypes(include=['number']).columns\n"," return df.groupby('top_n')[numeric_cols].mean()\n","\n","# Calculate and print the average hit ratio\n","average_hit_ratio_df = calculate_average_metrics(average_hit_ratio)\n","print(\"Average Hit Ratio:\")\n","print(average_hit_ratio_df)\n","\n","# Calculate and print the average intra-list diversity\n","average_intra_diversity_df = calculate_average_metrics(average_intra_diversity)\n","print(\"\\nAverage Intra-list Diversity:\")\n","print(average_intra_diversity_df)\n","\n","# Calculate and print the average inter-list diversity\n","average_inter_diversity_df = calculate_average_metrics(average_inter_diversity)\n","print(\"\\nAverage Inter-list Diversity:\")\n","average_inter_diversity_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":501},"id":"r7-DgF7QKIJB","executionInfo":{"status":"ok","timestamp":1735800134257,"user_tz":-420,"elapsed":289,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b6728df6-5316-49c1-9505-11bc7166d58a"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Hit Ratio:\n"," top_n hit_ratio\n","top_n \n","5 5.0 0.850645\n","10 10.0 0.928798\n","15 15.0 0.690492\n","20 20.0 0.566889\n","\n","Average Intra-list Diversity:\n"," top_n diversity\n","top_n \n","5 5.0 0.999315\n","10 10.0 0.994905\n","15 15.0 0.956109\n","20 20.0 0.943811\n","\n","Average Inter-list Diversity:\n"]},{"output_type":"execute_result","data":{"text/plain":[" top_n inter_diversity\n","top_n \n","5 5.0 0.997160\n","10 10.0 0.996431\n","15 15.0 0.990248\n","20 20.0 0.991499"],"text/html":["\n","
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top_ninter_diversity
top_n
55.00.997160
1010.00.996431
1515.00.990248
2020.00.991499
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top_nhit_ratiointra_diversityinter_diversity
050.8506450.9993150.997160
1100.9287980.9949050.996431
2150.6904920.9561090.990248
3200.5668890.9438110.991499
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Adjust figsize as needed\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_hit_ratio_df['hit_ratio'].idxmax()\n"," top_n_values = average_hit_ratio_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Hit Ratio ---\n"," axes[0].bar(average_hit_ratio_df.index, average_hit_ratio_df['hit_ratio'], width=4.0, color=colors)\n"," axes[0].set_xlabel(\"Top N Recommendations\")\n"," axes[0].set_ylabel(\"Average Hit Ratio\")\n"," axes[0].set_title(\"Average Hit Ratio for Different Top N Values\")\n"," axes[0].set_xticks(average_hit_ratio_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_intra_diversity_df['diversity'].idxmax()\n"," top_n_values = average_intra_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Intra-list Diversity ---\n"," axes[1].bar(average_intra_diversity_df.index, average_intra_diversity_df['diversity'], width=4.0, color=colors)\n"," axes[1].set_xlabel(\"Top N Recommendations\")\n"," axes[1].set_ylabel(\"Average Intra-list Diversity\")\n"," axes[1].set_title(\"Average Intra-list Diversity for Different Top N Values\")\n"," axes[1].set_xticks(average_intra_diversity_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_inter_diversity_df['inter_diversity'].idxmax()\n"," top_n_values = average_inter_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Inter-list Diversity ---\n"," axes[2].bar(average_inter_diversity_df.index, average_inter_diversity_df['inter_diversity'], width=4.0, color=colors)\n"," axes[2].set_xlabel(\"Top N Recommendations\")\n"," axes[2].set_ylabel(\"Average Inter-list Diversity\")\n"," axes[2].set_title(\"Average Inter-list Diversity for Different Top N Values\")\n"," axes[2].set_xticks(average_inter_diversity_df.index)\n","\n"," plt.tight_layout() # Adjust layout to prevent overlapping\n"," plt.show()\n","\n","plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H53yAPjpKxgM","executionInfo":{"status":"ok","timestamp":1735524295076,"user_tz":-420,"elapsed":1091,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7ffff1ee-81b8-414f-c5b8-921464fa09b6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Source Code/5. Reranking Algorithm SGD.ipynb b/Source Code/5. Reranking Algorithm SGD.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..efe676aa093bae2c9dba0f10d5b5ebb045de92a1 --- /dev/null +++ b/Source Code/5. Reranking Algorithm SGD.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1e7RrFWyvPr-7xIhv-HwCgtg_buMpn5Ew","timestamp":1735482820148},{"file_id":"1VDzeZyw5dQEni3I1oSoABOBF7dee8oGv","timestamp":1735482701187},{"file_id":"1ZYogLNhD2G2-R5S9dh-e0xpAtnl2-x3H","timestamp":1735482594838},{"file_id":"1FG7ZKNWvY9NzttqgpQZ3lrCo4bBNQfa0","timestamp":1735482435351}],"mount_file_id":"1hkVigVK6lDMH9vA4oXFeoKB25mlOmfPa","authorship_tag":"ABX9TyPKcmq04ZPxRtgl8csERWwN"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Reranking Applying\n","\n"],"metadata":{"id":"Y-MSLq08SvOf"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"],"metadata":{"id":"ndVraj2XhD0Y","executionInfo":{"status":"ok","timestamp":1735800116683,"user_tz":-420,"elapsed":481,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = {}\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items[item[\"item_id\"]] = item[\"playtime_forever\"]\n","\n"," user_item_interactions[data_dict['user_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"yPu3n3l6hRLA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800171901,"user_tz":-420,"elapsed":54762,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8b0300c7-9c26-422b-a61a-48ef157a9344"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - 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{'item_id': '252490', 'item_name': 'Rust', 'playtime_forever': 3483, 'playtime_2weeks': 0}, {'item_id': '252950', 'item_name': 'Rocket League', 'playtime_forever': 915, 'playtime_2weeks': 0}, {'item_id': '241540', 'item_name': 'State of Decay', 'playtime_forever': 2099, 'playtime_2weeks': 0}, {'item_id': '237890', 'item_name': 'Agarest: Generations of War', 'playtime_forever': 135, 'playtime_2weeks': 0}, {'item_id': '242050', 'item_name': \"Assassin's Creed IV Black Flag\", 'playtime_forever': 6221, 'playtime_2weeks': 0}, {'item_id': '234530', 'item_name': 'War of the Vikings', 'playtime_forever': 2509, 'playtime_2weeks': 0}, {'item_id': '257350', 'item_name': \"Baldur's Gate II: Enhanced Edition\", 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '243470', 'item_name': 'Watch_Dogs', 'playtime_forever': 2043, 'playtime_2weeks': 0}, {'item_id': '247730', 'item_name': 'Nether', 'playtime_forever': 3114, 'playtime_2weeks': 0}, {'item_id': '260730', 'item_name': 'Desperados - Wanted Dead or Alive', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261030', 'item_name': 'The Walking Dead: Season Two', 'playtime_forever': 637, 'playtime_2weeks': 0}, {'item_id': '242700', 'item_name': 'Injustice: Gods Among Us Ultimate Edition', 'playtime_forever': 368, 'playtime_2weeks': 0}, {'item_id': '262060', 'item_name': 'Darkest Dungeon', 'playtime_forever': 332, 'playtime_2weeks': 0}, {'item_id': '263440', 'item_name': 'The Stomping Land', 'playtime_forever': 142, 'playtime_2weeks': 0}, {'item_id': '108600', 'item_name': 'Project Zomboid', 'playtime_forever': 160, 'playtime_2weeks': 0}, {'item_id': '211820', 'item_name': 'Starbound', 'playtime_forever': 137, 'playtime_2weeks': 0}, {'item_id': '367540', 'item_name': 'Starbound - Unstable', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '41900', 'item_name': \"The Bard's Tale\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '240760', 'item_name': 'Wasteland 2', 'playtime_forever': 710, 'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = set()\n","\n"," if len(data_dict[\"items\"]) > 0:\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"ZnLJTSvOm6tH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800172198,"user_tz":-420,"elapsed":299,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0f4a7ac7-99d8-46da-8257-97cf85e91d67"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"id":"KwK2r2ospM7p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735800176882,"user_tz":-420,"elapsed":4685,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ab748590-77cb-418b-d010-ede17fa5383e"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"9HG4u_54plVH","executionInfo":{"status":"ok","timestamp":1735479680885,"user_tz":-420,"elapsed":730,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a732e27-e0bc-41f3-8417-703fc4de328b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: From data sentiment_counts i want to sorted from very positive to very negative\n","\n","import operator\n","\n","sorted_sentiment_counts = dict(sorted(sentiment_counts.items(), key=operator.itemgetter(1), reverse=True))\n","\n","sentiments, counts = zip(*sorted_sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary (Sorted)\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"sjR-gufgppmg","executionInfo":{"status":"ok","timestamp":1735479926795,"user_tz":-420,"elapsed":948,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e8acb122-9e3e-4b2d-fd64-15bfc992059f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["def generate_sentiment_value(sentimen_str):\n"," if sentimen_str == 'Overwhelmingly Positive':\n"," return 1.00\n"," elif sentimen_str == 'Very Positive':\n"," return 0.80\n"," elif sentimen_str == 'Positive':\n"," return 0.60\n"," elif sentimen_str == 'Mostly Positive':\n"," return 0.40\n"," elif sentimen_str == '9 user reviews':\n"," return 0.25\n"," elif sentimen_str == '8 user reviews':\n"," return 0.20\n"," elif sentimen_str == '7 user reviews':\n"," return 0.15\n"," elif sentimen_str == '6 user reviews':\n"," return 0.10\n"," elif sentimen_str == 'Mixed':\n"," return 0.5\n"," elif sentimen_str == '5 user reviews':\n"," return -0.5\n"," elif sentimen_str == '4 user reviews':\n"," return -0.10\n"," elif sentimen_str == '3 user reviews':\n"," return -0.15\n"," elif sentimen_str == '2 user reviews':\n"," return -0.20\n"," elif sentimen_str == '1 user review':\n"," return -0.25\n"," elif sentimen_str == 'Mostly Negative':\n"," return -0.40\n"," elif sentimen_str == 'Negative':\n"," return -0.60\n"," elif sentimen_str == 'Very Negative':\n"," return -0.85\n"," elif sentimen_str == 'Overwhelmingly Negative':\n"," return -1.00\n"," else:\n"," return 0.00"],"metadata":{"id":"Pd-DAKhQtwcR","executionInfo":{"status":"ok","timestamp":1735798787888,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n1KqBF9ifNWl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1735483116726,"user_tz":-420,"elapsed":167558,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"f3d64abe-f250-4867-f078-68345fb86511"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}],"source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_sgd.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_sgd.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"]},{"cell_type":"code","source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_sgd.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_sgd.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kSf3Stvdqu1m","executionInfo":{"status":"ok","timestamp":1735798980350,"user_tz":-420,"elapsed":192464,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d3a203e8-80b9-4ea0-a418-49337de4ffa6"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," count = len(data_dict['rec_bundles'])\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nz3SqNvj-F7","executionInfo":{"status":"ok","timestamp":1735798988907,"user_tz":-420,"elapsed":1953,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1b7aae22-923c-41eb-cdc6-70ce26dc6c88"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_sgd.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_sgd.json.gz'\n","Count: 130\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"TFglPf-ITJI9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Evaluate\n"],"metadata":{"id":"q1DFXWFOCsP0"}},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"],"metadata":{"id":"1M0JQWwPEAK5","executionInfo":{"status":"ok","timestamp":1735800176882,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit_ratios = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," hits = 0\n"," if user_id in user_item_interactions:\n"," user_items = set(user_item_interactions[user_id].keys())\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," hits += len(user_items.intersection(bundle_items))\n","\n"," hit_ratio = hits / top_n if top_n > 0 else 0 # Avoid division by zero\n"," hit_ratios.append({\"user_id\": user_id, \"top_n\": top_n, \"hit_ratio\": hit_ratio})\n"," return hit_ratios"],"metadata":{"id":"OBktUkbTEm5u","executionInfo":{"status":"ok","timestamp":1735800176882,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate diversity from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and diversity score.\n"," \"\"\"\n"," diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," total_items = 0\n"," unique_items = set()\n","\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," total_items += len(bundle_items)\n"," unique_items.update(bundle_items)\n","\n"," diversity = len(unique_items) / total_items if total_items > 0 else 0\n"," diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"diversity\": diversity})\n"," return diversity_scores"],"metadata":{"id":"Y9x5v5iNFzlZ","executionInfo":{"status":"ok","timestamp":1735800176882,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate inter_diversity from top_bundles_results by checkking how similar for each bundle\n","\n","import json\n","import gzip\n","import ast\n","import matplotlib.pyplot as plt\n","import operator\n","import pandas as pd\n","\n","# ... (Your existing code)\n","\n","def calculate_inter_diversity(top_bundles_results, bundle_data):\n"," \"\"\"\n"," Calculates the inter-diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and inter-diversity score.\n"," \"\"\"\n"," inter_diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," jaccard_similarities = []\n"," bundle_ids = list(top_bundles.keys())\n"," for i in range(len(bundle_ids)):\n"," for j in range(i + 1, len(bundle_ids)):\n"," bundle1_id = bundle_ids[i]\n"," bundle2_id = bundle_ids[j]\n"," if bundle1_id in bundle_data and bundle2_id in bundle_data:\n"," bundle1_items = bundle_data[bundle1_id]\n"," bundle2_items = bundle_data[bundle2_id]\n"," intersection = len(bundle1_items.intersection(bundle2_items))\n"," union = len(bundle1_items.union(bundle2_items))\n"," jaccard_similarity = intersection / union if union > 0 else 0\n"," jaccard_similarities.append(1 - jaccard_similarity) # 1-similarity to represent diversity\n","\n"," inter_diversity = sum(jaccard_similarities) / len(jaccard_similarities) if jaccard_similarities else 0\n"," inter_diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"inter_diversity\": inter_diversity})\n"," return inter_diversity_scores"],"metadata":{"id":"As-qpdgrGIDw","executionInfo":{"status":"ok","timestamp":1735800177380,"user_tz":-420,"elapsed":500,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["data_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"W5jfzwYKD21C","executionInfo":{"status":"ok","timestamp":1735524290762,"user_tz":-420,"elapsed":13,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a4e42bcc-f014-49c7-f558-92920a0e1db8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'bundle_final_price': '$4.77',\n"," 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page',\n"," 'bundle_price': '$5.97',\n"," 'bundle_name': 'The Ninjahtic Series',\n"," 'bundle_id': '594',\n"," 'items': [{'genre': 'Action, Adventure, Indie',\n"," 'item_id': '385230',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/385230',\n"," 'item_name': 'Ninjahtic'},\n"," {'genre': 'Action, Adventure, Indie',\n"," 'item_id': '387880',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/387880',\n"," 'item_name': 'Ninjahtic Mind Tricks'},\n"," {'genre': 'Action, Indie',\n"," 'item_id': '454130',\n"," 'discounted_price': '$1.99',\n"," 'item_url': 'http://store.steampowered.com/app/454130',\n"," 'item_name': 'Nil-Ninjahtic: Ronin'}],\n"," 'bundle_discount': '20%'}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_sgd.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"id":"jn67YIwICr93"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_sgd.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict[\"rec_bundles\"]) > 0:\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PlVDjnpiwBOe","executionInfo":{"status":"ok","timestamp":1735800208510,"user_tz":-420,"elapsed":31131,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0b1078c3-1fba-492f-e11c-500c56291a4c"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 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lists of dictionaries,\n"," each containing a 'top_n' and a metric value.\n","\n"," Returns:\n"," A pandas DataFrame with the average metrics for each 'top_n'.\n"," \"\"\"\n","\n"," all_metrics = []\n"," for user_id, user_metrics in metrics_dict.items():\n"," all_metrics.extend(user_metrics)\n","\n"," df = pd.DataFrame(all_metrics)\n","\n"," numeric_cols = df.select_dtypes(include=['number']).columns\n"," return df.groupby('top_n')[numeric_cols].mean()\n","\n","# Calculate and print the average hit ratio\n","average_hit_ratio_df = calculate_average_metrics(average_hit_ratio)\n","print(\"Average Hit Ratio:\")\n","print(average_hit_ratio_df)\n","\n","# Calculate and print the average intra-list diversity\n","average_intra_diversity_df = calculate_average_metrics(average_intra_diversity)\n","print(\"\\nAverage Intra-list Diversity:\")\n","print(average_intra_diversity_df)\n","\n","# Calculate and print the average inter-list diversity\n","average_inter_diversity_df = calculate_average_metrics(average_inter_diversity)\n","print(\"\\nAverage Inter-list Diversity:\")\n","average_inter_diversity_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":501},"id":"r7-DgF7QKIJB","executionInfo":{"status":"ok","timestamp":1735800209171,"user_tz":-420,"elapsed":664,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c6937f87-d1cc-4d2b-d69d-f1788e30a965"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Hit Ratio:\n"," top_n hit_ratio\n","top_n \n","5 5.0 0.798926\n","10 10.0 0.931415\n","15 15.0 0.700395\n","20 20.0 0.561868\n","\n","Average Intra-list Diversity:\n"," top_n diversity\n","top_n \n","5 5.0 0.999988\n","10 10.0 0.999887\n","15 15.0 0.963511\n","20 20.0 0.967506\n","\n","Average Inter-list Diversity:\n"]},{"output_type":"execute_result","data":{"text/plain":[" top_n inter_diversity\n","top_n \n","5 5.0 0.998071\n","10 10.0 0.998031\n","15 15.0 0.990497\n","20 20.0 0.993671"],"text/html":["\n","
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top_ninter_diversity
top_n
55.00.998071
1010.00.998031
1515.00.990497
2020.00.993671
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"average_inter_diversity_df","repr_error":"cannot insert top_n, already exists"}},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# prompt: combine average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df and make it colomn like top_n | hit_ratio | intra_diversity | inter_diversity just create from dictionary do not using merge function of pandas\n","\n","import pandas as pd\n","\n","def combine_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in average_hit_ratio_df.index:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': average_hit_ratio_df.loc[top_n, 'hit_ratio'],\n"," 'intra_diversity': average_intra_diversity_df.loc[top_n, 'diversity'],\n"," 'inter_diversity': average_inter_diversity_df.loc[top_n, 'inter_diversity']\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)\n","\n","# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)\n","combined_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"cdolqR3XOM3R","executionInfo":{"status":"ok","timestamp":1735800209171,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"13d2a46a-a5bf-4cf7-b051-e95ed41df551"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio intra_diversity inter_diversity\n","0 5 0.798926 0.999988 0.998071\n","1 10 0.931415 0.999887 0.998031\n","2 15 0.700395 0.963511 0.990497\n","3 20 0.561868 0.967506 0.993671"],"text/html":["\n","
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top_nhit_ratiointra_diversityinter_diversity
050.7989260.9999880.998071
1100.9314150.9998870.998031
2150.7003950.9635110.990497
3200.5618680.9675060.993671
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.15614720523295741,\n \"min\": 0.5618680984702664,\n \"max\": 0.9314152568128863,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9314152568128863,\n 0.5618680984702664,\n 0.7989261473001722\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"intra_diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.01994465687243573,\n \"min\": 0.9635108070778052,\n \"max\": 0.999988291167111,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9998869832895558,\n 0.9675056725420063,\n 0.999988291167111\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"inter_diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0036808583198750283,\n \"min\": 0.9904965385438248,\n \"max\": 0.9980710088715863,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9980307304438035,\n 0.9936708378111123,\n 0.9980710088715863\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# prompt: save combined_df as /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_native.csv\n","\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_sgd.csv', index=False)\n","combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns_sgd.csv', index=False)"],"metadata":{"id":"lfky5tCoPDIx","executionInfo":{"status":"ok","timestamp":1735800209171,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# prompt: generate bar chart for each average_hit_ratio_df, average_intra_diversity_df, and average_inter_diversity_df\n","# Make it dense space between bars, and change the color of bar for the best value\n","\n","# prompt: generate bar chart for each average_hit_ratio_df, average_intra_diversity_df, and average_inter_diversity_df\n","# Make it dense space between bars, and change the color of bar for the best value\n","\n","import matplotlib.pyplot as plt\n","\n","def plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df):\n"," \"\"\"Generates bar charts for average hit ratio, intra-diversity, and inter-diversity.\"\"\"\n","\n"," fig, axes = plt.subplots(3, 1, figsize=(8, 10)) # Adjust figsize as needed\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_hit_ratio_df['hit_ratio'].idxmax()\n"," top_n_values = average_hit_ratio_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Hit Ratio ---\n"," axes[0].bar(average_hit_ratio_df.index, average_hit_ratio_df['hit_ratio'], width=4.0, color=colors)\n"," axes[0].set_xlabel(\"Top N Recommendations\")\n"," axes[0].set_ylabel(\"Average Hit Ratio\")\n"," axes[0].set_title(\"Average Hit Ratio for Different Top N Values\")\n"," axes[0].set_xticks(average_hit_ratio_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_intra_diversity_df['diversity'].idxmax()\n"," top_n_values = average_intra_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Intra-list Diversity ---\n"," axes[1].bar(average_intra_diversity_df.index, average_intra_diversity_df['diversity'], width=4.0, color=colors)\n"," axes[1].set_xlabel(\"Top N Recommendations\")\n"," axes[1].set_ylabel(\"Average Intra-list Diversity\")\n"," axes[1].set_title(\"Average Intra-list Diversity for Different Top N Values\")\n"," axes[1].set_xticks(average_intra_diversity_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_inter_diversity_df['inter_diversity'].idxmax()\n"," top_n_values = average_inter_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Inter-list Diversity ---\n"," axes[2].bar(average_inter_diversity_df.index, average_inter_diversity_df['inter_diversity'], width=4.0, color=colors)\n"," axes[2].set_xlabel(\"Top N Recommendations\")\n"," axes[2].set_ylabel(\"Average Inter-list Diversity\")\n"," axes[2].set_title(\"Average Inter-list Diversity for Different Top N Values\")\n"," axes[2].set_xticks(average_inter_diversity_df.index)\n","\n"," plt.tight_layout() # Adjust layout to prevent overlapping\n"," plt.show()\n","\n","plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H53yAPjpKxgM","executionInfo":{"status":"ok","timestamp":1735524320405,"user_tz":-420,"elapsed":1010,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"59e0e36f-f368-41b9-a765-1e224b8f8f27"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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//792r17d4azyKQOgk0VEhKSbr1vv/1W7733nnbu3GnTH/nGvvpHjhyRi4tLmm3cPGvPqVOndP78eU2bNi3DaUJvjis9BQsWtJkpKFVWzjDz9NNPq0ePHkpKStJvv/2mESNG6PLly2kGMP/9998aMGCA1q9fn2YcQma6at3syJEjkqQyZcqkWVeuXDmtXr36jgYfHzlyJN3pjFO76hw5ckQVKlSwlmd0ftyJixcvSpLy5cvnlO3t379fkqxfFG92c3erXLlyqVixYmm2ER8fLz8/v3S3cfN5Wbx48TR1ChQokGY8hrMcOXJEpUuXTnO+3fh63ah06dJptvHggw9q4cKFdu23V69emjBhgoYMGaJly5bZ1bZDhw766quvtGzZMj333HP6+eefFR0dbdNt68qVKxo5cqRmzZqlY8eO2UxReyfvl/Rk9rOwbdu2+vTTT/XSSy+pX79+evTRR9WyZUu1bt36lhMVAP+LSCyAHG79+vU6ceKE5s+fr/nz56dZP3fuXD3++OOSpHbt2umTTz7RypUr1aJFCy1cuFBly5ZV5cqVrfVTUlJUsWJFjR8/Pt39BQYG2jxO79foH3/8Uc2bN1e9evU0efJkFS5cWG5ubpo1a5bmzZtn93NM/RXw+eeftw7OvVlqn/l7TbFixazJTJMmTVSwYEH16NFDDRs2VMuWLSVJ58+fV/369eXt7a1hw4apZMmS8vT01Pbt2/X222/n6F9BnXW1QpL++usvubq6Oi1ZST2uc+bMUUBAQJr1NyecHh4eab4opqSkyM/Pz2ayhBvd/KU0o5nGzC3u3ZATpV61GDJkiN1XLZ566in5+Pho3rx5eu655zRv3jy5urpax4FJ/13VnDVrlnr37q1atWrJx8dHFotF7dq1u+375cYfN26UnJxs8/pk9rMwd+7c2rRpkzZs2KAVK1Zo1apVWrBggRo1aqQ1a9Y4ZXY5IKcgsQByuLlz58rPz0+TJk1Ks27p0qX66quvNHXqVOXOnVv16tVT4cKFtWDBAtWtW1fr16+3DkROVbJkSf3xxx969NFHM/wDfDtLliyRp6enVq9ebXMfiFmzZtnUCwoKUkpKig4fPmzzS+mBAwds6hUqVEj58uVTcnJyulcc7pY7PR436tatmyZMmKABAwbomWeesc74c+bMGS1dutRmms0br0DZG0NQUJAkae/evWnW7dmzRwULFryjqVKDgoIy3OaN+3W2mJgY/fDDD6pVq5bTrlikDqD38/O74/OqZMmS+v7771WnTh2nJVHOOM9SBQUF6c8//1RKSopNUpTR65V6FedG+/btu+X9NDLSu3dvRUVFaejQoXZN8+vh4aHWrVvr888/V1xcnBYtWqRGjRrZJH+LFy9Wp06dNG7cOGvZ1atXrXdZv5UCBQqkW+/IkSMqUaKE9bE9n4UuLi569NFH9eijj2r8+PEaMWKE3n33XW3YsCFbP7OAu41rdEAOduXKFS1dulRPPfWUWrdunWbp0aOHLly4oOXLl0v6749f69at9c0332jOnDm6fv26TTcoSWrTpo2OHTum6dOnp7u/1H7Pt+Lq6iqLxWIzVWp0dHSaGaVS7+MwefJkm/KPPvoozfZatWqlJUuWpDv15alTp24bkzPkzZs3U19cbiVXrlx64403tHv3bmsXkdRfNG/81TopKSnNcUmNITNdPQoXLqwqVaros88+s4n5r7/+0po1a6z3grBXkyZNtHXrVm3ZssVadunSJU2bNk3BwcHpjrdx1NmzZ9W+fXslJyenSYQdER4eLm9vb40YMSLd6U0zc161adNGycnJGj58eJp1169fv6PzJTXhc/Rck/57vWJjY23GVl2/fl0fffSRvLy8VL9+fZv6X3/9tY4dO2Z9vHXrVv3666968skn7d536lWLZcuWaefOnXa17dChg65du6Zu3brp1KlTae5d4erqmuYqz0cffWTzmZORkiVL6pdfflFSUpK17Ntvv9XRo0dt6mX2s/Ds2bNp1lepUkWS0kxLC/yv44oFkIMtX75cFy5cUPPmzdNdX7NmTRUqVEhz5861JhBt27bVRx99pMGDB6tixYppprF84YUXtHDhQr3yyivasGGD6tSpo+TkZO3Zs0cLFy7U6tWrVa1atVvG1bRpU40fP15PPPGEnnvuOZ08eVKTJk1SqVKl9Oeff1rrhYaGqlWrVoqKitKZM2es083u27dPku0vt6NGjdKGDRsUFhamrl27qnz58jp79qy2b9+u77//Pt0/7s4WGhqqKVOm6L333lOpUqXk5+eXYf/8W4mIiNCgQYM0evRotWjRQrVr11aBAgXUqVMn9ezZUxaLRXPmzEm3e0xoaKgWLFigyMhIVa9eXV5eXmrWrFm6+xk7dqyefPJJ1apVS126dLFON+vj43NH9xmQpH79+unLL7/Uk08+qZ49e8rX11efffaZDh8+rCVLljjcp3zfvn364osvZIxRQkKC/vjjDy1atEgXL160nlPO4u3trSlTpuiFF17Qww8/rHbt2qlQoUKKiYnRihUrVKdOHX388ce33Eb9+vXVrVs3jRw5Ujt37tTjjz8uNzc37d+/X4sWLdLEiRPVunVru+KqUqWKXF1dNXr0aMXHx8vDw0ONGjXKcBzHrbz88sv65JNPFBERoW3btik4OFiLFy/WTz/9pKioqDRXf0qVKqW6devq1VdfVWJioqKiovTAAw/orbfesnvf0v+Ntfjjjz/sukJWv359FStWTMuWLVPu3Lmt3QZTPfXUU5ozZ458fHxUvnx5bdmyRd9//70eeOCB2277pZde0uLFi/XEE0+oTZs2OnjwoL744os0U0Bn9rNw2LBh2rRpk5o2baqgoCCdPHlSkydPVrFixWwmOQDuC9k2HxUAhzVr1sx4enqaS5cuZVgnIiLCuLm5WadpTUlJMYGBgUaSee+999Jtk5SUZEaPHm0eeugh4+HhYQoUKGBCQ0PN0KFDTXx8vLWebjE96IwZM0zp0qWNh4eHKVu2rJk1a5Z1ys8bXbp0yXTv3t34+voaLy8v06JFC7N3714jyYwaNcqmblxcnOnevbsJDAw0bm5uJiAgwDz66KNm2rRptz1WQUFBpmnTpumuu3GqzFTpTTcbGxtrmjZtavLly2ck3Xbq2VsdnyFDhthMg/nTTz+ZmjVrmty5c5siRYqYt956yzpF8I1TZV68eNE899xzJn/+/EaSdWrR9KZeNcaY77//3tSpU8fkzp3beHt7m2bNmpl//vnnlnHfLv6DBw+a1q1bm/z58xtPT09To0YN8+2339rUSe+YZmZ/qYuLi4vJnz+/qVq1qunVq1e6Uxo7Ot3sjbGGh4cbHx8f4+npaUqWLGkiIiLM77//bq1zu+lSp02bZkJDQ03u3LlNvnz5TMWKFc1bb71ljh8/bq2T0Tl48zSnxhgzffp0U6JECePq6mrX1LPpTUccFxdnOnfubAoWLGjc3d1NxYoV05wnqcdy7NixZty4cSYwMNB4eHiYRx55xDq9763c6vVOPfaZmW72Rm+++aaRZNq0aZNm3blz56zPycvLy4SHh5s9e/akmUo2velmjTFm3LhxpmjRosbDw8PUqVPH/P777+m+Dpn5LFy3bp15+umnTZEiRYy7u7spUqSIad++vdm3b59dzxf4X2Ax5n9sxBiAHG/nzp2qWrWqvvjiizRdIAA4X3R0tEJCQjR27Fj17ds3u8MBkEMxxgJAtrpy5UqasqioKLm4uNgMZAYAAPc2xlgAyFZjxozRtm3b1LBhQ+XKlUsrV67UypUr9fLLL6eZ2hYAANy7SCwAZKvatWtr7dq1Gj58uC5evKjixYtryJAhTp39BwAAZD3GWAAAAABwGGMsAAAAADjsvusKlZKSouPHjytfvnxOvbspAAAA8L/GGKMLFy6oSJEit71X0X2XWBw/fpwBoQAAAIAdjh49qmLFit2yzn2XWKTeZfTo0aPy9vbO5mgAAACAe1dCQoICAwOt36FvJVsTi02bNmns2LHatm2bTpw4oa+++kotWrS4ZZuNGzcqMjJSf//9twIDAzVgwABFRERkep+p3Z+8vb1JLAAAAIBMyMwQgmwdvH3p0iVVrlxZkyZNylT9w4cPq2nTpmrYsKF27typ3r1766WXXtLq1auzOFIAAAAAt5KtVyyefPJJPfnkk5muP3XqVIWEhGjcuHGSpHLlymnz5s2aMGGCwsPD022TmJioxMRE6+OEhATHggYAAACQRo6abnbLli1q3LixTVl4eLi2bNmSYZuRI0fKx8fHujBwGwAAAHC+HJVYxMbGyt/f36bM399fCQkJunLlSrpt+vfvr/j4eOty9OjRuxEqAAAAcF/5n58VysPDQx4eHtkdBgAAAPA/LUddsQgICFBcXJxNWVxcnLy9vZU7d+5sigoAAABAjkosatWqpXXr1tmUrV27VrVq1cqmiAAAAABI2ZxYXLx4UTt37tTOnTsl/Ted7M6dOxUTEyPpv/ERHTt2tNZ/5ZVXdOjQIb311lvas2ePJk+erIULF6pPnz7ZET4AAACA/y9bE4vff/9dVatWVdWqVSVJkZGRqlq1qgYNGiRJOnHihDXJkKSQkBCtWLFCa9euVeXKlTVu3Dh9+umnGU41CwAAAODusBhjTHYHcTclJCTIx8dH8fHx3HkbyCTL0NvfbRP3NzP4vvpTAgD3DXu+O+eoMRYAAAAA7k0kFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGEkFgAAAAAcRmIBAAAAwGG5sjuA+9WoHaezOwTkAP2qFszuEAAAADKFKxYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHJbticWkSZMUHBwsT09PhYWFaevWrbesHxUVpTJlyih37twKDAxUnz59dPXq1bsULQAAAID0ZGtisWDBAkVGRmrw4MHavn27KleurPDwcJ08eTLd+vPmzVO/fv00ePBg7d69WzNmzNCCBQv0zjvv3OXIAQAAANwoWxOL8ePHq2vXrurcubPKly+vqVOnKk+ePJo5c2a69X/++WfVqVNHzz33nIKDg/X444+rffv2t73KAQAAACBrZVtikZSUpG3btqlx48b/F4yLixo3bqwtW7ak26Z27dratm2bNZE4dOiQvvvuOzVp0iTD/SQmJiohIcFmAQAAAOBcubJrx6dPn1ZycrL8/f1tyv39/bVnz5502zz33HM6ffq06tatK2OMrl+/rldeeeWWXaFGjhypoUOHOjV2AAAAALayffC2PTZu3KgRI0Zo8uTJ2r59u5YuXaoVK1Zo+PDhGbbp37+/4uPjrcvRo0fvYsQAAADA/SHbrlgULFhQrq6uiouLsymPi4tTQEBAum0GDhyoF154QS+99JIkqWLFirp06ZJefvllvfvuu3JxSZsneXh4yMPDw/lPAAAAAIBVtl2xcHd3V2hoqNatW2ctS0lJ0bp161SrVq1021y+fDlN8uDq6ipJMsZkXbAAAAAAbinbrlhIUmRkpDp16qRq1aqpRo0aioqK0qVLl9S5c2dJUseOHVW0aFGNHDlSktSsWTONHz9eVatWVVhYmA4cOKCBAweqWbNm1gQDAAAAwN2XrYlF27ZtderUKQ0aNEixsbGqUqWKVq1aZR3QHRMTY3OFYsCAAbJYLBowYICOHTumQoUKqVmzZnr//fez6ykAAAAAkGQx91kfooSEBPn4+Cg+Pl7e3t7ZFseoHaezbd/IOfpVLZjdIUiSLEMt2R0C7nFm8H31pwQA7hv2fHfOUbNCAQAAALg3kVgAAAAAcBiJBQAAAACHkVgAAAAAcBiJBQAAAACH3dF0swcPHlRUVJR2794tSSpfvrx69eqlkiVLOjU4AAAAADmD3VcsVq9erfLly2vr1q2qVKmSKlWqpF9//VUPPfSQ1q5dmxUxAgAAALjH2X3Fol+/furTp49GjRqVpvztt9/WY4895rTgAAAAAOQMdl+x2L17t7p06ZKm/MUXX9Q///zjlKAAAAAA5Cx2JxaFChXSzp0705Tv3LlTfn5+zogJAAAAQA5jd1eorl276uWXX9ahQ4dUu3ZtSdJPP/2k0aNHKzIy0ukBAgAAALj32Z1YDBw4UPny5dO4cePUv39/SVKRIkU0ZMgQ9ezZ0+kBAgAAALj32Z1YWCwW9enTR3369NGFCxckSfny5XN6YAAA3IlRO05ndwi4x/WrWjC7QwD+J93RfSxSkVAAAAAAkDKZWDz88MNat26dChQooKpVq8pisWRYd/v27U4LDgAAAEDOkKnE4umnn5aHh4f1/7dKLAAAAADcfzKVWAwePNj6/yFDhmRVLAAAAAByKLvvY1GiRAmdOXMmTfn58+dVokQJpwQFAAAAIGexO7GIjo5WcnJymvLExET9+++/TgkKAAAAQM6S6Vmhli9fbv3/6tWr5ePjY32cnJysdevWKSQkxLnRAQAAAMgRMp1YtGjRQtJ/97Ho1KmTzTo3NzcFBwdr3LhxTg0OAAAAQM6Q6cQiJSVFkhQSEqLffvtNBQtycxkAAAAA/7H7BnmHDx/OijgAAAAA5GB3dOftS5cu6YcfflBMTIySkpJs1vXs2dMpgQEAAADIOexOLHbs2KEmTZro8uXLunTpknx9fXX69GnlyZNHfn5+JBYAAADAfcju6Wb79OmjZs2a6dy5c8qdO7d++eUXHTlyRKGhofrggw+yIkYAAAAA9zi7E4udO3fqjTfekIuLi1xdXZWYmKjAwECNGTNG77zzTlbECAAAAOAeZ3di4ebmJheX/5r5+fkpJiZGkuTj46OjR486NzoAAAAAOYLdYyyqVq2q3377TaVLl1b9+vU1aNAgnT59WnPmzFGFChWyIkYAAAAA9zi7r1iMGDFChQsXliS9//77KlCggF599VWdOnVKn3zyidMDBAAAAHDvs/uKRbVq1az/9/Pz06pVq5waEAAAAICcx+4rFhnZvn27nnrqKWdtDgAAAEAOYldisXr1avXt21fvvPOODh06JEnas2ePWrRooerVqyslJSVLggQAAABwb8t0V6gZM2aoa9eu8vX11blz5/Tpp59q/Pjxev3119W2bVv99ddfKleuXFbGCgAAAOAelekrFhMnTtTo0aN1+vRpLVy4UKdPn9bkyZO1a9cuTZ06laQCAAAAuI9lOrE4ePCgnn32WUlSy5YtlStXLo0dO1bFihXLsuAAAAAA5AyZTiyuXLmiPHnySJIsFos8PDys084CAAAAuL/ZNd3sp59+Ki8vL0nS9evXNXv2bBUsWNCmTs+ePZ0XHQAAAIAcIdOJRfHixTV9+nTr44CAAM2ZM8emjsVisTuxmDRpksaOHavY2FhVrlxZH330kWrUqJFh/fPnz+vdd9/V0qVLdfbsWQUFBSkqKkpNmjSxa78AAAAAnCfTiUV0dLTTd75gwQJFRkZq6tSpCgsLU1RUlMLDw7V37175+fmlqZ+UlKTHHntMfn5+Wrx4sYoWLaojR44of/78To8NAAAAQObZfedtZxo/fry6du2qzp07S5KmTp2qFStWaObMmerXr1+a+jNnztTZs2f1888/y83NTZIUHBx8y30kJiYqMTHR+jghIcF5TwAAAACAJCfeedteSUlJ2rZtmxo3bvx/wbi4qHHjxtqyZUu6bZYvX65atWqpe/fu8vf3V4UKFTRixAglJydnuJ+RI0fKx8fHugQGBjr9uQAAAAD3u2xLLE6fPq3k5GT5+/vblPv7+ys2NjbdNocOHdLixYuVnJys7777TgMHDtS4ceP03nvvZbif/v37Kz4+3rocPXrUqc8DAAAAQDZ3hbJXSkqK/Pz8NG3aNLm6uio0NFTHjh3T2LFjNXjw4HTbeHh4yMPD4y5HCgAAANxfsi2xKFiwoFxdXRUXF2dTHhcXp4CAgHTbFC5cWG5ubnJ1dbWWlStXTrGxsUpKSpK7u3uWxgwAAAAgfXZ3hXJ1ddXJkyfTlJ85c8bmC//tuLu7KzQ0VOvWrbOWpaSkaN26dapVq1a6berUqaMDBw4oJSXFWrZv3z4VLlyYpAIAAADIRnYnFsaYdMsTExPt/nIfGRmp6dOn67PPPtPu3bv16quv6tKlS9ZZojp27Kj+/ftb67/66qs6e/asevXqpX379mnFihUaMWKEunfvbu/TAAAAAOBEme4K9eGHH0r67yZ4N96BW5KSk5O1adMmlS1b1q6dt23bVqdOndKgQYMUGxurKlWqaNWqVdYB3TExMXJx+b/cJzAwUKtXr1afPn1UqVIlFS1aVL169dLbb79t134BAAAAOFemE4sJEyZI+u+KxdSpU226Pbm7uys4OFhTp061O4AePXqoR48e6a7buHFjmrJatWrpl19+sXs/AAAA95pRO05ndwi4x/WrWjC7Q8i0TCcWhw8fliQ1bNhQS5cuVYECBbIsKAAAAAA5i92zQm3YsCEr4gAAAACQg2UqsYiMjNTw4cOVN29eRUZG3rLu+PHjnRIYAAAAgJwjU4nFjh07dO3aNev/M2KxWJwTFQAAAIAcJVOJxY3dn+gKBQAAAOBmdt/HAgAAAABulunB2y1btsxUvaVLl95xMAAAAABypkwnFj4+PjaP582bp2bNmilfvnxODwoAAABAzpLpxGLWrFk2jxcvXqwxY8aoRIkSTg8KAAAAQM7CGAsAAAAADiOxAAAAAOAwEgsAAAAADsv0GIvly5fbPE5JSdG6dev0119/2ZQ3b97cOZEBAAAAyDEynVi0aNEiTVm3bt1sHlssFiUnJzscFAAAAICcJdOJRUpKSlbGAQAAACAHY4wFAAAAAIeRWAAAAABwGIkFAAAAAIeRWAAAAABwGIkFAAAAAIfdUWJx/vx5ffrpp+rfv7/Onj0rSdq+fbuOHTvm1OAAAAAA5AyZnm421Z9//qnGjRvLx8dH0dHR6tq1q3x9fbV06VLFxMTo888/z4o4AQAAANzD7L5iERkZqYiICO3fv1+enp7W8iZNmmjTpk1ODQ4AAABAzmB3YvHbb7+lueO2JBUtWlSxsbFOCQoAAABAzmJ3YuHh4aGEhIQ05fv27VOhQoWcEhQAAACAnMXuxKJ58+YaNmyYrl27JkmyWCyKiYnR22+/rVatWjk9QAAAAAD3PrsTi3HjxunixYvy8/PTlStXVL9+fZUqVUr58uXT+++/nxUxAgAAALjH2T0rlI+Pj9auXavNmzfrzz//1MWLF/Xwww+rcePGWREfAAAAgBzA7sQiVd26dVW3bl1nxgIAAAAgh7I7sfjwww/TLbdYLPL09FSpUqVUr149ubq6OhwcAAAAgJzB7sRiwoQJOnXqlC5fvqwCBQpIks6dO6c8efLIy8tLJ0+eVIkSJbRhwwYFBgY6PWAAAAAA9x67B2+PGDFC1atX1/79+3XmzBmdOXNG+/btU1hYmCZOnKiYmBgFBASoT58+WREvAAAAgHuQ3VcsBgwYoCVLlqhkyZLWslKlSumDDz5Qq1atdOjQIY0ZM4apZwEAAID7iN1XLE6cOKHr16+nKb9+/br1zttFihTRhQsXHI8OAAAAQI5gd2LRsGFDdevWTTt27LCW7dixQ6+++qoaNWokSdq1a5dCQkKcFyUAAACAe5rdicWMGTPk6+ur0NBQeXh4yMPDQ9WqVZOvr69mzJghSfLy8tK4ceOcHiwAAACAe5PdYywCAgK0du1a7dmzR/v27ZMklSlTRmXKlLHWadiwofMiBAAAAHDPs/uKRaqyZcuqefPmat68uU1ScScmTZqk4OBgeXp6KiwsTFu3bs1Uu/nz58tisahFixYO7R8AAACAY+7oztv//vuvli9frpiYGCUlJdmsGz9+vF3bWrBggSIjIzV16lSFhYUpKipK4eHh2rt3r/z8/DJsFx0drb59++qRRx65k6cAAAAAwInsTizWrVun5s2bq0SJEtqzZ48qVKig6OhoGWP08MMP2x3A+PHj1bVrV3Xu3FmSNHXqVK1YsUIzZ85Uv3790m2TnJysDh06aOjQofrxxx91/vx5u/cLAAAAwHns7grVv39/9e3bV7t27ZKnp6eWLFmio0ePqn79+nr22Wft2lZSUpK2bdumxo0b/19ALi5q3LixtmzZkmG7YcOGyc/PT126dLntPhITE5WQkGCzAAAAAHAuuxOL3bt3q2PHjpKkXLly6cqVK/Ly8tKwYcM0evRou7Z1+vRpJScny9/f36bc39/fek+Mm23evFkzZszQ9OnTM7WPkSNHysfHx7oEBgbaFSMAAACA27M7scibN691XEXhwoV18OBB67rTp087L7J0XLhwQS+88IKmT5+uggULZqpN//79FR8fb12OHj2apTECAAAA9yO7x1jUrFlTmzdvVrly5dSkSRO98cYb2rVrl5YuXaqaNWvata2CBQvK1dVVcXFxNuVxcXEKCAhIU//gwYOKjo5Ws2bNrGUpKSn/PZFcubR3716VLFnSpk3qvTYAAAAAZB27E4vx48fr4sWLkqShQ4fq4sWLWrBggUqXLm33jFDu7u4KDQ3VunXrrFPGpqSkaN26derRo0ea+mXLltWuXbtsygYMGKALFy5o4sSJdHMCAAAAsoldiUVycrL+/fdfVapUSdJ/3aKmTp3qUACRkZHq1KmTqlWrpho1aigqKkqXLl2yzhLVsWNHFS1aVCNHjpSnp6cqVKhg0z5//vySlKYcAAAAwN1jV2Lh6uqqxx9/XLt377Z+oXdU27ZtderUKQ0aNEixsbGqUqWKVq1aZR3QHRMTIxeXO76PHwAAAIC7wO6uUBUqVNChQ4cUEhLitCB69OiRbtcnSdq4ceMt286ePdtpcQAAAAC4M3ZfCnjvvffUt29fffvttzpx4gT3iAAAAABg/xWLJk2aSJKaN28ui8ViLTfGyGKxKDk52XnRAQAAAMgR7E4sNmzYkBVxAAAAAMjB7E4s6tevnxVxAAAAAMjB7mi6pR9//FHPP/+8ateurWPHjkmS5syZo82bNzs1OAAAAAA5g92JxZIlSxQeHq7cuXNr+/btSkxMlCTFx8drxIgRTg8QAAAAwL3vjmaFmjp1qqZPny43NzdreZ06dbR9+3anBgcAAAAgZ7A7sdi7d6/q1auXptzHx0fnz593RkwAAAAAchi7E4uAgAAdOHAgTfnmzZtVokQJpwQFAAAAIGexO7Ho2rWrevXqpV9//VUWi0XHjx/X3Llz1bdvX7366qtZESMAAACAe5zd083269dPKSkpevTRR3X58mXVq1dPHh4e6tu3r15//fWsiBEAAADAPc7uxMJisejdd9/Vm2++qQMHDujixYsqX768vLy8siI+AAAAADmA3V2hvvjiC12+fFnu7u4qX768atSoQVIBAAAA3OfsTiz69OkjPz8/Pffcc/ruu++UnJycFXEBAAAAyEHsTixOnDih+fPny2KxqE2bNipcuLC6d++un3/+OSviAwAAAJAD2J1Y5MqVS0899ZTmzp2rkydPasKECYqOjlbDhg1VsmTJrIgRAAAAwD3O7sHbN8qTJ4/Cw8N17tw5HTlyRLt373ZWXAAAAAByELuvWEjS5cuXNXfuXDVp0kRFixZVVFSUnnnmGf3999/Ojg8AAABADmD3FYt27drp22+/VZ48edSmTRsNHDhQtWrVyorYAAAAAOQQdicWrq6uWrhwocLDw+Xq6mqz7q+//lKFChWcFhwAAACAnMHuxGLu3Lk2jy9cuKAvv/xSn376qbZt28b0swAAAMB96I7GWEjSpk2b1KlTJxUuXFgffPCBGjVqpF9++cWZsQEAAADIIey6YhEbG6vZs2drxowZSkhIUJs2bZSYmKivv/5a5cuXz6oYAQAAANzjMn3FolmzZipTpoz+/PNPRUVF6fjx4/roo4+yMjYAAAAAOUSmr1isXLlSPXv21KuvvqrSpUtnZUwAAAAAcphMX7HYvHmzLly4oNDQUIWFhenjjz/W6dOnszI2AAAAADlEphOLmjVravr06Tpx4oS6deum+fPnq0iRIkpJSdHatWt14cKFrIwTAAAAwD3M7lmh8ubNqxdffFGbN2/Wrl279MYbb2jUqFHy8/NT8+bNsyJGAAAAAPe4O55uVpLKlCmjMWPG6N9//9WXX37prJgAAAAA5DAOJRapXF1d1aJFCy1fvtwZmwMAAACQwzglsQAAAABwfyOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOCweyKxmDRpkoKDg+Xp6amwsDBt3bo1w7rTp0/XI488ogIFCqhAgQJq3LjxLesDAAAAyHrZnlgsWLBAkZGRGjx4sLZv367KlSsrPDxcJ0+eTLf+xo0b1b59e23YsEFbtmxRYGCgHn/8cR07duwuRw4AAAAgVbYnFuPHj1fXrl3VuXNnlS9fXlOnTlWePHk0c+bMdOvPnTtXr732mqpUqaKyZcvq008/VUpKitatW5du/cTERCUkJNgsAAAAAJwrWxOLpKQkbdu2TY0bN7aWubi4qHHjxtqyZUumtnH58mVdu3ZNvr6+6a4fOXKkfHx8rEtgYKBTYgcAAADwf7I1sTh9+rSSk5Pl7+9vU+7v76/Y2NhMbePtt99WkSJFbJKTG/Xv31/x8fHW5ejRow7HDQAAAMBWruwOwBGjRo3S/PnztXHjRnl6eqZbx8PDQx4eHnc5MgAAAOD+kq2JRcGCBeXq6qq4uDib8ri4OAUEBNyy7QcffKBRo0bp+++/V6VKlbIyTAAAAAC3ka1dodzd3RUaGmoz8Dp1IHatWrUybDdmzBgNHz5cq1atUrVq1e5GqAAAAABuIdu7QkVGRqpTp06qVq2aatSooaioKF26dEmdO3eWJHXs2FFFixbVyJEjJUmjR4/WoEGDNG/ePAUHB1vHYnh5ecnLyyvbngcAAABwP8v2xKJt27Y6deqUBg0apNjYWFWpUkWrVq2yDuiOiYmRi8v/XViZMmWKkpKS1Lp1a5vtDB48WEOGDLmboQMAAAD4/7I9sZCkHj16qEePHumu27hxo83j6OjorA8IAAAAgF2y/QZ5AAAAAHI+EgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADiOxAAAAAOAwEgsAAAAADrsnEotJkyYpODhYnp6eCgsL09atW29Zf9GiRSpbtqw8PT1VsWJFfffdd3cpUgAAAADpyfbEYsGCBYqMjNTgwYO1fft2Va5cWeHh4Tp58mS69X/++We1b99eXbp00Y4dO9SiRQu1aNFCf/31112OHAAAAEAqizHGZGcAYWFhql69uj7++GNJUkpKigIDA/X666+rX79+aeq3bdtWly5d0rfffmstq1mzpqpUqaKpU6emqZ+YmKjExETr4/j4eBUvXlxHjx6Vt7d3FjyjzBn/x5ls2zdyjsjKD2R3CJIkn5E+2R0C7nHx/eOzOwQrPl9xO/fKZ6vE+Yrby+7zNSEhQYGBgTp//rx8fG7zfcBko8TEROPq6mq++uorm/KOHTua5s2bp9smMDDQTJgwwaZs0KBBplKlSunWHzx4sJHEwsLCwsLCwsLCwnKHy9GjR2/73T6XstHp06eVnJwsf39/m3J/f3/t2bMn3TaxsbHp1o+NjU23fv/+/RUZGWl9nJKSorNnz+qBBx6QxWJx8BnAWVKz4ey+kgRkBucrchLOV+QknK/3HmOMLly4oCJFity2brYmFneDh4eHPDw8bMry58+fPcHgtry9vfkgQY7B+YqchPMVOQnn673ltl2g/r9sHbxdsGBBubq6Ki4uzqY8Li5OAQEB6bYJCAiwqz4AAACArJetiYW7u7tCQ0O1bt06a1lKSorWrVunWrVqpdumVq1aNvUlae3atRnWBwAAAJD1sr0rVGRkpDp16qRq1aqpRo0aioqK0qVLl9S5c2dJUseOHVW0aFGNHDlSktSrVy/Vr19f48aNU9OmTTV//nz9/vvvmjZtWnY+DTjIw8NDgwcPTtNtDbgXcb4iJ+F8RU7C+ZqzZft0s5L08ccfa+zYsYqNjVWVKlX04YcfKiwsTJLUoEEDBQcHa/bs2db6ixYt0oABAxQdHa3SpUtrzJgxatKkSTZFDwAAAOCeSCwAAAAA5GzZfudtAAAAADkfiQUAAAAAh5FYAAAAAHAYiQUAAAAAh5FYINsMGTJEFovFZilbtmx2hwVIkjZt2qRmzZqpSJEislgs+vrrr23WG2M0aNAgFS5cWLlz51bjxo21f//+7AkW973bna8RERFpPm+feOKJ7AkW972RI0eqevXqypcvn/z8/NSiRQvt3bvXps7Vq1fVvXt3PfDAA/Ly8lKrVq3S3CAZ9x4SC2Srhx56SCdOnLAumzdvzu6QAEnSpUuXVLlyZU2aNCnd9WPGjNGHH36oqVOn6tdff1XevHkVHh6uq1ev3uVIgdufr5L0xBNP2Hzefvnll3cxQuD//PDDD+revbt++eUXrV27VteuXdPjjz+uS5cuWev06dNH33zzjRYtWqQffvhBx48fV8uWLbMxamRGtt8gD/e3XLlyKSAgILvDANJ48skn9eSTT6a7zhijqKgoDRgwQE8//bQk6fPPP5e/v7++/vprtWvX7m6GCtzyfE3l4eHB5y3uCatWrbJ5PHv2bPn5+Wnbtm2qV6+e4uPjNWPGDM2bN0+NGjWSJM2aNUvlypXTL7/8opo1a2ZH2MgErlggW+3fv19FihRRiRIl1KFDB8XExGR3SMBtHT58WLGxsWrcuLG1zMfHR2FhYdqyZUs2RgZkbOPGjfLz81OZMmX06quv6syZM9kdEiBJio+PlyT5+vpKkrZt26Zr167ZfMaWLVtWxYsX5zP2HkdigWwTFham2bNna9WqVZoyZYoOHz6sRx55RBcuXMju0IBbio2NlST5+/vblPv7+1vXAfeSJ554Qp9//rnWrVun0aNH64cfftCTTz6p5OTk7A4N97mUlBT17t1bderUUYUKFST99xnr7u6u/Pnz29TlM/beR1coZJsbL9tXqlRJYWFhCgoK0sKFC9WlS5dsjAwA/rfc2D2vYsWKqlSpkkqWLKmNGzfq0UcfzcbIcL/r3r27/vrrL8ZY/o/gigXuGfnz59eDDz6oAwcOZHcowC2l9lO/eYaSuLg4+rAjRyhRooQKFizI5y2yVY8ePfTtt99qw4YNKlasmLU8ICBASUlJOn/+vE19PmPvfSQWuGdcvHhRBw8eVOHChbM7FOCWQkJCFBAQoHXr1lnLEhIS9Ouvv6pWrVrZGBmQOf/++6/OnDnD5y2yhTFGPXr00FdffaX169crJCTEZn1oaKjc3NxsPmP37t2rmJgYPmPvcXSFQrbp27evmjVrpqCgIB0/flyDBw+Wq6ur2rdvn92hAbp48aLNr7mHDx/Wzp075evrq+LFi6t379567733VLp0aYWEhGjgwIEqUqSIWrRokX1B4751q/PV19dXQ4cOVatWrRQQEKCDBw/qrbfeUqlSpRQeHp6NUeN+1b17d82bN0/Lli1Tvnz5rOMmfHx8lDt3bvn4+KhLly6KjIyUr6+vvL299frrr6tWrVrMCHWvM0A2adu2rSlcuLBxd3c3RYsWNW3btjUHDhzI7rAAY4wxGzZsMJLSLJ06dTLGGJOSkmIGDhxo/P39jYeHh3n00UfN3r17szdo3Ldudb5evnzZPP7446ZQoULGzc3NBAUFma5du5rY2NjsDhv3qfTOVUlm1qxZ1jpXrlwxr732milQoIDJkyePeeaZZ8yJEyeyL2hkisUYY+5+OgMAAADgfwljLAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAAAAgMNILAAAAAA4jMQCAIBM2rhxoywWi86fP39PbAcA7iUkFgBwBywWyy2XIUOGOH2fDRo0kMVi0fz5823Ko6KiFBwcnOl4vb29Vb16dS1btszpMSKtBg0aqHfv3jZltWvX1okTJ+Tj45M9QQFAFiCxAIA7cOLECesSFRUlb29vm7K+fftmyX49PT01YMAAXbt2ze62s2bN0okTJ/T777+rTp06at26tXbt2pUFUeJ23N3dFRAQIIvFkt2hAIDTkFgAwB0ICAiwLj4+PrJYLNbHfn5+Gj9+vIoVKyYPDw9VqVJFq1atsraNjo62XnmoXbu2PD09VaFCBf3www+33W/79u11/vx5TZ8+3e6Y8+fPr4CAAD344IMaPny4rl+/rg0bNljXHz16VG3atFH+/Pnl6+urp59+WtHR0TbbmDlzph566CF5eHiocOHC6tGjh3VdTEyMnn76aXl5ecnb21tt2rRRXFycdf2QIUNUpUoVzZw5U8WLF5eXl5dee+01JScna8yYMdZj9/7779vs02Kx6JNPPtFTTz2lPHnyqFy5ctqyZYsOHDigBg0aKG/evKpdu7YOHjxo027ZsmV6+OGH5enpqRIlSmjo0KG6fv26zXY//fRTPfPMM8qTJ49Kly6t5cuX22zju+++04MPPqjcuXOrYcOGaY7HmTNn1L59exUtWlR58uRRxYoV9eWXX1rXR0RE6IcfftDEiROtV4yio6PT7Qq1ZMkS67ENDg7WuHHjbPYVHBysESNG6MUXX1S+fPlUvHhxTZs2zbo+KSlJPXr0UOHCheXp6amgoCCNHDlSAHDXGACAQ2bNmmV8fHysj8ePH2+8vb3Nl19+afbs2WPeeust4+bmZvbt22eMMebw4cNGkilWrJhZvHix+eeff8xLL71k8uXLZ06fPp3hfurXr2969eplxo8fb/z9/c3FixeNMcZMmDDBBAUF3TJGSearr74yxhhz7do1M2HCBCPJTJkyxRhjTFJSkilXrpx58cUXzZ9//mn++ecf89xzz5kyZcqYxMREY4wxkydPNp6eniYqKsrs3bvXbN261UyYMMEYY0xycrKpUqWKqVu3rvn999/NL7/8YkJDQ039+vWtMQwePNh4eXmZ1q1bm7///tssX77cuLu7m/DwcPP666+bPXv2mJkzZxpJ5pdffrGJvWjRombBggVm7969pkWLFiY4ONg0atTIrFq1yvzzzz+mZs2a5oknnrC22bRpk/H29jazZ882Bw8eNGvWrDHBwcFmyJAhNtstVqyYmTdvntm/f7/p2bOn8fLyMmfOnDHGGBMTE2M8PDxMZGSk2bNnj/niiy+Mv7+/kWTOnTtnjDHm33//NWPHjjU7duwwBw8eNB9++KFxdXU1v/76qzHGmPPnz5tatWqZrl27mhMnTpgTJ06Y69evmw0bNths5/fffzcuLi5m2LBhZu/evWbWrFkmd+7cZtasWdZ4g4KCjK+vr5k0aZLZv3+/GTlypHFxcTF79uwxxhgzduxYExgYaDZt2mSio6PNjz/+aObNm3fL8wIAnInEAgAcdHNiUaRIEfP+++/b1Klevbp57bXXjDH/l1iMGjXKuv7atWumWLFiZvTo0RnuJzWxuHr1qgkKCjLDhg0zxmQ+sfD09DR58+Y1Li4uRpIJDg62fomeM2eOKVOmjElJSbG2SUxMNLlz5zarV6+2Pq9333033e2vWbPGuLq6mpiYGGvZ33//bSSZrVu3GmP+Syzy5MljEhISrHXCw8NNcHCwSU5OtpaVKVPGjBw50ib2AQMGWB9v2bLFSDIzZsywln355ZfG09PT+vjRRx81I0aMsIlxzpw5pnDhwhlu9+LFi0aSWblypTHGmP79+5vy5cvbbOPtt9+2SQjS07RpU/PGG29YH6e+bje6ObF47rnnzGOPPWZT580337TZf1BQkHn++eetj1NSUoyfn581OXz99ddNo0aNbF5DALib6AoFAE6UkJCg48ePq06dOjblderU0e7du23KatWqZf1/rly5VK1atTR10uPh4aFhw4bpgw8+0OnTpzMd24QJE7Rz506tXLlS5cuX16effipfX19J0h9//KEDBw4oX7588vLykpeXl3x9fXX16lUdPHhQJ0+e1PHjx/Xoo4+mu+3du3crMDBQgYGB1rLy5csrf/78Ns8pODhY+fLlsz729/dX+fLl5eLiYlN28uRJm+1XqlTJZr0kVaxY0abs6tWrSkhIsD6fYcOGWZ+Ll5eXunbtqhMnTujy5cvpbjdv3rzy9va27nv37t0KCwuziePG10ySkpOTNXz4cFWsWFG+vr7y8vLS6tWrFRMTk+5xysju3bvTPWf279+v5OTkdONN7X6XGm9ERIR27typMmXKqGfPnlqzZo1dMQCAo3JldwAAAPs9//zz+uCDD/Tee+/ddkaoVAEBASpVqpRKlSqlWbNmqUmTJvrnn3/k5+enixcvKjQ0VHPnzk3TrlChQjZf/B3h5uZm89hisaRblpKSkmG71AHP6ZWltrt48aKGDh2qli1bponB09PzlvHcvO9bGTt2rCZOnKioqChVrFhRefPmVe/evZWUlJTpbdjjVvE+/PDDOnz4sFauXKnvv/9ebdq0UePGjbV48eIsiQUAbsYVCwBwIm9vbxUpUkQ//fSTTflPP/2k8uXL25T98ssv1v9fv35d27ZtU7ly5TK1HxcXF40cOVJTpkxJM6A4M2rUqKHQ0FDrQOmHH35Y+/fvl5+fnzX5SF18fHyUL18+BQcHa926delur1y5cjp69KiOHj1qLfvnn390/vz5NM/7bnj44Ye1d+/eNM+lVKlSmU6SypUrp61bt9qU3fiaSf+9rk8//bSef/55Va5cWSVKlNC+ffts6ri7u9tcdchoX+mdMw8++KBcXV0zFa/03/nXtm1bTZ8+XQsWLNCSJUt09uzZTLcHAEeQWACAk7355psaPXq0FixYoL1796pfv37auXOnevXqZVNv0qRJ+uqrr7Rnzx51795d586d04svvpjp/TRt2lRhYWH65JNP7ijO3r1765NPPtGxY8fUoUMHFSxYUE8//bR+/PFHHT58WBs3blTPnj3177//SvpvVqdx48bpww8/1P79+7V9+3Z99NFHkqTGjRurYsWK6tChg7Zv366tW7eqY8eOql+/vqpVq3ZH8Tli0KBB+vzzzzV06FD9/fff2r17t+bPn68BAwZkehuvvPKK9u/frzfffFN79+7VvHnzNHv2bJs6pUuX1tq1a/Xzzz9r9+7d6tatm81MWNJ/3b9+/fVXRUdH6/Tp0+leEXnjjTe0bt06DR8+XPv27dNnn32mjz/+2K5pi8ePH68vv/xSe/bs0b59+7Ro0SIFBAQof/78md4GADiCxAIAnKxnz56KjIzUG2+8oYoVK2rVqlVavny5SpcubVNv1KhRGjVqlCpXrqzNmzdr+fLlKliwoF37Gj16tK5evXpHcT7xxBMKCQnR+++/rzx58mjTpk0qXry4WrZsqXLlyqlLly66evWqvL29JUmdOnVSVFSUJk+erIceekhPPfWU9u/fL+m/LjnLli1TgQIFVK9ePTVu3FglSpTQggUL7ig2R4WHh+vbb7/VmjVrVL16ddWsWVMTJkxQUFBQprdRvHhxLVmyRF9//bUqV66sqVOnasSIETZ1BgwYoIcffljh4eFq0KCBAgIC1KJFC5s6ffv2laurq8qXL69ChQqlO/7i4Ycf1sKFCzV//nxVqFBBgwYN0rBhwxQREZHpePPly6cxY8aoWrVqql69uqKjo/Xdd985rRsbANyOxRhjsjsIALifREdHKyQkRDt27FCVKlWyOxwAAJyCnzEAAAAAOIzEAgAAAIDD6AoFAAAAwGFcsQAAAADgMBILAAAAAA4jsQAAAADgMBILAAAAAA4jsQAAAADgMBILAAAAAA4jsQAAAADgMBILAAAAAA4jsQAAAADgMBILAAAAAA4jsQAAAADgMBILAAAAAA4jsQAAAADgMBILAAAAAA4jsQBw34qOjpbFYtHs2bOtZUOGDJHFYsm+oDJwr8aVKr1jmVWuX7+ut956S4GBgXJxcVGLFi2yfJ/2Cg4OVkREhE3Z/v379fjjj8vHx0cWi0Vff/21JOm3335T7dq1lTdvXlksFu3cufOux4u7z2KxaMiQIdkdBuBUJBaAnSZPniyLxaKwsLDsDuWeExwcrKeeeuqO2n733Xc59o/s8ePHNWTIkEx/IZw9e7YsFot18fT0VJEiRRQeHq4PP/xQFy5cyNqA75Ksek1nzpypsWPHqnXr1vrss8/Up08fp+/jRg0aNLC+Vi4uLvL29laZMmX0wgsvaO3atZneTqdOnbRr1y69//77mjNnjqpVq6Zr167p2Wef1dmzZzVhwgTNmTNHQUFBWfhs7tzly5c1ZMgQbdy48bZ1g4ODbc7xjJa7kYim7mvcuHFp1qW+F3///fcM2/fs2VMWi0UHDhzIsM67774ri8WiP//80ykxAzmWAWCX2rVrm+DgYCPJ7N+/P7vDuacEBQWZpk2b3lHb7t27m7v9kXT48GEjycyaNctadu3aNXPlyhW7tvPbb7+l2c6tzJo1y0gyw4YNM3PmzDEzZ840I0aMMI8//rixWCwmKCjI/PHHHzZt7iSuuyklJcVcuXLFXL9+3VqWVa9p27ZtTdGiRZ2+3YzUr1/fFCtWzMyZM8fMmTPHTJ061fTt29eUKFHCSDJt2rQxSUlJNm2uXr1qU3b58mUjybz77rs29Xbv3m0kmenTp9+V5+KIU6dOGUlm8ODBt6371VdfWY/XnDlzTPv27Y0kM2HCBJvygwcPZnnckowk4+/vby5dumSzLvW9+Ntvv2XY/pdffjGSzNChQzOsExISYipWrGh3XJk5lkBOkuvupzJAznX48GH9/PPPWrp0qbp166a5c+dq8ODBdzWGlJQUJSUlydPT867u915y/fp1paSkyN3d3enbzpUrl3LlujsfjU8++aSqVatmfdy/f3+tX79eTz31lJo3b67du3crd+7cdz2uVMYYXb161RrDraReebkbTp48qfz58ztte5l5T/n4+Oj555+3KRs1apR69uypyZMnKzg4WKNHj7au8/DwsKl76tQpSUoT98mTJ9Mtd8SlS5eUN29ep23vTtzcPS02NlZffvmlWrRooeDg4LseT5UqVbRz505NnTpVkZGRdrUNCwtTqVKl9OWXX2rQoEFp1m/ZskWHDx/WqFGjnBUukGPRFQqww9y5c1WgQAE1bdpUrVu31ty5c63rrl27Jl9fX3Xu3DlNu4SEBHl6eqpv377WssTERA0ePFilSpWSh4eHAgMD9dZbbykxMdGmrcViUY8ePTR37lw99NBD8vDw0KpVqyRJH3zwgWrXrq0HHnhAuXPnVmhoqBYvXpxm/1euXFHPnj1VsGBB5cuXT82bN9exY8fS7eN77Ngxvfjii/L395eHh4ceeughzZw5846OV2q/+w8++EDTpk1TyZIl5eHhoerVq+u3336z1ouIiNCkSZOszzd1uXkbUVFR1m38888/SkpK0qBBgxQaGiofHx/lzZtXjzzyiDZs2HBH8Urpj2VYu3at6tatq/z588vLy0tlypTRO++8I0nauHGjqlevLknq3Lmzw108GjVqpIEDB+rIkSP64osvMoyrQoUKatiwYZr2KSkpKlq0qFq3bm1TFhUVpYceekienp7y9/dXt27ddO7cOZu2qV3ZVq9erWrVqil37tz65JNPbnsMpLRjLDJ6TY0xCg4O1tNPP50m9qtXr8rHx0fdunVL99ik7mPDhg36+++/rdtM7Zpz6dIlvfHGGwoMDJSHh4fKlCmjDz74QMYYm+3c6j1lD1dXV3344YcqX768Pv74Y8XHx9scy9QxFkOGDLF2b3rzzTdlsVis6+vXry9JevbZZ2WxWNSgQQPrNvbs2aPWrVvL19dXnp6eqlatmpYvX24TQ2pXnh9++EGvvfaa/Pz8VKxYMev6lStX6pFHHlHevHmVL18+NW3aVH///bfNNiIiIuTl5aVjx46pRYsW8vLyUqFChdS3b18lJydbj32hQoUkSUOHDrUee0e6ul2/fl3Dhw+3vqeDg4P1zjvvpPkMTD0v16xZoypVqsjT01Ply5fX0qVLM72vOnXqqFGjRhozZoyuXLlid6wdOnTQnj17tH379jTr5s2bJ4vFovbt2zv0mRQREZFu0pXR+KovvvhCoaGhyp07t3x9fdWuXTsdPXrUps7+/fvVqlUrBQQEyNPTU8WKFVO7du1szlXAmbhiAdhh7ty5atmypdzd3dW+fXtNmTJFv/32m6pXry43Nzc988wzWrp0qT755BObX9O//vprJSYmql27dpL++6LXvHlzbd68WS+//LLKlSunXbt2acKECdq3b591UGeq9evXa+HCherRo4cKFixo/eMzceJENW/eXB06dFBSUpLmz5+vZ599Vt9++62aNm1qbR8REaGFCxfqhRdeUM2aNfXDDz/YrE8VFxenmjVrWr94FSpUSCtXrlSXLl2UkJCg3r1739Fxmzdvni5cuKBu3brJYrFozJgxatmypQ4dOiQ3Nzd169ZNx48f19q1azVnzpx0tzFr1ixdvXpVL7/8sjw8POTr66uEhAR9+umnat++vbp27aoLFy5oxowZCg8P19atW1WlSpU7ivdGf//9t5566ilVqlRJw4YNk4eHhw4cOKCffvpJklSuXDkNGzZMgwYN0ssvv6xHHnlEklS7du073ucLL7ygd955R2vWrFHXrl3TrdO2bVsNGTJEsbGxCggIsJZv3rxZx48ft55rktStWzfNnj1bnTt3Vs+ePXX48GF9/PHH2rFjh3766Se5ublZ6+7du1ft27dXt27d1LVrV5UpU+a2xyA9Gb2mFotFzz//vMaMGaOzZ8/K19fXuu6bb75RQkJCmisDqQoVKqQ5c+bo/fff18WLFzVy5EhJ/70Gxhg1b95cGzZsUJcuXVSlShWtXr1ab775po4dO6YJEybYbCuj95S9XF1d1b59ew0cOFCbN29O933VsmVL5c+fX3369FH79u3VpEkTeXl5yd/fX0WLFtWIESPUs2dPVa9eXf7+/pL+O+/q1KmjokWLql+/fsqbN68WLlyoFi1aaMmSJXrmmWds9vHaa6+pUKFCGjRokC5duiRJmjNnjjp16qTw8HCNHj1aly9f1pQpU1S3bl3t2LHD5jknJycrPDxcYWFh+uCDD/T9999r3LhxKlmypF599VUVKlRIU6ZM0auvvqpnnnlGLVu2lCRVqlTpjo6bJL300kv67LPP1Lp1a73xxhv69ddfNXLkSO3evVtfffWVTd39+/erbdu2euWVV9SpUyfNmjVLzz77rFatWqXHHnssU/sbMmSI6tWrpylTpth91aJDhw4aOnSo5s2bp4cffthanpycrIULF+qRRx5R8eLFdfr06Sz/TJKk999/XwMHDlSbNm300ksv6dSpU/roo49Ur1497dixQ/nz51dSUpLCw8OVmJio119/XQEBATp27Ji+/fZbnT9/Xj4+Pk6JBbCRvT2xgJzj999/N5LM2rVrjTH/9SkvVqyY6dWrl7XO6tWrjSTzzTff2LRt0qSJKVGihPXxnDlzjIuLi/nxxx9t6k2dOtVIMj/99JO1TJJxcXExf//9d5qYLl++bPM4KSnJVKhQwTRq1Mhatm3bNiPJ9O7d26ZuREREmj6+Xbp0MYULFzanT5+2qduuXTvj4+OTZn83u3mMReoYhgceeMCcPXvWWr5s2bI0xymj/vip2/D29jYnT560WXf9+nWTmJhoU3bu3Dnj7+9vXnzxxVvGeuO2bxwbMXjwYJs4JkyYYCSZU6dOZbidOx1jcat+3T4+PqZq1aoZxrV3714jyXz00Uc27V577TXj5eVlfa1+/PFHI8nMnTvXpt6qVavSlAcFBRlJZtWqVTZ1M3MM0juWGb2mqbFPmTLFprx58+YmODjYpKSkZLgfY/4b8/DQQw/ZlH399ddGknnvvfdsylu3bm0sFos5cOCAtexW76nM7u9GX331lZFkJk6caC0LCgoynTp1sj5OPT5jx461abthwwYjySxatMim/NFHHzUVK1Y0V69etZalpKSY2rVrm9KlS1vLUs+lunXr2oxvuXDhgsmfP7/p2rWrzXZjY2ONj4+PTXmnTp2sY35uVLVqVRMaGmp9bM8Yi5uNHTvWSDKHDx82xhizc+dOI8m89NJLNvX69u1rJJn169dby1LPyyVLlljL4uPjTeHChW3eIxmRZLp3726MMaZhw4YmICDA+v7IzHsxVfXq1U2xYsVMcnKytSz1ffTJJ58YY+z7TLr5WHbq1MkEBQWl2e/N7/3o6Gjj6upq3n//fZt6u3btMrly5bKW79ixI91zC8hKdIUCMmnu3Lny9/e3dj+xWCxq27at5s+fb+0u0KhRIxUsWFALFiywtjt37pzWrl2rtm3bWssWLVqkcuXKqWzZsjp9+rR1adSokSSluWxev359lS9fPk1MN/Z9P3funOLj4/XII4/YXK5P7eLx2muv2bR9/fXXbR4bY7RkyRI1a9ZMxhibuMLDwxUfH59uN4DMaNu2rQoUKGB9nPqr/qFDhzK9jVatWlm7YqRydXW1XhlKSUnR2bNndf36dVWrVu2OY71Zat/3ZcuWKSUlxSnbzAwvL69bzg714IMPqkqVKjbnWnJyshYvXqxmzZpZz41FixbJx8dHjz32mM1rGhoaKi8vrzTnWkhIiMLDw23KnH0MHnzwQYWFhdl0JTx79qxWrlypDh063NG0ut99951cXV3Vs2dPm/I33nhDxhitXLnSpjyj99Sd8PLykiSnzeZ19uxZrV+/Xm3atNGFCxesr9mZM2cUHh6u/fv369ixYzZtunbtKldXV+vjtWvX6vz582rfvr3N6+7q6qqwsLB0u+a88sorNo8feeQRu96j9vjuu+8kKc2VgzfeeEOStGLFCpvyIkWK2Fyl8fb2VseOHbVjxw7FxsZmer+pV/mmTp1qd8zPP/+8/v33X23atMlaNm/ePLm7u+vZZ5+VdHc+k5YuXaqUlBS1adPG5rUNCAhQ6dKlra9t6hWJ1atX6/Lly07ZN3A7JBZAJiQnJ2v+/Plq2LChDh8+rAMHDujAgQMKCwtTXFyc1q1bJ+m/AbatWrXSsmXLrP2Ely5dqmvXrtkkFvv379fff/+tQoUK2SwPPvigpP8b0JkqJCQk3bi+/fZb1axZU56envL19bV2V7ix/+yRI0fk4uKSZhulSpWyeXzq1CmdP39e06ZNSxNX6riRm+PKrOLFi9s8Tk0ybu7jfysZHYPPPvtMlSpVkqenpx544AEVKlRIK1assDkGp06dUmxsrHW5ePFipvfbtm1b1alTRy+99JL8/f3Vrl07LVy4MMuTjIsXLypfvny3je2nn36yfsncuHGjTp48meZci4+Pl5+fX5rX9eLFi5k617LiGHTs2FE//fSTjhw5Ium/BOjatWt64YUX7mh7R44cUZEiRdIcs3LlylnX3yij8+lOpJ5Pt3u9MuvAgQMyxmjgwIFpXrPUySJu97rt379f0n8/dty8jTVr1qRp7+npmSZxL1CggF3vUXukfi7d/DkUEBCg/Pnzp3m9SpUqlSbhTP28jI6OzvR+69Wrp4YNG97RWIt27drJ1dVV8+bNk/TfmKCvvvpKTz75pM0PJ5n5THLE/v37ZYxR6dKl07y2u3fvtr62ISEhioyM1KeffqqCBQsqPDxckyZNYnwFshRjLIBMWL9+vU6cOKH58+dr/vz5adbPnTtXjz/+uKT//vh88sknWrlypVq0aKGFCxeqbNmyqly5srV+SkqKKlasqPHjx6e7v8DAQJvH6c3K8+OPP6p58+aqV6+eJk+erMKFC8vNzU2zZs2y/uGzR+qXxOeff16dOnVKt86d9qe+8ZfUG5mbBtXeSnrH4IsvvlBERIRatGihN998U35+fnJ1ddXIkSN18OBBa73q1avbfFEZPHhwpged5s6dW5s2bdKGDRu0YsUKrVq1SgsWLFCjRo20Zs2aDJ+bI/7991/Fx8en+dJ1s7Zt26p///5atGiRevfurYULF8rHx0dPPPGEtU5KSor8/Pxsrg7c6OYvk+kd56w4Bu3atVOfPn00d+5cvfPOO/riiy9UrVo1lSlTxu5t3YnMzHSVWX/99ZektMn6nUp9L/bt2zfN1aNUN+/r5ueTuo05c+bYjMFJdfMMY1lxHmdGdtz0cfDgwWrQoIE++eQTu2bj8vPz02OPPaYlS5Zo0qRJ+uabb3ThwgV16NDBWiezn0npyehYpF4RT5WSkiKLxaKVK1em+7qlXkGTpHHjxikiIkLLli3TmjVr1LNnT40cOVK//PKLzSB/wFlILIBMmDt3rvz8/Kyz3Nxo6dKl+uqrrzR16lTlzp1b9erVU+HChbVgwQLVrVtX69ev17vvvmvTpmTJkvrjjz/06KOP3vEf1iVLlsjT01OrV6+2mdpy1qxZNvWCgoKUkpKiw4cPq3Tp0tbym2/2VKhQIeXLl0/Jyclq3LjxHcXkiDs5DosXL1aJEiW0dOlSm/Y3TwE8d+5cm18nS5QoYdd+XFxc9Oijj+rRRx/V+PHjNWLECL377rvasGGDGjdu7PQvR6mDnTP6UpkqJCRENWrU0IIFC9SjRw8tXbpULVq0sDkfSpYsqe+//1516tRx6Mv07Y5Bem51XHx9fdW0aVPNnTtXHTp00E8//aSoqKg7ji8oKEjff/+9Lly4YHPlYM+ePdb1WSE5OVnz5s1Tnjx5VLduXadsM/X8dHNzu+P3YsmSJSX992XYWe9nZ57nqZ9L+/fvt15Vkv6bQOL8+fNpXq/Uqzg3xrBv3z5Jsnvgff369dWgQQONHj063eljb6VDhw5atWqVVq5cqXnz5snb21vNmjWzrs/sZ1J6ChQooPPnz6cpv/nqTcmSJWWMUUhIiPWqza1UrFhRFStW1IABA/Tzzz+rTp06mjp1qt57773btgXsRVco4DauXLmipUuX6qmnnlLr1q3TLD169NCFCxes00C6uLiodevW+uabbzRnzhxdv37dpmuKJLVp00bHjh3T9OnT091f6qwut+Lq6iqLxWLza1Z0dHSaGaVSv5xOnjzZpvyjjz5Ks71WrVppyZIl1l9gb5Q6D39WSZ13P70/rBlJ/bXuxisfv/76q7Zs2WJTr06dOmrcuLF1sSexOHv2bJqy1JldUru73UnsGVm/fr2GDx+ukJAQm19CM9K2bVv98ssvmjlzpk6fPp3uuZacnKzhw4enaXv9+vVMxZyZY5Ce2x2XF154Qf/884/efPNNubq62sxkZa8mTZooOTlZH3/8sU35hAkTZLFY9OSTT97xtjOSnJysnj17avfu3erZs6e8vb2dsl0/Pz/rL+onTpxIsz4z78Xw8HB5e3trxIgRunbt2h1t42Z58uSR5JzzvEmTJpKUJplMvYp78+xax48ft5kpKiEhQZ9//rmqVKmS7hWZ20kdazFt2jS72rVo0UJ58uTR5MmTtXLlSrVs2dLm/ieZ/UxKT8mSJRUfH29z9+4TJ06kmSGrZcuWcnV11dChQ9Nc9TXG6MyZM5L+O0bXr1+3WV+xYkW5uLjc8n0LOIIrFsBtLF++XBcuXFDz5s3TXV+zZk0VKlRIc+fOtX6pa9u2rT766CMNHjxYFStWtPlFTvrvC9XChQv1yiuvaMOGDapTp46Sk5O1Z88eLVy40HofgVtp2rSpxo8fryeeeELPPfecTp48qUmTJqlUqVI2f5hCQ0PVqlUrRUVF6cyZM9bpZlN/7bvxV7VRo0Zpw4YNCgsLU9euXVW+fHmdPXtW27dv1/fff5/uF0xnCQ0NlST17NlT4eHhmfqi+dRTT2np0qV65pln1LRpUx0+fFhTp05V+fLl7RpHcSvDhg3Tpk2b1LRpUwUFBenkyZOaPHmyihUrZv2FumTJksqfP7+mTp2qfPnyKW/evAoLC7ttP/6VK1dqz549un79uuLi4rR+/XqtXbtWQUFBWr58eaZuONemTRv17dtXffv2la+vb5pfp+vXr69u3bpp5MiR2rlzpx5//HG5ublp//79WrRokSZOnGhzz4s7PQbpud1r2rRpUz3wwANatGiRnnzySfn5+d32+WakWbNmatiwod59911FR0ercuXKWrNmjZYtW6bevXtbf8G/U/Hx8db7ily+fFkHDhzQ0qVLdfDgQbVr1y7dxM0RkyZNUt26dVWxYkV17dpVJUqUUFxcnLZs2aJ///1Xf/zxxy3be3t7a8qUKXrhhRf08MMPq127dipUqJBiYmK0YsUK1alTJ00Sdju5c+dW+fLltWDBAj344IPy9fVVhQoVVKFCBbufX+XKldWpUydNmzZN58+fV/369bV161Z99tlnatGiRZp7tDz44IPq0qWLfvvtN/n7+2vmzJmKi4tLc4U2s+rXr6/69evrhx9+sKudl5eXWrRoYe1uenPy78hnUrt27fT222/rmWeeUc+ePa3TAz/44IM2A79Lliyp9957T/3791d0dLRatGihfPny6fDhw/rqq6/08ssvq2/fvlq/fr169OihZ599Vg8++KCuX7+uOXPmWH9EArJE9kxGBeQczZo1M56enubSpUsZ1omIiDBubm7WaVpTUlJMYGBgutNfpkpKSjKjR482Dz30kPHw8DAFChQwoaGhZujQoSY+Pt5aTzdMlXizGTNmmNKlSxsPDw9TtmxZM2vWrDRTExpjzKVLl0z37t2Nr6+v8fLyMi1atLBO+Tlq1CibunFxcaZ79+4mMDDQuLm5mYCAAPPoo4+aadOm3fZYZTTd7M1TbKY+rxunWrx+/bp5/fXXTaFChYzFYrE+h1ttIyUlxYwYMcIEBQUZDw8PU7VqVfPtt99mOG3jzTIz3ey6devM008/bYoUKWLc3d1NkSJFTPv27c2+fftstrVs2TJTvnx5kytXrttOPZs6xWXq4u7ubgICAsxjjz1mJk6caBISEtK0Se91TVWnTp10p+680bRp00xoaKjJnTu3yZcvn6lYsaJ56623zPHjx611bn797DkG6R3LjF7TG7322mtGkpk3b16Gsd8so+lfL1y4YPr06WOKFCli3NzcTOnSpc3YsWPTTF97q/dURvu78fXy8vIypUuXNs8//7xZs2ZNum0cnW7WGGMOHjxoOnbsaAICAoybm5spWrSoeeqpp8zixYutdW43XeqGDRtMeHi48fHxMZ6enqZkyZImIiLC/P7779Y6nTp1Mnnz5k3TNr1z7ueffzahoaHG3d3drqlnb55u1hhjrl27ZoYOHWpCQkKMm5ubCQwMNP3797eZYteY/zsvV69ebSpVqmT9vMvsNKoZvd6px/5Wxy89K1asMJJM4cKFbaaeNca+z6T0jt+aNWtMhQoVjLu7uylTpoz54osvMnzvL1myxNStW9fkzZvX5M2b15QtW9Z0797d7N271xhjzKFDh8yLL75oSpYsaTw9PY2vr69p2LCh+f777zP9XAF7WYyxY/QkgP8ZO3fuVNWqVfXFF19kqssNkBX69OmjGTNmKDY21trVBrhRcHCwKlSooG+//Ta7QwFwG4yxAO4D6U2rGBUVJRcXF9WrVy8bIgL+m67ziy++UKtWrUgqAOB/AGMsgPvAmDFjtG3bNjVs2FC5cuXSypUrtXLlSr388stpprYFstrJkyf1/fffa/HixTpz5ox69eqV3SEBAJyAxAK4D9SuXVtr167V8OHDdfHiRRUvXlxDhgxJMw0ucDf8888/6tChg/z8/PThhx9aZ5gCAORsjLEAAAAA4DDGWAAAAABwGIkFAAAAAIfdd2MsUlJSdPz4ceXLl8/mxmAAAAAAbBljdOHCBRUpUkQuLre+JnHfJRbHjx9nFhwAAADADkePHlWxYsVuWee+Syzy5csn6b+D4+3tnc3RAAAAAPeuhIQEBQYGWr9D38p9l1ikdn/y9vYmsQAAAAAyITNDCBi8DQAAAMBhJBYAAAAAHEZiAQAAAMBhJBYAAAAAHJaticWmTZvUrFkzFSlSRBaLRV9//fVt22zcuFEPP/ywPDw8VKpUKc2ePTvL4wQAAABwa9maWFy6dEmVK1fWpEmTMlX/8OHDatq0qRo2bKidO3eqd+/eeumll7R69eosjhQAAADArWTrdLNPPvmknnzyyUzXnzp1qkJCQjRu3DhJUrly5bR582ZNmDBB4eHhWRUmAAAAgNvIUWMstmzZosaNG9uUhYeHa8uWLRm2SUxMVEJCgs0CAAAAwLly1A3yYmNj5e/vb1Pm7++vhIQEXblyRblz507TZuTIkRo6dOjdCjHTLENvf5MRwAw22R0CAABApuSoxOJO9O/fX5GRkdbHqbclB5B5o3aczu4QcI/rV7VgdocAAMhmOSqxCAgIUFxcnE1ZXFycvL29071aIUkeHh7y8PC4G+EBAAAA960cNcaiVq1aWrdunU3Z2rVrVatWrWyKCAAAAICUzYnFxYsXtXPnTu3cuVPSf9PJ7ty5UzExMZL+68bUsWNHa/1XXnlFhw4d0ltvvaU9e/Zo8uTJWrhwofr06ZMd4QMAAAD4/7I1sfj9999VtWpVVa1aVZIUGRmpqlWratCgQZKkEydOWJMMSQoJCdGKFSu0du1aVa5cWePGjdOnn37KVLMAAABANsvWMRYNGjSQMRnPepPeXbUbNGigHTt2ZGFUAAAAAOyVo8ZYAAAAALg3kVgAAAAAcBiJBQAAAACH5aj7WAAAcDvc0BG3ww0dgazBFQsAAAAADiOxAAAAAOAwEgsAAAAADmOMBQAAQDZhTBBuJyeNCeKKBQAAAACHkVgAAAAAcBiJBQAAAACHkVgAAAAAcBiJBQAAAACHkVgAAAAAcJjdicWhQ4eyIg4AAAAAOZjdiUWpUqXUsGFDffHFF7p69WpWxAQAAAAgh7E7sdi+fbsqVaqkyMhIBQQEqFu3btq6dWtWxAYAAAAgh7A7sahSpYomTpyo48ePa+bMmTpx4oTq1q2rChUqaPz48Tp16lRWxAkAAADgHnbHg7dz5cqlli1batGiRRo9erQOHDigvn37KjAwUB07dtSJEyecGScAAACAe9gdJxa///67XnvtNRUuXFjjx49X3759dfDgQa1du1bHjx/X008/7cw4AQAAANzDctnbYPz48Zo1a5b27t2rJk2a6PPPP1eTJk3k4vJfjhISEqLZs2crODjY2bECAAAAuEfZnVhMmTJFL774oiIiIlS4cOF06/j5+WnGjBkOBwcAAAAgZ7A7sVi7dq2KFy9uvUKRyhijo0ePqnjx4nJ3d1enTp2cFiQAAACAe5vdYyxKliyp06dPpyk/e/asQkJCnBIUAAAAgJzF7sTCGJNu+cWLF+Xp6elwQAAAAABynkx3hYqMjJQkWSwWDRo0SHny5LGuS05O1q+//qoqVao4PUAAAAAA975MJxY7duyQ9N8Vi127dsnd3d26zt3dXZUrV1bfvn2dHyEAAACAe16mE4sNGzZIkjp37qyJEyfK29s7y4ICAAAAkLPYPSvUrFmzsiIOAAAAADlYphKLli1bavbs2fL29lbLli1vWXfp0qVOCQwAAABAzpGpxMLHx0cWi8X6fwAAAAC4UaYSixu7P9EVCgAAAMDN7L6PxZUrV3T58mXr4yNHjigqKkpr1qxxamAAAAAAcg67E4unn35an3/+uSTp/PnzqlGjhsaNG6enn35aU6ZMcXqAAAAAAO59dicW27dv1yOPPCJJWrx4sQICAnTkyBF9/vnn+vDDD50eIAAAAIB7n92JxeXLl5UvXz5J0po1a9SyZUu5uLioZs2aOnLkiNMDBAAAAHDvszuxKFWqlL7++msdPXpUq1ev1uOPPy5JOnnyJDfNAwAAAO5TdicWgwYNUt++fRUcHKywsDDVqlVL0n9XL6pWrWp3AJMmTVJwcLA8PT0VFhamrVu33rJ+VFSUypQpo9y5cyswMFB9+vTR1atX7d4vAAAAAOex+87brVu3Vt26dXXixAlVrlzZWv7oo4/qmWeesWtbCxYsUGRkpKZOnaqwsDBFRUUpPDxce/fulZ+fX5r68+bNU79+/TRz5kzVrl1b+/btU0REhCwWi8aPH2/vUwEAAADgJHZdsbh27Zpy5cql06dPq2rVqnJx+b/mNWrUUNmyZe3a+fjx49W1a1d17txZ5cuX19SpU5UnTx7NnDkz3fo///yz6tSpo+eee07BwcF6/PHH1b59+1te5UhMTFRCQoLNAgAAAMC57Eos3NzcVLx4cSUnJzu846SkJG3btk2NGzf+v2BcXNS4cWNt2bIl3Ta1a9fWtm3brInEoUOH9N1336lJkyYZ7mfkyJHy8fGxLoGBgQ7HDgAAAMCW3WMs3n33Xb3zzjs6e/asQzs+ffq0kpOT5e/vb1Pu7++v2NjYdNs899xzGjZsmOrWrSs3NzeVLFlSDRo00DvvvJPhfvr376/4+HjrcvToUYfiBgAAAJCW3WMsPv74Yx04cEBFihRRUFCQ8ubNa7N++/btTgvuZhs3btSIESM0efJkhYWF6cCBA+rVq5eGDx+ugQMHptvGw8NDHh4eWRYTAAAAgDtILFq0aOGUHRcsWFCurq6Ki4uzKY+Li1NAQEC6bQYOHKgXXnhBL730kiSpYsWKunTpkl5++WW9++67NmM+AAAAANw9dicWgwcPdsqO3d3dFRoaqnXr1lmTlZSUFK1bt049evRIt83ly5fTJA+urq6SJGOMU+ICAAAAYD+7EwtJOn/+vBYvXqyDBw/qzTfflK+vr7Zv3y5/f38VLVo009uJjIxUp06dVK1aNdWoUUNRUVG6dOmSOnfuLEnq2LGjihYtqpEjR0qSmjVrpvHjx6tq1arWrlADBw5Us2bNrAkGAAAAgLvP7sTizz//VOPGjeXj46Po6Gh17dpVvr6+Wrp0qWJiYvT5559neltt27bVqVOnNGjQIMXGxqpKlSpatWqVdUB3TEyMzRWKAQMGyGKxaMCAATp27JgKFSqkZs2a6f3337f3aQAAAABwIrsTi8jISEVERGjMmDHKly+ftbxJkyZ67rnn7A6gR48eGXZ92rhxo83jXLlyafDgwU7rjgUAAADAOewe7fzbb7+pW7duacqLFi2a4TSxAAAAAP632Z1YeHh4pHv36n379qlQoUJOCQoAAABAzmJ3YtG8eXMNGzZM165dkyRZLBbFxMTo7bffVqtWrZweIAAAAIB7n92Jxbhx43Tx4kX5+fnpypUrql+/vkqVKqV8+fIxiBoAAAC4T9k9eNvHx0dr167V5s2b9eeff+rixYt6+OGH1bhx46yIDwAAAEAOYHdicfToUQUGBqpu3bqqW7duVsQEAAAAIIexuytUcHCw6tevr+nTp+vcuXNZERMAAACAHMbuxOL3339XjRo1NGzYMBUuXFgtWrTQ4sWLlZiYmBXxAQAAAMgB7E4sqlatqrFjxyomJkYrV65UoUKF9PLLL8vf318vvvhiVsQIAAAA4B5nd2KRymKxqGHDhpo+fbq+//57hYSE6LPPPnNmbAAAAAByiDtOLP7991+NGTNGVapUUY0aNeTl5aVJkyY5MzYAAAAAOYTds0J98sknmjdvnn766SeVLVtWHTp00LJlyxQUFJQV8QEAAADIAexOLN577z21b99eH374oSpXrpwVMQEAAADIYexOLGJiYmSxWLIiFgAAAAA5VKYSiz///FMVKlSQi4uLdu3adcu6lSpVckpgAAAAAHKOTCUWVapUUWxsrPz8/FSlShVZLBYZY6zrUx9bLBYlJydnWbAAAAAA7k2ZSiwOHz6sQoUKWf8PAAAAADfKVGJx44xPzP4EAAAA4GZ2D95ev369li5dqujoaFksFoWEhKh169aqV69eVsQHAAAAIAew6wZ5r7zyiho3bqwvv/xSZ86c0alTpzR37lw1bNhQr7/+elbFCAAAAOAel+nE4quvvtKsWbM0c+ZMnT59Wlu2bNEvv/yiU6dOafr06Zo2bZqWL1+elbECAAAAuEdlOrGYNWuWIiMjFRERYXMfCxcXF7344ovq3bu3ZsyYkSVBAgAAALi3ZTqx2L59u5555pkM17ds2VLbtm1zSlAAAAAAcpZMJxanT59WsWLFMlxfrFgxnTlzxilBAQAAAMhZMp1YJCUlyc3NLcP1uXLlUlJSklOCAgAAAJCz2DXd7MCBA5UnT550112+fNkpAQEAAADIeTKdWNSrV0979+69bR0AAAAA959MJxYbN27MwjAAAAAA5GR23SAPAAAAANJDYgEAAADAYSQWAAAAABxGYgEAAADAYXYnFjExMTLGpCk3xigmJsYpQQEAAADIWexOLEJCQnTq1Kk05WfPnlVISIhTggIAAACQs9idWBhjZLFY0pRfvHhRnp6eTgkKAAAAQM6S6ftYREZGSpIsFkuaO3AnJyfr119/VZUqVZweIAAAAIB7X6avWOzYsUM7duyQMUa7du2yPt6xY4f27NmjypUra/bs2XYHMGnSJAUHB8vT01NhYWHaunXrLeufP39e3bt3V+HCheXh4aEHH3xQ3333nd37BQAAAOA8mb5isWHDBklS586dNXHiRHl7ezu88wULFigyMlJTp05VWFiYoqKiFB4err1798rPzy9N/aSkJD322GPy8/PT4sWLVbRoUR05ckT58+d3OBYAAAAAdy7TiUWqWbNm2TxOSEjQ+vXrVbZsWZUtW9aubY0fP15du3ZV586dJUlTp07VihUrNHPmTPXr1y9N/ZkzZ+rs2bP6+eef5ebmJkkKDg629ykAAAAAcDK7B2+3adNGH3/8sSTpypUrqlatmtq0aaOKFStqyZIlmd5OUlKStm3bpsaNG/9fMC4uaty4sbZs2ZJum+XLl6tWrVrq3r27/P39VaFCBY0YMULJyckZ7icxMVEJCQk2CwAAAADnsjux2LRpkx555BFJ0ldffSVjjM6fP68PP/xQ7733Xqa3c/r0aSUnJ8vf39+m3N/fX7Gxsem2OXTokBYvXqzk5GR99913GjhwoMaNG3fL/Y4cOVI+Pj7WJTAwMNMxAgAAAMgcuxOL+Ph4+fr6SpJWrVqlVq1aKU+ePGratKn279/v9ABvlJKSIj8/P02bNk2hoaFq27at3n33XU2dOjXDNv3791d8fLx1OXr0aJbGCAAAANyP7B5jERgYqC1btsjX11erVq3S/PnzJUnnzp2z6z4WBQsWlKurq+Li4mzK4+LiFBAQkG6bwoULy83NTa6urtaycuXKKTY2VklJSXJ3d0/TxsPDQx4eHpmOCwAAAID97L5i0bt3b3Xo0EHFihVTkSJF1KBBA0n/dZGqWLFiprfj7u6u0NBQrVu3zlqWkpKidevWqVatWum2qVOnjg4cOKCUlBRr2b59+1S4cOF0kwoAAAAAd4fdicVrr72mLVu2aObMmdq8ebNcXP7bRIkSJewaYyH9d9O96dOn67PPPtPu3bv16quv6tKlS9ZZojp27Kj+/ftb67/66qs6e/asevXqpX379mnFihUaMWKEunfvbu/TAAAAAOBEdneFkqRq1aqpWrVqNmVNmza1eztt27bVqVOnNGjQIMXGxqpKlSpatWqVdUB3TEyMNXGR/uuGtXr1avXp00eVKlVS0aJF1atXL7399tt38jQAAAAAOEmmEovIyEgNHz5cefPmVWRk5C3rjh8/3q4AevTooR49eqS7buPGjWnKatWqpV9++cWufQAAAADIWplKLHbs2KFr165Z/58Ri8XinKgAAAAA5CiZSiw2bNiQ7v8BAAAAQLqDwdsAAAAAcLNMXbFo2bJlpje4dOnSOw4GAAAAQM6UqcTCx8cnq+MAAAAAkINlKrGYNWtWVscBAAAAIAdzaIzFqFGjdP78eSeFAgAAACCnciixGDFihM6ePeusWAAAAADkUA4lFsYYZ8UBAAAAIAdjulkAAAAADsvU4O2M/PPPPypSpIizYgEAAACQQzmUWAQGBjorDgAAAAA5mN2JRXJysiZMmKCFCxcqJiZGSUlJNusZzA0AAADcf+weYzF06FCNHz9ebdu2VXx8vCIjI9WyZUu5uLhoyJAhWRAiAAAAgHud3YnF3LlzNX36dL3xxhvKlSuX2rdvr08//VSDBg3SL7/8khUxAgAAALjH2Z1YxMbGqmLFipIkLy8vxcfHS5KeeuoprVixwrnRAQAAAMgR7E4sihUrphMnTkiSSpYsqTVr1kiSfvvtN3l4eDg3OgAAAAA5gt2JxTPPPKN169ZJkl5//XUNHDhQpUuXVseOHfXiiy86PUAAAAAA9z67Z4UaNWqU9f9t27ZVUFCQfv75Z5UuXVrNmjVzanAAAAAAcga7Eotr166pW7duGjhwoEJCQiRJNWvWVM2aNbMkOAAAAAA5g11dodzc3LRkyZKsigUAAABADmX3GIsWLVro66+/zoJQAAAAAORUdo+xKF26tIYNG6affvpJoaGhyps3r836nj17Oi04AAAAADmD3YnFjBkzlD9/fm3btk3btm2zWWexWEgsAAAAgPuQ3YnF4cOHsyIOAAAAADmY3WMshg0bpsuXL6cpv3LlioYNG+aUoAAAAADkLHYnFkOHDtXFixfTlF++fFlDhw51SlAAAAAAcha7EwtjjCwWS5ryP/74Q76+vk4JCgAAAEDOkukxFgUKFJDFYpHFYtGDDz5ok1wkJyfr4sWLeuWVV7IkSAAAAAD3tkwnFlFRUTLG6MUXX9TQoUPl4+NjXefu7q7g4GDVqlUrS4IEAAAAcG/LdGLRqVMnSVJISIhq164tNze3LAsKAAAAQM5i93Sz9evXV0pKivbt26eTJ08qJSXFZn29evWcFhwAAACAnMHuxOKXX37Rc889pyNHjsgYY7POYrEoOTnZacEBAAAAyBnsTixeeeUVVatWTStWrFDhwoXTnSEKAAAAwP3F7sRi//79Wrx4sUqVKpUV8QAAAADIgey+j0VYWJgOHDiQFbEAAAAAyKHsTixef/11vfHGG5o9e7a2bdumP//802a5E5MmTVJwcLA8PT0VFhamrVu3Zqrd/PnzZbFY1KJFizvaLwAAAADnsLsrVKtWrSRJL774orXMYrFY78ht7+DtBQsWKDIyUlOnTlVYWJiioqIUHh6uvXv3ys/PL8N20dHR6tu3rx555BF7nwIAAAAAJ7M7sTh8+LBTAxg/fry6du2qzp07S5KmTp2qFStWaObMmerXr1+6bZKTk9WhQwcNHTpUP/74o86fP+/UmAAAAADYx+7EIigoyGk7T0pK0rZt29S/f39rmYuLixo3bqwtW7Zk2G7YsGHy8/NTly5d9OOPP95yH4mJiUpMTLQ+TkhIcDxwAAAAADYynVgsX748U/WaN2+e6Z2fPn1aycnJ8vf3tyn39/fXnj170m2zefNmzZgxQzt37szUPkaOHKmhQ4dmOiYAAAAA9st0YpGZAdJZfYO8Cxcu6IUXXtD06dNVsGDBTLXp37+/IiMjrY8TEhIUGBiYVSECAAAA96VMJxYpKSlO33nBggXl6uqquLg4m/K4uDgFBASkqX/w4EFFR0erWbNmaeLKlSuX9u7dq5IlS9q08fDwkIeHh9NjBwAAAPB/7J5u1pnc3d0VGhqqdevWWctSUlK0bt061apVK039smXLateuXdq5c6d1ad68uRo2bKidO3dyJQIAAADIJnYP3na2yMhIderUSdWqVVONGjUUFRWlS5cuWWeJ6tixo4oWLaqRI0fK09NTFSpUsGmfP39+SUpTDgAAAODuyfbEom3btjp16pQGDRqk2NhYValSRatWrbIO6I6JiZGLS7ZeWAEAAABwG9meWEhSjx491KNHj3TXbdy48ZZtZ8+e7fyAAAAAANiFSwEAAAAAHHZHicX58+f16aefqn///jp79qwkafv27Tp27JhTgwMAAACQM9jdFerPP/9U48aN5ePjo+joaHXt2lW+vr5aunSpYmJi9Pnnn2dFnAAAAADuYXZfsYiMjFRERIT2798vT09Pa3mTJk20adMmpwYHAAAAIGewO7H47bff1K1btzTlRYsWVWxsrFOCAgAAAJCz2J1YeHh4KCEhIU35vn37VKhQIacEBQAAACBnsTuxaN68uYYNG6Zr165JkiwWi2JiYvT222+rVatWTg8QAAAAwL3P7sRi3Lhxunjxovz8/HTlyhXVr19fpUqVUr58+fT+++9nRYwAAAAA7nF2zwrl4+OjtWvXavPmzfrzzz918eJFPfzww2rcuHFWxAcAAAAgB7jjO2/XrVtXdevWdWYsAAAAAHIouxOLDz/8MN1yi8UiT09PlSpVSvXq1ZOrq6vDwQEAAADIGexOLCZMmKBTp07p8uXLKlCggCTp3LlzypMnj7y8vHTy5EmVKFFCGzZsUGBgoNMDBgAAAHDvsXvw9ogRI1S9enXt379fZ86c0ZkzZ7Rv3z6FhYVp4sSJiomJUUBAgPr06ZMV8QIAAAC4B9l9xWLAgAFasmSJSpYsaS0rVaqUPvjgA7Vq1UqHDh3SmDFjmHoWAAAAuI/YfcXixIkTun79epry69evW++8XaRIEV24cMHx6AAAAADkCHYnFg0bNlS3bt20Y8cOa9mOHTv06quvqlGjRpKkXbt2KSQkxHlRAgAAALin2Z1YzJgxQ76+vgoNDZWHh4c8PDxUrVo1+fr6asaMGZIkLy8vjRs3zunBAgAAALg32T3GIiAgQGvXrtWePXu0b98+SVKZMmVUpkwZa52GDRs6L0IAAAAA97w7vkFe2bJlVbZsWWfGAvy/9u47LIqrfxv4vSDs0pUoTRGwNwRFRTSChYglKlEfsMUaH5NYooiJxIJYwIpoLFgxMRbsmkRRQ7DFFls0URANiEZAURERAYXz/uHLPq6A7rKLy/68P9c1V9wzZ2bu3Z3d7JeZOUNEREREOqpMhcWdO3ewb98+pKSkID8/X2FeeHi4RoIREREREZHuULmwiI2NRc+ePVGrVi3Ex8ejSZMmSE5OhhACzZs3L4+MRERERERUwal88XZQUBACAwNx5coVyGQy7Ny5E7dv34aXlxf+85//lEdGIiIiIiKq4FQuLK5du4bBgwcDACpVqoRnz57B1NQUM2fOxLx58zQekIiIiIiIKj6VCwsTExP5dRW2tra4efOmfF5GRobmkhERERERkc5Q+RqL1q1b48SJE2jYsCG6deuGiRMn4sqVK9i1axdat25dHhmJiIiIiKiCU7mwCA8PR3Z2NgAgJCQE2dnZiI6ORt26dTkiFBERERHRe0qlwqKgoAB37txB06ZNAbw8LSoyMrJcghERERERke5Q6RoLfX19dO7cGY8ePSqvPEREREREpINUvni7SZMm+Oeff8ojCxERERER6SiVC4vZs2cjMDAQP//8M1JTU5GVlaUwERERERHR+0fli7e7desGAOjZsyckEom8XQgBiUSCgoICzaUjIiIiIiKdoHJhERcXVx45iIiIiIhIh6lcWHh5eZVHDiIiIiIi0mEqX2MBAMePH8egQYPQpk0b/PvvvwCAjRs34sSJExoNR0REREREukHlwmLnzp3w8fGBkZERLly4gLy8PADA48ePERoaqvGARERERERU8ZVpVKjIyEisWbMGBgYG8va2bdviwoULGg1HRERERES6QeXCIiEhAZ6ensXaLSwskJmZqYlMRERERESkY1QuLGxsbHDjxo1i7SdOnECtWrXKFGL58uVwdHSETCaDu7s7zp49W2rfNWvWoF27dqhSpQqqVKkCb2/vN/YnIiIiIqLyp3JhMXLkSHz11Vc4c+YMJBIJ7t69i02bNiEwMBBffPGFygGio6MREBCA4OBgXLhwAS4uLvDx8cG9e/dK7H/kyBH0798fcXFxOHXqFOzt7dG5c2f5ReRERERERPTuqTzc7OTJk1FYWIhOnTohJycHnp6ekEqlCAwMxNixY1UOEB4ejpEjR2LYsGEAgMjISPzyyy9Yv349Jk+eXKz/pk2bFB6vXbsWO3fuRGxsLAYPHlysf15envwCcwC8OzgRERERUTlQ+YiFRCLBlClT8PDhQ/z11184ffo07t+/j1mzZqm88fz8fJw/fx7e3t7/C6SnB29vb5w6dUqpdeTk5OD58+ewtLQscX5YWBgsLCzkk729vco5iYiIiIjozVQuLH788Ufk5OTA0NAQjRo1QqtWrWBqalqmjWdkZKCgoADW1tYK7dbW1khLS1NqHd988w3s7OwUipNXBQUF4fHjx/Lp9u3bZcpKRERERESlU7mwmDBhAqysrDBgwADs378fBQUF5ZFLKXPnzsXWrVuxe/duyGSyEvtIpVKYm5srTEREREREpFkqFxapqanYunUrJBIJ/Pz8YGtri9GjR+PkyZMqb7xq1arQ19dHenq6Qnt6ejpsbGzeuOzChQsxd+5cHDp0CE2bNlV520REREREpDkqFxaVKlXCxx9/jE2bNuHevXtYvHgxkpOT0aFDB9SuXVuldRkaGsLNzQ2xsbHytsLCQsTGxsLDw6PU5ebPn49Zs2YhJiYGLVq0UPUpEBERERGRhqk8KtSrjI2N4ePjg0ePHuHWrVu4du2ayusICAjAkCFD0KJFC7Rq1QoRERF4+vSpfJSowYMHo3r16ggLCwMAzJs3D9OnT8fmzZvh6OgovxbD1NS0zNd6EBERERGRespUWOTk5GD37t3YtGkTYmNjYW9vj/79+2PHjh0qr8vf3x/379/H9OnTkZaWBldXV8TExMgv6E5JSYGe3v8OrKxcuRL5+fno27evwnqCg4MxY8aMsjwdIiIiIiJSk8qFRb9+/fDzzz/D2NgYfn5+mDZt2htPW1LGmDFjMGbMmBLnHTlyROFxcnKyWtsiIiIiIiLNU7mw0NfXx7Zt2+Dj4wN9fX2FeX/99ReaNGmisXBERERERKQbVC4sXr/z9ZMnT7BlyxasXbsW58+f1+rws0REREREpB0qjwpV5NixYxgyZAhsbW2xcOFCdOzYEadPn9ZkNiIiIiIi0hEqHbFIS0vDhg0bsG7dOmRlZcHPzw95eXnYs2cPGjVqVF4ZiYiIiIioglP6iEWPHj1Qv359XL58GREREbh79y6+++678sxGREREREQ6QukjFgcOHMC4cePwxRdfoG7duuWZiYiIiIiIdIzSRyxOnDiBJ0+ewM3NDe7u7li2bBkyMjLKMxsREREREekIpQuL1q1bY82aNUhNTcWoUaOwdetW2NnZobCwEIcPH8aTJ0/KMycREREREVVgKo8KZWJiguHDh+PEiRO4cuUKJk6ciLlz58LKygo9e/Ysj4xERERERFTBlXm4WQCoX78+5s+fjzt37mDLli2aykRERERERDpGrcKiiL6+Pnx9fbFv3z5NrI6IiIiIiHSMRgoLIiIiIiJ6v7GwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitbGwICIiIiIitVWIwmL58uVwdHSETCaDu7s7zp49+8b+27dvR4MGDSCTyeDs7Iz9+/e/o6RERERERFQSrRcW0dHRCAgIQHBwMC5cuAAXFxf4+Pjg3r17JfY/efIk+vfvjxEjRuDixYvw9fWFr68v/vrrr3ecnIiIiIiIikiEEEKbAdzd3dGyZUssW7YMAFBYWAh7e3uMHTsWkydPLtbf398fT58+xc8//yxva926NVxdXREZGVmsf15eHvLy8uSPHz9+jJo1a+L27dswNzcvh2ekHIswC61tm3TH46DH2o4AAAj/84G2I1AFF+DygbYjyHF/pbfh/kq6RNv7a1ZWFuzt7ZGZmQkLi7f8fhValJeXJ/T19cXu3bsV2gcPHix69uxZ4jL29vZi8eLFCm3Tp08XTZs2LbF/cHCwAMCJEydOnDhx4sSJE6cyTrdv337rb/tK0KKMjAwUFBTA2tpaod3a2hrx8fElLpOWllZi/7S0tBL7BwUFISAgQP64sLAQDx8+xAcffACJRKLmMyBNKaqGtX0kiUgZ3F9Jl3B/JV3C/bXiEULgyZMnsLOze2tfrRYW74JUKoVUKlVoq1y5snbC0FuZm5vzi4R0BvdX0iXcX0mXcH+tWN56CtT/p9WLt6tWrQp9fX2kp6crtKenp8PGxqbEZWxsbFTqT0RERERE5U+rhYWhoSHc3NwQGxsrbyssLERsbCw8PDxKXMbDw0OhPwAcPny41P5ERERERFT+tH4qVEBAAIYMGYIWLVqgVatWiIiIwNOnTzFs2DAAwODBg1G9enWEhYUBAL766it4eXlh0aJF6N69O7Zu3Ypz585h9erV2nwapCapVIrg4OBip60RVUTcX0mXcH8lXcL9VbdpfbhZAFi2bBkWLFiAtLQ0uLq6YunSpXB3dwcAtG/fHo6OjtiwYYO8//bt2zF16lQkJyejbt26mD9/Prp166al9EREREREVCEKCyIiIiIi0m1av/M2ERERERHpPhYWRERERESkNhYWRERERESkNhYWRERERESkNhYWpDUzZsyARCJRmBo0aKDtWEQAgGPHjqFHjx6ws7ODRCLBnj17FOYLITB9+nTY2trCyMgI3t7eSExM1E5Yeu+9bX8dOnRose/bLl26aCcsvffCwsLQsmVLmJmZwcrKCr6+vkhISFDok5ubi9GjR+ODDz6Aqakp+vTpU+wGyVTxsLAgrWrcuDFSU1Pl04kTJ7QdiQgA8PTpU7i4uGD58uUlzp8/fz6WLl2KyMhInDlzBiYmJvDx8UFubu47Tkr09v0VALp06aLwfbtly5Z3mJDof44ePYrRo0fj9OnTOHz4MJ4/f47OnTvj6dOn8j4TJkzATz/9hO3bt+Po0aO4e/cuevfurcXUpAyt3yCP3m+VKlWCjY2NtmMQFdO1a1d07dq1xHlCCERERGDq1Kno1asXAOCHH36AtbU19uzZg379+r3LqERv3F+LSKVSft9ShRATE6PweMOGDbCyssL58+fh6emJx48fY926ddi8eTM6duwIAIiKikLDhg1x+vRptG7dWhuxSQk8YkFalZiYCDs7O9SqVQsDBw5ESkqKtiMRvVVSUhLS0tLg7e0tb7OwsIC7uztOnTqlxWREpTty5AisrKxQv359fPHFF3jw4IG2IxEBAB4/fgwAsLS0BACcP38ez58/V/iObdCgAWrWrMnv2AqOhQVpjbu7OzZs2ICYmBisXLkSSUlJaNeuHZ48eaLtaERvlJaWBgCwtrZWaLe2tpbPI6pIunTpgh9++AGxsbGYN28ejh49iq5du6KgoEDb0eg9V1hYiPHjx6Nt27Zo0qQJgJffsYaGhqhcubJCX37HVnw8FYq05tXD9k2bNoW7uzscHBywbds2jBgxQovJiIj+b3n19DxnZ2c0bdoUtWvXxpEjR9CpUyctJqP33ejRo/HXX3/xGsv/I3jEgiqMypUro169erhx44a2oxC9UdF56q+PUJKens5z2Ekn1KpVC1WrVuX3LWnVmDFj8PPPPyMuLg41atSQt9vY2CA/Px+ZmZkK/fkdW/GxsKAKIzs7Gzdv3oStra22oxC9kZOTE2xsbBAbGytvy8rKwpkzZ+Dh4aHFZETKuXPnDh48eMDvW9IKIQTGjBmD3bt347fffoOTk5PCfDc3NxgYGCh8xyYkJCAlJYXfsRUcT4UirQkMDESPHj3g4OCAu3fvIjg4GPr6+ujfv7+2oxEhOztb4a+5SUlJuHTpEiwtLVGzZk2MHz8es2fPRt26deHk5IRp06bBzs4Ovr6+2gtN76037a+WlpYICQlBnz59YGNjg5s3b+Lrr79GnTp14OPjo8XU9L4aPXo0Nm/ejL1798LMzEx+3YSFhQWMjIxgYWGBESNGICAgAJaWljA3N8fYsWPh4eHBEaEqOkGkJf7+/sLW1lYYGhqK6tWrC39/f3Hjxg1txyISQggRFxcnABSbhgwZIoQQorCwUEybNk1YW1sLqVQqOnXqJBISErQbmt5bb9pfc3JyROfOnUW1atWEgYGBcHBwECNHjhRpaWnajk3vqZL2VQAiKipK3ufZs2fiyy+/FFWqVBHGxsbik08+EampqdoLTUqRCCHEuy9niIiIiIjo/xJeY0FERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERKSkI0eOQCKRIDMzs0Ksh4ioImFhQURUBhKJ5I3TjBkzNL7N9u3bQyKRYOvWrQrtERERcHR0VDqvubk5WrZsib1792o8IxXXvn17jB8/XqGtTZs2SE1NhYWFhXZCERGVAxYWRERlkJqaKp8iIiJgbm6u0BYYGFgu25XJZJg6dSqeP3+u8rJRUVFITU3FuXPn0LZtW/Tt2xdXrlwph5T0NoaGhrCxsYFEItF2FCIijWFhQURUBjY2NvLJwsICEolE/tjKygrh4eGoUaMGpFIpXF1dERMTI182OTlZfuShTZs2kMlkaNKkCY4ePfrW7fbv3x+ZmZlYs2aNypkrV64MGxsb1KtXD7NmzcKLFy8QFxcnn3/79m34+fmhcuXKsLS0RK9evZCcnKywjvXr16Nx48aQSqWwtbXFmDFj5PNSUlLQq1cvmJqawtzcHH5+fkhPT5fPnzFjBlxdXbF+/XrUrFkTpqam+PLLL1FQUID58+fLX7s5c+YobFMikWDVqlX4+OOPYWxsjIYNG+LUqVO4ceMG2rdvDxMTE7Rp0wY3b95UWG7v3r1o3rw5ZDIZatWqhZCQELx48UJhvWvXrsUnn3wCY2Nj1K1bF/v27VNYx/79+1GvXj0YGRmhQ4cOxV6PBw8eoH///qhevTqMjY3h7OyMLVu2yOcPHToUR48exZIlS+RHjJKTk0s8FWrnzp3y19bR0RGLFi1S2JajoyNCQ0MxfPhwmJmZoWbNmli9erV8fn5+PsaMGQNbW1vIZDI4ODggLCwMRETvjCAiIrVERUUJCwsL+ePw8HBhbm4utmzZIuLj48XXX38tDAwMxPXr14UQQiQlJQkAokaNGmLHjh3i6tWr4rPPPhNmZmYiIyOj1O14eXmJr776SoSHhwtra2uRnZ0thBBi8eLFwsHB4Y0ZAYjdu3cLIYR4/vy5WLx4sQAgVq5cKYQQIj8/XzRs2FAMHz5cXL58WVy9elUMGDBA1K9fX+Tl5QkhhFixYoWQyWQiIiJCJCQkiLNnz4rFixcLIYQoKCgQrq6u4sMPPxTnzp0Tp0+fFm5ubsLLy0ueITg4WJiamoq+ffuKv//+W+zbt08YGhoKHx8fMXbsWBEfHy/Wr18vAIjTp08rZK9evbqIjo4WCQkJwtfXVzg6OoqOHTuKmJgYcfXqVdG6dWvRpUsX+TLHjh0T5ubmYsOGDeLmzZvi0KFDwtHRUcyYMUNhvTVq1BCbN28WiYmJYty4ccLU1FQ8ePBACCFESkqKkEqlIiAgQMTHx4sff/xRWFtbCwDi0aNHQggh7ty5IxYsWCAuXrwobt68KZYuXSr09fXFmTNnhBBCZGZmCg8PDzFy5EiRmpoqUlNTxYsXL0RcXJzCes6dOyf09PTEzJkzRUJCgoiKihJGRkYiKipKntfBwUFYWlqK5cuXi8TERBEWFib09PREfHy8EEKIBQsWCHt7e3Hs2DGRnJwsjh8/LjZv3vzG/YKISJNYWBARqen1wsLOzk7MmTNHoU/Lli3Fl19+KYT4X2Exd+5c+fznz5+LGjVqiHnz5pW6naLCIjc3Vzg4OIiZM2cKIZQvLGQymTAxMRF6enoCgHB0dJT/iN64caOoX7++KCwslC+Tl5cnjIyMxMGDB+XPa8qUKSWu/9ChQ0JfX1+kpKTI2/7++28BQJw9e1YI8bKwMDY2FllZWfI+Pj4+wtHRURQUFMjb6tevL8LCwhSyT506Vf741KlTAoBYt26dvG3Lli1CJpPJH3fq1EmEhoYqZNy4caOwtbUtdb3Z2dkCgDhw4IAQQoigoCDRqFEjhXV88803CgVBSbp37y4mTpwof1z0vr3q9cJiwIAB4qOPPlLoM2nSJIXtOzg4iEGDBskfFxYWCisrK3lxOHbsWNGxY0eF95CI6F3iqVBERBqUlZWFu3fvom3btgrtbdu2xbVr1xTaPDw85P+uVKkSWrRoUaxPSaRSKWbOnImFCxciIyND6WyLFy/GpUuXcODAATRq1Ahr166FpaUlAODPP//EjRs3YGZmBlNTU5iamsLS0hK5ubm4efMm7t27h7t376JTp04lrvvatWuwt7eHvb29vK1Ro0aoXLmywnNydHSEmZmZ/LG1tTUaNWoEPT09hbZ79+4prL9p06YK8wHA2dlZoS03NxdZWVny5zNz5kz5czE1NcXIkSORmpqKnJycEtdrYmICc3Nz+bavXbsGd3d3hRyvvmcAUFBQgFmzZsHZ2RmWlpYwNTXFwYMHkZKSUuLrVJpr166VuM8kJiaioKCgxLxFp98V5R06dCguXbqE+vXrY9y4cTh06JBKGYiI1FVJ2wGIiEh1gwYNwsKFCzF79uy3jghVxMbGBnXq1EGdOnUQFRWFbt264erVq7CyskJ2djbc3NywadOmYstVq1ZN4Ye/OgwMDBQeSySSEtsKCwtLXa7ogueS2oqWy87ORkhICHr37l0sg0wme2Oe17f9JgsWLMCSJUsQEREBZ2dnmJiYYPz48cjPz1d6Hap4U97mzZsjKSkJBw4cwK+//go/Pz94e3tjx44d5ZKFiOh1PGJBRKRB5ubmsLOzw++//67Q/vvvv6NRo0YKbadPn5b/+8WLFzh//jwaNmyo1Hb09PQQFhaGlStXFrugWBmtWrWCm5ub/ELp5s2bIzExEVZWVvLio2iysLCAmZkZHB0dERsbW+L6GjZsiNu3b+P27dvytqtXryIzM7PY834XmjdvjoSEhGLPpU6dOkoXSQ0bNsTZs2cV2l59z4CX72uvXr0waNAguLi4oFatWrh+/bpCH0NDQ4WjDqVtq6R9pl69etDX11cqL/By//P398eaNWsQHR2NnTt34uHDh0ovT0SkDhYWREQaNmnSJMybNw/R0dFISEjA5MmTcenSJXz11VcK/ZYvX47du3cjPj4eo0ePxqNHjzB8+HClt9O9e3e4u7tj1apVZco5fvx4rFq1Cv/++y8GDhyIqlWrolevXjh+/DiSkpJw5MgRjBs3Dnfu3AHwclSnRYsWYenSpUhMTMSFCxfw3XffAQC8vb3h7OyMgQMH4sKFCzh79iwGDx4MLy8vtGjRokz51DF9+nT88MMPCAkJwd9//41r165h69atmDp1qtLr+Pzzz5GYmIhJkyYhISEBmzdvxoYNGxT61K1bF4cPH8bJkydx7do1jBo1SmEkLODl6V9nzpxBcnIyMjIySjwiMnHiRMTGxmLWrFm4fv06vv/+eyxbtkylYYvDw8OxZcsWxMfH4/r169i+fTtsbGxQuXJlpddBRKQOFhZERBo2btw4BAQEYOLEiXB2dkZMTAz27duHunXrKvSbO3cu5s6dCxcXF5w4cQL79u1D1apVVdrWvHnzkJubW6acXbp0gZOTE+bMmQNjY2McO3YMNWvWRO/evdGwYUOMGDECubm5MDc3BwAMGTIEERERWLFiBRo3boyPP/4YiYmJAF6ekrN3715UqVIFnp6e8Pb2Rq1atRAdHV2mbOry8fHBzz//jEOHDqFly5Zo3bo1Fi9eDAcHB6XXUbNmTezcuRN79uyBi4sLIiMjERoaqtBn6tSpaN68OXx8fNC+fXvY2NjA19dXoU9gYCD09fXRqFEjVKtWrcTrL5o3b45t27Zh69ataNKkCaZPn46ZM2di6NChSuc1MzPD/Pnz0aJFC7Rs2RLJycnYv3+/xk5jIyJ6G4kQQmg7BBHR+yQ5ORlOTk64ePEiXF1dtR2HiIhII/hnDCIiIiIiUhsLCyIiIiIiUhtPhSIiIiIiIrXxiAUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQURvbckEglmzJghf7xhwwZIJBIkJydrLVNJKmquV73+WpanBQsWoFatWtDX14erq+s72aYq2rdvj/bt2yu0paeno2/fvvjggw8gkUgQEREBAEhMTETnzp1hYWEBiUSCPXv2vPO89O45Ojpi6NCh2o5BpHEsLIhUtGLFCkgkEri7u2s7SoXj6OiIjz/+uEzL7t+//539MNWknJwczJgxA0eOHFGq/5EjRyCRSOSTVCqFtbU12rdvj9DQUNy/f798A78jJ0+exIwZM5CZmanR9R46dAhff/012rZti6ioKISGhmp0/a8bOnSowvtlamqKWrVqoW/fvti5cycKCwuVWs+ECRNw8OBBBAUFYePGjejSpQsAYMiQIbhy5QrmzJmDjRs3okWLFuX5dNQSGhqqVOHTvn17hdestOldfN4dHR0hkUgwduzYYvOKPos7duwodfnw8HBIJBL8+uuvpfZZs2YNJBIJ9u3bp5HMRLqskrYDEOmaTZs2wdHREWfPnsWNGzdQp04dbUf6P2H//v1Yvny5VouLTz/9FP369YNUKlV6mZycHISEhABAsb9Sv8m4cePQsmVLFBQU4P79+zh58iSCg4MRHh6Obdu2oWPHjmrleteePXuGSpX+97+UkydPIiQkBEOHDkXlypU1tp3ffvsNenp6WLduHQwNDTW23jeRSqVYu3YtgJfP89atW/jpp5/Qt29ftG/fHnv37oW5ubm8/6FDh0rM3atXLwQGBsrbnj17hlOnTmHKlCkYM2ZM+T8RNYWGhqJv377w9fV9Y78pU6bgs88+kz/+448/sHTpUnz77bdo2LChvL1p06blFbWYNWvWICgoCHZ2diot169fP0yaNAmbN2+Gt7d3iX02b96MDz74AF27dtVEVCKdxsKCSAVJSUk4efIkdu3ahVGjRmHTpk0IDg5+pxkKCwuRn58PmUz2Treri4QQyM3NhZGRkVL99fX1oa+vX86pXmrXrh369u2r0Pbnn3+ic+fO6NOnD65evQpbW9t3nutVOTk5MDY2Vqrvu9of7927ByMjI40VFcrsI5UqVcKgQYMU2mbPno25c+ciKCgII0eORHR0tHxeSdnu3btXrMAqOjqlycIrNzcXhoaG0NPT3gkJH330kcJjmUyGpUuX4qOPPlKp+NaUxo0bIyEhAXPnzsXSpUtVWtbOzg4dOnTArl27sHLlymLF/b///otjx47hv//9LwwMDDQZm0gn8VQoIhVs2rQJVapUQffu3dG3b19s2rRJPu/58+ewtLTEsGHDii2XlZUFmUym8NfKvLw8BAcHo06dOpBKpbC3t8fXX3+NvLw8hWUlEgnGjBmDTZs2oXHjxpBKpYiJiQEALFy4EG3atMEHH3wAIyMjuLm5lXhY/9mzZxg3bhyqVq0KMzMz9OzZE//++2+JpyP8+++/GD58OKytrSGVStG4cWOsX7++TK9XcnIyJBIJFi5ciNWrV6N27dqQSqVo2bIl/vjjD3m/oUOHYvny5fLnWzQVKSwsREREBBo3bgyZTAZra2uMGjUKjx49Uthe0alYBw8eRIsWLWBkZIRVq1YpnbekaxnOnTsHHx8fVK1aFUZGRnBycsLw4cPlz69atWoAgJCQELVP8XBxcUFERAQyMzOxbNmyUnN9/PHHqFWrVonr8PDwKHY6zY8//gg3NzcYGRnB0tIS/fr1w+3btxX6tG/fHk2aNMH58+fh6ekJY2NjfPvtt299DYq8+rxnzJiBSZMmAQCcnJzkr0tycjK8vLzg4uJSYvb69evDx8en1NdHIpEgKioKT58+la9zw4YNAIAXL15g1qxZ8n3M0dER3377bbHPk7r7yKsmT56Mzp07Y/v27bh+/bq8/dVrLIreOyEEli9frrCPODg4AAAmTZoEiUQCR0dH+TqU+RwWncqzdetWTJ06FdWrV4exsTGysrIAAGfOnEGXLl1gYWEBY2NjeHl54ffff1dYx4wZMyCRSHDjxg350SULCwsMGzYMOTk5Cq/906dP8f3338ufg7rXCKxYsUL+nWZnZ4fRo0cXO3Xu1f2yTZs28v0vMjJS6e04Ojpi8ODBWLNmDe7evatyzkGDBuHx48f45Zdfis3bunUrCgsLMXDgQADKfye/ruh9eF1p11cdOHAA7dq1g4mJCczMzNC9e3f8/fffCn3S0tIwbNgw1KhRA1KpFLa2tujVq1eFvlaLdB8LCyIVbNq0Cb1794ahoSH69++PxMRE+Q9kAwMDfPLJJ9izZw/y8/MVltuzZw/y8vLQr18/AC9/KPfs2RMLFy5Ejx498N1338HX1xeLFy+Gv79/se3+9ttvmDBhAvz9/bFkyRL5D5AlS5agWbNmmDlzJkJDQ1GpUiX85z//KfY/wKFDh+K7775Dt27dMG/ePBgZGaF79+7FtpOeno7WrVvj119/xZgxY7BkyRLUqVMHI0aMkF9sWhabN2/GggULMGrUKMyePRvJycno3bs3nj9/DgAYNWqU/K+cGzdulE9FRo0ahUmTJqFt27ZYsmQJhg0bhk2bNsHHx0e+jiIJCQno378/PvroIyxZskSti3vv3buHzp07Izk5GZMnT8Z3332HgQMH4vTp0wCAatWqYeXKlQCATz75RJ67d+/eZd5m3759YWRkVOLpNEX8/f2RlJSkUJwBwK1bt3D69Gn5fgYAc+bMweDBg1G3bl2Eh4dj/PjxiI2NhaenZ7EfcQ8ePEDXrl3h6uqKiIgIdOjQ4a2vQUl69+6N/v37AwAWL14sf12qVauGTz/9FJcvX8Zff/2lsMwff/yB69evFzsy8KqNGzeiXbt2kEql8nV6enoCAD777DNMnz4dzZs3x+LFi+Hl5YWwsDCF16KIJveRTz/9FEIIHD58uMT5np6e8n35o48+UthHFi9eDADo378/Nm7cKP+Mqfo5nDVrFn755RcEBgYiNDQUhoaG+O233+Dp6YmsrCwEBwcjNDQUmZmZ6NixI86ePVtsHX5+fnjy5AnCwsLg5+eHDRs2yE/xA16+9lKpFO3atZM/h1GjRpX5dZsxYwZGjx4NOzs7LFq0CH369MGqVavQuXPnYp/pR48eoVu3bnBzc8P8+fNRo0YNfPHFFyr9wWPKlCl48eIF5s6dq3LW3r17QyaTYfPmzcXmbd68GQ4ODmjbti0A5b+T1bFx40Z0794dpqammDdvHqZNm4arV6/iww8/VCga+vTpg927d2PYsGFYsWIFxo0bhydPniAlJUVjWYiKEUSklHPnzgkA4vDhw0IIIQoLC0WNGjXEV199Je9z8OBBAUD89NNPCst269ZN1KpVS/5448aNQk9PTxw/flyhX2RkpAAgfv/9d3kbAKGnpyf+/vvvYplycnIUHufn54smTZqIjh07ytvOnz8vAIjx48cr9B06dKgAIIKDg+VtI0aMELa2tiIjI0Ohb79+/YSFhUWx7b3OwcFBdO/eXf44KSlJABAffPCBePjwobx97969xV6n0aNHi5K+ko4fPy4AiE2bNim0x8TEFGt3cHAQAERMTMwbcxZ5/flHRUUJACIpKUkIIcTu3bsFAPHHH3+Uuo779+8XW8+bxMXFCQBi+/btpfZxcXERVapUKTXX48ePhVQqFRMnTlRYbv78+UIikYhbt24JIYRITk4W+vr6Ys6cOQr9rly5IipVqqTQ7uXlJQCIyMhIhb7KvAZCFH8tFyxYoJC5SGZmppDJZOKbb75RaB83bpwwMTER2dnZb9zOkCFDhImJiULbpUuXBADx2WefKbQHBgYKAOK3336Tt6m6j5S0vVddvHhRABATJkyQt3l5eQkvLy+FfgDE6NGjFdqKPh8LFixQaFf2c1i0L9WqVUvhs1lYWCjq1q0rfHx8RGFhobw9JydHODk5iY8++kjeFhwcLACI4cOHK2zrk08+ER988IFCm4mJiRgyZEipr0Vptm/fLgCIuLg4IYQQ9+7dE4aGhqJz586ioKBA3m/ZsmUCgFi/fr28rWi/XLRokbwtLy9PuLq6CisrK5Gfn//Gbb/6nTRs2DAhk8nE3bt3hRDKfRaL/Oc//xEymUw8fvxY3hYfHy8AiKCgIHmbMt/JRblefS2L3ofXvf7Zf/LkiahcubIYOXKkQr+0tDRhYWEhb3/06FGJ+xZReeMRCyIlbdq0CdbW1ujQoQOAl6cG+Pv7Y+vWrSgoKAAAdOzYEVWrVlU43/rRo0c4fPiwwpGI7du3o2HDhmjQoAEyMjLkU9EFu3FxcQrb9vLyQqNGjYplevW88EePHuHx48do164dLly4IG8vOm3qyy+/VFj29VFShBDYuXMnevToASGEQi4fHx88fvxYYb2q8Pf3R5UqVeSP27VrBwD4559/3rrs9u3bYWFhgY8++kghk5ubG0xNTYu9Vk5OTm88nUYVRee+//zzz8X+ilqeTE1N8eTJk1Lnm5ubo2vXrti2bRuEEPL26OhotG7dGjVr1gQA7Nq1C4WFhfDz81N47WxsbFC3bt1ir51UKi12Kp+mXwMLCwv06tULW7ZskWcvKChAdHQ0fH19YWJiovI69+/fDwAICAhQaJ84cSIAFPtrsSb3EVNTUwB44/ulirJ8DocMGaLwXXDp0iUkJiZiwIABePDggXz5p0+folOnTjh27Fix0aw+//xzhcft2rXDgwcP5KdVadKvv/6K/Px8jB8/XuFakJEjR8Lc3LzY+1WpUiWFoyOGhoYYNWoU7t27h/Pnzyu93alTp5b5qMWgQYOQm5uLXbt2yduKjmAUnQYFKPedrI7Dhw8jMzMT/fv3V9g39PX14e7uLv9MF12HdOTIkWKnjBKVJxYWREooKCjA1q1b0aFDByQlJeHGjRu4ceMG3N3dkZ6ejtjYWAAv/wfYp08f7N27V35u965du/D8+XOFwiIxMRF///03qlWrpjDVq1cPwMtTcF7l5ORUYq6ff/4ZrVu3hkwmg6WlpfzUnMePH8v73Lp1C3p6esXW8fpoVvfv30dmZiZWr15dLFfRj83Xcymr6IdukaIiQ5n/4SUmJuLx48ewsrIqlis7O/utr9XDhw+RlpYmn159bd7Gy8sLffr0QUhICKpWrYpevXohKiqq2Hn7mpadnQ0zM7M39vH398ft27dx6tQpAMDNmzdx/vz5YvuZEAJ169Yt9tpdu3at2GtXvXr1Yhcel8drMHjwYKSkpOD48eMAXv7QTE9Px6efflqm9RXt46/v0zY2NqhcuTJu3bql0F7a56kssrOzAeCt75eyyvI5fP35JCYmAnhZcLy+jrVr1yIvL6/Y50Cdz6iqit6P+vXrK7QbGhqiVq1axd4vOzu7YgVn0XelKtcL1KpVC59++ilWr16N1NRUlTJ37doVlpaWCqdDbdmyBS4uLmjcuLG8TZnvZHUUvbcdO3Ys9t4eOnRIvm9IpVLMmzcPBw4cgLW1NTw9PTF//nykpaVpJAdRaTgqFJESfvvtN6SmpmLr1q3YunVrsfmbNm1C586dAbwcnnDVqlU4cOAAfH19sW3bNjRo0EDhgtXCwkI4OzsjPDy8xO3Z29srPC5pxJrjx4+jZ8+e8PT0xIoVK2BrawsDAwNERUWVeC7w2xT9BXPQoEEYMmRIiX3KOjxkaSMavfrX9jflsrKyUrhQ/lVFF08Xef216t27N44ePSp/PGTIEPkFv29TNMb96dOn8dNPP+HgwYMYPnw4Fi1ahNOnT8v/Wq1Jz58/x/Xr19GkSZM39uvRoweMjY2xbds2tGnTBtu2bYOenh7+85//yPsUFhZCIpHgwIEDJb4Hr+cvaT8rj9fAx8cH1tbW+PHHH+Hp6Ykff/wRNjY2pQ7nqaySLn4tibKjhCmj6FoRTQ07XZbP4evPp2gdCxYsKPX6kdffN3U+o7pkypQp2LhxI+bNm/fWYXNfZWBgAD8/P6xZswbp6elISUlBYmIi5s+fL++jzndyaftu0dHwIkXv7caNG2FjY1Os/6tDPo8fPx49evTAnj17cPDgQUybNg1hYWH47bff0KxZM6WfO5EqWFgQKWHTpk2wsrKSj1z0ql27dmH37t2IjIyEkZERPD09YWtri+joaHz44Yf47bffMGXKFIVlateujT///BOdOnVS+sfQ63bu3AmZTIaDBw8qDIEYFRWl0M/BwQGFhYVISkpC3bp15e03btxQ6FetWjWYmZmhoKBA7R94ZVHa61C7dm38+uuvaNu2bZl+EC5atEjhr66qjmMPAK1bt0br1q0xZ84cbN68GQMHDsTWrVvx2Weflfn9K82OHTvw7Nmzt56qY2Jigo8//hjbt29HeHg4oqOj0a5dO4XnV7t2bQgh4OTkJP8Lb1m96TUoyZteF319fQwYMAAbNmzAvHnzsGfPHowcObLMQ+oW7eOJiYkK90lIT09HZmamfPSl8rBx40ZIJJJiQ6yWlSY+h7Vr1wbw8pQ5TX6WNbWvF70fCQkJCqOb5efnIykpqVjmu3fv4unTpwpHLYpG4Xp1JC1l1K5dG4MGDcKqVatUvsnpwIEDERkZiejoaCQlJUEikcgHKQCU/04uSdERoszMTIXhh18/elP03lpZWSn13tauXRsTJ07ExIkTkZiYCFdXVyxatAg//vjjW5clKgueCkX0Fs+ePcOuXbvw8ccfo2/fvsWmMWPG4MmTJ/K7rurp6aFv37746aefsHHjRrx48aLYSE9+fn74999/sWbNmhK39/Tp07fm0tfXh0QiUfiLVnJycrE74xb9QF2xYoVC+3fffVdsfX369MHOnTuLjdgDoNzvCF30o+H1kYr8/PxQUFCAWbNmFVvmxYsXb72zs5ubG7y9veVTSdeqlObRo0fF/mJb9BfgolOBiu7zoIk7TP/5558YP348qlSpgtGjR7+1v7+/P+7evYu1a9fizz//LLaf9e7dG/r6+ggJCSn2PIQQePDgwVu3ocxrUJLS3s8in376KR49eoRRo0YhOzv7jaNBvU23bt0AoNiISUVHBEsaAU0T5s6di0OHDsHf31+haFeHJj6Hbm5uqF27NhYuXCg/VUvVdZTExMREI/u5t7c3DA0NsXTpUoV9a926dXj8+HGx9+vFixcKQwLn5+dj1apVqFatGtzc3FTe/tSpU/H8+XOFow3KaNu2LRwdHfHjjz8iOjoaXl5eqFGjhny+st/JJSkqGI4dOyZvKxre91U+Pj4wNzdHaGhoidc8Fb23OTk5yM3NLbYNMzOzcj+Vk95vPGJB9Bb79u3DkydP0LNnzxLnt27dGtWqVcOmTZvkP+z8/f3x3XffITg4GM7Ozgp/RQVe/qjatm0bPv/8c8TFxaFt27YoKChAfHw8tm3bJh9j/026d++O8PBwdOnSBQMGDMC9e/ewfPly1KlTB5cvX5b3c3NzQ58+fRAREYEHDx6gdevWOHr0qPwvfq/+FXLu3LmIi4uDu7s7Ro4ciUaNGuHhw4e4cOECfv31Vzx8+LBMr6Eyin4gjBs3Dj4+PtDX10e/fv3g5eWFUaNGISwsDJcuXULnzp1hYGCAxMREbN++HUuWLCl2ozlN+f7777FixQp88sknqF27Np48eYI1a9bA3Nxc/mPWyMgIjRo1QnR0NOrVqwdLS0s0adLkracyHT9+HLm5uSgoKMCDBw/w+++/Y9++fbCwsMDu3btLPM3hdd26dYOZmRkCAwPlP0hfVbt2bcyePRtBQUFITk6Gr68vzMzMkJSUhN27d+O///2vwr1VyvoalKTo/ZwyZQr69esHAwMD9OjRQ15wNGvWDE2aNJEPZNC8efO3Pt/SuLi4YMiQIVi9ejUyMzPh5eWFs2fP4vvvv4evr698wIWyevHihfwvvLm5ubh16xb27duHy5cvo0OHDli9erVa63+dup9DPT09rF27Fl27dkXjxo0xbNgwVK9eHf/++y/i4uJgbm6On376SeVcbm5u+PXXXxEeHg47Ozs4OTmp/Fd/4OVRmaCgIISEhKBLly7o2bMnEhISsGLFCrRs2bJYkWlnZ4d58+YhOTkZ9erVQ3R0NC5duoTVq1eX6aZ0RUctXv/R/jYSiQQDBgxAaGgoAGDmzJkK85X9Ti5J586dUbNmTYwYMQKTJk2Cvr4+1q9fj2rVqikMD2tubo6VK1fi008/RfPmzdGvXz95n19++QVt27bFsmXLcP36dXTq1Al+fn5o1KgRKlWqhN27dyM9Pb3EIZiJNEYLI1ER6ZQePXoImUwmnj59WmqfoUOHCgMDA/nwkIWFhcLe3l4AELNnzy5xmfz8fDFv3jzRuHFjIZVKRZUqVYSbm5sICQlRGNIQJQxTWWTdunWibt26QiqVigYNGoioqKgShy18+vSpGD16tLC0tBSmpqbC19dXJCQkCABi7ty5Cn3T09PF6NGjhb29vTAwMBA2NjaiU6dOYvXq1W99rUobbrakIQ/x2vCkL168EGPHjhXVqlUTEomk2HNYvXq1cHNzE0ZGRsLMzEw4OzuLr7/+Wj50ZEnbf5vXM7w+tOOFCxdE//79Rc2aNYVUKhVWVlbi448/FufOnVNYz8mTJ4Wbm5swNDR869CzRUNcFk0GBgaiWrVqwtPTU8yZM0fcu3ev2DKv53rVwIEDBQDh7e1d6jZ37twpPvzwQ2FiYiJMTExEgwYNxOjRo0VCQoK8j5eXl2jcuHGxZZV9DUp63rNmzRLVq1cXenp6JeafP3++ACBCQ0NLzf660oZ/ff78uQgJCRFOTk7CwMBA2Nvbi6CgIJGbm6vQT9V9ZMiQIQrvl7GxsXB0dBR9+vQRO3bsUBgutYi6w80Kodzn8G3DpV68eFH07t1bfPDBB0IqlQoHBwfh5+cnYmNj5X2Kvi/u37+vsGxJ+1x8fLzw9PQURkZGAoDSQ8++PtxskWXLlokGDRoIAwMDYW1tLb744gvx6NEjhT5F++W5c+eEh4eHkMlkwsHBQSxbtkypbZf2ficmJgp9fX2lh5st8vfffwsAQiqVFssqhPLfya8PNyvEy6HB3d3dhaGhoahZs6YIDw8v9bMfFxcnfHx8hIWFhZDJZKJ27dpi6NCh8s9lRkaGGD16tGjQoIEwMTERFhYWwt3dXWzbtk3p50pUFhIh/o9dmUVESrl06RKaNWuGH3/8UWG4RKJ3ZcmSJZgwYQKSk5OLjUpEBLy883ZGRkaJp4URUcXDayyI3gPPnj0r1hYREQE9PT35nYuJ3iUhBNatWwcvLy8WFURE/0fwGgui98D8+fNx/vx5dOjQAZUqVcKBAwdw4MAB/Pe//y02tC1ReXr69Cn27duHuLg4XLlyBXv37tV2JCIi0hAWFkTvgTZt2uDw4cOYNWsWsrOzUbNmTcyYMaPYMLhE5e3+/fsYMGAAKleujG+//bbUQRGIiEj38BoLIiIiIiJSG6+xICIiIiIitb13p0IVFhbi7t27MDMz0/gdc4mIiIiI/i8RQuDJkyews7ODnt6bj0m8d4XF3bt3ebEqEREREZEKbt++rXC3+ZK8d4WFmZkZgJcvjrm5uZbTEBERERFVXFlZWbC3t5f/hn6T966wKDr9ydzcnIUFEREREZESlLmEgBdvExERERGR2lhYEBERERGR2lhYEBERERGR2lhYEBERERGR2lhYEBERERGR2rRaWBw7dgw9evSAnZ0dJBIJ9uzZ89Zljhw5gubNm0MqlaJOnTrYsGFDueckIiIiIqI302ph8fTpU7i4uGD58uVK9U9KSkL37t3RoUMHXLp0CePHj8dnn32GgwcPlnNSIiIiIiJ6E63ex6Jr167o2rWr0v0jIyPh5OSERYsWAQAaNmyIEydOYPHixfDx8Slxmby8POTl5ckfZ2VlqReaiIiIiIiK0alrLE6dOgVvb2+FNh8fH5w6darUZcLCwmBhYSGf7O3tyzsmEREREdF7R6fuvJ2WlgZra2uFNmtra2RlZeHZs2cwMjIqtkxQUBACAgLkj4tuS65tkpC3372QSAQLbUcAAMy9mKHtCFTBTW5WVdsRiIhIy3SqsCgLqVQKqVSq7RhERERExfAPN/Q2uvSHG50qLGxsbJCenq7Qlp6eDnNz8xKPVhAR0fuHP9TobXTphxqRLtGpayw8PDwQGxur0Hb48GF4eHhoKREREREREQFaLiyys7Nx6dIlXLp0CcDL4WQvXbqElJQUAC+vjxg8eLC8/+eff45//vkHX3/9NeLj47FixQps27YNEyZM0EZ8IiIiIiL6/7RaWJw7dw7NmjVDs2bNAAABAQFo1qwZpk+fDgBITU2VFxkA4OTkhF9++QWHDx+Gi4sLFi1ahLVr15Y61CwREREREb0bWr3Gon379hCi9FFvSrqrdvv27XHx4sVyTEVERERERKrSqWssiIiIiIioYmJhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREamNhQUREREREalO5sPjnn3/KIwcREREREekwlQuLOnXqoEOHDvjxxx+Rm5tbHpmIiIiIiEjHqFxYXLhwAU2bNkVAQABsbGwwatQonD17tjyyERERERGRjlC5sHB1dcWSJUtw9+5drF+/Hqmpqfjwww/RpEkThIeH4/79++WRk4iIiIiIKrAyX7xdqVIl9O7dG9u3b8e8efNw48YNBAYGwt7eHoMHD0ZqaqomcxIRERERUQVW5sLi3Llz+PLLL2Fra4vw8HAEBgbi5s2bOHz4MO7evYtevXppMicREREREVVglVRdIDw8HFFRUUhISEC3bt3www8/oFu3btDTe1mjODk5YcOGDXB0dNR0ViIiIiIiqqBULixWrlyJ4cOHY+jQobC1tS2xj5WVFdatW6d2OCIiIiIi0g0qFxaHDx9GzZo15UcoigghcPv2bdSsWROGhoYYMmSIxkISEREREVHFpvI1FrVr10ZGRkax9ocPH8LJyUkjoYiIiIiISLeoXFgIIUpsz87OhkwmUzsQERERERHpHqVPhQoICAAASCQSTJ8+HcbGxvJ5BQUFOHPmDFxdXTUekIiIiIiIKj6lC4uLFy8CeHnE4sqVKzA0NJTPMzQ0hIuLCwIDAzWfkIiIiIiIKjylC4u4uDgAwLBhw7BkyRKYm5uXWygiIiIiItItKo8KFRUVVR45iIiIiIhIhylVWPTu3RsbNmyAubk5evfu/ca+u3bt0kgwIiIiIiLSHUoVFhYWFpBIJPJ/ExERERERvUqpwuLV0594KhQREREREb1O5ftYPHv2DDk5OfLHt27dQkREBA4dOqTRYEREREREpDtULix69eqFH374AQCQmZmJVq1aYdGiRejVqxdWrlyp8YBERERERFTxqVxYXLhwAe3atQMA7NixAzY2Nrh16xZ++OEHLF26VOMBiYiIiIio4lO5sMjJyYGZmRkA4NChQ+jduzf09PTQunVr3Lp1S+MBiYiIiIio4lO5sKhTpw727NmD27dv4+DBg+jcuTMA4N69e2W6ad7y5cvh6OgImUwGd3d3nD179o39IyIiUL9+fRgZGcHe3h4TJkxAbm6uytslIiIiIiLNUbmwmD59OgIDA+Ho6Ah3d3d4eHgAeHn0olmzZiqtKzo6GgEBAQgODsaFCxfg4uICHx8f3Lt3r8T+mzdvxuTJkxEcHIxr165h3bp1iI6Oxrfffqvq0yAiIiIiIg1SubDo27cvUlJScO7cOcTExMjbO3XqhMWLF6u0rvDwcIwcORLDhg1Do0aNEBkZCWNjY6xfv77E/idPnkTbtm0xYMAAODo6onPnzujfv/9bj3IQEREREVH5UqmweP78OSpVqoSMjAw0a9YMenr/W7xVq1Zo0KCB0uvKz8/H+fPn4e3t/b8wenrw9vbGqVOnSlymTZs2OH/+vLyQ+Oeff7B//35069at1O3k5eUhKytLYSIiIiIiIs1S6gZ5RQwMDFCzZk0UFBSoveGMjAwUFBTA2tpaod3a2hrx8fElLjNgwABkZGTgww8/hBACL168wOeff/7GU6HCwsIQEhKidl4iIiIiIiqdyqdCTZkyBd9++y0ePnxYHnne6MiRIwgNDcWKFStw4cIF7Nq1C7/88gtmzZpV6jJBQUF4/PixfLp9+/Y7TExERERE9H5Q6YgFACxbtgw3btyAnZ0dHBwcYGJiojD/woULSq2natWq0NfXR3p6ukJ7eno6bGxsSlxm2rRp+PTTT/HZZ58BAJydnfH06VP897//xZQpUxROzSoilUohlUqVykRERERERGWjcmHh6+urkQ0bGhrCzc0NsbGx8nUWFhYiNjYWY8aMKXGZnJycYsWDvr4+AEAIoZFcRERERESkOpULi+DgYI1tPCAgAEOGDEGLFi3QqlUrRERE4OnTpxg2bBgAYPDgwahevTrCwsIAAD169EB4eDiaNWsGd3d33LhxA9OmTUOPHj3kBQYREREREb17KhcWAJCZmYkdO3bg5s2bmDRpEiwtLXHhwgVYW1ujevXqSq/H398f9+/fx/Tp05GWlgZXV1fExMTIL+hOSUlROEIxdepUSCQSTJ06Ff/++y+qVauGHj16YM6cOWV5GkREREREpCEqFxaXL1+Gt7c3LCwskJycjJEjR8LS0hK7du1CSkoKfvjhB5XWN2bMmFJPfTpy5Ihi2EqVEBwcrNGjJkREREREpD6VR4UKCAjA0KFDkZiYCJlMJm/v1q0bjh07ptFwRERERESkG1QuLP744w+MGjWqWHv16tWRlpamkVBERERERKRbVC4spFJpiXevvn79OqpVq6aRUEREREREpFtULix69uyJmTNn4vnz5wAAiUSClJQUfPPNN+jTp4/GAxIRERERUcWncmGxaNEiZGdnw8rKCs+ePYOXlxfq1KkDMzMzjs5ERERERPSeUnlUKAsLCxw+fBgnTpzA5cuXkZ2djebNm8Pb27s88hERERERkQ5QubC4ffs27O3t8eGHH+LDDz8sj0xERERERKRjVD4VytHREV5eXlizZg0ePXpUHpmIiIiIiEjHqFxYnDt3Dq1atcLMmTNha2sLX19f7NixA3l5eeWRj4iIiIiIdIDKhUWzZs2wYMECpKSk4MCBA6hWrRr++9//wtraGsOHDy+PjEREREREVMGpXFgUkUgk6NChA9asWYNff/0VTk5O+P777zWZjYiIiIiIdESZC4s7d+5g/vz5cHV1RatWrWBqaorly5drMhsREREREekIlUeFWrVqFTZv3ozff/8dDRo0wMCBA7F37144ODiURz4iIiIiItIBKhcWs2fPRv/+/bF06VK4uLiURyYiIiIiItIxKhcWKSkpkEgk5ZGFiIiIiIh0lFKFxeXLl9GkSRPo6enhypUrb+zbtGlTjQQjIiIiIiLdoVRh4erqirS0NFhZWcHV1RUSiQRCCPn8oscSiQQFBQXlFpaIiIiIiCompQqLpKQkVKtWTf5vIiIiIiKiVylVWLw64hNHfyIiIiIiotepfPH2b7/9hl27diE5ORkSiQROTk7o27cvPD09yyMfERERERHpAJVukPf555/D29sbW7ZswYMHD3D//n1s2rQJHTp0wNixY8srIxERERERVXBKFxa7d+9GVFQU1q9fj4yMDJw6dQqnT5/G/fv3sWbNGqxevRr79u0rz6xERERERFRBKV1YREVFISAgAEOHDlW4j4Wenh6GDx+O8ePHY926deUSkoiIiIiIKjalC4sLFy7gk08+KXV+7969cf78eY2EIiIiIiIi3aJ0YZGRkYEaNWqUOr9GjRp48OCBRkIREREREZFuUbqwyM/Ph4GBQanzK1WqhPz8fI2EIiIiIiIi3aLScLPTpk2DsbFxifNycnI0EoiIiIiIiHSP0oWFp6cnEhIS3tqHiIiIiIjeP0oXFkeOHCnHGEREREREpMtUukEeERERERFRSVhYEBERERGR2lhYEBERERGR2lhYEBERERGR2lQuLFJSUiCEKNYuhEBKSopGQhERERERkW5RubBwcnLC/fv3i7U/fPgQTk5OGglFRERERES6ReXCQggBiURSrD07OxsymUwjoYiIiIiISLcofR+LgIAAAIBEIil2B+6CggKcOXMGrq6uKgdYvnw5FixYgLS0NLi4uOC7775Dq1atSu2fmZmJKVOmYNeuXXj48CEcHBwQERGBbt26qbxtIiIiIiLSDKULi4sXLwJ4ecTiypUrMDQ0lM8zNDSEi4sLAgMDVdp4dHQ0AgICEBkZCXd3d0RERMDHxwcJCQmwsrIq1j8/Px8fffQRrKyssGPHDlSvXh23bt1C5cqVVdouERERERFpltKFRVxcHABg2LBhWLJkCczNzdXeeHh4OEaOHIlhw4YBACIjI/HLL79g/fr1mDx5crH+69evx8OHD3Hy5EkYGBgAABwdHdXOQURERERE6lH5GouoqCiFoiIrKwt79uxBfHy8SuvJz8/H+fPn4e3t/b8wenrw9vbGqVOnSlxm37598PDwwOjRo2FtbY0mTZogNDQUBQUFpW4nLy8PWVlZChMREREREWmWyoWFn58fli1bBgB49uwZWrRoAT8/Pzg7O2Pnzp1KrycjIwMFBQWwtrZWaLe2tkZaWlqJy/zzzz/YsWMHCgoKsH//fkybNg2LFi3C7NmzS91OWFgYLCws5JO9vb3SGYmIiIiISDkqFxbHjh1Du3btAAC7d++GEAKZmZlYunTpG3/ga0JhYSGsrKywevVquLm5wd/fH1OmTEFkZGSpywQFBeHx48fy6fbt2+WakYiIiIjofaRyYfH48WNYWloCAGJiYtCnTx8YGxuje/fuSExMVHo9VatWhb6+PtLT0xXa09PTYWNjU+Iytra2qFevHvT19eVtDRs2RFpaGvLz80tcRiqVwtzcXGEiIiIiIiLNUrmwsLe3x6lTp/D06VPExMSgc+fOAIBHjx6pdB8LQ0NDuLm5ITY2Vt5WWFiI2NhYeHh4lLhM27ZtcePGDRQWFsrbrl+/DltbW4VRqoiIiIiI6N1SubAYP348Bg4ciBo1asDOzg7t27cH8PIUKWdnZ5XWFRAQgDVr1uD777/HtWvX8MUXX+Dp06fyUaIGDx6MoKAgef8vvvgCDx8+xFdffYXr16/jl19+QWhoKEaPHq3q0yAiIiIiIg1SerjZIl9++SVatWqF27dv46OPPoKe3svapFatWipfY+Hv74/79+9j+vTpSEtLg6urK2JiYuQXdKekpMjXD7w8WnLw4EFMmDABTZs2RfXq1fHVV1/hm2++UfVpEBERERGRBqlcWABAixYt0KJFC4W27t27lynAmDFjMGbMmBLnHTlypFibh4cHTp8+XaZtERERERFR+VCqsAgICMCsWbNgYmKCgICAN/YNDw/XSDAiIiIiItIdShUWFy9exPPnz+X/Lo1EItFMKiIiIiIi0ilKFRZxcXEl/puIiIiIiAgow6hQREREREREr1PqiEXv3r2VXuGuXbvKHIaIiIiIiHSTUoWFhYVFeecgIiIiIiIdplRhERUVVd45iIiIiIhIh6l1jcXcuXORmZmpoShERERERKSr1CosQkND8fDhQ01lISIiIiIiHaVWYSGE0FQOIiIiIiLSYSoVFkIIpKSkIDc3t7zyEBERERGRDlK5sKhTpw5u374NALh69SocHBzKJRgREREREekOlQoLPT091K1bFw8ePAAA2NvbQ19fv1yCERERERGR7lD5Gou5c+di0qRJ+Ouvv8ojDxERERER6SCl7mPxqsGDByMnJwcuLi4wNDSEkZGRwnyOEkVERERE9P5RubCIiIgohxhERERERKTLVC4shgwZUh45iIiIiIhIh5XpPhY3b97E1KlT0b9/f9y7dw8AcODAAfz9998aDUdERERERLpB5cLi6NGjcHZ2xpkzZ7Br1y5kZ2cDAP78808EBwdrPCAREREREVV8KhcWkydPxuzZs3H48GEYGhrK2zt27IjTp09rNBwREREREekGlQuLK1eu4JNPPinWbmVlhYyMDI2EIiIiIiIi3aJyYVG5cmWkpqYWa7948SKqV6+ukVBERERERKRbVC4s+vXrh2+++QZpaWmQSCQoLCzE77//jsDAQAwePLg8MhIRERERUQWncmERGhqKBg0awN7eHtnZ2WjUqBE8PT3Rpk0bTJ06tTwyEhERERFRBafyfSwMDQ2xZs0aTJ8+HVeuXEF2djaaNWuGunXrlkc+IiIiIiLSASofsZg5cyZycnJgb2+Pbt26wc/PD3Xr1sWzZ88wc+bM8shIREREREQVnMqFRUhIiPzeFa/KyclBSEiIRkIREREREZFuUbmwEEJAIpEUa//zzz9haWmpkVBERERERKRblL7GokqVKpBIJJBIJKhXr55CcVFQUIDs7Gx8/vnn5RKSiIiIiIgqNqULi4iICAghMHz4cISEhMDCwkI+z9DQEI6OjvDw8CiXkEREREREVLEpXVgMGTIEAODk5IQ2bdrAwMCg3EIREREREZFuUXm4WS8vLxQWFuL69eu4d+8eCgsLFeZ7enpqLBwREREREekGlQuL06dPY8CAAbh16xaEEArzJBIJCgoKNBaOiIiIiIh0g8qFxeeff44WLVrgl19+ga2tbYkjRBERERER0ftF5cIiMTERO3bsQJ06dcojDxERERER6SCV72Ph7u6OGzdulEcWIiIiIiLSUSofsRg7diwmTpyItLQ0ODs7FxsdqmnTphoLR0REREREukHlIxZ9+vTBtWvXMHz4cLRs2RKurq5o1qyZ/L9lsXz5cjg6OkImk8Hd3R1nz55VarmtW7dCIpHA19e3TNslIiIiIiLNUPmIRVJSkkYDREdHIyAgAJGRkXB3d0dERAR8fHyQkJAAKyurUpdLTk5GYGAg2rVrp9E8RERERESkOpULCwcHB40GCA8Px8iRIzFs2DAAQGRkJH755ResX78ekydPLnGZgoICDBw4ECEhITh+/DgyMzNLXX9eXh7y8vLkj7OysjSan4iIiIiIVCgs9u3bp1S/nj17Kr3x/Px8nD9/HkFBQfI2PT09eHt749SpU6UuN3PmTFhZWWHEiBE4fvz4G7cRFhaGkJAQpTMREREREZHqlC4slLmOQdUb5GVkZKCgoADW1tYK7dbW1oiPjy9xmRMnTmDdunW4dOmSUtsICgpCQECA/HFWVhbs7e2VzkhERERERG+ndGFRWFhYnjmU8uTJE3z66adYs2YNqlatqtQyUqkUUqm0nJMREREREb3fVL7GQpOqVq0KfX19pKenK7Snp6fDxsamWP+bN28iOTkZPXr0kLcVFTyVKlVCQkICateuXb6hiYiIiIioGJWHm9UkQ0NDuLm5ITY2Vt5WWFiI2NhYeHh4FOvfoEEDXLlyBZcuXZJPPXv2RIcOHXDp0iWe4kREREREpCVaPWIBAAEBARgyZAhatGiBVq1aISIiAk+fPpWPEjV48GBUr14dYWFhkMlkaNKkicLylStXBoBi7URERERE9O5ovbDw9/fH/fv3MX36dKSlpcHV1RUxMTHyC7pTUlKgp6fVAytERERERPQWWi8sAGDMmDEYM2ZMifOOHDnyxmU3bNig+UBERERERKSSMh0KyMzMxNq1axEUFISHDx8CAC5cuIB///1Xo+GIiIiIiEg3qHzE4vLly/D29oaFhQWSk5MxcuRIWFpaYteuXUhJScEPP/xQHjmJiIiIiKgCU/mIRUBAAIYOHYrExETIZDJ5e7du3XDs2DGNhiMiIiIiIt2gcmHxxx9/YNSoUcXaq1evjrS0NI2EIiIiIiIi3aJyYSGVSpGVlVWs/fr166hWrZpGQhERERERkW5RubDo2bMnZs6ciefPnwMAJBIJUlJS8M0336BPnz4aD0hERERERBWfyoXFokWLkJ2dDSsrKzx79gxeXl6oU6cOzMzMMGfOnPLISEREREREFZzKo0JZWFjg8OHDOHHiBC5fvozs7Gw0b94c3t7e5ZGPiIiIiIh0QJlvkPfhhx/iww8/1GQWIiIiIiLSUSoXFkuXLi2xXSKRQCaToU6dOvD09IS+vr7a4YiIiIiISDeoXFgsXrwY9+/fR05ODqpUqQIAePToEYyNjWFqaop79+6hVq1aiIuLg729vcYDExERERFRxaPyxduhoaFo2bIlEhMT8eDBAzx48ADXr1+Hu7s7lixZgpSUFNjY2GDChAnlkZeIiIiIiCoglY9YTJ06FTt37kTt2rXlbXXq1MHChQvRp08f/PPPP5g/fz6HniUiIiIieo+ofMQiNTUVL168KNb+4sUL+Z237ezs8OTJE/XTERERERGRTlC5sOjQoQNGjRqFixcvytsuXryIL774Ah07dgQAXLlyBU5OTppLSUREREREFZrKhcW6detgaWkJNzc3SKVSSKVStGjRApaWlli3bh0AwNTUFIsWLdJ4WCIiIiIiqphUvsbCxsYGhw8fRnx8PK5fvw4AqF+/PurXry/v06FDB80lJCIiIiKiCq/MN8hr0KABGjRooMksRERERESko8pUWNy5cwf79u1DSkoK8vPzFeaFh4drJBgREREREekOlQuL2NhY9OzZE7Vq1UJ8fDyaNGmC5ORkCCHQvHnz8shIREREREQVnMoXbwcFBSEwMBBXrlyBTCbDzp07cfv2bXh5eeE///lPeWQkIiIiIqIKTuXC4tq1axg8eDAAoFKlSnj27BlMTU0xc+ZMzJs3T+MBiYiIiIio4lO5sDAxMZFfV2Fra4ubN2/K52VkZGguGRERERER6QyVr7Fo3bo1Tpw4gYYNG6Jbt26YOHEirly5gl27dqF169blkZGIiIiIiCo4lQuL8PBwZGdnAwBCQkKQnZ2N6Oho1K1blyNCERERERG9p1QqLAoKCnDnzh00bdoUwMvToiIjI8slGBERERER6Q6VrrHQ19dH586d8ejRo/LKQ0REREREOkjli7ebNGmCf/75pzyyEBERERGRjlK5sJg9ezYCAwPx888/IzU1FVlZWQoTERERERG9f1S+eLtbt24AgJ49e0IikcjbhRCQSCQoKCjQXDoiIiIiItIJKhcWcXFx5ZGDiIiIiIh0mMqFhZeXV3nkICIiIiIiHabyNRYAcPz4cQwaNAht2rTBv//+CwDYuHEjTpw4odFwRERERESkG1QuLHbu3AkfHx8YGRnhwoULyMvLAwA8fvwYoaGhGg9IREREREQVX5lGhYqMjMSaNWtgYGAgb2/bti0uXLig0XBERERERKQbVC4sEhIS4OnpWazdwsICmZmZmshEREREREQ6RuXCwsbGBjdu3CjWfuLECdSqVatMIZYvXw5HR0fIZDK4u7vj7NmzpfZds2YN2rVrhypVqqBKlSrw9vZ+Y38iIiIiIip/KhcWI0eOxFdffYUzZ85AIpHg7t272LRpEwIDA/HFF1+oHCA6OhoBAQEIDg7GhQsX4OLiAh8fH9y7d6/E/keOHEH//v0RFxeHU6dOwd7eHp07d5ZfRE5ERERERO+eysPNTp48GYWFhejUqRNycnLg6ekJqVSKwMBAjB07VuUA4eHhGDlyJIYNGwYAiIyMxC+//IL169dj8uTJxfpv2rRJ4fHatWuxc+dOxMbGYvDgwcX65+XlyS8wB8C7gxMRERERlQOVj1hIJBJMmTIFDx8+xF9//YXTp0/j/v37mDVrlsobz8/Px/nz5+Ht7f2/QHp68Pb2xqlTp5RaR05ODp4/fw5LS8sS54eFhcHCwkI+2dvbq5yTiIiIiIjeTOXC4scff0ROTg4MDQ3RqFEjtGrVCqampmXaeEZGBgoKCmBtba3Qbm1tjbS0NKXW8c0338DOzk6hOHlVUFAQHj9+LJ9u375dpqxERERERFQ6lQuLCRMmwMrKCgMGDMD+/ftRUFBQHrmUMnfuXGzduhW7d++GTCYrsY9UKoW5ubnCREREREREmqVyYZGamoqtW7dCIpHAz88Ptra2GD16NE6ePKnyxqtWrQp9fX2kp6crtKenp8PGxuaNyy5cuBBz587FoUOH0LRpU5W3TUREREREmqNyYVGpUiV8/PHH2LRpE+7du4fFixcjOTkZHTp0QO3atVVal6GhIdzc3BAbGytvKywsRGxsLDw8PEpdbv78+Zg1axZiYmLQokULVZ8CERERERFpmMqjQr3K2NgYPj4+ePToEW7duoVr166pvI6AgAAMGTIELVq0QKtWrRAREYGnT5/KR4kaPHgwqlevjrCwMADAvHnzMH36dGzevBmOjo7yazFMTU3LfK0HERERERGpp0yFRU5ODnbv3o1NmzYhNjYW9vb26N+/P3bs2KHyuvz9/XH//n1Mnz4daWlpcHV1RUxMjPyC7pSUFOjp/e/AysqVK5Gfn4++ffsqrCc4OBgzZswoy9MhIiIiIiI1qVxY9OvXDz///DOMjY3h5+eHadOmvfG0JWWMGTMGY8aMKXHekSNHFB4nJyertS0iIiIiItI8lQsLfX19bNu2DT4+PtDX11eY99dff6FJkyYaC0dERERERLpB5cLi9TtfP3nyBFu2bMHatWtx/vx5rQ4/S0RERERE2qHyqFBFjh07hiFDhsDW1hYLFy5Ex44dcfr0aU1mIyIiIiIiHaHSEYu0tDRs2LAB69atQ1ZWFvz8/JCXl4c9e/agUaNG5ZWRiIiIiIgqOKWPWPTo0QP169fH5cuXERERgbt37+K7774rz2xERERERKQjlD5iceDAAYwbNw5ffPEF6tatW56ZiIiIiIhIxyh9xOLEiRN48uQJ3Nzc4O7ujmXLliEjI6M8sxERERERkY5QurBo3bo11qxZg9TUVIwaNQpbt26FnZ0dCgsLcfjwYTx58qQ8cxIRERERUQWm8qhQJiYmGD58OE6cOIErV65g4sSJmDt3LqysrNCzZ8/yyEhERERERBVcmYebBYD69etj/vz5uHPnDrZs2aKpTEREREREpGPUKiyK6Ovrw9fXF/v27dPE6oiIiIiISMdopLAgIiIiIqL3GwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSGwsLIiIiIiJSW4UoLJYvXw5HR0fIZDK4u7vj7Nmzb+y/fft2NGjQADKZDM7Ozti/f/87SkpERERERCXRemERHR2NgIAABAcH48KFC3BxcYGPjw/u3btXYv+TJ0+if//+GDFiBC5evAhfX1/4+vrir7/+esfJiYiIiIioiNYLi/DwcIwcORLDhg1Do0aNEBkZCWNjY6xfv77E/kuWLEGXLl0wadIkNGzYELNmzULz5s2xbNmyd5yciIiIiIiKVNLmxvPz83H+/HkEBQXJ2/T09ODt7Y1Tp06VuMypU6cQEBCg0Obj44M9e/aU2D8vLw95eXnyx48fPwYAZGVlqZleTbna3TzpBq3vp/9fbvYTbUegCi4ry1DbEeS4v9LbcH8lXaLt/bXot4gQ4q19tVpYZGRkoKCgANbW1grt1tbWiI+PL3GZtLS0EvunpaWV2D8sLAwhISHF2u3t7cuYmujdsZhroe0IREop/i1LVHFxfyVdUlH21ydPnsDC4s2/S7RaWLwLQUFBCkc4CgsL8fDhQ3zwwQeQSCRaTEavysrKgr29PW7fvg1zc3NtxyF6I+6vpEu4v5Iu4f5a8Qgh8OTJE9jZ2b21r1YLi6pVq0JfXx/p6ekK7enp6bCxsSlxGRsbG5X6S6VSSKVShbbKlSuXPTSVK3Nzc36RkM7g/kq6hPsr6RLurxXL245UFNHqxduGhoZwc3NDbGysvK2wsBCxsbHw8PAocRkPDw+F/gBw+PDhUvsTEREREVH50/qpUAEBARgyZAhatGiBVq1aISIiAk+fPsWwYcMAAIMHD0b16tURFhYGAPjqq6/g5eWFRYsWoXv37ti6dSvOnTuH1atXa/NpEBERERG917ReWPj7++P+/fuYPn060tLS4OrqipiYGPkF2ikpKdDT+9+BlTZt2mDz5s2YOnUqvv32W9StWxd79uxBkyZNtPUUSAOkUimCg4OLnbZGVBFxfyVdwv2VdAn3V90mEcqMHUVERERERPQGWr9BHhERERER6T4WFkREREREpDYWFkREREREpDYWFkREREREpDYWFqQ1M2bMgEQiUZgaNGig7VhEAIBjx46hR48esLOzg0QiwZ49exTmCyEwffp02NrawsjICN7e3khMTNROWHrvvW1/HTp0aLHv2y5dumgnLL33wsLC0LJlS5iZmcHKygq+vr5ISEhQ6JObm4vRo0fjgw8+gKmpKfr06VPsBslU8bCwIK1q3LgxUlNT5dOJEye0HYkIAPD06VO4uLhg+fLlJc6fP38+li5disjISJw5cwYmJibw8fFBbm7uO05K9Pb9FQC6dOmi8H27ZcuWd5iQ6H+OHj2K0aNH4/Tp0zh8+DCeP3+Ozp074+nTp/I+EyZMwE8//YTt27fj6NGjuHv3Lnr37q3F1KQMrd/Hgt5vlSpVgo2NjbZjEBXTtWtXdO3atcR5QghERERg6tSp6NWrFwDghx9+gLW1Nfbs2YN+/fq9y6hEb9xfi0ilUn7fUoUQExOj8HjDhg2wsrLC+fPn4enpicePH2PdunXYvHkzOnbsCACIiopCw4YNcfr0abRu3VobsUkJPGJBWpWYmAg7OzvUqlULAwcOREpKirYjEb1VUlIS0tLS4O3tLW+zsLCAu7s7Tp06pcVkRKU7cuQIrKysUL9+fXzxxRd48OCBtiMRAQAeP34MALC0tAQAnD9/Hs+fP1f4jm3QoAFq1qzJ79gKjoUFaY27uzs2bNiAmJgYrFy5EklJSWjXrh2ePHmi7WhEb5SWlgYAsLa2Vmi3traWzyOqSLp06YIffvgBsbGxmDdvHo4ePYquXbuioKBA29HoPVdYWIjx48ejbdu2aNKkCYCX37GGhoaoXLmyQl9+x1Z8PBWKtObVw/ZNmzaFu7s7HBwcsG3bNowYMUKLyYiI/m959fQ8Z2dnNG3aFLVr18aRI0fQqVMnLSaj993o0aPx119/8RrL/yN4xIIqjMqVK6NevXq4ceOGtqMQvVHReeqvj1CSnp7Oc9hJJ9SqVQtVq1bl9y1p1ZgxY/Dzzz8jLi4ONWrUkLfb2NggPz8fmZmZCv35HVvxsbCgCiM7Oxs3b96Era2ttqMQvZGTkxNsbGwQGxsrb8vKysKZM2fg4eGhxWREyrlz5w4ePHjA71vSCiEExowZg927d+O3336Dk5OTwnw3NzcYGBgofMcmJCQgJSWF37EVHE+FIq0JDAxEjx494ODggLt37yI4OBj6+vro37+/tqMRITs7W+GvuUlJSbh06RIsLS1Rs2ZNjB8/HrNnz0bdunXh5OSEadOmwc7ODr6+vtoLTe+tN+2vlpaWCAkJQZ8+fWBjY4ObN2/i66+/Rp06deDj46PF1PS+Gj16NDZv3oy9e/fCzMxMft2EhYUFjIyMYGFhgREjRiAgIACWlpYwNzfH2LFj4eHhwRGhKjpBpCX+/v7C1tZWGBoaiurVqwt/f39x48YNbcciEkIIERcXJwAUm4YMGSKEEKKwsFBMmzZNWFtbC6lUKjp16iQSEhK0G5reW2/aX3NyckTnzp1FtWrVhIGBgXBwcBAjR44UaWlp2o5N76mS9lUAIioqSt7n2bNn4ssvvxRVqlQRxsbG4pNPPhGpqanaC01KkQghxLsvZ4iIiIiI6P8SXmNBRERERERqY2FBRERERERqY2FBRERERERqY2FBRERERERqY2FBRERERERqY2FBRERERERqY2FBRERERERqY2FBRERERERqY2FBRESkpCNHjkAikSAzM7NCrIeIqCJhYUFEVAYSieSN04wZMzS+zfbt20MikWDr1q0K7REREXB0dFQ6r7m5OVq2bIm9e/dqPCMV1759e4wfP16hrU2bNkhNTYWFhYV2QhERlQMWFkREZZCamiqfIiIiYG5urtAWGBhYLtuVyWSYOnUqnj9/rvKyUVFRSE1Nxblz59C2bVv07dsXV65cKYeU9DaGhoawsbGBRCLRdhQiIo1hYUFEVAY2NjbyycLCAhKJRP7YysoK4eHhqFGjBqRSKVxdXRETEyNfNjk5WX7koU2bNpDJZGjSpAmOHj361u32798fmZmZWLNmjcqZK1euDBsbG9SrVw+zZs3CixcvEBcXJ59/+/Zt+Pn5oXLlyrC0tESvXr2QnJyssI7169ejcePGkEqlsLW1xZgxY+TzUlJS0KtXL5iamsLc3Bx+fn5IT0+Xz58xYwZcXV2xfv161KxZE6ampvjyyy9RUFCA+fPny1+7OXPmKGxTIpFg1apV+Pjjj2FsbIyGDRvi1KlTuHHjBtq3bw8TExO0adMGN2/eVFhu7969aN68OWQyGWrVqoWQkBC8ePFCYb1r167FJ598AmNjY9StWxf79u1TWMf+/ftRr149GBkZoUOHDsVejwcPHqB///6oXr06jI2N4ezsjC1btsjnDx06FEePHsWSJUvkR4ySk5NLPBVq586d8tfW0dERixYtUtiWo6MjQkNDMXz4cJiZmaFmzZpYvXq1fH5+fj7GjBkDW1tbyGQyODg4ICwsDERE74wgIiK1REVFCQsLC/nj8PBwYW5uLrZs2SLi4+PF119/LQwMDMT169eFEEIkJSUJAKJGjRpix44d4urVq+Kzzz4TZmZmIiMjo9TteHl5ia+++kqEh4cLa2trkZ2dLYQQYvHixcLBweGNGQGI3bt3CyGEeP78uVi8eLEAIFauXCmEECI/P180bNhQDB8+XFy+fFlcvXpVDBgwQNSvX1/k5eUJIYRYsWKFkMlkIiIiQiQkJIizZ8+KxYsXCyGEKCgoEK6uruLDDz8U586dE6dPnxZubm7Cy8tLniE4OFiYmpqKvn37ir///lvs27dPGBoaCh8fHzF27FgRHx8v1q9fLwCI06dPK2SvXr26iI6OFgkJCcLX11c4OjqKjh07ipiYGHH16lXRunVr0aVLF/kyx44dE+bm5mLDhg3i5s2b4tChQ8LR0VHMmDFDYb01atQQmzdvFomJiWLcuHHC1NRUPHjwQAghREpKipBKpSIgIEDEx8eLH3/8UVhbWwsA4tGjR0IIIe7cuSMWLFggLl68KG7evCmWLl0q9PX1xZkzZ4QQQmRmZgoPDw8xcuRIkZqaKlJTU8WLFy9EXFycwnrOnTsn9PT0xMyZM0VCQoKIiooSRkZGIioqSp7XwcFBWFpaiuXLl4vExEQRFhYm9PT0RHx8vBBCiAULFgh7e3tx7NgxkZycLI4fPy42b978xv2CiEiTWFgQEanp9cLCzs5OzJkzR6FPy5YtxZdffimE+F9hMXfuXPn858+fixo1aoh58+aVup2iwiI3N1c4ODiImTNnCiGULyxkMpkwMTERenp6AoBwdHSU/4jeuHGjqF+/vigsLJQvk5eXJ4yMjMTBgwflz2vKlCklrv/QoUNCX19fpKSkyNv+/vtvAUCcPXtWCPGysDA2NhZZWVnyPj4+PsLR0VEUFBTI2+rXry/CwsIUsk+dOlX++NSpUwKAWLdunbxty5YtQiaTyR936tRJhIaGKmTcuHGjsLW1LXW92dnZAoA4cOCAEEKIoKAg0ahRI4V1fPPNNwoFQUm6d+8uJk6cKH9c9L696vXCYsCAAeKjjz5S6DNp0iSF7Ts4OIhBgwbJHxcWFgorKyt5cTh27FjRsWNHhfeQiOhd4qlQREQalJWVhbt376Jt27YK7W3btsW1a9cU2jw8POT/rlSpElq0aFGsT0mkUilmzpyJhQsXIiMjQ+lsixcvxqVLl3DgwAE0atQIa9euhaWlJQDgzz//xI0bN2BmZgZTU1OYmprC0tISubm5uHnzJu7du4e7d++iU6dOJa772rVrsLe3h729vbytUaNGqFy5ssJzcnR0hJmZmfyxtbU1GjVqBD09PYW2e/fuKay/adOmCvMBwNnZWaEtNzcXWVlZ8uczc+ZM+XMxNTXFyJEjkZqaipycnBLXa2JiAnNzc/m2r127Bnd3d4Ucr75nAFBQUIBZs2bB2dkZlpaWMDU1xcGDB5GSklLi61Saa9eulbjPJCYmoqCgoMS8RaffFeUdOnQoLl26hPr162PcuHE4dOiQShmIiNRVSdsBiIhIdYMGDcLChQsxe/bst44IVcTGxgZ16tRBnTp1EBUVhW7duuHq1auwsrJCdnY23NzcsGnTpmLLVatWTeGHvzoMDAwUHkskkhLbCgsLS12u6ILnktqKlsvOzkZISAh69+5dLINMJntjnte3/SYLFizAkiVLEBERAWdnZ5iYmGD8+PHIz89Xeh2qeFPe5s2bIykpCQcOHMCvv/4KPz8/eHt7Y8eOHeWShYjodTxiQUSkQebm5rCzs8Pvv/+u0P7777+jUaNGCm2nT5+W//vFixc4f/48GjZsqNR29PT0EBYWhpUrVxa7oFgZrVq1gpubm/xC6ebNmyMxMRFWVlby4qNosrCwgJmZGRwdHREbG1vi+ho2bIjbt2/j9u3b8rarV68iMzOz2PN+F5o3b46EhIRiz6VOnTpKF0kNGzbE2bNnFdpefc+Al+9rr169MGjQILi4uKBWrVq4fv26Qh9DQ0OFow6lbaukfaZevXrQ19dXKi/wcv/z9/fHmjVrEB0djZ07d+Lhw4dKL09EpA4WFkREGjZp0iTMmzcP0dHRSEhIwOTJk3Hp0iV89dVXCv2WL1+O3bt3Iz4+HqNHj8ajR48wfPhwpbfTvXt3uLu7Y9WqVWXKOX78eKxatQr//vsvBg4ciKpVq6JXr144fvw4kpKScOTIEYwbNw537twB8HJUp0WLFmHp0qVITEzEhQsX8N133wEAvL294ezsjIEDB+LChQs4e/YsBg8eDC8vL7Ro0aJM+dQxffp0/PDDDwgJCcHff/+Na9euYevWrZg6darS6/j888+RmJiISZMmISEhAZs3b8aGDRsU+tStWxeHDx/GyZMnce3aNYwaNUphJCzg5elfZ86cQXJyMjIyMko8IjJx4kTExsZi1qxZuH79Or7//nssW7ZMpWGLw8PDsWXLFsTHx+P69evYvn07bGxsULlyZaXXQUSkDhYWREQaNm7cOAQEBGDixIlwdnZGTEwM9u3bh7p16yr0mzt3LubOnQsXFxecOHEC+/btQ9WqVVXa1rx585Cbm1umnF26dIGTkxPmzJkDY2NjHDt2DDVr1kTv3r3RsGFDjBgxArm5uTA3NwcADBkyBBEREVixYgUaN26Mjz/+GImJiQBenpKzd+9eVKlSBZ6envD29katWrUQHR1dpmzq8vHxwc8//4xDhw6hZcuWaN26NRYvXgwHBwel11GzZk3s3LkTe/bsgYuLCyIjIxEaGqrQZ+rUqWjevDl8fHzQvn172NjYwNfXV6FPYGAg9PX10ahRI1SrVq3E6y+aN2+Obdu2YevWrWjSpAmmT5+OmTNnYujQoUrnNTMzw/z589GiRQu0bNkSycnJ2L9/v8ZOYyMiehuJEEJoOwQR0fskOTkZTk5OuHjxIlxdXbUdh4iISCP4ZwwiIiIiIlIbCwsiIiIiIlIbT4UiIiIiIiK18YgFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGpjYUFERERERGp7f8BpE32yyAoeE0AAAAASUVORK5CYII=\n"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Source Code/5. Reranking Algorithm.ipynb b/Source Code/5. Reranking Algorithm.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5bca747946ef16a9532e6ee2b62aeb9f69b8ecee --- /dev/null +++ b/Source Code/5. Reranking Algorithm.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1FG7ZKNWvY9NzttqgpQZ3lrCo4bBNQfa0","authorship_tag":"ABX9TyNXRBYTo1C1DeiT3ZYJGnUN"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Reranking Applying\n","\n"],"metadata":{"id":"ZCBc33R3CnAx"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets\"\n","files = [\n"," \"australian_user_reviews.json.gz\",\n"," \"australian_users_items.json.gz\",\n"," \"bundle_data.json.gz\",\n"," \"steam_games.json.gz\"\n","]"],"metadata":{"id":"ndVraj2XhD0Y","executionInfo":{"status":"ok","timestamp":1735800177456,"user_tz":-420,"elapsed":336,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[1]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = {}\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items[item[\"item_id\"]] = item[\"playtime_forever\"]\n","\n"," user_item_interactions[data_dict['user_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yPu3n3l6hRLA","executionInfo":{"status":"ok","timestamp":1735800222533,"user_tz":-420,"elapsed":44051,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c251ce3c-372d-4810-e2af-7f502a7b5d0b"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - 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{'item_id': '252490', 'item_name': 'Rust', 'playtime_forever': 3483, 'playtime_2weeks': 0}, {'item_id': '252950', 'item_name': 'Rocket League', 'playtime_forever': 915, 'playtime_2weeks': 0}, {'item_id': '241540', 'item_name': 'State of Decay', 'playtime_forever': 2099, 'playtime_2weeks': 0}, {'item_id': '237890', 'item_name': 'Agarest: Generations of War', 'playtime_forever': 135, 'playtime_2weeks': 0}, {'item_id': '242050', 'item_name': \"Assassin's Creed IV Black Flag\", 'playtime_forever': 6221, 'playtime_2weeks': 0}, {'item_id': '234530', 'item_name': 'War of the Vikings', 'playtime_forever': 2509, 'playtime_2weeks': 0}, {'item_id': '257350', 'item_name': \"Baldur's Gate II: Enhanced Edition\", 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '243470', 'item_name': 'Watch_Dogs', 'playtime_forever': 2043, 'playtime_2weeks': 0}, {'item_id': '247730', 'item_name': 'Nether', 'playtime_forever': 3114, 'playtime_2weeks': 0}, {'item_id': '260730', 'item_name': 'Desperados - Wanted Dead or Alive', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261030', 'item_name': 'The Walking Dead: Season Two', 'playtime_forever': 637, 'playtime_2weeks': 0}, {'item_id': '242700', 'item_name': 'Injustice: Gods Among Us Ultimate Edition', 'playtime_forever': 368, 'playtime_2weeks': 0}, {'item_id': '262060', 'item_name': 'Darkest Dungeon', 'playtime_forever': 332, 'playtime_2weeks': 0}, {'item_id': '263440', 'item_name': 'The Stomping Land', 'playtime_forever': 142, 'playtime_2weeks': 0}, {'item_id': '108600', 'item_name': 'Project Zomboid', 'playtime_forever': 160, 'playtime_2weeks': 0}, {'item_id': '211820', 'item_name': 'Starbound', 'playtime_forever': 137, 'playtime_2weeks': 0}, {'item_id': '367540', 'item_name': 'Starbound - Unstable', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '41900', 'item_name': \"The Bard's Tale\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '240760', 'item_name': 'Wasteland 2', 'playtime_forever': 710, 'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","data_items = None\n","with gzip.open(f\"{file_path}/{files[2]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," data_items = set()\n","\n"," if len(data_dict[\"items\"]) > 0:\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," for item in data_dict[\"items\"]:\n"," data_items.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = data_items\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZnLJTSvOm6tH","executionInfo":{"status":"ok","timestamp":1735800222993,"user_tz":-420,"elapsed":463,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e84e6c80-9319-40a6-e2f7-a8c2cf19366d"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n","{'bundle_final_price': '$4.77', 'bundle_url': 'http://store.steampowered.com/bundle/594/?utm_source=SteamDB&utm_medium=SteamDB&utm_campaign=SteamDB%20Bundles%20Page', 'bundle_price': '$5.97', 'bundle_name': 'The Ninjahtic Series', 'bundle_id': '594', 'items': [{'genre': 'Action, Adventure, Indie', 'item_id': '385230', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/385230', 'item_name': 'Ninjahtic'}, {'genre': 'Action, Adventure, Indie', 'item_id': '387880', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/387880', 'item_name': 'Ninjahtic Mind Tricks'}, {'genre': 'Action, Indie', 'item_id': '454130', 'discounted_price': '$1.99', 'item_url': 'http://store.steampowered.com/app/454130', 'item_name': 'Nil-Ninjahtic: Ronin'}], 'bundle_discount': '20%'}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}/{files[3]}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KwK2r2ospM7p","executionInfo":{"status":"ok","timestamp":1735800232382,"user_tz":-420,"elapsed":9391,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"53b97217-eaef-465d-bd1f-b7006c65d1e4"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# prompt: Show me distribution of sentiment attribute on items_dictionary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'items_dictionary' is defined and accessible\n","\n","sentiment_counts = {}\n","for item_id, item_data in items_dictionary.items():\n"," if 'sentiment' in item_data:\n"," sentiment = item_data['sentiment']\n"," sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1\n","\n","sentiments, counts = zip(*sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"9HG4u_54plVH","executionInfo":{"status":"ok","timestamp":1735479680885,"user_tz":-420,"elapsed":730,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2a732e27-e0bc-41f3-8417-703fc4de328b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: From data sentiment_counts i want to sorted from very positive to very negative\n","\n","import operator\n","\n","sorted_sentiment_counts = dict(sorted(sentiment_counts.items(), key=operator.itemgetter(1), reverse=True))\n","\n","sentiments, counts = zip(*sorted_sentiment_counts.items())\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(sentiments, counts, color='skyblue')\n","plt.xlabel(\"Sentiment\")\n","plt.ylabel(\"Number of Items\")\n","plt.title(\"Distribution of Sentiment Attribute in items_dictionary (Sorted)\")\n","plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability\n","plt.tight_layout() # Adjust layout to prevent labels from overlapping\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"id":"sjR-gufgppmg","executionInfo":{"status":"ok","timestamp":1735479926795,"user_tz":-420,"elapsed":948,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"e8acb122-9e3e-4b2d-fd64-15bfc992059f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["def generate_sentiment_value(sentimen_str):\n"," if sentimen_str == 'Overwhelmingly Positive':\n"," return 1.00\n"," elif sentimen_str == 'Very Positive':\n"," return 0.80\n"," elif sentimen_str == 'Positive':\n"," return 0.60\n"," elif sentimen_str == 'Mostly Positive':\n"," return 0.40\n"," elif sentimen_str == '9 user reviews':\n"," return 0.25\n"," elif sentimen_str == '8 user reviews':\n"," return 0.20\n"," elif sentimen_str == '7 user reviews':\n"," return 0.15\n"," elif sentimen_str == '6 user reviews':\n"," return 0.10\n"," elif sentimen_str == 'Mixed':\n"," return 0.5\n"," elif sentimen_str == '5 user reviews':\n"," return -0.5\n"," elif sentimen_str == '4 user reviews':\n"," return -0.10\n"," elif sentimen_str == '3 user reviews':\n"," return -0.15\n"," elif sentimen_str == '2 user reviews':\n"," return -0.20\n"," elif sentimen_str == '1 user review':\n"," return -0.25\n"," elif sentimen_str == 'Mostly Negative':\n"," return -0.40\n"," elif sentimen_str == 'Negative':\n"," return -0.60\n"," elif sentimen_str == 'Very Negative':\n"," return -0.85\n"," elif sentimen_str == 'Overwhelmingly Negative':\n"," return -1.00\n"," else:\n"," return 0.00"],"metadata":{"id":"Pd-DAKhQtwcR","executionInfo":{"status":"ok","timestamp":1735798825806,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n1KqBF9ifNWl","executionInfo":{"status":"ok","timestamp":1735482576696,"user_tz":-420,"elapsed":198542,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0d6dd5dc-6645-4217-97ad-8f535fd8623e"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}],"source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs/recommendation_data_native.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_native.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"]},{"cell_type":"code","source":["import pandas as pd\n","import json\n","import gzip\n","import operator\n","\n","filepath_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns/recommendation_data_ann.json.gz'\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz'\n","\n","data_dict = None\n","\n","users_ids = set()\n","users_bundle_candidate_bfr = set()\n","users_bundle_candidate_bfr_sentiment = {}\n","\n","with gzip.open(filepath_rec_data, 'rt', encoding='utf-8') as file, gzip.open(filepath_rarank_rec_data, 'wt', encoding='utf-8') as outfile:\n"," for line in file:\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n","\n"," if int(data_dict[\"is_recommended\"]) == 1:\n"," users_bundle_candidate_bfr.add(data_dict['bundle_id'])\n","\n"," if data_dict['user_id'] not in users_ids:\n"," # Handle for the first checkking\n"," if len(users_ids) == 0:\n"," users_ids.add(data_dict['user_id'])\n"," else:\n"," # Do Reranking here\n"," for bundle_id in users_bundle_candidate_bfr:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] = 0\n"," for item_id in bundle_data[bundle_id]:\n"," if item_id in items_dictionary and \"sentiment\" in items_dictionary[item_id]:\n"," users_bundle_candidate_bfr_sentiment[bundle_id] += generate_sentiment_value(items_dictionary[item_id]['sentiment'])\n","\n"," users_bundle_candidate_bfr = sorted(users_bundle_candidate_bfr_sentiment.items(), key=operator.itemgetter(1), reverse=True)\n","\n"," json.dump({ \"user_id\": data_dict['user_id'], \"rec_bundles\": dict(users_bundle_candidate_bfr) }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n","\n"," # Reset data\n"," users_ids.add(data_dict['user_id'])\n"," users_bundle_candidate_bfr = set()\n"," users_bundle_candidate_bfr_sentiment = {}\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON line: {e}\")\n"," # Handle the error as needed (e.g., skip the line, log the error, etc.)\n","\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hPscaGLMq2tQ","executionInfo":{"status":"ok","timestamp":1735799025428,"user_tz":-420,"elapsed":199625,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"11134129-bfeb-4480-a848-a4aa80e2bb71"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["{'user_id': '76561198030000787', 'bundle_id': '594', 'is_recommended': '0'}\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," count = len(data_dict['rec_bundles'])\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nz3SqNvj-F7","executionInfo":{"status":"ok","timestamp":1735482292981,"user_tz":-420,"elapsed":307,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c1eba6cf-dd5d-41e3-b6c7-f57eac0768b1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_native.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_native.json.gz'\n","Count: 419\n"]}]},{"cell_type":"markdown","source":["## Evaluate\n"],"metadata":{"id":"q1DFXWFOCsP0"}},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}\n","\n","top_bundles_results = get_top_rec_bundles(data_dict)\n","\n","for result in top_bundles_results:\n"," print(f\"Top {result['top_n']} recommended bundles for user {result['user_id']}:\")\n"," print(result['top_rec_bundles'])\n"," print(\"-\" * 20)"],"metadata":{"id":"1M0JQWwPEAK5","executionInfo":{"status":"ok","timestamp":1735800232382,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit_ratios = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," hits = 0\n"," if user_id in user_item_interactions:\n"," user_items = set(user_item_interactions[user_id].keys())\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," hits += len(user_items.intersection(bundle_items))\n","\n"," hit_ratio = hits / top_n if top_n > 0 else 0 # Avoid division by zero\n"," hit_ratios.append({\"user_id\": user_id, \"top_n\": top_n, \"hit_ratio\": hit_ratio})\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n","hit_ratios"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OBktUkbTEm5u","executionInfo":{"status":"ok","timestamp":1735800232382,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"30a20d96-7890-4e9b-a7c8-cb23c18c6253"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# prompt: Calculate diversity from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data):\n"," \"\"\"\n"," Calculates the diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and diversity score.\n"," \"\"\"\n"," diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," total_items = 0\n"," unique_items = set()\n","\n"," for bundle_id in top_bundles:\n"," if bundle_id in bundle_data:\n"," bundle_items = bundle_data[bundle_id]\n"," total_items += len(bundle_items)\n"," unique_items.update(bundle_items)\n","\n"," diversity = len(unique_items) / total_items if total_items > 0 else 0\n"," diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"diversity\": diversity})\n"," return diversity_scores\n","\n","# Example usage\n","diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n","diversity_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y9x5v5iNFzlZ","executionInfo":{"status":"ok","timestamp":1735800232382,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"96d3a68b-a488-4d48-d2c6-db5d83edf1af"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["# prompt: Calculate inter_diversity from top_bundles_results by checkking how similar for each bundle\n","\n","import json\n","import gzip\n","import ast\n","import matplotlib.pyplot as plt\n","import operator\n","import pandas as pd\n","\n","# ... (Your existing code)\n","\n","def calculate_inter_diversity(top_bundles_results, bundle_data):\n"," \"\"\"\n"," Calculates the inter-diversity of recommended bundles.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and inter-diversity score.\n"," \"\"\"\n"," inter_diversity_scores = []\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," jaccard_similarities = []\n"," bundle_ids = list(top_bundles.keys())\n"," for i in range(len(bundle_ids)):\n"," for j in range(i + 1, len(bundle_ids)):\n"," bundle1_id = bundle_ids[i]\n"," bundle2_id = bundle_ids[j]\n"," if bundle1_id in bundle_data and bundle2_id in bundle_data:\n"," bundle1_items = bundle_data[bundle1_id]\n"," bundle2_items = bundle_data[bundle2_id]\n"," intersection = len(bundle1_items.intersection(bundle2_items))\n"," union = len(bundle1_items.union(bundle2_items))\n"," jaccard_similarity = intersection / union if union > 0 else 0\n"," jaccard_similarities.append(1 - jaccard_similarity) # 1-similarity to represent diversity\n","\n"," inter_diversity = sum(jaccard_similarities) / len(jaccard_similarities) if jaccard_similarities else 0\n"," inter_diversity_scores.append({\"user_id\": user_id, \"top_n\": top_n, \"inter_diversity\": inter_diversity})\n"," return inter_diversity_scores\n","\n","# Example usage\n","inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","inter_diversity_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"As-qpdgrGIDw","executionInfo":{"status":"ok","timestamp":1735800232664,"user_tz":-420,"elapsed":285,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"909223e6-163f-41de-90ee-c78b0efe353c"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["data_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"W5jfzwYKD21C","executionInfo":{"status":"ok","timestamp":1735520132696,"user_tz":-420,"elapsed":382,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7cede590-f529-4c0f-de13-ca0f7617eb5a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'user_id': 'js41637',\n"," 'rec_bundles': {'362': 45.94999999999998,\n"," '383': 21.400000000000002,\n"," '646': 20.200000000000006,\n"," '356': 19.70000000000001,\n"," '1468': 18.700000000000006,\n"," '1418': 15.5,\n"," '616': 14.299999999999999,\n"," '268': 13.650000000000002,\n"," '232': 13.600000000000003,\n"," '813': 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1.9500000000000002,\n"," '1144': 1.9500000000000002,\n"," '462': 1.9,\n"," '1055': 1.9,\n"," '420': 1.8000000000000003,\n"," '1217': 1.8000000000000003,\n"," '1034': 1.8,\n"," '1291': 1.8,\n"," '944': 1.8,\n"," '302': 1.8,\n"," '236': 1.8,\n"," '233': 1.8,\n"," '1182': 1.8,\n"," '1336': 1.8,\n"," '139': 1.8,\n"," '331': 1.7999999999999998,\n"," '433': 1.7499999999999998,\n"," '454': 1.7000000000000002,\n"," '364': 1.7000000000000002,\n"," '776': 1.6500000000000004,\n"," '836': 1.6,\n"," '727': 1.6,\n"," '93': 1.6,\n"," '622': 1.6,\n"," '1204': 1.6,\n"," '1013': 1.6,\n"," '861': 1.6,\n"," '429': 1.6,\n"," '781': 1.6,\n"," '1399': 1.6,\n"," '169': 1.6,\n"," '1085': 1.6,\n"," '823': 1.6,\n"," '1390': 1.6,\n"," '445': 1.6,\n"," '243': 1.6,\n"," '799': 1.6,\n"," '357': 1.6,\n"," '827': 1.6,\n"," '526': 1.6,\n"," '1385': 1.6,\n"," '365': 1.6,\n"," '1207': 1.6,\n"," '494': 1.6,\n"," '338': 1.6,\n"," '779': 1.6,\n"," '428': 1.6,\n"," '934': 1.6,\n"," '1440': 1.55,\n"," '434': 1.5,\n"," '664': 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0.5,\n"," '394': 0.5,\n"," '264': 0.5,\n"," '270': 0.5,\n"," '166': 0.5,\n"," '1140': 0.5,\n"," '949': 0.5,\n"," '941': 0.5,\n"," '977': 0.5,\n"," '510': 0.5,\n"," '1121': 0.4,\n"," '137': 0.4,\n"," '297': 0.4,\n"," '488': 0.4,\n"," '1226': 0.4,\n"," '303': 0.4,\n"," '316': 0.4,\n"," '1477': 0.4,\n"," '472': 0.4,\n"," '568': 0.4,\n"," '624': 0.4,\n"," '251': 0.4,\n"," '1271': 0.4,\n"," '552': 0.4,\n"," '843': 0.35,\n"," '893': 0.35,\n"," '954': 0.30000000000000004,\n"," '718': 0.30000000000000004,\n"," '656': 0.30000000000000004,\n"," '1404': 0.3,\n"," '1040': 0.3,\n"," '548': 0.3,\n"," '914': 0.25,\n"," '369': 0.25,\n"," '1386': 0.25,\n"," '1130': 0.25,\n"," '543': 0.25,\n"," '430': 0.2,\n"," '280': 0.19999999999999996,\n"," '963': 0.19999999999999996,\n"," '321': 0.19999999999999996,\n"," '531': 0.19999999999999996,\n"," '355': 0.15,\n"," '258': 0.15,\n"," '567': 0.10000000000000009,\n"," '332': 0.09999999999999998,\n"," '1350': 0.09999999999999998,\n"," '1222': 0.09999999999999998,\n"," '1275': 0.09999999999999998,\n"," '309': 0.05000000000000007,\n"," '432': 0.04999999999999993,\n"," '845': 2.7755575615628914e-17,\n"," '844': 2.7755575615628914e-17,\n"," '491': 0,\n"," '414': 0.0,\n"," '1295': 0,\n"," '492': 0.0,\n"," '409': 0.0,\n"," '1143': -0.04999999999999999,\n"," '540': -0.05000000000000002,\n"," '328': -0.15000000000000002,\n"," '1406': -0.2,\n"," '424': -0.2,\n"," '245': -0.2,\n"," '326': -0.25,\n"," '634': -0.2500000000000001,\n"," '254': -0.4,\n"," '536': -0.4,\n"," '1005': -0.4,\n"," '846': -0.4,\n"," '439': -0.5,\n"," '1437': -0.55,\n"," '440': -0.55,\n"," '570': -0.7500000000000001,\n"," '329': -0.7999999999999999,\n"," '539': -0.8,\n"," '701': -0.8,\n"," '436': -0.8,\n"," '955': -1.0,\n"," '327': -1.15,\n"," '956': -2.25}}"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_native.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jn67YIwICr93","executionInfo":{"status":"ok","timestamp":1735521755846,"user_tz":-420,"elapsed":30056,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4b0c4a56-c2c4-4ca3-e477-b6596d02cdd0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 9881\n","4908 / 9881\n","4909 / 9881\n","4910 / 9881\n","4911 / 9881\n","4912 / 9881\n","4913 / 9881\n","4914 / 9881\n","4915 / 9881\n","4916 / 9881\n","4917 / 9881\n","4918 / 9881\n","4919 / 9881\n","4920 / 9881\n","4921 / 9881\n","4922 / 9881\n","4923 / 9881\n","4924 / 9881\n","4925 / 9881\n","4926 / 9881\n","4927 / 9881\n","4928 / 9881\n","4929 / 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Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_native.json.gz' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_native.json.gz'\n","Count: 9880\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz'\n","\n","average_hit_ratio = {}\n","average_intra_diversity = {}\n","average_inter_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","intra_diversity_scores = []\n","inter_diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict[\"rec_bundles\"]) > 0:\n","\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, user_item_interactions, bundle_data)\n"," intra_diversity_scores = calculate_intra_diversity(top_bundles_results, user_item_interactions, bundle_data)\n"," inter_diversity_scores = calculate_inter_diversity(top_bundles_results, bundle_data)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_intra_diversity[data_dict['user_id']] = intra_diversity_scores\n"," average_inter_diversity[data_dict['user_id']] = inter_diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9rJQUrLHwTJz","executionInfo":{"status":"ok","timestamp":1735800300598,"user_tz":-420,"elapsed":30404,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"855de8e2-fba7-4076-a864-5aaecf4e7a88"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 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metric value.\n","\n"," Returns:\n"," A pandas DataFrame with the average metrics for each 'top_n'.\n"," \"\"\"\n","\n"," all_metrics = []\n"," for user_id, user_metrics in metrics_dict.items():\n"," all_metrics.extend(user_metrics)\n","\n"," df = pd.DataFrame(all_metrics)\n","\n"," numeric_cols = df.select_dtypes(include=['number']).columns\n"," return df.groupby('top_n')[numeric_cols].mean()\n","\n","# Calculate and print the average hit ratio\n","average_hit_ratio_df = calculate_average_metrics(average_hit_ratio)\n","print(\"Average Hit Ratio:\")\n","print(average_hit_ratio_df)\n","\n","# Calculate and print the average intra-list diversity\n","average_intra_diversity_df = calculate_average_metrics(average_intra_diversity)\n","print(\"\\nAverage Intra-list Diversity:\")\n","print(average_intra_diversity_df)\n","\n","# Calculate and print the average inter-list diversity\n","average_inter_diversity_df = calculate_average_metrics(average_inter_diversity)\n","print(\"\\nAverage Inter-list Diversity:\")\n","average_inter_diversity_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":501},"id":"r7-DgF7QKIJB","executionInfo":{"status":"ok","timestamp":1735800301101,"user_tz":-420,"elapsed":525,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4827b716-1171-46d6-e180-85c6d4ee0744"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Hit Ratio:\n"," top_n hit_ratio\n","top_n \n","5 5.0 0.795115\n","10 10.0 0.895338\n","15 15.0 0.675268\n","20 20.0 0.542384\n","\n","Average Intra-list Diversity:\n"," top_n diversity\n","top_n \n","5 5.0 0.999928\n","10 10.0 0.999577\n","15 15.0 0.963669\n","20 20.0 0.965174\n","\n","Average Inter-list Diversity:\n"]},{"output_type":"execute_result","data":{"text/plain":[" top_n inter_diversity\n","top_n \n","5 5.0 0.998234\n","10 10.0 0.998081\n","15 15.0 0.990658\n","20 20.0 0.993605"],"text/html":["\n","
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top_ninter_diversity
top_n
55.00.998234
1010.00.998081
1515.00.990658
2020.00.993605
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"average_inter_diversity_df","repr_error":"cannot insert top_n, already exists"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# prompt: combine average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df and make it colomn like top_n | hit_ratio | intra_diversity | inter_diversity just create from dictionary do not using merge function of pandas\n","\n","import pandas as pd\n","\n","def combine_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in average_hit_ratio_df.index:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': average_hit_ratio_df.loc[top_n, 'hit_ratio'],\n"," 'intra_diversity': average_intra_diversity_df.loc[top_n, 'diversity'],\n"," 'inter_diversity': average_inter_diversity_df.loc[top_n, 'inter_diversity']\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)\n","\n","# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)\n","combined_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"cdolqR3XOM3R","executionInfo":{"status":"ok","timestamp":1735800301101,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"88078b51-3c47-4454-dbd9-e139f3a09a86"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio intra_diversity inter_diversity\n","0 5 0.795115 0.999928 0.998234\n","1 10 0.895338 0.999577 0.998081\n","2 15 0.675268 0.963669 0.990658\n","3 20 0.542384 0.965174 0.993605"],"text/html":["\n","
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top_nhit_ratiointra_diversityinter_diversity
050.7951150.9999280.998234
1100.8953380.9995770.998081
2150.6752680.9636690.990658
3200.5423840.9651740.993605
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1524650127003355,\n \"min\": 0.5423837032532685,\n \"max\": 0.8953379953379953,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8953379953379953,\n 0.5423837032532685,\n 0.7951150298976386\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"intra_diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.020408169113056922,\n \"min\": 0.9636694730845546,\n \"max\": 0.9999284585780975,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9995772945736154,\n 0.9651740832224865,\n 0.9999284585780975\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"inter_diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0036817899801428536,\n \"min\": 0.9906584211813163,\n \"max\": 0.9982344721475157,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9980809110723352,\n 0.9936045677343909,\n 0.9982344721475157\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["# prompt: save combined_df as /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_native.csv\n","\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_native.csv', index=False)\n","combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns_ann.csv', index=False)"],"metadata":{"id":"lfky5tCoPDIx","executionInfo":{"status":"ok","timestamp":1735800301101,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["# prompt: generate bar chart for each average_hit_ratio_df, average_intra_diversity_df, and average_inter_diversity_df\n","# Make it dense space between bars, and change the color of bar for the best value\n","\n","# prompt: generate bar chart for each average_hit_ratio_df, average_intra_diversity_df, and average_inter_diversity_df\n","# Make it dense space between bars, and change the color of bar for the best value\n","\n","import matplotlib.pyplot as plt\n","\n","def plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df):\n"," \"\"\"Generates bar charts for average hit ratio, intra-diversity, and inter-diversity.\"\"\"\n","\n"," fig, axes = plt.subplots(3, 1, figsize=(8, 10)) # Adjust figsize as needed\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_hit_ratio_df['hit_ratio'].idxmax()\n"," top_n_values = average_hit_ratio_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Hit Ratio ---\n"," axes[0].bar(average_hit_ratio_df.index, average_hit_ratio_df['hit_ratio'], width=4.0, color=colors)\n"," axes[0].set_xlabel(\"Top N Recommendations\")\n"," axes[0].set_ylabel(\"Average Hit Ratio\")\n"," axes[0].set_title(\"Average Hit Ratio for Different Top N Values\")\n"," axes[0].set_xticks(average_hit_ratio_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_intra_diversity_df['diversity'].idxmax()\n"," top_n_values = average_intra_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Intra-list Diversity ---\n"," axes[1].bar(average_intra_diversity_df.index, average_intra_diversity_df['diversity'], width=4.0, color=colors)\n"," axes[1].set_xlabel(\"Top N Recommendations\")\n"," axes[1].set_ylabel(\"Average Intra-list Diversity\")\n"," axes[1].set_title(\"Average Intra-list Diversity for Different Top N Values\")\n"," axes[1].set_xticks(average_intra_diversity_df.index)\n","\n"," # Find the index of the maximum value\n"," best_value_index = average_inter_diversity_df['inter_diversity'].idxmax()\n"," top_n_values = average_inter_diversity_df.index\n","\n"," colors = ['skyblue'] * len(top_n_values)\n"," colors[top_n_values.get_loc(best_value_index)] = 'green' # Highlight the best value\n","\n"," # --- Inter-list Diversity ---\n"," axes[2].bar(average_inter_diversity_df.index, average_inter_diversity_df['inter_diversity'], width=4.0, color=colors)\n"," axes[2].set_xlabel(\"Top N Recommendations\")\n"," axes[2].set_ylabel(\"Average Inter-list Diversity\")\n"," axes[2].set_title(\"Average Inter-list Diversity for Different Top N Values\")\n"," axes[2].set_xticks(average_inter_diversity_df.index)\n","\n"," plt.tight_layout() # Adjust layout to prevent overlapping\n"," plt.show()\n","\n","plot_metrics(average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"H53yAPjpKxgM","executionInfo":{"status":"ok","timestamp":1735522753701,"user_tz":-420,"elapsed":707,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a24c5067-5d77-458c-b5c6-d7fffae7c306"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Source Code/6. Evaluations.ipynb b/Source Code/6. Evaluations.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..56a3fbf333bbad52b777bd05e38d9e23529b6793 --- /dev/null +++ b/Source Code/6. Evaluations.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1_pppHhIqCp4y2z-qf9nhiocpMpQVBRRX","authorship_tag":"ABX9TyM91kr96KtFrhRpjoXmxJYg"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oWuWoOOiV4Vn","executionInfo":{"status":"ok","timestamp":1735875774569,"user_tz":-420,"elapsed":4079,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7f3bd69c-2920-47a5-de44-0b2639a97dd2"},"outputs":[{"output_type":"stream","name":"stdout","text":[" top_n hit_ratio intra_diversity inter_diversity model\n","0 5 0.795724 0.999928 0.998539 dt\n","1 10 0.895541 0.999588 0.998394 dt\n","2 15 0.675429 0.963657 0.990966 dt\n","3 20 0.542511 0.965154 0.993914 dt\n","4 5 0.513340 0.995296 0.776677 knn\n"]}],"source":["# prompt: Load all csv file from this folder /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation and combine it to one pandas dataframe and add new column name \"model\" that represents the source of file for example native\n","\n","import pandas as pd\n","import os\n","import glob\n","\n","def load_and_combine_csv(folder_path, model_name):\n"," all_files = glob.glob(os.path.join(folder_path, \"*.csv\"))\n"," df_list = []\n"," count = 0\n"," for filename in all_files:\n"," try:\n"," df = pd.read_csv(filename, index_col=None, header=0)\n"," df['model'] = model_name[count] # Add the 'model' column\n"," df_list.append(df)\n"," count += 1\n"," except pd.errors.EmptyDataError:\n"," print(f\"Warning: Skipping empty file {filename}\")\n"," except pd.errors.ParserError:\n"," print(f\"Warning: Skipping file with parsing error {filename}\")\n"," except Exception as e:\n"," print(f\"Error loading file {filename}: {e}\")\n","\n"," if df_list:\n"," combined_df = pd.concat(df_list, axis=0, ignore_index=True)\n"," return combined_df\n"," else:\n"," print(\"Warning: No valid CSV files found in the specified directory\")\n"," return pd.DataFrame() # Return an empty DataFrame if no valid files are found\n","\n","# Example Usage\n","folder_path = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns'\n","# folder_path = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation'\n","model_name = [\"dt\", \"knn\", \"logreg\", \"bnb\", \"sgd\", \"ann\"] # replace with the desired name\n","# model_name = [\"ann\", \"dt\", \"knn\", \"logreg\", \"bnb\", \"sgd\"] # replace with the desired name\n","combined_df = load_and_combine_csv(folder_path, model_name)\n","\n","if not combined_df.empty:\n"," print(combined_df.head())\n"," # Further processing or saving of combined_df"]},{"cell_type":"code","source":["import pandas as pd\n","import os\n","import glob\n","\n","folder_path = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns'\n","all_files = glob.glob(os.path.join(folder_path, \"*.csv\"))"],"metadata":{"id":"ToQTSALixKb3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["all_files"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hl2pQivwxPQv","executionInfo":{"status":"ok","timestamp":1735875782331,"user_tz":-420,"elapsed":290,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a2656ad5-7a4e-42cd-b5a7-3c292cafe4c7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns/price_patterns_dt.csv',\n"," '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns/price_patterns_knn.csv',\n"," '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns/price_patterns_logreg.csv',\n"," '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns/price_patterns_bnb.csv',\n"," '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns/price_patterns_sgd.csv',\n"," '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/price_patterns/price_patterns_ann.csv']"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["# prompt: From this dataframe combined_df please create three diagram for represent hit ratio, intra_diversity, inter_diversity for each type of top_n and models, i want to see each models\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Assuming combined_df is already loaded as in the previous code\n","\n","def plot_metrics(df, metric_name):\n"," plt.figure(figsize=(10, 6))\n"," sns.lineplot(data=df, x='top_n', y=metric_name, hue='model', marker='o')\n"," plt.title(f'{metric_name} for different models and top_n values')\n"," plt.xlabel('Top N')\n"," plt.ylabel(metric_name)\n"," plt.show()\n","\n","# Create the plots\n","plot_metrics(combined_df, 'hit_ratio')\n","plot_metrics(combined_df, 'intra_diversity')\n","plot_metrics(combined_df, 'inter_diversity')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"vI302M2JXh4P","executionInfo":{"status":"ok","timestamp":1735800510118,"user_tz":-420,"elapsed":4016,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"01135479-1d27-4366-bc2d-264e495cbd24"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: save the combine\n","\n","# Assuming combined_df is already loaded as in the previous code\n","\n","def save_combined_csv(df, output_filename=\"combined_data.csv\"):\n"," \"\"\"Saves the combined DataFrame to a CSV file.\n","\n"," Args:\n"," df: The DataFrame to save.\n"," output_filename: The name of the output CSV file (default: \"combined_data.csv\").\n"," \"\"\"\n"," try:\n"," df.to_csv(output_filename, index=False)\n"," print(f\"Combined DataFrame saved to {output_filename}\")\n"," except Exception as e:\n"," print(f\"Error saving DataFrame: {e}\")\n","\n","# Example usage\n","if not combined_df.empty:\n"," save_combined_csv(combined_df, \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/combined_metrics.csv\") # Specify the desired output filename"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0encup05Z-nB","executionInfo":{"status":"ok","timestamp":1735875789347,"user_tz":-420,"elapsed":299,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"250ae2a2-43fe-42ac-c987-d95617a1d62d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Combined DataFrame saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/combined_metrics.csv\n"]}]},{"cell_type":"code","source":["# prompt: filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/\n","# Load all data from as .json.gz\n","\n","import pandas as pd\n","import os\n","import glob\n","import json\n","import gzip\n","\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","\n","def load_json_gz_data(filepath):\n"," all_files = glob.glob(os.path.join(filepath, \"*.json.gz\"))\n"," df_list = []\n"," index = 0\n"," for filename in all_files:\n"," df_list.append(0)\n"," try:\n"," with gzip.open(filename, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n"," count = len(data_dict['rec_bundles'])\n"," df_list[index] += count\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n"," except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n"," except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n"," print(f\"Filename: {filename} | Count: {count}\")\n"," index += 1\n","\n","combined_df = load_json_gz_data(filepath_rarank_rec_data)\n","combined_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UzZ5d7x7T-r9","executionInfo":{"status":"ok","timestamp":1736749399911,"user_tz":-420,"elapsed":8161,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"f6071bc9-71fd-43cd-cf2c-82fedc687567"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz | Count: 137\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz | Count: 102\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz | Count: 138\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz | Count: 137\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_sgd.json.gz | Count: 130\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz | Count: 0\n"]}]},{"cell_type":"code","source":["filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","combined_df = load_json_gz_data(filepath_rarank_rec_data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bgFXVO-OVtab","executionInfo":{"status":"ok","timestamp":1736749574066,"user_tz":-420,"elapsed":8494,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"99bb0bcb-fdf8-4b4a-f8ce-c57a6dad8411"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz | Count: 219\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz | Count: 169\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz | Count: 450\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_sgd.json.gz | Count: 223\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz | Count: 21\n","File '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/' successfully processed and saved to '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/'\n","Filename: /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz | Count: 405\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Convergence/IMPACT-P ANN.ipynb b/Source Code/Convergence/IMPACT-P ANN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1c5976746fb29a18721e0c1b96fb93c76d0c63a1 --- /dev/null +++ b/Source Code/Convergence/IMPACT-P ANN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["_T8fPo2P6CFY"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777438447776,"user_tz":-420,"elapsed":21049,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"3600c744-9395-447b-c77f-997aa5a1fb16"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cbi9HIU2YNKA","executionInfo":{"status":"ok","timestamp":1777438482095,"user_tz":-420,"elapsed":34304,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"3bbd6a06-acd2-486c-c2a9-1ac1662f54d7"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777438482096,"user_tz":-420,"elapsed":9,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"eFg-8moUb52J"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"Egw06Nzgb8g1","executionInfo":{"status":"ok","timestamp":1777438493980,"user_tz":-420,"elapsed":11889,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# prompt: Distribution on len user_tags_and_specs_features\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming user_tags_and_specs_features is already loaded as a NumPy array\n","\n","lengths = [len(x) for x in user_tags_and_specs_features]\n","\n","plt.hist(lengths, bins=50) # Adjust the number of bins as needed\n","plt.xlabel('Length of user_tags_and_specs_features')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Lengths')\n","plt.show()\n","print(f\"The average length is {np.mean(lengths)}\")\n","print(f\"The median length is {np.median(lengths)}\")\n","print(f\"The standard deviation of length is {np.std(lengths)}\")\n","print(f\"The maximum length is {np.max(lengths)}\")\n","print(f\"The minimum length is {np.min(lengths)}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":559},"id":"yJpD_1HALjO_","executionInfo":{"status":"ok","timestamp":1735287312801,"user_tz":-420,"elapsed":1396,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"013d14e0-23b4-45d5-e0de-da2a8b0a36c9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["The average length is 225.0\n","The median length is 225.0\n","The standard deviation of length is 0.0\n","The maximum length is 225\n","The minimum length is 225\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import matplotlib.pyplot as plt\n","import os\n","from sklearn.model_selection import KFold\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score\n","\n","# 1. LOAD DATA\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 2. SETUP\n","kf = KFold(n_splits=5, shuffle=True, random_state=42)\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# 3. TRAINING LOOP\n","fold_accuracies = []\n","best_history = None\n","\n","for fold, (train_idx, val_idx) in enumerate(kf.split(X, y)):\n"," X_train, X_val = X[train_idx], X[val_idx]\n"," y_train_fold, y_val_fold = y[train_idx], y[val_idx]\n","\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n"," # Pastikan metrics=['accuracy']\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n"," # Train 20 epochs\n"," history = model.fit(X_train, y_train_fold, epochs=20, batch_size=128,\n"," validation_data=(X_val, y_val_fold), verbose=0)\n","\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\")\n"," fold_accuracies.append(accuracy_score(y_val_fold, y_pred))\n","\n"," # Simpan history dari fold terakhir untuk plotting\n"," if fold == 4: best_history = history\n","\n","# 4. SAVE CONVERGENCE DATA (Accuracy Focus)\n","history_df = pd.DataFrame(best_history.history)\n","history_df.to_csv(os.path.join(dir_path, 'ann_tags_accuracy_convergence.csv'), index_label='epoch')\n","\n","# 5. HD PLOTTING (Accuracy vs Epochs)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(history_df['accuracy'], label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(history_df['val_accuracy'], label='Validation Accuracy', marker='s', linewidth=2)\n","plt.title('Accuracy Convergence: ANN (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Epochs', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'ann_tags_accuracy_convergence.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. FINAL STABILITY OUTPUT\n","print(\"\\n--- STABILITY SUMMARY (ANN) ---\")\n","print(f\"Mean Accuracy: {np.mean(fold_accuracies):.4f}\")\n","print(f\"Std Dev: {np.std(fold_accuracies):.4f}\")\n","\n","# 7. SAVE MODEL\n","model.save(os.path.join(dir_path, 'best_ann_tags_model.keras'))\n","print(f\"\\nModel dan Data Convergence (Accuracy) berhasil disimpan.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"qpcxK_8thvGV","executionInfo":{"status":"ok","timestamp":1777441488251,"user_tz":-420,"elapsed":343475,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"42e1d460-454e-4147-b442-3e95ecba7dcf"},"execution_count":7,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY (ANN) ---\n","Mean Accuracy: 0.8733\n","Std Dev: 0.0021\n","\n","Model dan Data Convergence (Accuracy) berhasil disimpan.\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/neural_networks_tags_specifications.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cdFS0AWac8Q4","executionInfo":{"status":"ok","timestamp":1734855843779,"user_tz":-420,"elapsed":6440,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"db40b6ea-e2b3-4c19-9d57-d9936ea19682"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step\n"," precision recall f1-score support\n","\n"," 0 0.88 0.44 0.59 18645\n"," 1 0.62 0.94 0.75 18147\n","\n"," accuracy 0.69 36792\n"," macro avg 0.75 0.69 0.67 36792\n","weighted avg 0.75 0.69 0.67 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"FZMag36kctY2"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"nY0oy9x2cy8P","executionInfo":{"status":"ok","timestamp":1777442077868,"user_tz":-420,"elapsed":12067,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import matplotlib.pyplot as plt\n","import os\n","from sklearn.model_selection import KFold\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score\n","import joblib\n","\n","# 1. LOAD & SUBSAMPLE\n","X_full = user_price_features\n","y_full = user_price_label\n","sample_limit = 50000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Using {len(X)} samples for ANN (Price) evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SETUP\n","kf = KFold(n_splits=5, shuffle=True, random_state=42)\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# 4. TRAINING LOOP\n","fold_accuracies = []\n","best_history = None\n","\n","for fold, (train_idx, val_idx) in enumerate(kf.split(X, y)):\n"," X_train, X_val = X[train_idx], X[val_idx]\n"," y_train_fold, y_val_fold = y[train_idx], y[val_idx]\n","\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n"," # Train 20 epochs\n"," history = model.fit(X_train, y_train_fold, epochs=20, batch_size=128,\n"," validation_data=(X_val, y_val_fold), verbose=0)\n","\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\")\n"," fold_accuracies.append(accuracy_score(y_val_fold, y_pred))\n","\n"," # Simpan history dari fold terakhir untuk plotting\n"," if fold == 4: best_history = history\n","\n","# 5. SAVE CONVERGENCE DATA (Accuracy Focus)\n","history_df = pd.DataFrame(best_history.history)\n","history_df.to_csv(os.path.join(dir_path, 'ann_price_accuracy_convergence.csv'), index_label='epoch')\n","\n","# 6. HD PLOTTING (Accuracy)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(history_df['accuracy'], label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(history_df['val_accuracy'], label='Validation Accuracy', marker='s', linewidth=2)\n","plt.title('Convergence Analysis: ANN Accuracy (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Epochs', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'ann_price_accuracy_convergence.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","print(\"\\n--- STABILITY SUMMARY (ANN PRICE) ---\")\n","print(f\"Mean Accuracy: {np.mean(fold_accuracies):.4f}\")\n","print(f\"Std Dev: {np.std(fold_accuracies):.4f}\")\n","\n","# 8. SAVE MODEL & SCALER\n","model.save(os.path.join(dir_path, 'ann_price_model.keras'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Data Convergence (Accuracy) berhasil disimpan.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"twU-lTQPcvo8","executionInfo":{"status":"ok","timestamp":1777442453882,"user_tz":-420,"elapsed":280252,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"44374f1e-bc14-4c9c-efe9-c7575b9b4572"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 50000 samples for ANN (Price) evaluation.\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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IUM8GBGAwi7zdUTmz1kc6dixo6pVq2YtX7x40bDtlzvcuHHD5iav0aNHO9U283ErVqxQamqq22ID4H6ZP5M5c+aMhyIBAOdxvQLkjYKarDxr1ixDeejQoSpWjE1EARQOBWtGBQAnLF++3FDu0qVLtqsT3n7s7datW6fExMQ8i+3+++93um3m2JYtW+aWmADkndDQUJu6a9eueSASAHAe1yvAnWP27NmGcs+ePVW6dGkPRQOgsEtISLBuW3yLs9cRJpPJZrstd19HZP6Mp3bt2k6vSFK1alXVrFnTWo6Pj9f69evdGh8A98r8mQyfxwAoDLheAe4cR48e1ZYtWwx17BoBoDAhoRBAobNnzx5DuXXr1k63LV++vKpWrWotp6am6uDBg26Jy2KxaN++fTmOrU2bNoby3r1782T7HwDuc/78eZu6UqVKeSASAHAO1yvAneP69etavHixoY4PrgHkxoEDB5SWlmYtV6tWTWXLlnW6febriMyf7+RWbj4vkvI+PgDulfkzGT6PAVAYcL0C3Dky3+TZqFEj1a9f30PRAIDrWE8VQKFz6NAhQ7lu3bouta9bt64iIyMN52vWrFmu4zp9+rRhq9OAgABVrlzZ6fZVqlSRv7+/9RyJiYk6e/asS+cAkL82btxoKFepUkU+Pj751n9sbKzOnTun69evq0SJEipVqpQqVqzo9KqtAPLfjRs3dPLkSV2+fFne3t4qVaqUypcvL39//3zpn+sV4M7xww8/KDk52VoOCwtT9+7dPRgRgMLOHZ/HZHW+3Cro8QFwH4vFot9//91QV6tWrXzrPz09XSdPnlRMTIxMJpNKlSqlcuXKKSgoKN9iAOC6CxcuKCoqSomJiQoNDVXp0qVVrly5fI2B6xXgzpCRkaE5c+YY6rjJE0BhQ0IhgEIlOTlZZ86cMdRVqlTJpXNkPv7IkSO5jsveeVyN61ab289z5MgRvqAHCrAZM2YYyvn1JX10dLTq1q1r9wOjkiVL6t5779WQIUM0YMAAeXl55UtMALI3btw4nTx5UikpKYb6YsWKqUmTJurWrZueeOIJlSlTJs9i4HoFuHPMmjXLUB46dKiKFcvfj4G4+QEoWnJ7HZH5+NOnTyslJUW+vr65jk1yf3zu+rwIgPutW7dOp06dspZNJpO6du2aL32/+eabeuqppxQfH2+oN5vNqlevnrp06aInn3zS6S1MAeS9P//8U9WrVzfMG7eULVtW7du318iRI/NlHuF6Bbgz/Pbbbzp79qy17O3trSFDhuR7HNwEASA32PIYQKESGxtr2FbP29tbYWFhLp2jQoUKhnJ0dLRbYst8nooVK7p8jryKDYD7/fzzz9qwYYOhLr/uMEtOTnZ49+mVK1f0448/atCgQapdu7bWr1+fLzEByN7Bgwdtkgkl6ebNm9q6datee+01ValSRf/617+Unp6eJzFwvQLcGY4fP65NmzYZ6kaNGpVv/d+6+aFMmTJq1KiR2rdvr0aNGqly5coqXbq0+vbtqx9++CHP5joAeSO31xHh4eGGxOaMjAxdvnzZLbFJuY+PaxygcMjIyNDEiRMNdV27dnVpC/bcOHbsmE0y4a249u7dq/fee0933XWXxo4da1gtGoDnXLlyxW4yoSRdvHhR33//vbp166bGjRvrzz//zNNYuF4B7gyZb/Ls1auXSpcuna8xvPnmmwoNDVWtWrXUpk0btW7dWrVr11ZISIgaNmyo5557TqdPn87XmAAULiQUAihUEhISDGV/f3+XV7cICAjI8pw5lfk8mftxRl7FBsC9rly5oscff9xQ17dvXzVv3txDEdl34sQJderUSVOnTvV0KACclJycrP/85z/q3LlznlwHcL0C3Blmz55tKDdu3Fj16tXLt/65+QEomnJ7HWEymeTn55flOXMjt/FxjQMUDu+99562bt1qLZvNZr3xxhsejMjWzZs39dlnn6lFixa6cOGCp8MB4KTdu3erRYsWmj9/fp71wfUKUPTFx8dr8eLFhjpPbHfMTRAAcouEQgCFSuZ/jnKyLU5efXhdkGMD4D4ZGRkaNmyYzp07Z60LDg7WtGnT8rzvEiVKaODAgfrqq6+0Y8cOXb58WWlpabp27ZoOHTqkr776Sm3btjW0SU9P19NPP6158+bleXwAbJlMJrVu3VpvvPGGVq9erXPnzikpKUkpKSk6f/68li5dqscff9zmumHdunUaPHiw21fv4noFKPosFou+/vprQ50nPrh2Bjc/AIVLQb+OyG18XOMABd/GjRv18ssvG+omTJigRo0a5XnfDRs21CuvvKKff/5ZkZGRSkhIUGpqqi5evKjVq1fr2WefVXBwsKHNn3/+qV69eikxMTHP4wNgq3Tp0ho5cqS++eYb7du3T1euXFFaWpquXr2qvXv36uOPP1aDBg0MbZKTkzVs2DCbnWnchesVoOj74YcflJSUZC2Hh4erW7duHozIMW6CAJCVYtkfAgAFR+ZtAn18fFw+R/HixQ1ld911UZBjA+A+zz33nFasWGGo+/zzz1WpUqU87ffdd99Vt27dFBgYaPNciRIlVKJECUVERGj06NFavHixRo8erbi4OEl/JRY8/PDD6tChQ75tAQRAuv/++zVkyBDVqlXL7vPly5dX+fLl1bNnT73yyisaPHiwYYvS5cuX65NPPtE//vEPt8XE9QpQ9P322286c+aMtezj46MhQ4bkS98lSpRQ165d9cADD6hBgwaqVq2aSpQooaSkJEVFRWnz5s2aOXOmfv/9d2ubWzc/hIeHa/DgwfkSJ4CcKejXEbmNj2scoGA7efKk+vfvr5s3b1rrGjZsqDfffDNP+23cuLF27typxo0b230+PDxc4eHh6ty5s1566SWNHj1aS5YssT6/c+dO/etf/9L777+fp3ECMPrmm2/04IMP2r0eCAkJUUhIiOrXr69x48bp888/1/jx43Xjxg1JUmpqqoYMGaLjx4/n6AaKrHC9AhR9mbc7Hjp0qIoVy7+0nIYNG6pnz55q3bq16tatq9KlS8vHx0dXrlzRn3/+qZUrV2r69Om6du2atc2tmyDWr1+fox1tABRNrFAIoFDJ/M9bamqqy+e49U+ho3PmVEGODYB7TJs2TR988IGh7vnnn9egQYPyvO8HH3zQbjKhPf369dOKFSsMd6wmJSUVuC2AgKKudevWDpMJM6tYsaJ+/fVXtWrVylD/+uuvG+5ozS2uV4CiL/N2xz179lSpUqXyvN93331X58+f1/fff6/Ro0erSZMmKlmypIoVK2a48WHjxo1atGiRQkJCrG1v3fxw8eLFPI8TQM4V9OuI3MbHNQ5QcMXGxqpbt26KjY211oWHh2vRokU2yTXuVr9+fYfJhJmFhoZq4cKF6t+/v6H+k08+0fnz5/MiPAAODB061Olkvccff1zfffedzOb/+9r8/Pnz+t///uf2uLheAYq2EydOGG6ilPJv14hbN0Hs3r1b//nPf9StWzdVqVJFAQEB8vb2tt4A8e677+rUqVPq27evof2tmyAA4BYSCgEUKpmTaTLfzeWMzHdsOZugk52CHBuA3Pvuu+80YcIEQ93IkSP19ttveyagbLRs2VLPP/+8oe67775TRkaGhyICkB1fX199/fXXhjtWo6OjtWrVKrf1wfUKULQlJCRo0aJFhrr8+uCamx+Aoq+gX0fkNj6ucYCCKT4+Xt26ddPRo0etdcHBwVq5cqWqVavmwcjsM5vN+vLLLw03T6SkpGj+/PmeCwpAtvr376+HHnrIUDdnzhy398P1ClC0Zb7Js3HjxqpXr16+9M1NEADcjYRCAIVK5n+OkpKSZLFYXDpHYmJilufMqcznydyPM/IqNgC5s2zZMo0YMcIw3/Tv319ffvmlTCaTByPL2vjx4+Xl5WUtX7lyRTt27PBgRACyc9ddd6l3796GurxMKOR6BSha5s+fb3iPhoeHq1u3bh6MyDFufgAKn9xeR1gslnxNKHQ1Pq5xgIInJSVFvXv3NnyW4e/vr+XLl6tBgwYejCxroaGhGj16tKHOnf/XAcgbzzzzjKG8b98+Xbp0ya19cL0CFF0Wi8UmETm/bvLMCW6CAJAdEgoBFCqlS5c2JO+kpaUpOjrapXNkvrMiLCzMLbFlPs+5c+dcPkdexQYg59auXasHH3xQN2/etNZ16dJFc+fONSTrFUShoaE2d6QdOXLEQ9EAcFanTp0MZXe+b7leAYq2WbNmGcpDhw41rHpa0HDzA1C45PY64tKlS4b/q8xms0qXLu2W2KTcx8c1DlCwpKWlaeDAgVq3bp21zsfHR4sWLVKbNm08F5iT8vL/OgB5o169eoa//xaLxbA6qjtwvQIUXevWrVNkZKS17OPjoyFDhnguICdwEwSArJBQCKBQ8fPzU+XKlQ11Z86ccekcmY+PiIjIdVySVLt2bUP57NmzLp8jcxt3xQYgZ7Zu3arevXsbtp5o3bq1Fi9eLB8fHw9G5rxKlSoZyjExMR6KBICz8vJ9y/UKUHSdPHlSGzduNNQV5DvhJW5+AAqbzNcRuf08pkqVKvL19c11XLe4Oz6ucQDPycjI0PDhw7V06VJrnZeXl7777js98MADHozMeXweAxROFStWNJTd/d7legUoujLf5NmzZ0+VKlXKM8G4gJsgADhCQiGAQifzP0gHDx50qf2hQ4eyPF9OValSRX5+ftZyYmKiTp8+7XT706dPKykpyVoOCAiw+eAJQP7Zt2+funXrpoSEBGtdo0aN9PPPPysgIMCDkbnG29vbUE5LS/NQJACclZfvW65XgKLr66+/lsVisZYbN26sevXqeTAi5/BlO1B4FNTPYxydr6DFB8A5FotFjz32mObNm2etM5lM+vLLLzVgwAAPRuYaPo8BCqe8fu9yvQIUTQkJCVq4cKGhrqDf5HkLn8sAcISEQgCFTsOGDQ3lzZs3O932woULhuWmvb29VbduXbfEZTKZVL9+/RzHtmnTJkO5fv36hu2dAeSfI0eOqEuXLrp6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STABILITY SUMMARY (ANN PRICE) ---\n","Mean Accuracy: 0.9949\n","Std Dev: 0.0034\n","\n","Model dan Data Convergence (Accuracy) berhasil disimpan.\n"]}]},{"cell_type":"code","source":["\n","# Example prediction (replace with your actual data)\n","example_features = user_price_features[0] # Example datapoint\n","prediction = (model.predict(np.array([example_features])) > 0.5).astype(\"int32\")\n","print(f\"Prediction for the example datapoint: {prediction}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FXgmBUNTWiOJ","executionInfo":{"status":"ok","timestamp":1734887544151,"user_tz":-420,"elapsed":379,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"28a5c81c-e433-4a69-ef99-509a40fc8fe6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n","Prediction for the example datapoint: [[0]]\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FMRD6RqjGNbK","executionInfo":{"status":"ok","timestamp":1735051114521,"user_tz":-420,"elapsed":91190,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"386467ef-11cc-48e9-dee6-d7ee94ca95d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2ms/step\n"," precision recall f1-score support\n","\n"," 0 1.00 1.00 1.00 602313\n"," 1 1.00 1.00 1.00 612435\n","\n"," accuracy 1.00 1214748\n"," macro avg 1.00 1.00 1.00 1214748\n","weighted avg 1.00 1.00 1.00 1214748\n","\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XfpLJxOdF79c","executionInfo":{"status":"ok","timestamp":1735050941077,"user_tz":-420,"elapsed":19421,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"804fb55e-d0c2-4955-acf1-949a0e3e6594"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: save model best_mlp_classifier\n","\n","import joblib\n","\n","# Assuming neural_networks is defined as in your provided code.\n","# Assuming 'neural_networks' is your trained model\n","joblib.dump(model, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl')\n","print(\"Model saved to neural_networks.pkl\")"],"metadata":{"id":"FoYSaGaJR1wo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1734887466457,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3bf0e99e-5628-471d-cc09-85a630a08376"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to neural_networks.pkl\n"]}]},{"cell_type":"markdown","source":["### Predic Bundle"],"metadata":{"id":"unvcsZe_xUw-"}},{"cell_type":"code","source":[],"metadata":{"id":"6EVDa1hXxYv6"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Source Code/Convergence/IMPACT-P Decision Tree.ipynb b/Source Code/Convergence/IMPACT-P Decision Tree.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..46d1f1fed351dbc0c4859d9fe0c122444d1be887 --- /dev/null +++ b/Source Code/Convergence/IMPACT-P Decision Tree.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777433062145,"user_tz":-420,"elapsed":15705,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"b6189af7-ec79-405e-b827-d0c31a7dede3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nWPptAf-L46u","executionInfo":{"status":"ok","timestamp":1777433238530,"user_tz":-420,"elapsed":8900,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"a2533dfd-6717-4664-c478-5cbf4371bb31"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777433241604,"user_tz":-420,"elapsed":8,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"iRZwe19LYEPm"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"u4jFU5HCYDmw","executionInfo":{"status":"ok","timestamp":1777433250542,"user_tz":-420,"elapsed":6428,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. SETUP\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","dt = DecisionTreeClassifier(random_state=42)\n","param_grid = {\n"," 'max_depth': [10, 20, 30, None],\n"," 'min_samples_split': [5, 10],\n"," 'min_samples_leaf': [2, 4]\n","}\n","grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'dt_learning_curve.csv'), index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Decision Tree (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'dt_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Decision Tree\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":707},"id":"ZNJX8CrOYc_d","executionInfo":{"status":"ok","timestamp":1777433692483,"user_tz":-420,"elapsed":434372,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"b405ec6c-8335-40cc-f028-fdae00f877dd"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","Decision Tree 0.872029 0.001922\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"-uHARtrfYAse"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"MA0bSAHwsT-0","executionInfo":{"status":"ok","timestamp":1777433744035,"user_tz":-420,"elapsed":9120,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. LOAD & SUBSAMPLE\n","X_full = user_price_features\n","y_full = user_price_label\n","sample_limit = 100000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Using {len(X)} samples for Decision Tree (Price) evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 3. HYPERPARAMETER TUNING\n","dt = DecisionTreeClassifier(random_state=42)\n","param_grid = {\n"," 'max_depth': [10, 20, 30, None],\n"," 'min_samples_split': [5, 10],\n"," 'min_samples_leaf': [2, 4]\n","}\n","grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 4. LEARNING CURVE ANALYSIS (Convergence Proof)\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 5. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'dt_price_learning_curve.csv'), index=False)\n","\n","# 6. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Decision Tree (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'dt_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Decision Tree (Price)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"id":"PyQaMKU0LLqx","colab":{"base_uri":"https://localhost:8080/","height":736},"executionInfo":{"status":"ok","timestamp":1777433810143,"user_tz":-420,"elapsed":20861,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"fce3b2fc-a8eb-4aa6-be35-54ee2bce1363"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 100000 samples for Decision Tree (Price) evaluation.\n","Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","Decision Tree (Price) 0.997263 0.00048\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}}]} \ No newline at end of file diff --git a/Source Code/Convergence/IMPACT-P KNN.ipynb b/Source Code/Convergence/IMPACT-P KNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4fcc2fceccb579b27aa66d612c95f095de1b9d53 --- /dev/null +++ b/Source Code/Convergence/IMPACT-P KNN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777429297139,"user_tz":-420,"elapsed":19569,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"84b2d86f-e387-4f8f-e5b8-5fd1b55c1774"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DBRXr-Gf2tzx","executionInfo":{"status":"ok","timestamp":1777429332294,"user_tz":-420,"elapsed":35150,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"e18c6517-ed06-4b40-ffed-54d0167ca436"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777429332296,"user_tz":-420,"elapsed":8,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777429345790,"user_tz":-420,"elapsed":13492,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. LOAD & SAMPLE DATA\n","sample_size = 10000\n","X_full = user_tags_and_specs_features\n","y_full = user_tags_and_specs_label\n","\n","X = X_full[:sample_size]\n","y = y_full[:sample_size]\n","print(f\"Using {len(X)} samples for KNN evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 3. HYPERPARAMETER TUNING\n","knn = KNeighborsClassifier(algorithm='ball_tree', n_jobs=-1)\n","param_grid = {'n_neighbors': [3, 5, 7, 9], 'weights': ['uniform', 'distance']}\n","grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 4. LEARNING CURVE ANALYSIS (Convergence/Saturation Proof)\n","# Using 'best_model' ensures the analysis reflects your chosen configuration\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 5. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'knn_learning_curve.csv'), index=False)\n","\n","# 6. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: KNN (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'knn_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"KNN (Tags/Specs)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":528},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777430578101,"user_tz":-420,"elapsed":692287,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"6dfeeb73-85df-4d2f-e3be-c625e2891e2e"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 10000 samples for KNN evaluation.\n","Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","KNN (Tags/Specs) 0.849 0.015117\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777432001494,"user_tz":-420,"elapsed":10961,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. LOAD & SUBSAMPLE\n","X_full = user_price_features\n","y_full = user_price_label\n","sample_limit = 50000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Using {len(X)} samples for KNN (Price) evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 3. HYPERPARAMETER TUNING\n","knn = KNeighborsClassifier(algorithm='ball_tree', n_jobs=-1)\n","param_grid = {'n_neighbors': [3, 5, 7, 9], 'weights': ['uniform', 'distance']}\n","grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 4. LEARNING CURVE ANALYSIS\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 5. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'knn_price_learning_curve.csv'), index=False)\n","\n","# 6. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: KNN (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'knn_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"KNN (Price)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777432847818,"user_tz":-420,"elapsed":391657,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/","height":532},"outputId":"064fdb1e-ab04-4efc-c386-ff0d969b1689"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 50000 samples for KNN (Price) evaluation.\n","Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","KNN (Price) 0.9764 0.001952\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model best_logreg_classifier\n","import joblib\n","\n","# Assuming 'best_logreg_classifier' is your trained model\n","# joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/knn_tags_specifications.pkl')\n","joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/knn_price_patterns.pkl')\n","print(\"Model saved to best_logreg_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1734869567483,"user_tz":-420,"elapsed":11523,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b5e5fa97-9fbe-4063-f4c7-43d12fd24f8b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_logreg_classifier.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Convergence/IMPACT-P Logistic Regression.ipynb b/Source Code/Convergence/IMPACT-P Logistic Regression.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..11ccc03b08c9013b116bc901e7d249a85cac6954 --- /dev/null +++ b/Source Code/Convergence/IMPACT-P Logistic Regression.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1RfBBMApu-iF","executionInfo":{"status":"ok","timestamp":1777427567302,"user_tz":-420,"elapsed":24057,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"a5a1d00c-36c3-4f18-b70d-e169f36b334d"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777427611674,"user_tz":-420,"elapsed":8362,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"bd50abdf-8d66-46b2-83e7-c22e537b97f2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777427641079,"user_tz":-420,"elapsed":49,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777427655304,"user_tz":-420,"elapsed":10658,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","# 1. SETUP DATA\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1],\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear'] # 'newton-cg' can be slow, removed for efficiency\n","}\n","\n","logreg = LogisticRegression(max_iter=1000, random_state=42)\n","grid_search = GridSearchCV(logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS (Convergence Proof)\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE DATA TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv('logreg_learning_curve.csv', index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Logistic Regression (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","\n","plt.savefig(os.path.join(dir_path, 'logreg_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. OUTPUT STABILITY TABLE\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Logistic Regression\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(summary_table.to_string(index=False))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":471},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777428032173,"user_tz":-420,"elapsed":203045,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"7779d4fd-5a28-4f47-9be1-81f8bf0dd4f7"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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Model Mean Accuracy Std Dev\n","Logistic Regression 0.863909 0.001346\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777428106784,"user_tz":-420,"elapsed":11064,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","# 1. SETUP\n","X = user_price_features\n","y = user_price_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1],\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear']\n","}\n","\n","logreg = LogisticRegression(max_iter=1000, random_state=42)\n","grid_search = GridSearchCV(logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS (Convergence Proof)\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'logreg_price_learning_curve.csv'), index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Logistic Regression (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'logreg_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. OUTPUT STABILITY TABLE\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Logistic Regression (Price)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777429133050,"user_tz":-420,"elapsed":696921,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/","height":506},"outputId":"36c4c2eb-3479-4bc8-d57b-413c9d68d85f"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","Logistic Regression (Price) 0.899441 0.00027\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Convergence/IMPACT-P Naive Bayes.ipynb b/Source Code/Convergence/IMPACT-P Naive Bayes.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c900fe76fe4caa5159f0890ee43693af29e12b5f --- /dev/null +++ b/Source Code/Convergence/IMPACT-P Naive Bayes.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777421617504,"user_tz":-420,"elapsed":15790,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"701c2de7-84ef-4b70-f8f3-d3dccde84cdd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W2dzyUI5m-pc","executionInfo":{"status":"ok","timestamp":1777422260669,"user_tz":-420,"elapsed":47316,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"e8a3ca2a-c3a3-4d0d-d976-1739e6a419cb"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777422274396,"user_tz":-420,"elapsed":5,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"NRnsh1W3oa_Q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777422290519,"user_tz":-420,"elapsed":12647,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","import os\n","\n","# 1. SETUP\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. GRID SEARCH\n","param_grid = {'alpha': [0.1, 0.5, 1.0, 2.0, 5.0], 'binarize': [0.0, 0.5, 1.0]}\n","bnb = BernoulliNB()\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS\n","# This computes accuracy for different training set sizes\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE TO CSV (For your Appendix/Reviewer Proof)\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1),\n"," 'train_score_std': np.std(train_scores, axis=1),\n"," 'test_score_std': np.std(test_scores, axis=1)\n","})\n","lc_df.to_csv('nb_learning_curve.csv', index=False)\n","print(\"\\nLearning curve data saved to 'nb_learning_curve.csv'\")\n","\n","# 5. OPTIONAL: Plotting (Uncomment to visualize)\n","plt.figure(figsize=(8, 5))\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy')\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy')\n","plt.title('Learning Curve (Naive Bayes)')\n","plt.xlabel('Training Set Size')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.grid(True)\n","plt.show()\n","\n","# 6. PRINT FINAL REPORT\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, best_model.predict(X_test)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777422376726,"user_tz":-420,"elapsed":84317,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"7c96a605-2ba9-4911-e59e-12e3a8591847"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","\n","Learning curve data saved to 'nb_learning_curve.csv'\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Classification Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.81 0.85 0.83 18645\n"," 1 0.84 0.79 0.82 18147\n","\n"," accuracy 0.82 36792\n"," macro avg 0.83 0.82 0.82 36792\n","weighted avg 0.83 0.82 0.82 36792\n","\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# 1. Atur font agar terlihat profesional dan ukuran figure yang lebih besar\n","plt.rcParams.update({'font.size': 12, 'font.family': 'serif'})\n","\n","# 2. Gunakan figsize (10, 6) dan DPI 300 (standar HD untuk paper)\n","plt.figure(figsize=(10, 6), dpi=300)\n","\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","\n","plt.title('Learning Curve (Naive Bayes)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","\n","# 3. SIMPAN SEBAGAI PDF ATAU SVG (PENTING!)\n","# Simpan sebagai PDF agar tidak pecah saat di-zoom atau dimasukkan ke MS Word/LaTeX\n","plt.savefig('learning_curve_nb.pdf', bbox_inches='tight', dpi=300)\n","plt.savefig('learning_curve_nb.png', bbox_inches='tight', dpi=300) # Tetap simpan PNG sebagai cadangan\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":600},"id":"1Ji8VkFHovQE","executionInfo":{"status":"ok","timestamp":1777422503249,"user_tz":-420,"elapsed":4778,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"ce275028-e30f-45d9-eb7a-5f2ac0aa5f10"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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joblib\n","import os\n","\n","# Ambil model terbaik\n","best_bnb_tags = grid_search.best_estimator_\n","\n","# Pastikan direktori tujuan ada\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/retrain_tags_specifications/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan model\n","joblib.dump(best_bnb_tags, os.path.join(dir_path, 'nb_tags_specifications.pkl'))\n","print(\"Model Tags & Specifications berhasil disimpan!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zlSBDwGdsfU6","executionInfo":{"status":"ok","timestamp":1777339663796,"user_tz":-420,"elapsed":67,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"d0b57519-8107-4c0e-8314-70fd13f30106"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Tags & Specifications berhasil disimpan!\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777422560173,"user_tz":-420,"elapsed":12769,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# 1. SETUP & PREPROCESSING\n","X = user_price_features\n","y = user_price_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","param_grid = {'alpha': [0.1, 0.5, 1.0, 2.0, 5.0], 'binarize': [0.0, 0.5, 1.0]}\n","bnb = BernoulliNB()\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS (Convergence Proof)\n","# Ini menganalisis performa model seiring bertambahnya data\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE TO CSV (Untuk bukti di Appendix)\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1),\n"," 'train_score_std': np.std(train_scores, axis=1),\n"," 'test_score_std': np.std(test_scores, axis=1)\n","})\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/nb_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","lc_df.to_csv(os.path.join(dir_path, 'nb_price_learning_curve.csv'), index=False)\n","print(f\"\\nLearning curve data saved to: {dir_path}/nb_price_learning_curve.csv\")\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Naive Bayes (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","\n","# Simpan sebagai PDF (Vector - HD) dan PNG\n","plt.savefig(os.path.join(dir_path, 'nb_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.savefig(os.path.join(dir_path, 'nb_price_learning_curve.png'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. FINAL REPORT\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, best_model.predict(X_test)))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777422871532,"user_tz":-420,"elapsed":169128,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/","height":856},"outputId":"b002c1c4-df49-4b0a-b750-04a2e726a390"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","\n","Learning curve data saved to: /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/nb_price//nb_price_learning_curve.csv\n"]},{"output_type":"display_data","data":{"text/plain":["
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Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.70 0.60 0.65 602313\n"," 1 0.66 0.75 0.70 612435\n","\n"," accuracy 0.68 1214748\n"," macro avg 0.68 0.68 0.67 1214748\n","weighted avg 0.68 0.68 0.67 1214748\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["import joblib\n","import os\n","\n","# Ambil model terbaik\n","best_bnb_price = grid_search.best_estimator_\n","\n","# Pastikan direktori tujuan ada\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/retrain_price_pattern/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan model\n","joblib.dump(best_bnb_price, os.path.join(dir_path, 'nb_price_patterns.pkl'))\n","print(\"Model Price Patterns berhasil disimpan!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1777339771773,"user_tz":-420,"elapsed":83,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"192b8e83-2e8d-4bc2-9b1c-fff03e9cd85a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Price Patterns berhasil disimpan!\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Not use3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation SGD.ipynb b/Source Code/Not use3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation SGD.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..26ddb4ccb91406975c4110b14c0fde4283c06687 --- /dev/null +++ b/Source Code/Not use3. Preliminary 1 IMPACT-P Implicit Matching and Constraint Tuning for Bundle Recommendation SGD.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["_T8fPo2P6CFY"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1734853764685,"user_tz":-420,"elapsed":13491,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8c970e9c-18ae-423f-e0a7-e2ce9f75e19d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Downloading ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/114.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.3.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"Aw7ApgIGajKn"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"6rfmeKiLal00"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt:\n","\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import classification_report\n","from sklearn.linear_model import SGDClassifier\n","from sklearn.metrics import accuracy_score\n","\n","\n","# Assuming 'new_bundle_recommendations' is defined as in the previous code\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Hyperparameter tuning with GridSearchCV for SGD Classifier\n","param_grid = {\n"," 'loss': ['hinge', 'log_loss'], # Different loss functions\n"," 'penalty': ['l2'], # Different penalties\n"," 'alpha': [0.001, 0.01, 0.1], # Regularization parameter\n"," 'learning_rate': ['constant'], # Learning rate schedule\n"," 'eta0': [0.001, 0.01, 0.1] # Initial learning rate\n","}\n","\n","sgd_classifier = SGDClassifier(max_iter=1000, random_state=42)\n","\n","grid_search = GridSearchCV(estimator=sgd_classifier, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=True)\n","grid_search.fit(X_train, y_train)\n","\n","\n","best_sgd_classifier = grid_search.best_estimator_\n","print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n","\n","# Evaluate the best model using cross-validation\n","cv_scores = cross_val_score(best_sgd_classifier, user_tags_and_specs_features, user_tags_and_specs_label, cv=5, scoring='accuracy', n_jobs=-1, verbose=True)\n","print(f\"Cross-Validation Scores: {cv_scores}\")\n","print(f\"Mean Cross-Validation Score: {np.mean(cv_scores)}\")\n","\n","# Predict on the test set and evaluate\n","y_pred = best_sgd_classifier.predict(X_test)\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Test Accuracy: {accuracy}\")\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f-lO2C21aoxt","executionInfo":{"status":"ok","timestamp":1734855149741,"user_tz":-420,"elapsed":98226,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0375b395-ccd3-4bd4-fb72-771cff6ccdf0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 18 candidates, totalling 90 fits\n","Best Hyperparameters: {'alpha': 0.001, 'eta0': 0.01, 'learning_rate': 'constant', 'loss': 'hinge', 'penalty': 'l2'}\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"]},{"output_type":"stream","name":"stdout","text":["Cross-Validation Scores: [0.84882583 0.85273973 0.86187215 0.85974831 0.86635318]\n","Mean Cross-Validation Score: 0.8579078392122865\n","Test Accuracy: 0.8636931941726462\n"," precision recall f1-score support\n","\n"," 0 0.84 0.91 0.87 18645\n"," 1 0.90 0.82 0.86 18147\n","\n"," accuracy 0.86 36792\n"," macro avg 0.87 0.86 0.86 36792\n","weighted avg 0.87 0.86 0.86 36792\n","\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 6.6s finished\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"yur49rzBah8E"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt:\n","\n","from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n","from sklearn.metrics import classification_report\n","from sklearn.linear_model import SGDClassifier\n","from sklearn.metrics import accuracy_score\n","\n","\n","# Assuming 'new_bundle_recommendations' is defined as in the previous code\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Hyperparameter tuning with GridSearchCV for SGD Classifier\n","param_grid = {\n"," 'loss': ['hinge', 'log_loss'], # Different loss functions\n"," 'penalty': ['l2'], # Different penalties\n"," 'alpha': [0.001, 0.01, 0.1], # Regularization parameter\n"," 'learning_rate': ['constant'], # Learning rate schedule\n"," 'eta0': [0.001, 0.01, 0.1] # Initial learning rate\n","}\n","\n","sgd_classifier = SGDClassifier(max_iter=1000, random_state=42)\n","\n","grid_search = GridSearchCV(estimator=sgd_classifier, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=True)\n","grid_search.fit(X_train, y_train)\n","\n","\n","best_sgd_classifier = grid_search.best_estimator_\n","print(f\"Best Hyperparameters: {grid_search.best_params_}\")\n","\n","# Evaluate the best model using cross-validation\n","cv_scores = cross_val_score(best_sgd_classifier, user_price_features, user_price_label, cv=5, scoring='accuracy', n_jobs=-1, verbose=True)\n","print(f\"Cross-Validation Scores: {cv_scores}\")\n","print(f\"Mean Cross-Validation Score: {np.mean(cv_scores)}\")\n","\n","# Predict on the test set and evaluate\n","y_pred = best_sgd_classifier.predict(X_test)\n","accuracy = accuracy_score(y_test, y_pred)\n","print(f\"Test Accuracy: {accuracy}\")\n","print(classification_report(y_test, y_pred))"],"metadata":{"id":"hnRTUGdExdM8","executionInfo":{"status":"ok","timestamp":1734854699989,"user_tz":-420,"elapsed":816520,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3da50ce1-e14b-49e8-a06d-c776c9f25618"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 18 candidates, totalling 90 fits\n","Best Hyperparameters: {'alpha': 0.001, 'eta0': 0.001, 'learning_rate': 'constant', 'loss': 'log_loss', 'penalty': 'l2'}\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n","[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 47.0s finished\n"]},{"output_type":"stream","name":"stdout","text":["Cross-Validation Scores: [0.90129228 0.88836944 0.89182365 0.88014963 0.90706056]\n","Mean Cross-Validation Score: 0.8937391129682863\n","Test Accuracy: 0.9033659656159138\n"," precision recall f1-score support\n","\n"," 0 0.89 0.92 0.90 602313\n"," 1 0.92 0.89 0.90 612435\n","\n"," accuracy 0.90 1214748\n"," macro avg 0.90 0.90 0.90 1214748\n","weighted avg 0.90 0.90 0.90 1214748\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model\n","\n","import joblib\n","\n","# Assuming 'best_sgd_classifier' is your trained model\n","joblib.dump(best_sgd_classifier, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/best_sgd_classifier.pkl')\n","print(\"Model saved to best_sgd_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SnVH0_fBnE-H","executionInfo":{"status":"ok","timestamp":1734778798575,"user_tz":-420,"elapsed":440,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9ec570e6-77ae-4282-89cd-61a21c444c73"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_sgd_classifier.pkl\n"]}]},{"cell_type":"code","source":["# prompt: Save model\n","\n","import joblib\n","\n","# Assuming 'best_sgd_classifier' is your trained model\n","joblib.dump(best_sgd_classifier, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/best_sgd_classifier_price_patterns.pkl')\n","print(\"Model saved to best_sgd_classifier_price_patterns.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"00b0sMZCkFr3","executionInfo":{"status":"ok","timestamp":1734854945432,"user_tz":-420,"elapsed":787,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a4cabc55-4ccb-4122-9dc3-47e8a40b8593"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_sgd_classifier_price_patterns.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Our Proposed/ANN.ipynb b/Source Code/Our Proposed/ANN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b1990cefb51b220374ac2a3bac895b54d3c02481 --- /dev/null +++ b/Source Code/Our Proposed/ANN.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","outputId":"b4b0da36-0cd1-4bed-9baa-92b2e19b5c9e","executionInfo":{"status":"ok","timestamp":1736583588395,"user_tz":-420,"elapsed":16150,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736583894130,"user_tz":-420,"elapsed":363,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"4f04fd18-0aeb-4ec5-c04d-9d0699724e56","executionInfo":{"status":"ok","timestamp":1736583896309,"user_tz":-420,"elapsed":1352,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"2b7b63ca-cef2-4a43-a7c0-59db06f5d3c7","executionInfo":{"status":"ok","timestamp":1736583900660,"user_tz":-420,"elapsed":4005,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"d97877e3-987c-4ea1-d90c-ac2b74b7bc43","executionInfo":{"status":"ok","timestamp":1736583924720,"user_tz":-420,"elapsed":24062,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736583924721,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736583924721,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1736583924721,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1736583926788,"user_tz":-420,"elapsed":2070,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1736583926788,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736583927255,"user_tz":-420,"elapsed":468,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"c4816cae-56a9-43d8-eba9-48103e43594e","executionInfo":{"status":"ok","timestamp":1736583927255,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"b14de3d1-e8a5-4583-d5f7-bceecba899bd","executionInfo":{"status":"ok","timestamp":1736583927256,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","execution_count":14,"metadata":{"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736583927256,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","outputId":"5d186803-5dc1-4b29-9d63-433076ea06db","executionInfo":{"status":"ok","timestamp":1736584014062,"user_tz":-420,"elapsed":86809,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357284 sha256=7cc8521c609d9d0ab276ade75fa5bcaeb51d5b9b3d05ea082c0083393d2e0f48\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736584015094,"user_tz":-420,"elapsed":1035,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"bba35cce-fd78-4738-c3f7-c9d22c303c97","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"35a30a03-e084-484e-c8e0-086dca5cebe3","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":6,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","execution_count":21,"metadata":{"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"xUuraXZlT3f-","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8","executionInfo":{"status":"ok","timestamp":1736584015095,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0","executionInfo":{"status":"ok","timestamp":1736584015096,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","outputId":"481ae679-af9d-4265-807d-8b108bd68789","executionInfo":{"status":"ok","timestamp":1736585279502,"user_tz":-420,"elapsed":990134,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 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0.069771657067377, 10: 0.18081310105743048, 15: 0.23031802692321748, 20: 0.24765561679400952}\n","Mean Diversity: {5: 0.7239472522597888, 10: 0.7783845717782079, 15: 0.8393916735422627, 20: 0.8521412561853942}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"c8f6e11c-816a-496b-8186-aaeb0cf233a3","executionInfo":{"status":"ok","timestamp":1736585279502,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.069772 0.723947\n","1 10 0.180813 0.778385\n","2 15 0.230318 0.839392\n","3 20 0.247656 0.852141"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0697720.723947
1100.1808130.778385
2150.2303180.839392
3200.2476560.852141
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.08008708484248589,\n \"min\": 0.069771657067377,\n \"max\": 0.24765561679400952,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.18081310105743048,\n 0.24765561679400952,\n 0.069771657067377\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0591953021939797,\n \"min\": 0.7239472522597888,\n \"max\": 0.8521412561853942,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.7783845717782079,\n 0.8521412561853942,\n 0.7239472522597888\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":32}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736585279502,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_ann_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"54f42785-b246-40d8-86d9-4c57aa7a8b19","id":"gt-Dl92KbiMa","executionInfo":{"status":"ok","timestamp":1736586253487,"user_tz":-420,"elapsed":973988,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 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9875\n"]}]},{"cell_type":"code","execution_count":35,"metadata":{"id":"2_46IbCBbiMa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736586253488,"user_tz":-420,"elapsed":25,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c95c9b28-0a35-4060-be91-9dc1145b1a37"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.06662603203656858, 10: 0.16892391849437932, 15: 0.2161954496113333, 20: 0.23619830502793274}\n","Mean Diversity: {5: 0.7699626770784954, 10: 0.8129473208641467, 15: 0.8547486336174472, 20: 0.8567684295943925}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"GXzwwOcgbiMa","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1736586253488,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"536a6a48-d8a8-4d3f-f168-f43d53cf4cd9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.066626 0.769963\n","1 10 0.168924 0.812947\n","2 15 0.216195 0.854749\n","3 20 0.236198 0.856768"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0666260.769963
1100.1689240.812947
2150.2161950.854749
3200.2361980.856768
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0.8129473208641467,\n 0.8567684295943925,\n 0.7699626770784954\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":36}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"g6EVJI0AbiMb","executionInfo":{"status":"ok","timestamp":1736586253488,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_ann_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Our Proposed/DT.ipynb b/Source Code/Our Proposed/DT.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..58b4d22ec257eca6237d92fd0ab04788a231222c --- /dev/null +++ b/Source Code/Our Proposed/DT.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","executionInfo":{"status":"ok","timestamp":1736581022111,"user_tz":-420,"elapsed":37967,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"52ac479e-ef9b-4aff-ca06-6dd90f421265"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":4,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736581026042,"user_tz":-420,"elapsed":321,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"19662a85-9c9c-457a-cdbc-050ac7dd9fc5","executionInfo":{"status":"ok","timestamp":1736581029780,"user_tz":-420,"elapsed":1634,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"7ff53056-120b-4896-a265-84be4d283e0f","executionInfo":{"status":"ok","timestamp":1736581036980,"user_tz":-420,"elapsed":5843,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"fbae6b14-45c3-4589-c55e-213096977883","executionInfo":{"status":"ok","timestamp":1736581097539,"user_tz":-420,"elapsed":25005,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736581097539,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736581097539,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1736581644530,"user_tz":-420,"elapsed":305,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1736581647390,"user_tz":-420,"elapsed":1587,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1736581648400,"user_tz":-420,"elapsed":532,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736581673122,"user_tz":-420,"elapsed":662,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"31ebeb77-4491-449f-b30e-d548ad0b1111","executionInfo":{"status":"ok","timestamp":1736581677374,"user_tz":-420,"elapsed":321,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"f04b0ea5-538f-4a78-d8d2-1e9f1768ca42","executionInfo":{"status":"ok","timestamp":1736581681841,"user_tz":-420,"elapsed":548,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","execution_count":21,"metadata":{"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736581936396,"user_tz":-420,"elapsed":327,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","executionInfo":{"status":"ok","timestamp":1736581868115,"user_tz":-420,"elapsed":88645,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ee113329-5bc7-488a-e287-3ebfda6f61c2"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m153.6/154.4 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357278 sha256=b7b0f463d95670ecaa3b22ccf3ed4801f496b3765b94808d108a2424e046719f\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736581915578,"user_tz":-420,"elapsed":1022,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736581943439,"user_tz":-420,"elapsed":328,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736581945189,"user_tz":-420,"elapsed":309,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"990e75f3-6581-47bc-ab2c-fa760ee81e4d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"62e31734-5749-46c6-9e90-f5192a981574"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","execution_count":25,"metadata":{"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736581961747,"user_tz":-420,"elapsed":301,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"xUuraXZlT3f-","executionInfo":{"status":"ok","timestamp":1736581968822,"user_tz":-420,"elapsed":361,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8","executionInfo":{"status":"ok","timestamp":1736583227617,"user_tz":-420,"elapsed":304,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0","executionInfo":{"status":"ok","timestamp":1736583228715,"user_tz":-420,"elapsed":1,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":34,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","executionInfo":{"status":"ok","timestamp":1736583212341,"user_tz":-420,"elapsed":1012503,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"bb817aa8-57d3-4a8b-d012-29d0bf8d7865"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 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0.0556114290327098, 10: 0.0816885452491297, 15: 0.11506830602591385, 20: 0.1487330570114376}\n","Mean Diversity: {5: 0.7144831493964885, 10: 0.8006985304590102, 15: 0.8421280313921458, 20: 0.8582217942359223}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"a45303f5-de99-4234-b487-0ab66bb8fd50","executionInfo":{"status":"ok","timestamp":1736583245291,"user_tz":-420,"elapsed":309,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.055611 0.714483\n","1 10 0.081689 0.800699\n","2 15 0.115068 0.842128\n","3 20 0.148733 0.858222"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0556110.714483
1100.0816890.800699
2150.1150680.842128
3200.1487330.858222
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0.8006985304590102,\n 0.8582217942359223,\n 0.7144831493964885\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":36}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736583275223,"user_tz":-420,"elapsed":304,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_dt_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736584367255,"user_tz":-420,"elapsed":1026576,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"610c4bbc-a181-4575-e354-8d5b94793a17","id":"gt-Dl92KbiMa"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 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9875\n"]}]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3a868bf2-da45-4f21-a626-f3c6b4c66e31","executionInfo":{"status":"ok","timestamp":1736584367255,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"id":"2_46IbCBbiMa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.0666313271232474, 10: 0.16897354007188056, 15: 0.2162528312287415, 20: 0.23626969812760135}\n","Mean Diversity: {5: 0.7702137210359932, 10: 0.8131784532509084, 15: 0.8549625423774154, 20: 0.8569741611555853}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"outputId":"5c7f2dc5-4165-4976-ac21-06d2b6519c14","executionInfo":{"status":"ok","timestamp":1736584367255,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"id":"GXzwwOcgbiMa"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.066631 0.770214\n","1 10 0.168974 0.813178\n","2 15 0.216253 0.854963\n","3 20 0.236270 0.856974"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0666310.770214
1100.1689740.813178
2150.2162530.854963
3200.2362700.856974
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0.8131784532509084,\n 0.8569741611555853,\n 0.7702137210359932\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":40}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":41,"metadata":{"executionInfo":{"status":"ok","timestamp":1736584367255,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"id":"g6EVJI0AbiMb"},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_dt_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Our Proposed/LR.ipynb b/Source Code/Our Proposed/LR.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..993315ebd222ac0c82e770a02b3428edc6a5ee36 --- /dev/null +++ b/Source Code/Our Proposed/LR.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","outputId":"ec150dc5-48bc-423a-c36a-3d84fc75e334","executionInfo":{"status":"ok","timestamp":1736583568745,"user_tz":-420,"elapsed":17073,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736583749020,"user_tz":-420,"elapsed":463,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"151c75d4-eaac-4b53-92ba-e88c58481f6e","executionInfo":{"status":"ok","timestamp":1736583751565,"user_tz":-420,"elapsed":1304,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"14f7340f-3d3c-4bdd-bcb2-55fb0508dd8c","executionInfo":{"status":"ok","timestamp":1736583755237,"user_tz":-420,"elapsed":3673,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"f6581124-5cbb-434a-d087-525401762edf","executionInfo":{"status":"ok","timestamp":1736583776565,"user_tz":-420,"elapsed":21330,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736583776565,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736583776565,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1736583776565,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1736583778418,"user_tz":-420,"elapsed":1855,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1736583778418,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736583778418,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"cd6932ec-8cc5-4ca7-8e71-bf68f16c4a0e","executionInfo":{"status":"ok","timestamp":1736583778418,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"0b25a271-e1c0-4aab-c332-0dc21bac5108","executionInfo":{"status":"ok","timestamp":1736583778704,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","execution_count":14,"metadata":{"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736583778704,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","outputId":"33fbbc06-6ead-4e1f-cee9-9c6eb7ba412c","executionInfo":{"status":"ok","timestamp":1736583862326,"user_tz":-420,"elapsed":83623,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357292 sha256=7683eb64071fa9de9ce2a079f007e5fa48500b0c593a906adf3ba471799e0c37\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":1452,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"855d72a7-86be-42ae-939d-e40c0631b3e6","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"234b5a57-43de-4dc3-c4cd-364fc5f81905","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","execution_count":21,"metadata":{"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"xUuraXZlT3f-","executionInfo":{"status":"ok","timestamp":1736583863776,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8","executionInfo":{"status":"ok","timestamp":1736583864204,"user_tz":-420,"elapsed":1,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0","executionInfo":{"status":"ok","timestamp":1736583864205,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","outputId":"229c6e4f-b173-41da-cfb8-99c819c642ff","executionInfo":{"status":"ok","timestamp":1736584865383,"user_tz":-420,"elapsed":1001180,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 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0.0670395122632774, 10: 0.179765497172958, 15: 0.22854428430113963, 20: 0.2497146590092032}\n","Mean Diversity: {5: 0.7699666715323202, 10: 0.8116886827565335, 15: 0.8549011113505084, 20: 0.8561813748315994}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"5faa5042-bfb6-49d8-aea0-0db0433748a5","executionInfo":{"status":"ok","timestamp":1736584866022,"user_tz":-420,"elapsed":641,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.067040 0.769967\n","1 10 0.179765 0.811689\n","2 15 0.228544 0.854901\n","3 20 0.249715 0.856181"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0670400.769967
1100.1797650.811689
2150.2285440.854901
3200.2497150.856181
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0.8116886827565335,\n 0.8561813748315994,\n 0.7699666715323202\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":27}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736584866022,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_lr_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6b8ed01b-676d-4769-d2be-f07d5f29e5fb","id":"gt-Dl92KbiMa","executionInfo":{"status":"ok","timestamp":1736585852294,"user_tz":-420,"elapsed":986275,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 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9875\n"]}]},{"cell_type":"code","execution_count":30,"metadata":{"id":"2_46IbCBbiMa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736585852294,"user_tz":-420,"elapsed":38,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"fe3404b0-4c1f-4312-a14e-564243c35ac0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.0669209423764569, 10: 0.17649593060999474, 15: 0.2242545911001547, 20: 0.24498734244898784}\n","Mean Diversity: {5: 0.77001134039417, 10: 0.812976846389913, 15: 0.854439763819919, 20: 0.8555448223397129}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"GXzwwOcgbiMa","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1736585852294,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7272f107-51cf-4ad4-86c7-417b0d036b39"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.066921 0.770011\n","1 10 0.176496 0.812977\n","2 15 0.224255 0.854440\n","3 20 0.244987 0.855545"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0669210.770011
1100.1764960.812977
2150.2242550.854440
3200.2449870.855545
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0.812976846389913,\n 0.8555448223397129,\n 0.77001134039417\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":31}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"g6EVJI0AbiMb","executionInfo":{"status":"ok","timestamp":1736585852294,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_lr_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Our Proposed/NB.ipynb b/Source Code/Our Proposed/NB.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2293f27c46776d5eb5b42ac28a0cbbba26e50876 --- /dev/null +++ b/Source Code/Our Proposed/NB.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","outputId":"7e7a298a-1bc3-44e4-eae5-461adfca4c43","executionInfo":{"status":"ok","timestamp":1736583563194,"user_tz":-420,"elapsed":22241,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736583586773,"user_tz":-420,"elapsed":519,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"5c7085c8-3b6e-4804-9640-b1cdcadff450","executionInfo":{"status":"ok","timestamp":1736583590022,"user_tz":-420,"elapsed":2518,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"513118c4-6b05-4533-f7b0-9690223ccb51","executionInfo":{"status":"ok","timestamp":1736583593972,"user_tz":-420,"elapsed":3952,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"29458ca3-e1fc-405e-f1ad-b0ed1f1458b8","executionInfo":{"status":"ok","timestamp":1736583617407,"user_tz":-420,"elapsed":23437,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736583617407,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736583617408,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1736583617408,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1736583620921,"user_tz":-420,"elapsed":3515,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1736583620921,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736583621735,"user_tz":-420,"elapsed":816,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"5b6da858-fffc-4999-8757-e3e25953d11b","executionInfo":{"status":"ok","timestamp":1736583621735,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"03228577-a2d2-4f02-ac60-4c81689216f5","executionInfo":{"status":"ok","timestamp":1736583621735,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","execution_count":14,"metadata":{"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736583621735,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","outputId":"8cf4d9c0-4b22-49c4-d26e-045b0ac70d17","executionInfo":{"status":"ok","timestamp":1736583705336,"user_tz":-420,"elapsed":83603,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357268 sha256=aa0130d01b95c8757a5c116e73a54145d22e3831a0bf7c5c7b55765b6e3e5bee\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":1491,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"34bf8d45-4855-4ac9-f21c-b0590fe41060","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"b0c0d679-defc-429f-a8ac-d26e429a4774","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","execution_count":21,"metadata":{"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"xUuraXZlT3f-","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0","executionInfo":{"status":"ok","timestamp":1736583707454,"user_tz":-420,"elapsed":631,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","outputId":"f7941a18-5248-430b-c9c3-8998e0ba20fa","executionInfo":{"status":"ok","timestamp":1736584668974,"user_tz":-420,"elapsed":934271,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 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0.053942361726539934, 10: 0.08902889679004895, 15: 0.11498034956661733, 20: 0.12808298843942734}\n","Mean Diversity: {5: 0.6843654482632244, 10: 0.7765075478059327, 15: 0.8217431667991754, 20: 0.84256695870694}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"31ff560b-bd82-42d1-df03-4c9ffc1ef5c3","executionInfo":{"status":"ok","timestamp":1736584668975,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.053942 0.684365\n","1 10 0.089029 0.776508\n","2 15 0.114980 0.821743\n","3 20 0.128083 0.842567"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0539420.684365
1100.0890290.776508
2150.1149800.821743
3200.1280830.842567
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0.7765075478059327,\n 0.84256695870694,\n 0.6843654482632244\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":27}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736584669374,"user_tz":-420,"elapsed":402,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_nb_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"619630f9-260c-411a-ac67-b0096f40a051","id":"gt-Dl92KbiMa","executionInfo":{"status":"ok","timestamp":1736585649875,"user_tz":-420,"elapsed":980502,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 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9875\n"]}]},{"cell_type":"code","execution_count":30,"metadata":{"id":"2_46IbCBbiMa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736585649875,"user_tz":-420,"elapsed":21,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ea921254-8fb3-4a7e-e969-7f8a0c597987"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.07050337583630341, 10: 0.17805464489464837, 15: 0.21590067946889757, 20: 0.24205086277439458}\n","Mean Diversity: {5: 0.7386372634185411, 10: 0.801307555194398, 15: 0.8374426517721162, 20: 0.8529219105105518}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"GXzwwOcgbiMa","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1736585650556,"user_tz":-420,"elapsed":682,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"788eb539-4720-411e-ccba-44560d717de3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.070503 0.738637\n","1 10 0.178055 0.801308\n","2 15 0.215901 0.837443\n","3 20 0.242051 0.852922"],"text/html":["\n","
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top_nhit_ratiodiversity
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0.801307555194398,\n 0.8529219105105518,\n 0.7386372634185411\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":31}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"g6EVJI0AbiMb","executionInfo":{"status":"ok","timestamp":1736585650556,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_nb_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Our Proposed/Temporary.ipynb b/Source Code/Our Proposed/Temporary.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0d8505ee1ec864ed2859f104db50f7dcfdeca60 --- /dev/null +++ b/Source Code/Our Proposed/Temporary.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","outputId":"aca4a1c1-f834-48ce-9014-823e994e59a0","executionInfo":{"status":"ok","timestamp":1750660716532,"user_tz":-420,"elapsed":17429,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1750660719400,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"72b6bb60-94cc-4c93-b0a5-6d03af8cfffb","executionInfo":{"status":"ok","timestamp":1750660725123,"user_tz":-420,"elapsed":4421,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"23d511bf-84e3-4c31-9aad-4e5d9946be96","executionInfo":{"status":"ok","timestamp":1750660728751,"user_tz":-420,"elapsed":3629,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"ea8c6d28-c742-47a3-8c11-642d9084cc14","executionInfo":{"status":"ok","timestamp":1750660748753,"user_tz":-420,"elapsed":20002,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n","An unexpected error occurred: [Errno 107] Transport endpoint is not connected\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1750660748753,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1750660748783,"user_tz":-420,"elapsed":29,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1750660748810,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1750660754550,"user_tz":-420,"elapsed":5739,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1750660754647,"user_tz":-420,"elapsed":99,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1750660755010,"user_tz":-420,"elapsed":362,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"5d6070c5-bcde-49dd-81e6-28e27378f275","executionInfo":{"status":"ok","timestamp":1750660755011,"user_tz":-420,"elapsed":46,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"76e68741-3c40-4ef1-b135-811fcc2fe3e3","executionInfo":{"status":"ok","timestamp":1750660755011,"user_tz":-420,"elapsed":43,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","source":["# prompt: Save user_bundle_histories as csv\n","\n","import pandas as pd\n","\n","df_user_bundle_histories = pd.DataFrame(user_bundle_histories)\n","df_user_bundle_histories.to_csv('/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_bundle_histories.csv', index=False)\n","\n","print(\"user_bundle_histories saved as CSV.\")\n","print(df_user_bundle_histories.head())\n","print(df_user_bundle_histories.info())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MoakRZBHgv76","executionInfo":{"status":"ok","timestamp":1750660761723,"user_tz":-420,"elapsed":563,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"47f0f0b6-a4e5-40e3-98cd-ba265acf0841"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["user_bundle_histories saved as CSV.\n"," user_id bundle_id label rating\n","0 76561197970982479 232 1 0.0\n","1 76561197970982479 383 0 0.0\n","2 76561197970982479 231 1 0.0\n","3 76561197970982479 362 1 0.0\n","4 76561197970982479 237 1 0.0\n","\n","RangeIndex: 103453 entries, 0 to 103452\n","Data columns (total 4 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 user_id 103453 non-null object \n"," 1 bundle_id 103453 non-null object \n"," 2 label 103453 non-null int64 \n"," 3 rating 103453 non-null float64\n","dtypes: float64(1), int64(1), object(2)\n","memory usage: 3.2+ MB\n","None\n"]}]},{"cell_type":"code","source":["# prompt: Load data /content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_bundle_histories.cs\n","\n","df_user_bundle_histories = pd.read_csv('/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_bundle_histories.csv')\n","\n","print(df_user_bundle_histories.head())\n","print(df_user_bundle_histories.info())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"Y-WBhuRTg7_j","executionInfo":{"status":"ok","timestamp":1750660915388,"user_tz":-420,"elapsed":39,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"77beaaab-934c-4ca0-ff54-899596270a2a"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of unique bundle_ids for each user_id:\n"]},{"output_type":"execute_result","data":{"text/plain":["user_id\n","-Azsael- 24\n","-GM-Dragon 7\n","-Thyme- 12\n","00690069006900 25\n","05041129 5\n"," ..\n","zevlupine 32\n","ziass 7\n","zile98 6\n","zmagma 1\n","zombiehackerbrah 62\n","Name: bundle_id, Length: 4781, dtype: int64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
bundle_id
user_id
-Azsael-24
-GM-Dragon7
-Thyme-12
0069006900690025
050411295
......
zevlupine32
ziass7
zile986
zmagma1
zombiehackerbrah62
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4781 rows × 1 columns

\n","

"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["# prompt: save user_bundle_histories\n","\n","output_file_path = \"/content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_bundle_histories.json.gz\"\n","with gzip.open(output_file_path, 'wt', encoding='utf-8') as f:\n"," json.dump(user_bundle_histories, f, indent=2)\n","\n","print(f\"Saved user_bundle_histories to {output_file_path}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e9_X_fUKci3a","executionInfo":{"status":"ok","timestamp":1750659723291,"user_tz":-420,"elapsed":1174,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"21c2195c-d174-47cc-a7a1-0c26170e6684"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Saved user_bundle_histories to /content/drive/MyDrive/Projects/Disertation/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_bundle_histories.json.gz\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"flYvFh9kThB6"},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","outputId":"8cf4d9c0-4b22-49c4-d26e-045b0ac70d17","executionInfo":{"status":"ok","timestamp":1736583705336,"user_tz":-420,"elapsed":83603,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357268 sha256=aa0130d01b95c8757a5c116e73a54145d22e3831a0bf7c5c7b55765b6e3e5bee\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"34bf8d45-4855-4ac9-f21c-b0590fe41060","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"b0c0d679-defc-429f-a8ac-d26e429a4774","executionInfo":{"status":"ok","timestamp":1736583706825,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hfTbC2xdTWNg"},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xUuraXZlT3f-"},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","outputId":"f7941a18-5248-430b-c9c3-8998e0ba20fa","executionInfo":{"status":"ok","timestamp":1736584668974,"user_tz":-420,"elapsed":934271,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 9881\n","4908 / 9881\n","4909 / 9881\n","4910 / 9881\n","4911 / 9881\n","4912 / 9881\n","4913 / 9881\n","4914 / 9881\n","4915 / 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0.6843654482632244, 10: 0.7765075478059327, 15: 0.8217431667991754, 20: 0.84256695870694}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"31ff560b-bd82-42d1-df03-4c9ffc1ef5c3","executionInfo":{"status":"ok","timestamp":1736584668975,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.053942 0.684365\n","1 10 0.089029 0.776508\n","2 15 0.114980 0.821743\n","3 20 0.128083 0.842567"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0539420.684365
1100.0890290.776508
2150.1149800.821743
3200.1280830.842567
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0.7765075478059327,\n 0.84256695870694,\n 0.6843654482632244\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":27}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JImcjAVFawOX"},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_nb_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"619630f9-260c-411a-ac67-b0096f40a051","id":"gt-Dl92KbiMa","executionInfo":{"status":"ok","timestamp":1736585649875,"user_tz":-420,"elapsed":980502,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 / 9881\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 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9875\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2_46IbCBbiMa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736585649875,"user_tz":-420,"elapsed":21,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ea921254-8fb3-4a7e-e969-7f8a0c597987"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.07050337583630341, 10: 0.17805464489464837, 15: 0.21590067946889757, 20: 0.24205086277439458}\n","Mean Diversity: {5: 0.7386372634185411, 10: 0.801307555194398, 15: 0.8374426517721162, 20: 0.8529219105105518}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GXzwwOcgbiMa","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1736585650556,"user_tz":-420,"elapsed":682,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"788eb539-4720-411e-ccba-44560d717de3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.070503 0.738637\n","1 10 0.178055 0.801308\n","2 15 0.215901 0.837443\n","3 20 0.242051 0.852922"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0705030.738637
1100.1780550.801308
2150.2159010.837443
3200.2420510.852922
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07546956449306909,\n \"min\": 0.07050337583630341,\n \"max\": 0.24205086277439458,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.17805464489464837,\n 0.24205086277439458,\n 0.07050337583630341\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.050794065541156716,\n \"min\": 0.7386372634185411,\n \"max\": 0.8529219105105518,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.801307555194398,\n 0.8529219105105518,\n 0.7386372634185411\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":31}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g6EVJI0AbiMb"},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_nb_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Our Proposed/k-NN.ipynb b/Source Code/Our Proposed/k-NN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8ec8f8fe49696f0a04d60984b34d644ff519c98d --- /dev/null +++ b/Source Code/Our Proposed/k-NN.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdwkZ0q5StF9","outputId":"25a01e04-20ee-47f8-8971-8ed53632394f","executionInfo":{"status":"ok","timestamp":1736583579297,"user_tz":-420,"elapsed":16759,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736583826082,"user_tz":-420,"elapsed":263,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","outputId":"c12af171-a95a-4ed5-e8e8-d8724d30e600","executionInfo":{"status":"ok","timestamp":1736583828601,"user_tz":-420,"elapsed":1525,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","outputId":"4b10552c-604e-4938-a0f9-5c72f6150ce0","executionInfo":{"status":"ok","timestamp":1736583832312,"user_tz":-420,"elapsed":3713,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","outputId":"b6b55b64-ab83-4eb8-f5ed-2299c3c1ef4a","executionInfo":{"status":"ok","timestamp":1736583852785,"user_tz":-420,"elapsed":20475,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736583852785,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736583852785,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"DY0CG_yqVPk0","executionInfo":{"status":"ok","timestamp":1736583852785,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"3o9OG-TGVOfx","executionInfo":{"status":"ok","timestamp":1736583855125,"user_tz":-420,"elapsed":2342,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"4lLCuDGbVJ_Z","executionInfo":{"status":"ok","timestamp":1736583855126,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736583855439,"user_tz":-420,"elapsed":315,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9qLjFCOB99Y","outputId":"b26d1435-415d-489c-b1d4-e0a1d48a8fae","executionInfo":{"status":"ok","timestamp":1736583855440,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","outputId":"69667604-988a-473f-81f5-e63b8e568acc","executionInfo":{"status":"ok","timestamp":1736583855440,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","execution_count":14,"metadata":{"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736583855440,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cQr5la-EVvSI","outputId":"ff029718-4312-4e7c-98a2-3876b007c4f4","executionInfo":{"status":"ok","timestamp":1736583935015,"user_tz":-420,"elapsed":79577,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m102.4/154.4 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357265 sha256=7b935b22c28c023142d2c122fdd2684cc3745e2b0063717f01d97888d2a91b61\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","import pandas as pd\n","from surprise.model_selection import train_test_split\n","from surprise import Dataset, Reader\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":1174,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":6,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":6,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","outputId":"7272f96a-947e-43dc-9824-46a7835b57ea","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":6,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","outputId":"a93544f5-9256-46d3-e051-56334f2aea82","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","execution_count":21,"metadata":{"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","# hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","# hit_ratios"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"xUuraXZlT3f-","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","# gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","# gini_simpson_result"]},{"cell_type":"code","source":["import numpy as np\n","\n","def calculate_mean(average):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"XYJbHB_0a-n8","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)"],"metadata":{"id":"eC-WiqV-bGT0","executionInfo":{"status":"ok","timestamp":1736583936186,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":["### Tags and Specs"],"metadata":{"id":"qarAR1Xhamk_"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_rVWLDRqV4mt","outputId":"e229cf8d-7cfa-4b30-8488-056169b62381","executionInfo":{"status":"ok","timestamp":1736584833363,"user_tz":-420,"elapsed":896750,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4883 / 9881\n","4884 / 9881\n","4885 / 9881\n","4886 / 9881\n","4887 / 9881\n","4888 / 9881\n","4889 / 9881\n","4890 / 9881\n","4891 / 9881\n","4892 / 9881\n","4893 / 9881\n","4894 / 9881\n","4895 / 9881\n","4896 / 9881\n","4897 / 9881\n","4898 / 9881\n","4899 / 9881\n","4900 / 9881\n","4901 / 9881\n","4902 / 9881\n","4903 / 9881\n","4904 / 9881\n","4905 / 9881\n","4906 / 9881\n","4907 / 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0.045415533653788656, 10: 0.06659065441356157, 15: 0.11162056719588316, 20: 0.12641311751863013}\n","Mean Diversity: {5: 0.7617941715937238, 10: 0.7970695947998078, 15: 0.8448309974154098, 20: 0.8682237534421361}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","outputId":"a43812f4-8d1e-4657-8e24-a127adc49b94","executionInfo":{"status":"ok","timestamp":1736584833363,"user_tz":-420,"elapsed":8,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.045416 0.761794\n","1 10 0.066591 0.797070\n","2 15 0.111621 0.844831\n","3 20 0.126413 0.868224"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0454160.761794
1100.0665910.797070
2150.1116210.844831
3200.1264130.868224
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0.7970695947998078,\n 0.8682237534421361,\n 0.7617941715937238\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":27}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736584833364,"user_tz":-420,"elapsed":6,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_knn_tags_specs.csv', index=False)"]},{"cell_type":"markdown","source":["### Price Patterns"],"metadata":{"id":"IefsuBOVbiMa"}},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","try:\n"," with gzip.open(filepath_rarank_rec_data, 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"{count} / 9881\")\n"," print(f\"File '{filepath_rarank_rec_data}' successfully processed and saved to '{filepath_rarank_rec_data}'\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {filepath_rarank_rec_data}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n","print(f\"Count: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"dcc7d7ec-6a59-4af2-ac5d-eb0d9764230a","id":"gt-Dl92KbiMa","executionInfo":{"status":"ok","timestamp":1736585271463,"user_tz":-420,"elapsed":438105,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","4878 / 9881\n","4879 / 9881\n","4880 / 9881\n","4881 / 9881\n","4882 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9875\n"]}]},{"cell_type":"code","execution_count":30,"metadata":{"id":"2_46IbCBbiMa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736585271463,"user_tz":-420,"elapsed":27,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7d24d451-b1d1-4302-fd38-13f985c6cb0a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.04258495370775438, 10: 0.06258701925290794, 15: 0.07333514800313988, 20: 0.07661074047725566}\n","Mean Diversity: {5: 0.5738410237478444, 10: 0.6033223797667215, 15: 0.6181434932668557, 20: 0.6235527997446176}\n"]}],"source":["# Example usage:\n","mean_hit_ratio = calculate_mean(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"GXzwwOcgbiMa","colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"status":"ok","timestamp":1736585271463,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"81acce02-67df-4943-b528-8cbb3c845c70"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.042585 0.573841\n","1 10 0.062587 0.603322\n","2 15 0.073335 0.618143\n","3 20 0.076611 0.623553"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0425850.573841
1100.0625870.603322
2150.0733350.618143
3200.0766110.623553
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0.6033223797667215,\n 0.6235527997446176,\n 0.5738410237478444\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":31}],"source":["# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"g6EVJI0AbiMb","executionInfo":{"status":"ok","timestamp":1736585271780,"user_tz":-420,"elapsed":320,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/our_proposed/bundle_recommendation_knn_price_patterns.csv', index=False)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Overfitting Test/IMPACT-P ANN.ipynb b/Source Code/Overfitting Test/IMPACT-P ANN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d1adfeca4059275d02a946012223151fbccd576a --- /dev/null +++ b/Source Code/Overfitting Test/IMPACT-P ANN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["_T8fPo2P6CFY"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777537055944,"user_tz":-420,"elapsed":18617,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"14262a6b-f9a5-489c-9fb7-85de942c0e59"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cbi9HIU2YNKA","executionInfo":{"status":"ok","timestamp":1777537076756,"user_tz":-420,"elapsed":20806,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"c39c995a-7239-4f6a-ae5f-5d42cb9e8da0"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777537076785,"user_tz":-420,"elapsed":15,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"eFg-8moUb52J"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"Egw06Nzgb8g1","executionInfo":{"status":"ok","timestamp":1777537085570,"user_tz":-420,"elapsed":8783,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# prompt: Distribution on len user_tags_and_specs_features\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming user_tags_and_specs_features is already loaded as a NumPy array\n","\n","lengths = [len(x) for x in user_tags_and_specs_features]\n","\n","plt.hist(lengths, bins=50) # Adjust the number of bins as needed\n","plt.xlabel('Length of user_tags_and_specs_features')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Lengths')\n","plt.show()\n","print(f\"The average length is {np.mean(lengths)}\")\n","print(f\"The median length is {np.median(lengths)}\")\n","print(f\"The standard deviation of length is {np.std(lengths)}\")\n","print(f\"The maximum length is {np.max(lengths)}\")\n","print(f\"The minimum length is {np.min(lengths)}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":559},"id":"yJpD_1HALjO_","executionInfo":{"status":"ok","timestamp":1735287312801,"user_tz":-420,"elapsed":1396,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"013d14e0-23b4-45d5-e0de-da2a8b0a36c9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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qCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECqhV3Q0QEb0s7KfvLLMmZYFPFXRCRNWBV6qIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKaBaQ1V0dDT69u0LGxsbqFQqbN++XbZepVKV+li4cKFUY29vX2L9ggULZPs5c+YM3nrrLRgaGsLW1hYhISEletmyZQuaNWsGQ0NDuLi4YNeuXbL1QgjMmjUL1tbWMDIygpeXFy5duqTcYBAREZFOq9ZQlZOTgzZt2mDFihWlrk9LS5M9fvjhB6hUKgwYMEBWN3fuXFnd+PHjpXVarRY9evSAnZ0d4uLisHDhQgQHB2P16tVSzdGjR+Hn54eAgACcOnUKvr6+8PX1xblz56SakJAQLF++HKGhoYiNjYWxsTG8vb3x8OFDhUeFiIiIdFGt6jx4r1690KtXr6eut7Kykj3/7bff4Onpiddff1223MTEpERtsfXr1yMvLw8//PADDAwM0LJlS8THx2Px4sUYPXo0AGDZsmXo2bMnPvnkEwDAl19+iYiICHz99dcIDQ2FEAJLly7FzJkz0a9fPwDAunXrYGlpie3bt2PQoEGVHgMiIiJ6OejMnKqMjAzs3LkTAQEBJdYtWLAA9evXR7t27bBw4UIUFBRI62JiYtC5c2cYGBhIy7y9vZGYmIi7d+9KNV5eXrJ9ent7IyYmBgCQnJyM9PR0WY2pqSnc3NykmtLk5uZCq9XKHkRERPRyqtYrVRWxdu1amJiYoH///rLlEyZMQPv27WFubo6jR49ixowZSEtLw+LFiwEA6enpcHBwkG1jaWkpratXrx7S09OlZY/XpKenS3WPb1daTWnmz5+POXPmVOJsiYiISNfoTKj64YcfMGTIEBgaGsqWBwUFSX9u3bo1DAwM8M9//hPz58+HWq2u6jZlZsyYIetPq9XC1ta2GjsiIiKiF0Un3v47dOgQEhMTMXLkyDJr3dzcUFBQgJSUFACP5mVlZGTIaoqfF8/DelrN4+sf3660mtKo1WpoNBrZg4iIiF5OOhGqvv/+e7i6uqJNmzZl1sbHx0NPTw8WFhYAAHd3d0RHRyM/P1+qiYiIgLOzM+rVqyfVREZGyvYTEREBd3d3AICDgwOsrKxkNVqtFrGxsVINERERvdqq9e2/7OxsJCUlSc+Tk5MRHx8Pc3NzvPbaawAehZctW7Zg0aJFJbaPiYlBbGwsPD09YWJigpiYGEyePBkffvihFJgGDx6MOXPmICAgANOmTcO5c+ewbNkyLFmyRNrPxIkT0aVLFyxatAg+Pj7YuHEjTpw4Id12QaVSYdKkSZg3bx6cnJzg4OCAL774AjY2NvD19X2BI0RERES6olpD1YkTJ+Dp6Sk9L55/5O/vj7CwMADAxo0bIYSAn59fie3VajU2btyI4OBg5ObmwsHBAZMnT5bNYzI1NcXevXsRGBgIV1dXNGjQALNmzZJupwAA//jHP7BhwwbMnDkTn332GZycnLB9+3a0atVKqvn000+Rk5OD0aNHIzMzEx4eHggPDy8xx4uIiIheTSohhKjuJl4VWq0WpqamyMrK4vwqopeQ/fSdZdakLPCpgk6ISEnl/f2tE3OqiIiIiGo6hioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgB1RqqoqOj0bdvX9jY2EClUmH79u2y9cOHD4dKpZI9evbsKau5c+cOhgwZAo1GAzMzMwQEBCA7O1tWc+bMGbz11lswNDSEra0tQkJCSvSyZcsWNGvWDIaGhnBxccGuXbtk64UQmDVrFqytrWFkZAQvLy9cunRJmYEgIiIinVetoSonJwdt2rTBihUrnlrTs2dPpKWlSY+ff/5Ztn7IkCE4f/48IiIisGPHDkRHR2P06NHSeq1Wix49esDOzg5xcXFYuHAhgoODsXr1aqnm6NGj8PPzQ0BAAE6dOgVfX1/4+vri3LlzUk1ISAiWL1+O0NBQxMbGwtjYGN7e3nj48KGCI0JERES6SiWEENXdBACoVCps27YNvr6+0rLhw4cjMzOzxBWsYhcuXECLFi1w/PhxdOjQAQAQHh6O3r174/r167CxscGqVavw+eefIz09HQYGBgCA6dOnY/v27UhISAAADBw4EDk5OdixY4e0744dO6Jt27YIDQ2FEAI2NjaYMmUKpk6dCgDIysqCpaUlwsLCMGjQoHKdo1arhampKbKysqDRaCo6RERUw9lP31lmTcoCnyrohIiUVN7f3zV+TtWBAwdgYWEBZ2dnjB07Frdv35bWxcTEwMzMTApUAODl5QU9PT3ExsZKNZ07d5YCFQB4e3sjMTERd+/elWq8vLxkx/X29kZMTAwAIDk5Genp6bIaU1NTuLm5STVERET0aqtV3Q08S8+ePdG/f384ODjg8uXL+Oyzz9CrVy/ExMRAX18f6enpsLCwkG1Tq1YtmJubIz09HQCQnp4OBwcHWY2lpaW0rl69ekhPT5eWPV7z+D4e3660mtLk5uYiNzdXeq7Vaity+kRERKRDanSoevxtNRcXF7Ru3RqOjo44cOAAunXrVo2dlc/8+fMxZ86c6m6DiIiIqkCNf/vvca+//joaNGiApKQkAICVlRVu3rwpqykoKMCdO3dgZWUl1WRkZMhqip+XVfP4+se3K62mNDNmzEBWVpb0uHbtWoXOl4iIiHSHToWq69ev4/bt27C2tgYAuLu7IzMzE3FxcVJNVFQUioqK4ObmJtVER0cjPz9fqomIiICzszPq1asn1URGRsqOFRERAXd3dwCAg4MDrKysZDVarRaxsbFSTWnUajU0Go3sQURERC+nag1V2dnZiI+PR3x8PIBHE8Lj4+Nx9epVZGdn45NPPsGff/6JlJQUREZGol+/fmjSpAm8vb0BAM2bN0fPnj0xatQoHDt2DEeOHMG4ceMwaNAg2NjYAAAGDx4MAwMDBAQE4Pz589i0aROWLVuGoKAgqY+JEyciPDwcixYtQkJCAoKDg3HixAmMGzcOwKNPJk6aNAnz5s3D77//jrNnz2LYsGGwsbGRfVqRiIiIXl3VOqfqxIkT8PT0lJ4XBx1/f3+sWrUKZ86cwdq1a5GZmQkbGxv06NEDX375JdRqtbTN+vXrMW7cOHTr1g16enoYMGAAli9fLq03NTXF3r17ERgYCFdXVzRo0ACzZs2S3cvqH//4BzZs2ICZM2fis88+g5OTE7Zv345WrVpJNZ9++ilycnIwevRoZGZmwsPDA+Hh4TA0NHyRQ0REREQ6osbcp+pVwPtUEb3ceJ8qopfTS3OfKiIiIiJdwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAHVGqqio6PRt29f2NjYQKVSYfv27dK6/Px8TJs2DS4uLjA2NoaNjQ2GDRuG1NRU2T7s7e2hUqlkjwULFshqzpw5g7feeguGhoawtbVFSEhIiV62bNmCZs2awdDQEC4uLti1a5dsvRACs2bNgrW1NYyMjODl5YVLly4pNxhERESk06o1VOXk5KBNmzZYsWJFiXX379/HyZMn8cUXX+DkyZPYunUrEhMT8c4775SonTt3LtLS0qTH+PHjpXVarRY9evSAnZ0d4uLisHDhQgQHB2P16tVSzdGjR+Hn54eAgACcOnUKvr6+8PX1xblz56SakJAQLF++HKGhoYiNjYWxsTG8vb3x8OFDhUeFiIiIdJFKCCGquwkAUKlU2LZtG3x9fZ9ac/z4cbz55pu4cuUKXnvtNQCPrlRNmjQJkyZNKnWbVatW4fPPP0d6ejoMDAwAANOnT8f27duRkJAAABg4cCBycnKwY8cOabuOHTuibdu2CA0NhRACNjY2mDJlCqZOnQoAyMrKgqWlJcLCwjBo0KBynaNWq4WpqSmysrKg0WjKtQ0R6Q776TvLrElZ4FMFnRCRksr7+1un5lRlZWVBpVLBzMxMtnzBggWoX78+2rVrh4ULF6KgoEBaFxMTg86dO0uBCgC8vb2RmJiIu3fvSjVeXl6yfXp7eyMmJgYAkJycjPT0dFmNqakp3NzcpJrS5ObmQqvVyh5ERET0cqpV3Q2U18OHDzFt2jT4+fnJUuKECRPQvn17mJub4+jRo5gxYwbS0tKwePFiAEB6ejocHBxk+7K0tJTW1atXD+np6dKyx2vS09Oluse3K62mNPPnz8ecOXMqecZERESkS3QiVOXn5+ODDz6AEAKrVq2SrQsKCpL+3Lp1axgYGOCf//wn5s+fD7VaXdWtysyYMUPWn1arha2tbTV2RERERC9KjX/7rzhQXblyBREREWXORXJzc0NBQQFSUlIAAFZWVsjIyJDVFD+3srJ6Zs3j6x/frrSa0qjVamg0GtmDiIiIXk41OlQVB6pLly5h3759qF+/fpnbxMfHQ09PDxYWFgAAd3d3REdHIz8/X6qJiIiAs7Mz6tWrJ9VERkbK9hMREQF3d3cAgIODA6ysrGQ1Wq0WsbGxUg0RERG92qr17b/s7GwkJSVJz5OTkxEfHw9zc3NYW1vjvffew8mTJ7Fjxw4UFhZK85fMzc1hYGCAmJgYxMbGwtPTEyYmJoiJicHkyZPx4YcfSoFp8ODBmDNnDgICAjBt2jScO3cOy5Ytw5IlS6TjTpw4EV26dMGiRYvg4+ODjRs34sSJE9JtF1QqFSZNmoR58+bByckJDg4O+OKLL2BjY/PMTysSERHRq6Nab6lw4MABeHp6llju7++P4ODgEhPMi+3fvx9du3bFyZMn8fHHHyMhIQG5ublwcHDA0KFDERQUJJtPdebMGQQGBuL48eNo0KABxo8fj2nTpsn2uWXLFsycORMpKSlwcnJCSEgIevfuLa0XQmD27NlYvXo1MjMz4eHhgZUrV6Jp06blPl/eUoHo5cZbKhC9nMr7+7vG3KfqVcBQRfRyY6giejm9lPepIiIiIqqpGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECqhUqPr777+V7oOIiIhIp1UqVDVp0gSenp746aef8PDhQ6V7IiIiItI5lQpVJ0+eROvWrREUFAQrKyv885//xLFjx5TujYiIiEhnVCpUtW3bFsuWLUNqaip++OEHpKWlwcPDA61atcLixYtx69YtpfskIiIiqtGea6J6rVq10L9/f2zZsgX//ve/kZSUhKlTp8LW1hbDhg1DWlqaUn0SERER1WjPFapOnDiBjz/+GNbW1li8eDGmTp2Ky5cvIyIiAqmpqejXr59SfRIRERHVaLUqs9HixYuxZs0aJCYmonfv3li3bh169+4NPb1HGc3BwQFhYWGwt7dXslciIiKiGqtSoWrVqlX46KOPMHz4cFhbW5daY2Fhge+///65miMiIiLSFZUKVZcuXSqzxsDAAP7+/pXZPREREZHOqdScqjVr1mDLli0llm/ZsgVr16597qaIiIiIdE2lQtX8+fPRoEGDEsstLCzwr3/967mbIiIiItI1lQpVV69ehYODQ4nldnZ2uHr16nM3RURERKRrKhWqLCwscObMmRLLT58+jfr16z93U0RERES6plKhys/PDxMmTMD+/ftRWFiIwsJCREVFYeLEiRg0aJDSPRIRERHVeJX69N+XX36JlJQUdOvWDbVqPdpFUVERhg0bxjlVRERE9EqqVKgyMDDApk2b8OWXX+L06dMwMjKCi4sL7OzslO6PiIiISCdUKlQVa9q0KZo2bapUL0REREQ6q1KhqrCwEGFhYYiMjMTNmzdRVFQkWx8VFaVIc0RERES6olKhauLEiQgLC4OPjw9atWoFlUqldF9EREREOqVSoWrjxo3YvHkzevfurXQ/RERERDqpUrdUMDAwQJMmTZTuhYiIiEhnVSpUTZkyBcuWLYMQQul+iIiIiHRSpULV4cOHsX79ejg6OqJv377o37+/7FFe0dHR6Nu3L2xsbKBSqbB9+3bZeiEEZs2aBWtraxgZGcHLywuXLl2S1dy5cwdDhgyBRqOBmZkZAgICkJ2dLas5c+YM3nrrLRgaGsLW1hYhISEletmyZQuaNWsGQ0NDuLi4YNeuXRXuhYiIiF5dlQpVZmZmePfdd9GlSxc0aNAApqamskd55eTkoE2bNlixYkWp60NCQrB8+XKEhoYiNjYWxsbG8Pb2xsOHD6WaIUOG4Pz584iIiMCOHTsQHR2N0aNHS+u1Wi169OgBOzs7xMXFYeHChQgODsbq1aulmqNHj8LPzw8BAQE4deoUfH194evri3PnzlWoFyIiInp1qUQNeQ9PpVJh27Zt8PX1BfDoypCNjQ2mTJmCqVOnAgCysrJgaWmJsLAwDBo0CBcuXECLFi1w/PhxdOjQAQAQHh6O3r174/r167CxscGqVavw+eefIz09HQYGBgCA6dOnY/v27UhISAAADBw4EDk5OdixY4fUT8eOHdG2bVuEhoaWq5fy0Gq1MDU1RVZWFjQajSLjRkQ1h/30nWXWpCzwqYJOiEhJ5f39XakrVQBQUFCAffv24ZtvvsG9e/cAAKmpqSXeequs5ORkpKenw8vLS1pmamoKNzc3xMTEAABiYmJgZmYmBSoA8PLygp6eHmJjY6Wazp07S4EKALy9vZGYmIi7d+9KNY8fp7im+Djl6aU0ubm50Gq1sgcRERG9nCoVqq5cuQIXFxf069cPgYGBuHXrFgDg3//+t3Ql53mlp6cDACwtLWXLLS0tpXXp6emwsLCQra9VqxbMzc1lNaXt4/FjPK3m8fVl9VKa+fPny94WtbW1LeOsiYiISFdVKlRNnDgRHTp0wN27d2FkZCQtf/fddxEZGalYc7puxowZyMrKkh7Xrl2r7paIiIjoBanUzT8PHTqEo0ePyt5SAwB7e3vcuHFDkcasrKwAABkZGbC2tpaWZ2RkoG3btlLNzZs3ZdsVFBTgzp070vZWVlbIyMiQ1RQ/L6vm8fVl9VIatVoNtVpdrvMlIiIi3VapK1VFRUUoLCwssfz69eswMTF57qYAwMHBAVZWVrIrX1qtFrGxsXB3dwcAuLu7IzMzE3FxcVJNVFQUioqK4ObmJtVER0cjPz9fqomIiICzszPq1asn1Tx5hS0iIkI6Tnl6ISIioldbpUJVjx49sHTpUum5SqVCdnY2Zs+eXaGvrsnOzkZ8fDzi4+MBPJoQHh8fj6tXr0KlUmHSpEmYN28efv/9d5w9exbDhg2DjY2N9AnB5s2bo2fPnhg1ahSOHTuGI0eOYNy4cRg0aBBsbGwAAIMHD4aBgQECAgJw/vx5bNq0CcuWLUNQUJDUx8SJExEeHo5FixYhISEBwcHBOHHiBMaNGyedX1m9EBER0autUrdUuH79Ory9vSGEwKVLl9ChQwdcunQJDRo0QHR0dInJ409z4MABeHp6llju7++PsLAwCCEwe/ZsrF69GpmZmfDw8MDKlSvRtGlTqfbOnTsYN24c/vjjD+jp6WHAgAFYvnw56tatK9WcOXMGgYGBOH78OBo0aIDx48dj2rRpsmNu2bIFM2fOREpKCpycnBASEiILiOXppSy8pQLRy423VCB6OZX393el71NVUFCAjRs34syZM8jOzkb79u0xZMgQ2cR1kmOoInq5MVQRvZzK+/u7UhPVgUe3Lvjwww8ruzkRERHRS6VSoWrdunXPXD9s2LBKNUNERESkqyoVqiZOnCh7np+fj/v378PAwAB16tRhqCIiIqJXTqU+/Xf37l3ZIzs7G4mJifDw8MDPP/+sdI9ERERENV6lv/vvSU5OTliwYEGJq1hERERErwLFQhXwaPJ6amqqkrskIiIi0gmVmlP1+++/y54LIZCWloavv/4anTp1UqQxIiIiIl1SqVD15F3EVSoVGjZsiLfffhuLFi1Soi8iIiIinVKpUFVUVKR0H0REREQ6TdE5VURERESvqkpdqXr8y4jLsnjx4socgoiIiEinVCpUnTp1CqdOnUJ+fj6cnZ0BABcvXoS+vj7at28v1alUKmW6JCIiIqrhKhWq+vbtCxMTE6xduxb16tUD8OiGoCNGjMBbb72FKVOmKNokERERUU1XqTlVixYtwvz586VABQD16tXDvHnz+Ok/IiIieiVVKlRptVrcunWrxPJbt27h3r17z90UERERka6pVKh69913MWLECGzduhXXr1/H9evX8euvvyIgIAD9+/dXukciIiKiGq9Sc6pCQ0MxdepUDB48GPn5+Y92VKsWAgICsHDhQkUbJCIiItIFlQpVderUwcqVK7Fw4UJcvnwZAODo6AhjY2NFmyMiIiLSFc9188+0tDSkpaXByckJxsbGEEIo1RcRERGRTqlUqLp9+za6deuGpk2bonfv3khLSwMABAQE8HYKRERE9EqqVKiaPHkyateujatXr6JOnTrS8oEDByI8PFyx5oiIiIh0RaXmVO3duxd79uxB48aNZcudnJxw5coVRRojIiIi0iWVulKVk5Mju0JV7M6dO1Cr1c/dFBEREZGuqVSoeuutt7Bu3TrpuUqlQlFREUJCQuDp6alYc0RERES6olJv/4WEhKBbt244ceIE8vLy8Omnn+L8+fO4c+cOjhw5onSPRERERDVepa5UtWrVChcvXoSHhwf69euHnJwc9O/fH6dOnYKjo6PSPRIRERHVeBW+UpWfn4+ePXsiNDQUn3/++YvoiYiIiEjnVPhKVe3atXHmzJkX0QsRERGRzqrU238ffvghvv/+e6V7ISIiItJZlZqoXlBQgB9++AH79u2Dq6trie/8W7x4sSLNEREREemKCoWqv//+G/b29jh37hzat28PALh48aKsRqVSKdcdERERkY6oUKhycnJCWloa9u/fD+DR19IsX74clpaWL6Q5IiIiIl1RoTlVQgjZ8927dyMnJ0fRhoiIiIh0UaUmqhd7MmQRERERvaoqFKpUKlWJOVOcQ0VERERUwTlVQggMHz5c+tLkhw8fYsyYMSU+/bd161blOiQiIiLSARUKVf7+/rLnH374oaLNEBEREemqCr39t2bNmnI9lGRvby+97fj4IzAwEADQtWvXEuvGjBkj28fVq1fh4+ODOnXqwMLCAp988gkKCgpkNQcOHED79u2hVqvRpEkThIWFlehlxYoVsLe3h6GhIdzc3HDs2DFFz5WIiIh013NNVK8Kx48fR1pamvSIiIgAALz//vtSzahRo2Q1ISEh0rrCwkL4+PggLy8PR48exdq1axEWFoZZs2ZJNcnJyfDx8YGnpyfi4+MxadIkjBw5Env27JFqNm3ahKCgIMyePRsnT55EmzZt4O3tjZs3b1bBKBAREVFNpxI69hG+SZMmYceOHbh06RJUKhW6du2Ktm3bYunSpaXW7969G3369EFqaqp0P63Q0FBMmzYNt27dgoGBAaZNm4adO3fi3Llz0naDBg1CZmYmwsPDAQBubm5444038PXXXwMAioqKYGtri/Hjx2P69Onl6l2r1cLU1BRZWVnQaDTPMQpEVBPZT99ZZk3KAp8q6ISIlFTe3981/krV4/Ly8vDTTz/ho48+kn3qcP369WjQoAFatWqFGTNm4P79+9K6mJgYuLi4yG5Q6u3tDa1Wi/Pnz0s1Xl5esmN5e3sjJiZGOm5cXJysRk9PD15eXlJNaXJzc6HVamUPIiIiejlV6rv/qsv27duRmZmJ4cOHS8sGDx4MOzs72NjY4MyZM5g2bRoSExOlTyCmp6eXuON78fP09PRn1mi1Wjx48AB3795FYWFhqTUJCQlP7Xf+/PmYM2dOpc+XiIiIdIdOharvv/8evXr1go2NjbRs9OjR0p9dXFxgbW2Nbt264fLly3B0dKyONiUzZsxAUFCQ9Fyr1cLW1rYaOyIiIqIXRWdC1ZUrV7Bv374y74Hl5uYGAEhKSoKjoyOsrKxKfEovIyMDAGBlZSX9t3jZ4zUajQZGRkbQ19eHvr5+qTXF+yiNWq2W7ulFRERELzedmVO1Zs0aWFhYwMfn2ZM84+PjAQDW1tYAAHd3d5w9e1b2Kb2IiAhoNBq0aNFCqomMjJTtJyIiAu7u7gAAAwMDuLq6ymqKiooQGRkp1RAREdGrTSdCVVFREdasWQN/f3/UqvX/F9cuX76ML7/8EnFxcUhJScHvv/+OYcOGoXPnzmjdujUAoEePHmjRogWGDh2K06dPY8+ePZg5cyYCAwOlq0hjxozB33//jU8//RQJCQlYuXIlNm/ejMmTJ0vHCgoKwrfffou1a9fiwoULGDt2LHJycjBixIiqHQwiIiKqkXTi7b99+/bh6tWr+Oijj2TLDQwMsG/fPixduhQ5OTmwtbXFgAEDMHPmTKlGX18fO3bswNixY+Hu7g5jY2P4+/tj7ty5Uo2DgwN27tyJyZMnY9myZWjcuDG+++47eHt7SzUDBw7ErVu3MGvWLKSnp6Nt27YIDw8vMXmdiIiIXk06d58qXcb7VBG93HifKqKX00t5nyoiIiKimoqhioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECanSoCg4Ohkqlkj2aNWsmrX/48CECAwNRv3591K1bFwMGDEBGRoZsH1evXoWPjw/q1KkDCwsLfPLJJygoKJDVHDhwAO3bt4darUaTJk0QFhZWopcVK1bA3t4ehoaGcHNzw7Fjx17IORMREZFuqtGhCgBatmyJtLQ06XH48GFp3eTJk/HHH39gy5YtOHjwIFJTU9G/f39pfWFhIXx8fJCXl4ejR49i7dq1CAsLw6xZs6Sa5ORk+Pj4wNPTE/Hx8Zg0aRJGjhyJPXv2SDWbNm1CUFAQZs+ejZMnT6JNmzbw9vbGzZs3q2YQiIiIqMZTCSFEdTfxNMHBwdi+fTvi4+NLrMvKykLDhg2xYcMGvPfeewCAhIQENG/eHDExMejYsSN2796NPn36IDU1FZaWlgCA0NBQTJs2Dbdu3YKBgQGmTZuGnTt34ty5c9K+Bw0ahMzMTISHhwMA3Nzc8MYbb+Drr78GABQVFcHW1hbjx4/H9OnTy30+Wq0WpqamyMrKgkajqeywEFENZT99Z5k1KQt8qqATIlJSeX9/1/grVZcuXYKNjQ1ef/11DBkyBFevXgUAxMXFIT8/H15eXlJts2bN8NprryEmJgYAEBMTAxcXFylQAYC3tze0Wi3Onz8v1Ty+j+Ka4n3k5eUhLi5OVqOnpwcvLy+p5mlyc3Oh1WplDyIiIno51ehQ5ebmhrCwMISHh2PVqlVITk7GW2+9hXv37iE9PR0GBgYwMzOTbWNpaYn09HQAQHp6uixQFa8vXvesGq1WiwcPHuB///sfCgsLS60p3sfTzJ8/H6amptLD1ta2wmNAREREuqFWdTfwLL169ZL+3Lp1a7i5ucHOzg6bN2+GkZFRNXZWPjNmzEBQUJD0XKvVMlgRERG9pGr0laonmZmZoWnTpkhKSoKVlRXy8vKQmZkpq8nIyICVlRUAwMrKqsSnAYufl1Wj0WhgZGSEBg0aQF9fv9Sa4n08jVqthkajkT2IiIjo5aRToSo7OxuXL1+GtbU1XF1dUbt2bURGRkrrExMTcfXqVbi7uwMA3N3dcfbsWdmn9CIiIqDRaNCiRQup5vF9FNcU78PAwACurq6ymqKiIkRGRko1RERERDU6VE2dOhUHDx5ESkoKjh49infffRf6+vrw8/ODqakpAgICEBQUhP379yMuLg4jRoyAu7s7OnbsCADo0aMHWrRogaFDh+L06dPYs2cPZs6cicDAQKjVagDAmDFj8Pfff+PTTz9FQkICVq5cic2bN2Py5MlSH0FBQfj222+xdu1aXLhwAWPHjkVOTg5GjBhRLeNCRERENU+NnlN1/fp1+Pn54fbt22jYsCE8PDzw559/omHDhgCAJUuWQE9PDwMGDEBubi68vb2xcuVKaXt9fX3s2LEDY8eOhbu7O4yNjeHv74+5c+dKNQ4ODti5cycmT56MZcuWoXHjxvjuu+/g7e0t1QwcOBC3bt3CrFmzkJ6ejrZt2yI8PLzE5HUiIiJ6ddXo+1S9bHifKqKXG+9TRfRyemnuU0VERESkCxiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmgRoeq+fPn44033oCJiQksLCzg6+uLxMREWU3Xrl2hUqlkjzFjxshqrl69Ch8fH9SpUwcWFhb45JNPUFBQIKs5cOAA2rdvD7VajSZNmiAsLKxEPytWrIC9vT0MDQ3h5uaGY8eOKX7OREREpJtqdKg6ePAgAgMD8eeffyIiIgL5+fno0aMHcnJyZHWjRo1CWlqa9AgJCZHWFRYWwsfHB3l5eTh69CjWrl2LsLAwzJo1S6pJTk6Gj48PPD09ER8fj0mTJmHkyJHYs2ePVLNp0yYEBQVh9uzZOHnyJNq0aQNvb2/cvHnzxQ8EERER1XgqIYSo7ibK69atW7CwsMDBgwfRuXNnAI+uVLVt2xZLly4tdZvdu3ejT58+SE1NhaWlJQAgNDQU06ZNw61bt2BgYIBp06Zh586dOHfunLTdoEGDkJmZifDwcACAm5sb3njjDXz99dcAgKKiItja2mL8+PGYPn16ufrXarUwNTVFVlYWNBpNZYeBiGoo++k7y6xJWeBTBZ0QkZLK+/u7Rl+pelJWVhYAwNzcXLZ8/fr1aNCgAVq1aoUZM2bg/v370rqYmBi4uLhIgQoAvL29odVqcf78eanGy8tLtk9vb2/ExMQAAPLy8hAXFyer0dPTg5eXl1RTmtzcXGi1WtmDiIiIXk61qruB8ioqKsKkSZPQqVMntGrVSlo+ePBg2NnZwcbGBmfOnMG0adOQmJiIrVu3AgDS09NlgQqA9Dw9Pf2ZNVqtFg8ePMDdu3dRWFhYak1CQsJTe54/fz7mzJlT+ZMmIiIinaEzoSowMBDnzp3D4cOHZctHjx4t/dnFxQXW1tbo1q0bLl++DEdHx6puU2bGjBkICgqSnmu1Wtja2lZjR0RERPSi6ESoGjduHHbs2IHo6Gg0btz4mbVubm4AgKSkJDg6OsLKyqrEp/QyMjIAAFZWVtJ/i5c9XqPRaGBkZAR9fX3o6+uXWlO8j9Ko1Wqo1erynSQRERHptBo9p0oIgXHjxmHbtm2IioqCg4NDmdvEx8cDAKytrQEA7u7uOHv2rOxTehEREdBoNGjRooVUExkZKdtPREQE3N3dAQAGBgZwdXWV1RQVFSEyMlKqISIioldbjb5SFRgYiA0bNuC3336DiYmJNAfK1NQURkZGuHz5MjZs2IDevXujfv36OHPmDCZPnozOnTujdevWAIAePXqgRYsWGDp0KEJCQpCeno6ZM2ciMDBQuoo0ZswYfP311/j000/x0UcfISoqCps3b8bOnf//SZ6goCD4+/ujQ4cOePPNN7F06VLk5ORgxIgRVT8wREREVOPU6FC1atUqAI9um/C4NWvWYPjw4TAwMMC+ffukgGNra4sBAwZg5syZUq2+vj527NiBsWPHwt3dHcbGxvD398fcuXOlGgcHB+zcuROTJ0/GsmXL0LhxY3z33Xfw9vaWagYOHIhbt25h1qxZSE9PR9u2bREeHl5i8joRERG9mnTqPlW6jvepInq58T5VRC+nl/I+VUREREQ1FUMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIiIiIgUwVBEREREpgKGKiIiISAEMVUREREQKYKgiIiIiUgBDFREREZECGKqIiIiIFMBQRURERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFREREpACGKiIiIiIFMFQRERERKYChioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIaqClqxYgXs7e1haGgINzc3HDt2rLpbIiIiohqAoaoCNm3ahKCgIMyePRsnT55EmzZt4O3tjZs3b1Z3a0RERFTNGKoqYPHixRg1ahRGjBiBFi1aIDQ0FHXq1MEPP/xQ3a0RERFRNWOoKqe8vDzExcXBy8tLWqanpwcvLy/ExMRUY2dERERUE9Sq7gZ0xf/+9z8UFhbC0tJSttzS0hIJCQmlbpObm4vc3FzpeVZWFgBAq9W+uEaJqNoU5d4vs4b//xPpnuL/b4UQz6xjqHqB5s+fjzlz5pRYbmtrWw3dEFFNYLq0ujsgosq6d+8eTE1Nn7qeoaqcGjRoAH19fWRkZMiWZ2RkwMrKqtRtZsyYgaCgIOl5UVER7ty5g/r160OlUr3QfnWBVquFra0trl27Bo1GU93tvLQ4zlWD41w1OM5Vg+MsJ4TAvXv3YGNj88w6hqpyMjAwgKurKyIjI+Hr6wvgUUiKjIzEuHHjSt1GrVZDrVbLlpmZmb3gTnWPRqPh/7RVgONcNTjOVYPjXDU4zv/vWVeoijFUVUBQUBD8/f3RoUMHvPnmm1i6dClycnIwYsSI6m6NiIiIqhlDVQUMHDgQt27dwqxZs5Ceno62bdsiPDy8xOR1IiIievUwVFXQuHHjnvp2H1WMWq3G7NmzS7xFSsriOFcNjnPV4DhXDY5z5ahEWZ8PJCIiIqIy8eafRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgUwFBFFTZ//ny88cYbMDExgYWFBXx9fZGYmCitv3PnDsaPHw9nZ2cYGRnhtddew4QJE6TvPnzS7du30bhxY6hUKmRmZpZ5/J07d8LNzQ1GRkaoV6+edDPWl011jvPFixfRr18/NGjQABqNBh4eHti/f7+Sp1djKDXOKpWqxGPjxo3PPPadO3cwZMgQaDQamJmZISAgANnZ2S/kPKtbdY1zSkoKAgIC4ODgACMjIzg6OmL27NnIy8t7Yeda3arzNV0sNzcXbdu2hUqlQnx8vJKnV6MxVFGFHTx4EIGBgfjzzz8RERGB/Px89OjRAzk5OQCA1NRUpKam4j//+Q/OnTuHsLAwhIeHIyAgoNT9BQQEoHXr1uU69q+//oqhQ4dixIgROH36NI4cOYLBgwcrdm41SXWOc58+fVBQUICoqCjExcWhTZs26NOnD9LT0xU7v5pCyXFes2YN0tLSpEdZgX/IkCE4f/48IiIisGPHDkRHR2P06NEv4jSrXXWNc0JCAoqKivDNN9/g/PnzWLJkCUJDQ/HZZ5+9qFOtdtX5mi726aeflvmVLi8lQfScbt68KQCIgwcPPrVm8+bNwsDAQOTn58uWr1y5UnTp0kVERkYKAOLu3btP3Ud+fr5o1KiR+O6775RqXadU1TjfunVLABDR0dHSMq1WKwCIiIiI5z6Pmq6y4wxAbNu2rdzH+euvvwQAcfz4cWnZ7t27hUqlEjdu3KhU77qkqsa5NCEhIcLBweG59qFLqnqsd+3aJZo1aybOnz8vAIhTp05VomvdxCtV9NyKLxmbm5s/s0aj0aBWrf+/3+xff/2FuXPnYt26ddDTK/ulePLkSdy4cQN6enpo164drK2t0atXL5w7d+75T0IHVNU4169fH87Ozli3bh1ycnJQUFCAb775BhYWFnB1dX3+E6nhKjvOABAYGIgGDRrgzTffxA8//ADxjNsAxsTEwMzMDB06dJCWeXl5QU9PD7Gxsc95FjVfVY3z0/b7rOO+bKpyrDMyMjBq1Cj8+OOPqFOnzvM3r2uqOdSRjissLBQ+Pj6iU6dOT625deuWeO2118Rnn30mLXv48KFo3bq1+PHHH4UQQuzfv7/MKyg///yzACBee+018csvv4gTJ04IPz8/Ub9+fXH79m3FzqkmqspxFkKIa9euCVdXV6FSqYS+vr6wtrYWJ0+eVORcarLKjrMQQsydO1ccPnxYnDx5UixYsECo1WqxbNmyp+7nq6++Ek2bNi2xvGHDhmLlypWVPwkdUJXj/KRLly4JjUYjVq9eXen+dUlVjnVRUZHo2bOn+PLLL4UQQiQnJ79yV6oYqui5jBkzRtjZ2Ylr166Vuj4rK0u8+eabomfPniIvL09aPnnyZDFw4EDpeXl+2a9fv14AEN9884207OHDh6JBgwYiNDT0+U+mBqvKcS4qKhLvvPOO6NWrlzh8+LCIi4sTY8eOFY0aNRKpqamKnVNNVNlxLs0XX3whGjdu/NT1r3Koqspxftz169eFo6OjCAgIqHDPuqoqx3rZsmWiU6dOoqCgQAjBUEVUIYGBgaJx48bi77//LnW9VqsV7u7uolu3buLBgweydW3atBF6enpCX19f6OvrCz09PQFA6Ovri1mzZpW6v6ioKAFAHDp0SLb8zTffLPEvrJdJVY/zvn37hJ6ensjKypItb9KkiZg/f74yJ1UDPc84l2bHjh0CgHj48GGp67///nthZmYmW5afny/09fXF1q1bK34COqKqx7nYjRs3hJOTkxg6dKgoLCysVO+6pqrHul+/frK/b/T19aW/b4YNG/Zc56Ir+IXKVGFCCIwfPx7btm3DgQMH4ODgUKJGq9XC29sbarUav//+OwwNDWXrf/31Vzx48EB6fvz4cXz00Uc4dOgQHB0dSz2uq6sr1Go1EhMT4eHhAQDIz89HSkoK7OzsFDzDmqG6xvn+/fsAUGL+lZ6eHoqKip73tGocJca5NPHx8ahXr95Tv5DW3d0dmZmZiIuLk+aqRUVFoaioCG5ubs93UjVQdY0zANy4cQOenp5wdXXFmjVryjW3UJdV11gvX74c8+bNk56npqbC29sbmzZteilf06Wq1khHOmns2LHC1NRUHDhwQKSlpUmP+/fvCyEeXU52c3MTLi4uIikpSVZTfFn4SaW9LRUbGyucnZ3F9evXpWUTJ04UjRo1Env27BEJCQkiICBAWFhYiDt37rzQc64O1TXOt27dEvXr1xf9+/cX8fHxIjExUUydOlXUrl1bxMfHv/DzrmpKjPPvv/8uvv32W3H27Flx6dIlsXLlSlGnTh3Z1cDSXs89e/YU7dq1E7GxseLw4cPCyclJ+Pn5Ve0AVJHqGufr16+LJk2aiG7duonr16/L9vuyqs7X9OP49h9ROQAo9bFmzRohxP//4i7tkZycXOo+S/tlX7zs8W3y8vLElClThIWFhTAxMRFeXl7i3LlzL+5kq1F1jvPx48dFjx49hLm5uTAxMREdO3YUu3btenEnW42UGOfdu3eLtm3birp16wpjY2PRpk0bERoaKnubqbRxvn37tvDz8xN169YVGo1GjBgxQty7d68Kz77qVNc4r1mz5qn7fVlV52v6ca9iqFIJUcHPohIRERFRCS/3G8tEREREVYShioiIiEgBDFVERERECmCoIiIiIlIAQxURERGRAhiqiIiIiBTAUEVERESkAIYqIh01fPhw+Pr6Kr7f9PR0dO/eHcbGxjAzM1N8/6QMlUqF7du3V3cbL9T9+/cxYMAAaDQaqFQqZGZmVndLRM/EUEX0DC8quFRESkoKVCoV4uPjq+R4S5YsQVpaGuLj43Hx4sUqOaYSgoOD0bZt2+pugxS0du1aHDp0CEePHkVaWhpMTU0V2W/Xrl0xadIkRfZF9Dh+oTIRyVy+fBmurq5wcnKq7lYkQggUFhaiVi3+lfUquXz5Mpo3b45WrVpVdyulysvLg4GBQXW3QTUIr1QRPYdz586hV69eqFu3LiwtLTF06FD873//k9Z37doVEyZMwKeffgpzc3NYWVkhODhYto+EhAR4eHjA0NAQLVq0wL59+2Rv7RR/w3y7du2gUqnQtWtX2fb/+c9/YG1tjfr16yMwMBD5+fnP7HnVqlVwdHSEgYEBnJ2d8eOPP0rr7O3t8euvv2LdunVQqVQYPnx4qfso7V/6vr6+svqVK1fCyckJhoaGsLS0xHvvvSetKyoqwvz58+Hg4AAjIyO0adMGv/zyi7T+wIEDUKlU2L17N1xdXaFWq3H48OGnnlNYWBjmzJmD06dPQ6VSQaVSISwsDACwePFiuLi4wNjYGLa2tvj444+RnZ0t2/7bb7+Fra0t6tSpg3fffReLFy+WvfV5+vRpeHp6wsTEBBqNBq6urjhx4sRT+yl2+/Zt+Pn5oVGjRqhTpw5cXFzw888/lxjLsl4jly5dQufOnaXXSERERJnHLpaXl4dx48bB2toahoaGsLOzw/z586X1KpUKq1atQq9evWBkZITXX39d9rMAgGvXruGDDz6AmZkZzM3N0a9fP6SkpMhqfvjhB7Rs2RJqtRrW1tYYN24cgEeBODg4GK+99hrUajVsbGwwYcKEMvvu2rUrFi1ahOjoaNnrPjc3F1OnTkWjRo1gbGwMNzc3HDhwQNqurDEfPnw4Dh48iGXLlkmvlZSUFISFhZV4u3v79u1QqVTS8+Krod999x0cHBxgaGgIAMjMzMTIkSPRsGFDaDQavP322zh9+rS0XWVfP6SDqverB4lqNn9/f9GvX79S1929e1c0bNhQzJgxQ1y4cEGcPHlSdO/eXXh6eko1Xbp0ERqNRgQHB4uLFy+KtWvXCpVKJfbu3SuEEKKgoEA4OzuL7t27i/j4eHHo0CHx5ptvCgBi27ZtQgghjh07JgCIffv2ibS0NHH79m2pN41GI8aMGSMuXLgg/vjjD1GnTh2xevXqp57P1q1bRe3atcWKFStEYmKiWLRokdDX1xdRUVFCCCFu3rwpevbsKT744AORlpYmMjMzS91Ply5dxMSJE2XL+vXrJ/z9/YUQj76QWV9fX2zYsEGkpKSIkydPimXLlkm18+bNE82aNRPh4eHi8uXLYs2aNUKtVosDBw4IIf7/i1pbt24t9u7dK5KSkqTzLs39+/fFlClTRMuWLUVaWppIS0sT9+/fF0IIsWTJEhEVFSWSk5NFZGSkcHZ2FmPHjpW2PXz4sNDT0xMLFy4UiYmJYsWKFcLc3FyYmppKNS1bthQffvihuHDhgrh48aLYvHmziI+Pf2o/xa5fvy4WLlwoTp06JS5fviyWL18u9PX1RWxsrGwsn/UaKSwsFK1atRLdunUT8fHx4uDBg6Jdu3ay18izLFy4UNja2oro6GiRkpIiDh06JDZs2CCtByDq168vvv32W5GYmChmzpwp9PX1xV9//SWEePQl5s2bNxcfffSROHPmjPjrr7/E4MGDhbOzs8jNzRVCCLFy5UphaGgoli5dKhITE8WxY8fEkiVLhBBCbNmyRWg0GrFr1y5x5coVERsb+8zXaLHbt2+LUaNGCXd3d9nrfuTIkeIf//iHiI6OFklJSWLhwoVCrVaLixcvlmvMMzMzhbu7uxg1apT0WikoKBBr1qyR/cyFEGLbtm2yL16ePXu2MDY2Fj179hQnT54Up0+fFkII4eXlJfr27SuOHz8uLl68KKZMmSLq168v9VzZ1w/pHoYqomd4Vqj68ssvRY8ePWTLrl27JgCIxMREIcSjX5geHh6ymjfeeENMmzZNCPHom+Br1aol0tLSpPURERGyX5hP+6Z3f39/YWdnJwoKCqRl77//vhg4cOBTz+cf//iHGDVqlGzZ+++/L3r37i09fzwcPU1ZoerXX38VGo1GaLXaEts+fPhQ1KlTRxw9elS2PCAgQPj5+Qkh/j9Ubd++/Zl9PG727NmiTZs2ZdZt2bJF1K9fX3o+cOBA4ePjI6sZMmSI7BesiYmJCAsLK3cvz+Lj4yOmTJkiPS/rNbJnzx5Rq1YtcePGDWn97t27yx2qxo8fL95++21RVFRU6noAYsyYMbJlbm5uUvD88ccfhbOzs2z73NxcYWRkJPbs2SOEEMLGxkZ8/vnnpe5/0aJFomnTpiIvL6/MXp80ceJE0aVLF+n5lStXhL6+vmwshBCiW7duYsaMGU/dT2lj/uTrt7yhqnbt2uLmzZvSskOHDgmNRiMePnwo29bR0VF88803QghlXz9Us/HtP6JKOn36NPbv34+6detKj2bNmgF4NBekWOvWrWXbWVtb4+bNmwCAxMRE2NrawsrKSlr/5ptvlruHli1bQl9fv9R9l+bChQvo1KmTbFmnTp1w4cKFch+zPLp37w47Ozu8/vrrGDp0KNavX4/79+8DAJKSknD//n10795dNnbr1q2TjRsAdOjQ4bl72bdvH7p164ZGjRrBxMQEQ4cOxe3bt6V+EhMTS4z5k8+DgoIwcuRIeHl5YcGCBSX6fJrCwkJ8+eWXcHFxgbm5OerWrYs9e/bg6tWrsrpnvUYuXLgAW1tb2NjYSOvd3d3Ld/J49HZXfHw8nJ2dMWHCBOzdu7dEzZP7c3d3l14Tp0+fRlJSEkxMTKSflbm5OR4+fIjLly/j5s2bSE1NRbdu3Uo9/vvvv48HDx7g9ddfx6hRo7Bt2zYUFBSUu//HnT17FoWFhWjatKnstXPw4EHpZ1LeMa8sOzs7NGzYUHp++vRpZGdno379+rKekpOTpZ4q+/oh3cNZn0SVlJ2djb59++Lf//53iXXW1tbSn2vXri1bp1KpUFRUpEgPL3Lfz6KnpwchhGzZ43O5TExMcPLkSRw4cAB79+7FrFmzEBwcjOPHj0vzmXbu3IlGjRrJ9qFWq2XPjY2Nn6vPlJQU9OnTB2PHjsVXX30Fc3NzHD58GAEBAcjLy0OdOnXKtZ/g4GAMHjwYO3fuxO7duzF79mxs3LgR77777jO3W7hwIZYtW4alS5dK87omTZqEvLw8Wd2L/Dm2b98eycnJ2L17N/bt24cPPvgAXl5eJeZNPU12djZcXV2xfv36EusaNmwIPb1n/9vc1tYWiYmJ2LdvHyIiIvDxxx9j4cKFOHjwYInzLk8v+vr6iIuLk/1jAgDq1q0LoPxj/qSyXtPFnnxNZmdnw9raWjavq1jxHK3Kvn5I9zBUEVVS+/bt8euvv8Le3r7Sn0pzdnbGtWvXkJGRAUtLSwDA8ePHZTXFny4qLCx8voYBNG/eHEeOHIG/v7+07MiRI2jRokWF9tOwYUOkpaVJzwsLC3Hu3Dl4enpKy2rVqgUvLy94eXlh9uzZMDMzQ1RUFLp37w61Wo2rV6+iS5cuz31OxQwMDEqMUVxcHIqKirBo0SLpl//mzZtlNc7OziXG/MnnANC0aVM0bdoUkydPhp+fH9asWVPmL8UjR46gX79++PDDDwE8mqB/8eLFCo138+bNce3aNaSlpUlh/c8//yz39gCg0WgwcOBADBw4EO+99x569uyJO3fuwNzcXNrfsGHDpPo///wT7dq1A/Dodb5p0yZYWFhAo9GUun97e3tERkbKfv6PMzIyQt++fdG3b18EBgaiWbNmOHv2LNq3b1+h82jXrh0KCwtx8+ZNvPXWW6XWlGfMS3utNGzYEPfu3UNOTo4UnMpzG5P27dsjPT0dtWrVgr29/VPrKvP6Id3DUEVUhqysrBJ/uRZ/0u7bb7+Fn5+f9MmtpKQkbNy4Ed99912Jf0mXpnv37nB0dIS/vz9CQkJw7949zJw5EwCkTx1ZWFjAyMgI4eHhaNy4MQwNDSt9v55PPvkEH3zwAdq1awcvLy/88ccf2Lp1K/bt21eh/bz99tsICgrCzp074ejoiMWLF8tuzLhjxw78/fff6Ny5M+rVq4ddu3ahqKgIzs7OMDExwdSpUzF58mQUFRXBw8MDWVlZOHLkCDQajSzwVYS9vT2Sk5MRHx+Pxo0bw8TEBE2aNEF+fj7++9//om/fvjhy5AhCQ0Nl240fPx6dO3fG4sWL0bdvX0RFRWH37t3S+D948ACffPIJ3nvvPTg4OOD69es4fvw4BgwYUGZPTk5O+OWXX3D06FHUq1cPixcvRkZGRoVClZeXF5o2bQp/f38sXLgQWq0Wn3/+ebm3X7x4MaytrdGuXTvo6elhy5YtsLKykn3SbcuWLejQoQM8PDywfv16HDt2DN9//z0AYMiQIVi4cCH69euHuXPnonHjxrhy5Qq2bt2KTz/9FI0bN0ZwcDDGjBkDCwsL9OrVC/fu3cORI0cwfvx4hIWFobCwEG5ubqhTpw5++uknGBkZwc7OrtznUKxp06YYMmQIhg0bhkWLFqFdu3a4desWIiMj0bp1a/j4+JRrzO3t7REbG4uUlBTp7czi/j777DNMmDABsbGx0idIn8XLywvu7u7w9fVFSEgImjZtitTUVOzcuRPvvvsuWrZsWenXD+mg6p7URVST+fv7CwAlHgEBAUIIIS5evCjeffddYWZmJoyMjESzZs3EpEmTpEm9ZU3oFkKICxcuiE6dOgkDAwPRrFkz8ccffwgAIjw8XKr59ttvha2trdDT05Mm7pY2if7Jib2lWblypXj99ddF7dq1RdOmTcW6deue2V9p8vLyxNixY4W5ubmwsLAQ8+fPl2136NAh0aVLF1GvXj1hZGQkWrduLTZt2iRtX1RUJJYuXSqcnZ1F7dq1RcOGDYW3t7c4ePCgEOL/J6rfvXv3mX087uHDh2LAgAHCzMxMABBr1qwRQgixePFiYW1tLYyMjIS3t7dYt25diX2vXr1aNGrUSBgZGQlfX18xb948YWVlJYR4NCl70KBBwtbWVhgYGAgbGxsxbtw48eDBgzJ7un37tujXr5+oW7eusLCwEDNnzhTDhg2T/dzK8xpJTEwUHh4ewsDAQDRt2lSEh4eXe6L66tWrRdu2bYWxsbHQaDSiW7du4uTJk9J6AGLFihWie/fuQq1WC3t7e9nPSggh0tLSxLBhw0SDBg2EWq0Wr7/+uhg1apTIysqSakJDQ6Wfp7W1tRg/frwQ4tFkbzc3N6HRaISxsbHo2LGj2LdvX5l9C1H66zkvL0/MmjVL2NvbS8d69913xZkzZ4QQ5RvzxMRE0bFjR2FkZCQAiOTkZKnXJk2aCCMjI9GnTx+xevXqEhPVS/swhFarFePHjxc2Njaidu3awtbWVgwZMkRcvXr1uV4/pHtUQjzxJjIRVasjR47Aw8MDSUlJcHR0rO52XkmjRo1CQkICDh06VN2tvHAqlQrbtm2r9m8OIHoZ8O0/omq2bds21K1bF05OTkhKSsLEiRPRqVMnBqoq9J///Ef6vsPdu3dj7dq1WLlyZXW3RUQ6hrdUIKpm9+7dkybvDh8+HG+88QZ+++236m6rxmnZsqXsI+uPP0r7ZFpFHDt2DN27d4eLiwtCQ0OxfPlyjBw5ssztiu+mX9rjX//613P1VF7/+te/ntpDr169qqSHyjh06NBT+y7+JB+RruHbf0SkE65cufLUr+CxtLSEiYlJFXcE3LhxAw8ePCh1nbm5ufTpuhfpzp07uHPnTqnrjIyMSty2oqZ48OABbty48dT1TZo0qcJuiJTBUEVERESkAL79R0RERKQAhioiIiIiBTBUERERESmAoYqIiIhIAQxVRERERApgqCIiIiJSAEMVERERkQIYqoiIiIgU8H8yUWXZd4qsWgAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["The average length is 225.0\n","The median length is 225.0\n","The standard deviation of length is 0.0\n","The maximum length is 225\n","The minimum length is 225\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","import os\n","\n","# 1. LOAD DATA (Sesuaikan path)\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. KONFIGURASI HYPERPARAMETER TUNING\n","# Kita akan simpan hasil per kombinasi untuk tabel summary\n","results_list = []\n","param_grid = {'epochs': [10, 20], 'batch_size': [64, 128]} # Batch size lebih besar lebih cepat\n","\n","# 4. CROSS VALIDATION LOOP\n","kf = KFold(n_splits=5, shuffle=True, random_state=42)\n","\n","for epochs in param_grid['epochs']:\n"," for batch_size in param_grid['batch_size']:\n"," fold_accuracies = []\n","\n"," print(f\"\\nTraining ANN: Epochs={epochs}, Batch={batch_size}\")\n","\n"," for fold, (train_idx, val_idx) in enumerate(kf.split(X, y)):\n"," X_train, X_val = X[train_idx], X[val_idx]\n"," y_train, y_val = y[train_idx], y[train_idx] # y_train perlu diperbaiki aksesnya jika y adalah numpy\n"," y_train_fold, y_val_fold = y[train_idx], y[val_idx]\n","\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n","\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n"," # Tambahkan EarlyStopping agar tidak membuang waktu\n"," callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=2)\n","\n"," model.fit(X_train, y_train_fold, epochs=epochs, batch_size=batch_size, verbose=0, callbacks=[callback])\n","\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\")\n"," acc = accuracy_score(y_val_fold, y_pred)\n"," fold_accuracies.append(acc)\n","\n"," results_list.append({\n"," \"Params\": f\"Ep:{epochs}, B:{batch_size}\",\n"," \"Fold 1\": fold_accuracies[0], \"Fold 2\": fold_accuracies[1],\n"," \"Fold 3\": fold_accuracies[2], \"Fold 4\": fold_accuracies[3],\n"," \"Fold 5\": fold_accuracies[4],\n"," \"Mean\": np.mean(fold_accuracies),\n"," \"Std\": np.std(fold_accuracies)\n"," })\n","\n","# 5. TABEL RINGKASAN\n","summary_df = pd.DataFrame(results_list)\n","print(\"\\n--- TABEL STABILITAS ANN (CROSS-VALIDATION) ---\")\n","print(summary_df.to_string(index=False))\n","\n","# 6. TRAIN FINAL MODEL (Gunakan params terbaik dari tabel di atas)\n","best_p = summary_df.loc[summary_df['Mean'].idxmax()]\n","print(f\"\\nBest Config: {best_p['Params']}\")\n","\n","final_model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n","])\n","final_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","final_model.fit(X, y, epochs=int(best_p['Params'].split(',')[0].split(':')[1]),\n"," batch_size=int(best_p['Params'].split(',')[1].split(':')[1]), verbose=1)\n","\n","# 7. EVALUASI AKHIR & CLASSIFICATION REPORT (4 DIGITS)\n","y_final_pred = (final_model.predict(X, verbose=0) > 0.5).astype(\"int32\")\n","print(\"\\nFinal Classification Report (Full Dataset):\")\n","# Tambahkan digits=4 di sini\n","print(classification_report(y, y_final_pred, digits=4))\n","\n","# 7. SIMPAN MODEL\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","final_model.save(os.path.join(dir_path, 'ann_model.keras'))\n","print(f\"Model ANN berhasil disimpan di {dir_path}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qpcxK_8thvGV","executionInfo":{"status":"ok","timestamp":1777540604120,"user_tz":-420,"elapsed":3390689,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"66c3e839-3be7-4330-8c25-bcacde8eb759"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Training ANN: Epochs=10, Batch=64\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n","\n","Training ANN: Epochs=10, Batch=128\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n","\n","Training ANN: Epochs=20, Batch=64\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n","\n","Training ANN: Epochs=20, Batch=128\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n","\n","--- TABEL STABILITAS ANN (CROSS-VALIDATION) ---\n"," Params Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std\n"," Ep:10, B:64 0.876441 0.873587 0.870706 0.875894 0.871463 0.873618 0.002292\n","Ep:10, B:128 0.876441 0.873016 0.871548 0.875133 0.870865 0.873400 0.002109\n"," Ep:20, B:64 0.875843 0.872554 0.871902 0.875051 0.872197 0.873509 0.001615\n","Ep:20, B:128 0.876332 0.873070 0.872173 0.875268 0.872061 0.873781 0.001719\n","\n","Best Config: Ep:20, B:128\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.8646 - loss: 0.3108\n","Epoch 2/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.8701 - loss: 0.2950\n","Epoch 3/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.8717 - loss: 0.2913\n","Epoch 4/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.8726 - loss: 0.2893\n","Epoch 5/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.8731 - loss: 0.2875\n","Epoch 6/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - accuracy: 0.8739 - loss: 0.2867\n","Epoch 7/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.8739 - loss: 0.2860\n","Epoch 8/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.8746 - loss: 0.2849\n","Epoch 9/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 7ms/step - accuracy: 0.8743 - loss: 0.2847\n","Epoch 10/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 7ms/step - accuracy: 0.8747 - loss: 0.2841\n","Epoch 11/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.8746 - loss: 0.2837\n","Epoch 12/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.8750 - loss: 0.2834\n","Epoch 13/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 7ms/step - accuracy: 0.8753 - loss: 0.2831\n","Epoch 14/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 7ms/step - accuracy: 0.8753 - loss: 0.2828\n","Epoch 15/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.8752 - loss: 0.2825\n","Epoch 16/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - accuracy: 0.8752 - loss: 0.2824\n","Epoch 17/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 7ms/step - accuracy: 0.8757 - loss: 0.2818\n","Epoch 18/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.8758 - loss: 0.2819\n","Epoch 19/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - accuracy: 0.8757 - loss: 0.2816\n","Epoch 20/20\n","\u001b[1m1438/1438\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.8757 - loss: 0.2813\n","\n","Final Classification Report (Full Dataset):\n"," precision recall f1-score support\n","\n"," 0 0.8690 0.8900 0.8794 93176\n"," 1 0.8842 0.8624 0.8731 90782\n","\n"," accuracy 0.8763 183958\n"," macro avg 0.8766 0.8762 0.8763 183958\n","weighted avg 0.8765 0.8763 0.8763 183958\n","\n","Model ANN berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_model/\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/neural_networks_tags_specifications.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cdFS0AWac8Q4","executionInfo":{"status":"ok","timestamp":1734855843779,"user_tz":-420,"elapsed":6440,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"db40b6ea-e2b3-4c19-9d57-d9936ea19682"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step\n"," precision recall f1-score support\n","\n"," 0 0.88 0.44 0.59 18645\n"," 1 0.62 0.94 0.75 18147\n","\n"," accuracy 0.69 36792\n"," macro avg 0.75 0.69 0.67 36792\n","weighted avg 0.75 0.69 0.67 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"FZMag36kctY2"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"nY0oy9x2cy8P","executionInfo":{"status":"ok","timestamp":1777540613923,"user_tz":-420,"elapsed":9776,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score\n","import os\n","\n","# 1. LOAD DATA\n","sample_limit = 50000\n","X = user_price_features[:sample_limit]\n","y = user_price_label[:sample_limit]\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. KONFIGURASI\n","param_grid = {'epochs': [10, 20], 'batch_size': [64, 128]}\n","results_list = []\n","kf = KFold(n_splits=5, shuffle=True, random_state=42)\n","\n","# 4. CROSS-VALIDATION LOOP\n","for epochs in param_grid['epochs']:\n"," for batch_size in param_grid['batch_size']:\n"," fold_accuracies = []\n","\n"," print(f\"\\nTraining ANN (Price): Epochs={epochs}, Batch={batch_size}\")\n","\n"," for fold, (train_idx, val_idx) in enumerate(kf.split(X, y)):\n"," X_train, X_val = X[train_idx], X[val_idx]\n"," y_train, y_val = y[train_idx], y[val_idx]\n","\n"," # Model Architecture\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n","\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n"," # EarlyStopping: Jika loss tidak turun dalam 2 epoch, stop untuk efisiensi\n"," callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=2)\n","\n"," model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0, callbacks=[callback])\n","\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\")\n"," acc = accuracy_score(y_val, y_pred)\n"," fold_accuracies.append(acc)\n","\n"," results_list.append({\n"," \"Params\": f\"Ep:{epochs}, B:{batch_size}\",\n"," \"Fold 1\": fold_accuracies[0], \"Fold 2\": fold_accuracies[1],\n"," \"Fold 3\": fold_accuracies[2], \"Fold 4\": fold_accuracies[3],\n"," \"Fold 5\": fold_accuracies[4],\n"," \"Mean\": np.mean(fold_accuracies),\n"," \"Std\": np.std(fold_accuracies)\n"," })\n","\n","# 5. TABEL STABILITAS\n","summary_df = pd.DataFrame(results_list)\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS ANN (PRICE FEATURES) ---\")\n","print(summary_df.to_string(index=False))\n","\n","# 6. TRAIN FINAL MODEL (Gunakan params terbaik)\n","best_p = summary_df.loc[summary_df['Mean'].idxmax()]\n","print(f\"\\nBest Config: {best_p['Params']}\")\n","\n","final_model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n","])\n","final_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n","# Train pada seluruh data\n","final_model.fit(X, y, epochs=int(best_p['Params'].split(',')[0].split(':')[1]),\n"," batch_size=int(best_p['Params'].split(',')[1].split(':')[1]), verbose=1)\n","# 7. EVALUASI AKHIR & CLASSIFICATION REPORT (4 DIGITS)\n","y_final_pred = (final_model.predict(X, verbose=0) > 0.5).astype(\"int32\")\n","print(\"\\nFinal Classification Report (Full Dataset):\")\n","# Tambahkan digits=4 di sini\n","print(classification_report(y, y_final_pred, digits=4))\n","\n","# 7. SIMPAN MODEL\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","final_model.save(os.path.join(dir_path, 'ann_price_model.keras'))\n","# Simpan scaler agar bisa dipakai inferensi nanti\n","import joblib\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel ANN berhasil disimpan di {dir_path}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"twU-lTQPcvo8","executionInfo":{"status":"ok","timestamp":1777541384206,"user_tz":-420,"elapsed":770271,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"b849868e-aec4-4fe4-da99-9829d8c38346"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Training ANN (Price): Epochs=10, Batch=64\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n","\n","Training ANN (Price): Epochs=10, Batch=128\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n","\n","Training ANN (Price): Epochs=20, Batch=64\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n","\n","Training ANN (Price): Epochs=20, Batch=128\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n","\n","--- TABEL STABILITAS ANN (PRICE FEATURES) ---\n"," Params Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std\n"," Ep:10, B:64 0.9961 0.9898 0.9914 0.9918 0.9940 0.9926 0.0022\n","Ep:10, B:128 0.9904 0.9950 0.9883 0.9884 0.9888 0.9902 0.0025\n"," Ep:20, B:64 0.9940 0.9965 0.9921 0.9942 0.9939 0.9941 0.0014\n","Ep:20, B:128 0.9956 0.9930 0.9903 0.9943 0.9968 0.9940 0.0022\n","\n","Best Config: Ep:20, B:64\n","Epoch 1/20\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6ms/step - accuracy: 0.9090 - loss: 0.2186\n","Epoch 2/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9532 - loss: 0.1158\n","Epoch 3/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9691 - loss: 0.0769\n","Epoch 4/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9801 - loss: 0.0513\n","Epoch 5/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9836 - loss: 0.0418\n","Epoch 6/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9881 - loss: 0.0309\n","Epoch 7/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9896 - loss: 0.0274\n","Epoch 8/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9915 - loss: 0.0217\n","Epoch 9/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9898 - loss: 0.0268\n","Epoch 10/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9932 - loss: 0.0188\n","Epoch 11/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.9934 - loss: 0.0174\n","Epoch 12/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9935 - loss: 0.0168\n","Epoch 13/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9946 - loss: 0.0151\n","Epoch 14/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9949 - loss: 0.0139\n","Epoch 15/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6ms/step - accuracy: 0.9943 - loss: 0.0139\n","Epoch 16/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9962 - loss: 0.0113\n","Epoch 17/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9955 - loss: 0.0118\n","Epoch 18/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9965 - loss: 0.0096\n","Epoch 19/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9971 - loss: 0.0088\n","Epoch 20/20\n","\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9966 - loss: 0.0092\n","\n","Final Classification Report (Full Dataset):\n"," precision recall f1-score support\n","\n"," 0 0.9982 0.9973 0.9977 22129\n"," 1 0.9978 0.9986 0.9982 27871\n","\n"," accuracy 0.9980 50000\n"," macro avg 0.9980 0.9979 0.9980 50000\n","weighted avg 0.9980 0.9980 0.9980 50000\n","\n","\n","Model ANN berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_price/\n"]}]},{"cell_type":"code","source":["\n","# Example prediction (replace with your actual data)\n","example_features = user_price_features[0] # Example datapoint\n","prediction = (model.predict(np.array([example_features])) > 0.5).astype(\"int32\")\n","print(f\"Prediction for the example datapoint: {prediction}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FXgmBUNTWiOJ","executionInfo":{"status":"ok","timestamp":1734887544151,"user_tz":-420,"elapsed":379,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"28a5c81c-e433-4a69-ef99-509a40fc8fe6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n","Prediction for the example datapoint: [[0]]\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FMRD6RqjGNbK","executionInfo":{"status":"ok","timestamp":1735051114521,"user_tz":-420,"elapsed":91190,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"386467ef-11cc-48e9-dee6-d7ee94ca95d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2ms/step\n"," precision recall f1-score support\n","\n"," 0 1.00 1.00 1.00 602313\n"," 1 1.00 1.00 1.00 612435\n","\n"," accuracy 1.00 1214748\n"," macro avg 1.00 1.00 1.00 1214748\n","weighted avg 1.00 1.00 1.00 1214748\n","\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XfpLJxOdF79c","executionInfo":{"status":"ok","timestamp":1735050941077,"user_tz":-420,"elapsed":19421,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"804fb55e-d0c2-4955-acf1-949a0e3e6594"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: save model best_mlp_classifier\n","\n","import joblib\n","\n","# Assuming neural_networks is defined as in your provided code.\n","# Assuming 'neural_networks' is your trained model\n","joblib.dump(model, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl')\n","print(\"Model saved to neural_networks.pkl\")"],"metadata":{"id":"FoYSaGaJR1wo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1734887466457,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3bf0e99e-5628-471d-cc09-85a630a08376"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to neural_networks.pkl\n"]}]},{"cell_type":"markdown","source":["### Predic Bundle"],"metadata":{"id":"unvcsZe_xUw-"}},{"cell_type":"code","source":[],"metadata":{"id":"6EVDa1hXxYv6"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Source Code/Overfitting Test/IMPACT-P Decision Tree.ipynb b/Source Code/Overfitting Test/IMPACT-P Decision Tree.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9168820ac868b1001d72080e0f175fac05d4e808 --- /dev/null +++ b/Source Code/Overfitting Test/IMPACT-P Decision Tree.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777535742291,"user_tz":-420,"elapsed":19477,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"eb07152a-8df3-4711-d210-685db1ccaa1a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nWPptAf-L46u","executionInfo":{"status":"ok","timestamp":1777535764363,"user_tz":-420,"elapsed":22047,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"87cdffef-f291-48c7-d177-b631c78ea4de"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777535764382,"user_tz":-420,"elapsed":32,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"iRZwe19LYEPm"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"u4jFU5HCYDmw","executionInfo":{"status":"ok","timestamp":1777535775717,"user_tz":-420,"elapsed":9382,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","import joblib\n","import os\n","\n","# 1. LOAD DATA\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","\n","# 2. PREPROCESSING\n","# Meskipun Decision Tree secara teori tidak butuh scaling, tetap dilakukan untuk konsistensi pipeline\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","# Menambahkan min_samples_leaf untuk mencegah overfitting\n","dt = DecisionTreeClassifier(random_state=42)\n","param_grid = {\n"," 'max_depth': [10, 20, 30, None],\n"," 'min_samples_split': [5, 10],\n"," 'min_samples_leaf': [2, 4]\n","}\n","\n","grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Decision Tree\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Standard Deviation\": [np.std(fold_scores)]\n","})\n","\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (DECISION TREE) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","best_model = grid_search.best_estimator_\n","y_pred_train = best_model.predict(X_train)\n","y_pred_test = best_model.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# 8. MENYIMPAN MODEL & SCALER\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","joblib.dump(best_model, os.path.join(dir_path, 'best_dt_model.pkl'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Scaler berhasil disimpan di {dir_path}\")\n","\n","# 9. CLASSIFICATION REPORT\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZNJX8CrOYc_d","executionInfo":{"status":"ok","timestamp":1777536322599,"user_tz":-420,"elapsed":343970,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"6a2b516d-86c5-47d1-a0ed-716712a24232"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 16 candidates, totalling 80 fits\n","\n","--- TABEL STABILITAS MODEL (DECISION TREE) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","Decision Tree {'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 10} 0.8722 0.8711 0.8755 0.8717 0.8697 0.8720 0.0019\n","\n","--- ANALISIS GENERALIZATION GAP ---\n","Training Accuracy : 0.8765\n","Testing Accuracy : 0.8746\n","Generalization Gap : 0.0019\n","\n","Model dan Scaler berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_model/\n","\n","Classification Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.8572 0.9030 0.8795 18645\n"," 1 0.8946 0.8454 0.8693 18147\n","\n"," accuracy 0.8746 36792\n"," macro avg 0.8759 0.8742 0.8744 36792\n","weighted avg 0.8756 0.8746 0.8745 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"-uHARtrfYAse"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"MA0bSAHwsT-0","executionInfo":{"status":"ok","timestamp":1777536330988,"user_tz":-420,"elapsed":8383,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","import joblib\n","import os\n","\n","# 1. LOAD DATA\n","X_full = user_price_features\n","y_full = user_price_label\n","\n","# --- SUBSAMPLING (Wajib untuk dataset 600k) ---\n","sample_limit = 100000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Menggunakan {len(X)} data untuk Decision Tree.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","dt = DecisionTreeClassifier(random_state=42)\n","param_grid = {\n"," 'max_depth': [10, 20, 30, None],\n"," 'min_samples_split': [5, 10],\n"," 'min_samples_leaf': [2, 4]\n","}\n","\n","grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Decision Tree (Price)\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Standard Deviation\": [np.std(fold_scores)]\n","})\n","\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (DECISION TREE - PRICE) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","best_model = grid_search.best_estimator_\n","y_pred_train = best_model.predict(X_train)\n","y_pred_test = best_model.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# 8. MENYIMPAN MODEL & SCALER\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","joblib.dump(best_model, os.path.join(dir_path, 'best_dt_price_model.pkl'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Scaler berhasil disimpan di {dir_path}\")\n","\n","# 9. CLASSIFICATION REPORT\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"id":"PyQaMKU0LLqx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1777536348504,"user_tz":-420,"elapsed":17529,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"e8c17a01-5cee-41c6-9463-36a3918866ac"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Menggunakan 100000 data untuk Decision Tree.\n","Fitting 5 folds for each of 16 candidates, totalling 80 fits\n","\n","--- TABEL STABILITAS MODEL (DECISION TREE - PRICE) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","Decision Tree (Price) {'max_depth': 30, 'min_samples_leaf': 2, 'min_samples_split': 5} 0.9976 0.9970 0.9972 0.9979 0.9966 0.9973 0.0005\n","\n","--- ANALISIS GENERALIZATION GAP ---\n","Training Accuracy : 0.9994\n","Testing Accuracy : 0.9976\n","Generalization Gap : 0.0019\n","\n","Model dan Scaler berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_price/\n","\n","Classification Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.9963 0.9978 0.9971 8352\n"," 1 0.9985 0.9973 0.9979 11648\n","\n"," accuracy 0.9976 20000\n"," macro avg 0.9974 0.9976 0.9975 20000\n","weighted avg 0.9976 0.9976 0.9976 20000\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}}]} \ No newline at end of file diff --git a/Source Code/Overfitting Test/IMPACT-P KNN.ipynb b/Source Code/Overfitting Test/IMPACT-P KNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..406cfcf24a95cb58f19014d35f934f2774fb2f0e --- /dev/null +++ b/Source Code/Overfitting Test/IMPACT-P KNN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777536662131,"user_tz":-420,"elapsed":18434,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"5bb99207-55ea-4e7e-c535-1069ba5fe44a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DBRXr-Gf2tzx","executionInfo":{"status":"ok","timestamp":1777536690657,"user_tz":-420,"elapsed":28523,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"dc00b77d-6253-444f-f1c4-1174fa1e016d"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777536690664,"user_tz":-420,"elapsed":3,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777536702501,"user_tz":-420,"elapsed":11828,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","import joblib\n","import os\n","\n","# 1. LOAD DATA\n","# Tambahkan ini setelah loading data untuk membatasi jumlah sampel\n","sample_size = 10000\n","X = user_tags_and_specs_features[:sample_size]\n","y = user_tags_and_specs_label[:sample_size]\n","\n","print(f\"Data dipangkas menjadi {sample_size} baris untuk keperluan evaluasi KNN.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","# Optimasi: Menambahkan algorithm='ball_tree' agar lebih cepat dari brute-force pada dataset besar\n","knn = KNeighborsClassifier(algorithm='ball_tree', n_jobs=-1)\n","\n","param_grid = {\n"," 'n_neighbors': [3, 5, 7, 9],\n"," 'weights': ['uniform', 'distance']\n","}\n","\n","grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","# Mengambil score dari masing-masing fold untuk model terbaik\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"KNN (Tags/Specs)\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Standard Deviation\": [np.std(fold_scores)]\n","})\n","\n","# Menampilkan tabel\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (KNN) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","best_model = grid_search.best_estimator_\n","y_pred_train = best_model.predict(X_train)\n","y_pred_test = best_model.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# 8. MENYIMPAN MODEL & SCALER\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","joblib.dump(best_model, os.path.join(dir_path, 'best_knn_model.pkl'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Scaler berhasil disimpan di {dir_path}\")\n","\n","# 9. CLASSIFICATION REPORT\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777536851094,"user_tz":-420,"elapsed":148581,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"9ff55688-e9cc-4f47-e32f-fa36bf8ba115"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Data dipangkas menjadi 10000 baris untuk keperluan evaluasi KNN.\n","Fitting 5 folds for each of 8 candidates, totalling 40 fits\n","\n","--- TABEL STABILITAS MODEL (KNN) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","KNN (Tags/Specs) {'n_neighbors': 7, 'weights': 'distance'} 0.8200 0.8569 0.8531 0.8638 0.8512 0.8490 0.0151\n","\n","--- ANALISIS GENERALIZATION GAP ---\n","Training Accuracy : 0.8815\n","Testing Accuracy : 0.8550\n","Generalization Gap : 0.0265\n","\n","Model dan Scaler berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_model/\n","\n","Classification Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.8508 0.8542 0.8525 981\n"," 1 0.8591 0.8557 0.8574 1019\n","\n"," accuracy 0.8550 2000\n"," macro avg 0.8549 0.8550 0.8550 2000\n","weighted avg 0.8550 0.8550 0.8550 2000\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777536861362,"user_tz":-420,"elapsed":10263,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","import joblib\n","import os\n","\n","# 1. LOAD DATA\n","X_full = user_price_features\n","y_full = user_price_label\n","\n","# --- SUBSAMPLING UNTUK MENCEGAH HANG ---\n","# Kita ambil 50.000 sampel agar GridSearchCV berjalan cepat\n","sample_limit = 50000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Menggunakan {len(X)} data untuk KNN (Price Features).\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","# Menggunakan ball_tree agar pencarian tetangga lebih cepat\n","knn = KNeighborsClassifier(algorithm='ball_tree', n_jobs=-1)\n","\n","param_grid = {\n"," 'n_neighbors': [3, 5, 7, 9],\n"," 'weights': ['uniform', 'distance']\n","}\n","\n","grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"KNN (Price Features)\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Standard Deviation\": [np.std(fold_scores)]\n","})\n","\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (KNN - PRICE) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","best_model = grid_search.best_estimator_\n","y_pred_train = best_model.predict(X_train)\n","y_pred_test = best_model.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# 8. MENYIMPAN MODEL & SCALER\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","joblib.dump(best_model, os.path.join(dir_path, 'best_knn_price_model.pkl'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Scaler berhasil disimpan di {dir_path}\")\n","\n","# 9. CLASSIFICATION REPORT\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777536955892,"user_tz":-420,"elapsed":94526,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"12256928-9558-4435-ef72-2f799bf70073"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Menggunakan 50000 data untuk KNN (Price Features).\n","Fitting 5 folds for each of 8 candidates, totalling 40 fits\n","\n","--- TABEL STABILITAS MODEL (KNN - PRICE) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","KNN (Price Features) {'n_neighbors': 5, 'weights': 'distance'} 0.9784 0.9782 0.9772 0.9739 0.9742 0.9764 0.0020\n","\n","--- ANALISIS GENERALIZATION GAP ---\n","Training Accuracy : 1.0000\n","Testing Accuracy : 0.9781\n","Generalization Gap : 0.0219\n","\n","Model dan Scaler berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_price/\n","\n","Classification Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.9648 0.9873 0.9759 4498\n"," 1 0.9894 0.9706 0.9799 5502\n","\n"," accuracy 0.9781 10000\n"," macro avg 0.9771 0.9789 0.9779 10000\n","weighted avg 0.9784 0.9781 0.9781 10000\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model best_logreg_classifier\n","import joblib\n","\n","# Assuming 'best_logreg_classifier' is your trained model\n","# joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/knn_tags_specifications.pkl')\n","joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/knn_price_patterns.pkl')\n","print(\"Model saved to best_logreg_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1734869567483,"user_tz":-420,"elapsed":11523,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b5e5fa97-9fbe-4063-f4c7-43d12fd24f8b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_logreg_classifier.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Overfitting Test/IMPACT-P Logistic Regression.ipynb b/Source Code/Overfitting Test/IMPACT-P Logistic Regression.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1ede8a64de69a6de208143a376d0076f38e1c703 --- /dev/null +++ b/Source Code/Overfitting Test/IMPACT-P Logistic Regression.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1RfBBMApu-iF","executionInfo":{"status":"ok","timestamp":1777529855810,"user_tz":-420,"elapsed":19805,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"c59c29a7-5b79-490e-9f45-f74f74e888d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777529867444,"user_tz":-420,"elapsed":11630,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"427b82d3-b1bc-4554-9159-c2405fa3df5f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma logistic regression dan gunakan cross validation dan hyperparameter tuning\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Prepare the data for logistic regression\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1],\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear', 'newton-cg']\n","}\n","\n","logreg = LogisticRegression(max_iter=1000, random_state=42)\n","grid_search = GridSearchCV(logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","# Mengambil score dari masing-masing fold untuk model terbaik\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Logistic Regression\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Standard Deviation\": [np.std(fold_scores)]\n","})\n","\n","# Menampilkan tabel\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (LOGISTIC REGRESSION) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","y_pred_train = grid_search.best_estimator_.predict(X_train)\n","y_pred_test = grid_search.best_estimator_.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# 8. MENYIMPAN MODEL TERBAIK & SCALER\n","import joblib\n","import os\n","\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","joblib.dump(grid_search.best_estimator_, os.path.join(dir_path, 'best_logreg_classifier.pkl'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Scaler berhasil disimpan di {dir_path}\")\n","\n","# Predict on the test set and evaluate\n","accuracy = accuracy_score(y_test, y_pred_test)\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777530086104,"user_tz":-420,"elapsed":208489,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"8fbbb403-9646-4ba9-daed-44eb63817063"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 9 candidates, totalling 45 fits\n","\n","--- TABEL STABILITAS MODEL (LOGISTIC REGRESSION) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","Logistic Regression {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'} 0.8648 0.8622 0.8660 0.8635 0.8630 0.8639 0.0013\n","\n","--- ANALISIS GENERALIZATION GAP ---\n","Training Accuracy : 0.8644\n","Testing Accuracy : 0.8667\n","Generalization Gap : 0.0023\n","\n","Model dan Scaler berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_model/\n"," precision recall f1-score support\n","\n"," 0 0.8468 0.8998 0.8725 18645\n"," 1 0.8900 0.8328 0.8604 18147\n","\n"," accuracy 0.8667 36792\n"," macro avg 0.8684 0.8663 0.8665 36792\n","weighted avg 0.8681 0.8667 0.8665 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dari new_bundle_recommendations terapkan algoritma logistic regression dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score\n","import joblib\n","import os\n","\n","X = user_price_features\n","y = user_price_label\n","\n","# Prepare the data for logistic regression\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1],\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear', 'newton-cg']\n","}\n","\n","logreg = LogisticRegression(max_iter=1000, random_state=42)\n","grid_search = GridSearchCV(logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","# Mengambil score dari masing-masing fold untuk model terbaik\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Logistic Regression (Price)\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Standard Deviation\": [np.std(fold_scores)]\n","})\n","\n","# Menampilkan tabel\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (LOGREG PRICE) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","y_pred_train = grid_search.best_estimator_.predict(X_train)\n","y_pred_test = grid_search.best_estimator_.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP (PRICE) ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# 8. MENYIMPAN MODEL TERBAIK & SCALER\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","joblib.dump(grid_search.best_estimator_, os.path.join(dir_path, 'best_logreg_price.pkl'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Scaler berhasil disimpan di {dir_path}\")\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777530642348,"user_tz":-420,"elapsed":546108,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9487a6b8-7c09-4bdf-de49-737b6404b7fb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 9 candidates, totalling 45 fits\n","\n","--- TABEL STABILITAS MODEL (LOGREG PRICE) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","Logistic Regression (Price) {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'} 0.8993 0.8993 0.8992 0.8994 0.9000 0.8994 0.0003\n","\n","--- ANALISIS GENERALIZATION GAP (PRICE) ---\n","Training Accuracy : 0.9002\n","Testing Accuracy : 0.9003\n","Generalization Gap : 0.0001\n","\n","Model dan Scaler berhasil disimpan di /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_price/\n"," precision recall f1-score support\n","\n"," 0 0.9087 0.8883 0.8984 602313\n"," 1 0.8925 0.9122 0.9023 612435\n","\n"," accuracy 0.9003 1214748\n"," macro avg 0.9006 0.9002 0.9003 1214748\n","weighted avg 0.9005 0.9003 0.9003 1214748\n","\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Overfitting Test/IMPACT-P Naive Bayes.ipynb b/Source Code/Overfitting Test/IMPACT-P Naive Bayes.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5d787026da1e606481230b075b680e315cf31405 --- /dev/null +++ b/Source Code/Overfitting Test/IMPACT-P Naive Bayes.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777529457874,"user_tz":-420,"elapsed":21788,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"bd4c2b45-8e43-4618-a8c4-de8dd3c866ea"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W2dzyUI5m-pc","executionInfo":{"status":"ok","timestamp":1777529478070,"user_tz":-420,"elapsed":20182,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"63bc2eea-d5b6-4e3a-84a3-70f6c9895d70"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777529478075,"user_tz":-420,"elapsed":3,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"NRnsh1W3oa_Q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777529493046,"user_tz":-420,"elapsed":14971,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dengan menggunakan user_tags_and_specs_features sebagai X dan user_tags_and_specs_label sebagai y untuk kasus binary classification terapkan algoritma naive bayes yang ringan dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA (Sesuai permintaan Anda)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","param_grid = {\n"," 'alpha': [0.1, 0.5, 1.0, 2.0, 5.0],\n"," 'binarize': [0.0, 0.5, 1.0]\n","}\n","\n","bnb = BernoulliNB()\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","# Mengambil indeks dari model terbaik\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","# Mengambil score dari masing-masing fold untuk parameter terbaik\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# Menghitung Mean dan Std Dev\n","mean_acc = np.mean(fold_scores)\n","std_acc = np.std(fold_scores)\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Bernoulli Naive Bayes\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [mean_acc],\n"," \"Standard Deviation\": [std_acc]\n","})\n","\n","# Menampilkan tabel dengan format 4 angka di belakang koma\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","y_pred_train = grid_search.best_estimator_.predict(X_train)\n","y_pred_test = grid_search.best_estimator_.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","print(\"\\nClassification Report (Test Set):\")\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777529578297,"user_tz":-420,"elapsed":85246,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"d89a9629-b28b-47b3-efda-18d360161ec2"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","\n","--- TABEL STABILITAS MODEL ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","Bernoulli Naive Bayes {'alpha': 1.0, 'binarize': 0.0} 0.8201 0.8194 0.8250 0.8192 0.8214 0.8210 0.0021\n","\n","--- ANALISIS GENERALIZATION GAP ---\n","Training Accuracy : 0.8206\n","Testing Accuracy : 0.8246\n","Generalization Gap : 0.0040\n","\n","Classification Report (Test Set):\n"," precision recall f1-score support\n","\n"," 0 0.8097 0.8549 0.8317 18645\n"," 1 0.8418 0.7935 0.8170 18147\n","\n"," accuracy 0.8246 36792\n"," macro avg 0.8257 0.8242 0.8243 36792\n","weighted avg 0.8255 0.8246 0.8244 36792\n","\n"]}]},{"cell_type":"code","source":["import joblib\n","import os\n","\n","# Ambil model terbaik\n","best_bnb_tags = grid_search.best_estimator_\n","\n","# Pastikan direktori tujuan ada\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/retrain_tags_specifications/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan model\n","joblib.dump(best_bnb_tags, os.path.join(dir_path, 'nb_tags_specifications.pkl'))\n","print(\"Model Tags & Specifications berhasil disimpan!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zlSBDwGdsfU6","executionInfo":{"status":"ok","timestamp":1777339663796,"user_tz":-420,"elapsed":67,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"d0b57519-8107-4c0e-8314-70fd13f30106"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Tags & Specifications berhasil disimpan!\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777529589239,"user_tz":-420,"elapsed":10916,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Ok sekarang dengan menggunakan user_tags_and_specs_features sebagai X dan user_tags_and_specs_label sebagai y untuk kasus binary classification terapkan algoritma naive bayes yang ringan dan gunakan cross validation dan hyperparameter tuning\n","\n","import pandas as pd\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Load data (assuming it's already loaded as numpy arrays)\n","X = user_price_features\n","y = user_price_label\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SPLIT DATA\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 4. HYPERPARAMETER TUNING & CROSS-VALIDATION\n","param_grid = {\n"," 'alpha': [0.1, 0.5, 1.0, 2.0, 5.0],\n"," 'binarize': [0.0, 0.5, 1.0]\n","}\n","\n","bnb = BernoulliNB()\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# 5. EKSTRAKSI HASIL PER FOLD\n","best_idx = grid_search.best_index_\n","results = grid_search.cv_results_\n","\n","# Mengambil score dari masing-masing fold untuk parameter terbaik\n","fold_scores = [results[f'split{i}_test_score'][best_idx] for i in range(5)]\n","\n","# Menghitung Mean dan Std Dev\n","mean_acc = np.mean(fold_scores)\n","std_acc = np.std(fold_scores)\n","\n","# 6. MEMBUAT TABEL RINGKASAN\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Bernoulli Naive Bayes (Price)\"],\n"," \"Best Hyperparameter\": [str(grid_search.best_params_)],\n"," \"Fold 1\": [fold_scores[0]],\n"," \"Fold 2\": [fold_scores[1]],\n"," \"Fold 3\": [fold_scores[2]],\n"," \"Fold 4\": [fold_scores[3]],\n"," \"Fold 5\": [fold_scores[4]],\n"," \"Mean Accuracy\": [mean_acc],\n"," \"Standard Deviation\": [std_acc]\n","})\n","\n","# Menampilkan tabel\n","pd.options.display.float_format = '{:.4f}'.format\n","print(\"\\n--- TABEL STABILITAS MODEL (PRICE FEATURES) ---\")\n","print(summary_table.to_string(index=False))\n","\n","# 7. ANALISIS GENERALIZATION GAP\n","y_pred_train = grid_search.best_estimator_.predict(X_train)\n","y_pred_test = grid_search.best_estimator_.predict(X_test)\n","\n","train_acc = accuracy_score(y_train, y_pred_train)\n","test_acc = accuracy_score(y_test, y_pred_test)\n","\n","print(\"\\n--- ANALISIS GENERALIZATION GAP (PRICE FEATURES) ---\")\n","print(f\"Training Accuracy : {train_acc:.4f}\")\n","print(f\"Testing Accuracy : {test_acc:.4f}\")\n","print(f\"Generalization Gap : {abs(train_acc - test_acc):.4f}\")\n","\n","# Print classification report and accuracy\n","print(classification_report(y_test, y_pred_test, digits=4))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777529682322,"user_tz":-420,"elapsed":93076,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6032807a-2b6f-4c15-ce96-a5fbad6e79be"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","\n","--- TABEL STABILITAS MODEL (PRICE FEATURES) ---\n"," Model Best Hyperparameter Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Accuracy Standard Deviation\n","Bernoulli Naive Bayes (Price) {'alpha': 0.1, 'binarize': 0.0} 0.6753 0.6752 0.6751 0.6760 0.6748 0.6753 0.0004\n","\n","--- ANALISIS GENERALIZATION GAP (PRICE FEATURES) ---\n","Training Accuracy : 0.6753\n","Testing Accuracy : 0.6757\n","Generalization Gap : 0.0004\n"," precision recall f1-score support\n","\n"," 0 0.7027 0.5996 0.6471 602313\n"," 1 0.6559 0.7505 0.7000 612435\n","\n"," accuracy 0.6757 1214748\n"," macro avg 0.6793 0.6750 0.6735 1214748\n","weighted avg 0.6791 0.6757 0.6737 1214748\n","\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["import joblib\n","import os\n","\n","# Ambil model terbaik\n","best_bnb_price = grid_search.best_estimator_\n","\n","# Pastikan direktori tujuan ada\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/retrain_price_pattern/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan model\n","joblib.dump(best_bnb_price, os.path.join(dir_path, 'nb_price_patterns.pkl'))\n","print(\"Model Price Patterns berhasil disimpan!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1777339771773,"user_tz":-420,"elapsed":83,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"192b8e83-2e8d-4bc2-9b1c-fff03e9cd85a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Price Patterns berhasil disimpan!\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Parameter Sensitivity/IMPACT-P ANN.ipynb b/Source Code/Parameter Sensitivity/IMPACT-P ANN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1c5976746fb29a18721e0c1b96fb93c76d0c63a1 --- /dev/null +++ b/Source Code/Parameter Sensitivity/IMPACT-P ANN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["_T8fPo2P6CFY"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777438447776,"user_tz":-420,"elapsed":21049,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"3600c744-9395-447b-c77f-997aa5a1fb16"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cbi9HIU2YNKA","executionInfo":{"status":"ok","timestamp":1777438482095,"user_tz":-420,"elapsed":34304,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"3bbd6a06-acd2-486c-c2a9-1ac1662f54d7"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777438482096,"user_tz":-420,"elapsed":9,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"eFg-8moUb52J"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"Egw06Nzgb8g1","executionInfo":{"status":"ok","timestamp":1777438493980,"user_tz":-420,"elapsed":11889,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# prompt: Distribution on len user_tags_and_specs_features\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming user_tags_and_specs_features is already loaded as a NumPy array\n","\n","lengths = [len(x) for x in user_tags_and_specs_features]\n","\n","plt.hist(lengths, bins=50) # Adjust the number of bins as needed\n","plt.xlabel('Length of user_tags_and_specs_features')\n","plt.ylabel('Frequency')\n","plt.title('Distribution of Lengths')\n","plt.show()\n","print(f\"The average length is {np.mean(lengths)}\")\n","print(f\"The median length is {np.median(lengths)}\")\n","print(f\"The standard deviation of length is {np.std(lengths)}\")\n","print(f\"The maximum length is {np.max(lengths)}\")\n","print(f\"The minimum length is {np.min(lengths)}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":559},"id":"yJpD_1HALjO_","executionInfo":{"status":"ok","timestamp":1735287312801,"user_tz":-420,"elapsed":1396,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"013d14e0-23b4-45d5-e0de-da2a8b0a36c9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["The average length is 225.0\n","The median length is 225.0\n","The standard deviation of length is 0.0\n","The maximum length is 225\n","The minimum length is 225\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import matplotlib.pyplot as plt\n","import os\n","from sklearn.model_selection import KFold\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score\n","\n","# 1. LOAD DATA\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 2. SETUP\n","kf = KFold(n_splits=5, shuffle=True, random_state=42)\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# 3. TRAINING LOOP\n","fold_accuracies = []\n","best_history = None\n","\n","for fold, (train_idx, val_idx) in enumerate(kf.split(X, y)):\n"," X_train, X_val = X[train_idx], X[val_idx]\n"," y_train_fold, y_val_fold = y[train_idx], y[val_idx]\n","\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n"," # Pastikan metrics=['accuracy']\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n"," # Train 20 epochs\n"," history = model.fit(X_train, y_train_fold, epochs=20, batch_size=128,\n"," validation_data=(X_val, y_val_fold), verbose=0)\n","\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\")\n"," fold_accuracies.append(accuracy_score(y_val_fold, y_pred))\n","\n"," # Simpan history dari fold terakhir untuk plotting\n"," if fold == 4: best_history = history\n","\n","# 4. SAVE CONVERGENCE DATA (Accuracy Focus)\n","history_df = pd.DataFrame(best_history.history)\n","history_df.to_csv(os.path.join(dir_path, 'ann_tags_accuracy_convergence.csv'), index_label='epoch')\n","\n","# 5. HD PLOTTING (Accuracy vs Epochs)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(history_df['accuracy'], label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(history_df['val_accuracy'], label='Validation Accuracy', marker='s', linewidth=2)\n","plt.title('Accuracy Convergence: ANN (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Epochs', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'ann_tags_accuracy_convergence.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. FINAL STABILITY OUTPUT\n","print(\"\\n--- STABILITY SUMMARY (ANN) ---\")\n","print(f\"Mean Accuracy: {np.mean(fold_accuracies):.4f}\")\n","print(f\"Std Dev: {np.std(fold_accuracies):.4f}\")\n","\n","# 7. SAVE MODEL\n","model.save(os.path.join(dir_path, 'best_ann_tags_model.keras'))\n","print(f\"\\nModel dan Data Convergence (Accuracy) berhasil disimpan.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"qpcxK_8thvGV","executionInfo":{"status":"ok","timestamp":1777441488251,"user_tz":-420,"elapsed":343475,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"42e1d460-454e-4147-b442-3e95ecba7dcf"},"execution_count":7,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY (ANN) ---\n","Mean Accuracy: 0.8733\n","Std Dev: 0.0021\n","\n","Model dan Data Convergence (Accuracy) berhasil disimpan.\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/neural_networks_tags_specifications.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_tags_and_specs_features, user_tags_and_specs_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cdFS0AWac8Q4","executionInfo":{"status":"ok","timestamp":1734855843779,"user_tz":-420,"elapsed":6440,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"db40b6ea-e2b3-4c19-9d57-d9936ea19682"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1150/1150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step\n"," precision recall f1-score support\n","\n"," 0 0.88 0.44 0.59 18645\n"," 1 0.62 0.94 0.75 18147\n","\n"," accuracy 0.69 36792\n"," macro avg 0.75 0.69 0.67 36792\n","weighted avg 0.75 0.69 0.67 36792\n","\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"FZMag36kctY2"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"nY0oy9x2cy8P","executionInfo":{"status":"ok","timestamp":1777442077868,"user_tz":-420,"elapsed":12067,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import matplotlib.pyplot as plt\n","import os\n","from sklearn.model_selection import KFold\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score\n","import joblib\n","\n","# 1. LOAD & SUBSAMPLE\n","X_full = user_price_features\n","y_full = user_price_label\n","sample_limit = 50000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Using {len(X)} samples for ANN (Price) evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","# 3. SETUP\n","kf = KFold(n_splits=5, shuffle=True, random_state=42)\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/ann_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# 4. TRAINING LOOP\n","fold_accuracies = []\n","best_history = None\n","\n","for fold, (train_idx, val_idx) in enumerate(kf.split(X, y)):\n"," X_train, X_val = X[train_idx], X[val_idx]\n"," y_train_fold, y_val_fold = y[train_idx], y[val_idx]\n","\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(X.shape[1],)),\n"," tf.keras.layers.Dense(256, activation=\"relu\"),\n"," tf.keras.layers.Dense(128, activation=\"relu\"),\n"," tf.keras.layers.Dense(1, activation=\"sigmoid\")\n"," ])\n"," model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n"," # Train 20 epochs\n"," history = model.fit(X_train, y_train_fold, epochs=20, batch_size=128,\n"," validation_data=(X_val, y_val_fold), verbose=0)\n","\n"," y_pred = (model.predict(X_val) > 0.5).astype(\"int32\")\n"," fold_accuracies.append(accuracy_score(y_val_fold, y_pred))\n","\n"," # Simpan history dari fold terakhir untuk plotting\n"," if fold == 4: best_history = history\n","\n","# 5. SAVE CONVERGENCE DATA (Accuracy Focus)\n","history_df = pd.DataFrame(best_history.history)\n","history_df.to_csv(os.path.join(dir_path, 'ann_price_accuracy_convergence.csv'), index_label='epoch')\n","\n","# 6. HD PLOTTING (Accuracy)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(history_df['accuracy'], label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(history_df['val_accuracy'], label='Validation Accuracy', marker='s', linewidth=2)\n","plt.title('Convergence Analysis: ANN Accuracy (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Epochs', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'ann_price_accuracy_convergence.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","print(\"\\n--- STABILITY SUMMARY (ANN PRICE) ---\")\n","print(f\"Mean Accuracy: {np.mean(fold_accuracies):.4f}\")\n","print(f\"Std Dev: {np.std(fold_accuracies):.4f}\")\n","\n","# 8. SAVE MODEL & SCALER\n","model.save(os.path.join(dir_path, 'ann_price_model.keras'))\n","joblib.dump(scaler, os.path.join(dir_path, 'scaler.pkl'))\n","print(f\"\\nModel dan Data Convergence (Accuracy) berhasil disimpan.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"twU-lTQPcvo8","executionInfo":{"status":"ok","timestamp":1777442453882,"user_tz":-420,"elapsed":280252,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"44374f1e-bc14-4c9c-efe9-c7575b9b4572"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 50000 samples for ANN (Price) evaluation.\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY (ANN PRICE) ---\n","Mean Accuracy: 0.9949\n","Std Dev: 0.0034\n","\n","Model dan Data Convergence (Accuracy) berhasil disimpan.\n"]}]},{"cell_type":"code","source":["\n","# Example prediction (replace with your actual data)\n","example_features = user_price_features[0] # Example datapoint\n","prediction = (model.predict(np.array([example_features])) > 0.5).astype(\"int32\")\n","print(f\"Prediction for the example datapoint: {prediction}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FXgmBUNTWiOJ","executionInfo":{"status":"ok","timestamp":1734887544151,"user_tz":-420,"elapsed":379,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"28a5c81c-e433-4a69-ef99-509a40fc8fe6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n","Prediction for the example datapoint: [[0]]\n"]}]},{"cell_type":"code","source":["import joblib\n","import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# Load the saved model\n","with open('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl', 'rb') as file:\n"," loaded_model = joblib.load(file)\n","\n","# Split data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(user_price_features, user_price_label, test_size=0.2, random_state=42)\n","\n","# Now you can use the loaded_model to make predictions\n","# For example:\n","y_pred = loaded_model.predict(X_test)\n","y_pred = (y_pred > 0.5).astype(int) # Convert probabilities to binary predictions (0 or 1)\n","y_pred = y_pred.flatten() # Flatten y_pred to a 1D array\n","\n","print(classification_report(y_test, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FMRD6RqjGNbK","executionInfo":{"status":"ok","timestamp":1735051114521,"user_tz":-420,"elapsed":91190,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"386467ef-11cc-48e9-dee6-d7ee94ca95d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m37961/37961\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2ms/step\n"," precision recall f1-score support\n","\n"," 0 1.00 1.00 1.00 602313\n"," 1 1.00 1.00 1.00 612435\n","\n"," accuracy 1.00 1214748\n"," macro avg 1.00 1.00 1.00 1214748\n","weighted avg 1.00 1.00 1.00 1214748\n","\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XfpLJxOdF79c","executionInfo":{"status":"ok","timestamp":1735050941077,"user_tz":-420,"elapsed":19421,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"804fb55e-d0c2-4955-acf1-949a0e3e6594"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: save model best_mlp_classifier\n","\n","import joblib\n","\n","# Assuming neural_networks is defined as in your provided code.\n","# Assuming 'neural_networks' is your trained model\n","joblib.dump(model, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/neural_networks_price_patterns.pkl')\n","print(\"Model saved to neural_networks.pkl\")"],"metadata":{"id":"FoYSaGaJR1wo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1734887466457,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3bf0e99e-5628-471d-cc09-85a630a08376"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to neural_networks.pkl\n"]}]},{"cell_type":"markdown","source":["### Predic Bundle"],"metadata":{"id":"unvcsZe_xUw-"}},{"cell_type":"code","source":[],"metadata":{"id":"6EVDa1hXxYv6"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Source Code/Parameter Sensitivity/IMPACT-P Decision Tree.ipynb b/Source Code/Parameter Sensitivity/IMPACT-P Decision Tree.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..46d1f1fed351dbc0c4859d9fe0c122444d1be887 --- /dev/null +++ b/Source Code/Parameter Sensitivity/IMPACT-P Decision Tree.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777433062145,"user_tz":-420,"elapsed":15705,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"b6189af7-ec79-405e-b827-d0c31a7dede3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nWPptAf-L46u","executionInfo":{"status":"ok","timestamp":1777433238530,"user_tz":-420,"elapsed":8900,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"a2533dfd-6717-4664-c478-5cbf4371bb31"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777433241604,"user_tz":-420,"elapsed":8,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"iRZwe19LYEPm"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"u4jFU5HCYDmw","executionInfo":{"status":"ok","timestamp":1777433250542,"user_tz":-420,"elapsed":6428,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. SETUP\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","dt = DecisionTreeClassifier(random_state=42)\n","param_grid = {\n"," 'max_depth': [10, 20, 30, None],\n"," 'min_samples_split': [5, 10],\n"," 'min_samples_leaf': [2, 4]\n","}\n","grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'dt_learning_curve.csv'), index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Decision Tree (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'dt_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Decision Tree\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":707},"id":"ZNJX8CrOYc_d","executionInfo":{"status":"ok","timestamp":1777433692483,"user_tz":-420,"elapsed":434372,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"b405ec6c-8335-40cc-f028-fdae00f877dd"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","Decision Tree 0.872029 0.001922\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"-uHARtrfYAse"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"MA0bSAHwsT-0","executionInfo":{"status":"ok","timestamp":1777433744035,"user_tz":-420,"elapsed":9120,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. LOAD & SUBSAMPLE\n","X_full = user_price_features\n","y_full = user_price_label\n","sample_limit = 100000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Using {len(X)} samples for Decision Tree (Price) evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 3. HYPERPARAMETER TUNING\n","dt = DecisionTreeClassifier(random_state=42)\n","param_grid = {\n"," 'max_depth': [10, 20, 30, None],\n"," 'min_samples_split': [5, 10],\n"," 'min_samples_leaf': [2, 4]\n","}\n","grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 4. LEARNING CURVE ANALYSIS (Convergence Proof)\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 5. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/dt_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'dt_price_learning_curve.csv'), index=False)\n","\n","# 6. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Decision Tree (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'dt_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Decision Tree (Price)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"id":"PyQaMKU0LLqx","colab":{"base_uri":"https://localhost:8080/","height":736},"executionInfo":{"status":"ok","timestamp":1777433810143,"user_tz":-420,"elapsed":20861,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"fce3b2fc-a8eb-4aa6-be35-54ee2bce1363"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 100000 samples for Decision Tree (Price) evaluation.\n","Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","Decision Tree (Price) 0.997263 0.00048\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}}]} \ No newline at end of file diff --git a/Source Code/Parameter Sensitivity/IMPACT-P KNN.ipynb b/Source Code/Parameter Sensitivity/IMPACT-P KNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4fcc2fceccb579b27aa66d612c95f095de1b9d53 --- /dev/null +++ b/Source Code/Parameter Sensitivity/IMPACT-P KNN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777429297139,"user_tz":-420,"elapsed":19569,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"84b2d86f-e387-4f8f-e5b8-5fd1b55c1774"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DBRXr-Gf2tzx","executionInfo":{"status":"ok","timestamp":1777429332294,"user_tz":-420,"elapsed":35150,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"e18c6517-ed06-4b40-ffed-54d0167ca436"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777429332296,"user_tz":-420,"elapsed":8,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777429345790,"user_tz":-420,"elapsed":13492,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. LOAD & SAMPLE DATA\n","sample_size = 10000\n","X_full = user_tags_and_specs_features\n","y_full = user_tags_and_specs_label\n","\n","X = X_full[:sample_size]\n","y = y_full[:sample_size]\n","print(f\"Using {len(X)} samples for KNN evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 3. HYPERPARAMETER TUNING\n","knn = KNeighborsClassifier(algorithm='ball_tree', n_jobs=-1)\n","param_grid = {'n_neighbors': [3, 5, 7, 9], 'weights': ['uniform', 'distance']}\n","grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 4. LEARNING CURVE ANALYSIS (Convergence/Saturation Proof)\n","# Using 'best_model' ensures the analysis reflects your chosen configuration\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 5. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'knn_learning_curve.csv'), index=False)\n","\n","# 6. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: KNN (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'knn_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"KNN (Tags/Specs)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":528},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777430578101,"user_tz":-420,"elapsed":692287,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"6dfeeb73-85df-4d2f-e3be-c625e2891e2e"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 10000 samples for KNN evaluation.\n","Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","KNN (Tags/Specs) 0.849 0.015117\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777432001494,"user_tz":-420,"elapsed":10961,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import accuracy_score, classification_report\n","\n","# 1. LOAD & SUBSAMPLE\n","X_full = user_price_features\n","y_full = user_price_label\n","sample_limit = 50000\n","X = X_full[:sample_limit]\n","y = y_full[:sample_limit]\n","print(f\"Using {len(X)} samples for KNN (Price) evaluation.\")\n","\n","# 2. PREPROCESSING\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 3. HYPERPARAMETER TUNING\n","knn = KNeighborsClassifier(algorithm='ball_tree', n_jobs=-1)\n","param_grid = {'n_neighbors': [3, 5, 7, 9], 'weights': ['uniform', 'distance']}\n","grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 4. LEARNING CURVE ANALYSIS\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 5. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/knn_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'knn_price_learning_curve.csv'), index=False)\n","\n","# 6. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: KNN (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'knn_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 7. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"KNN (Price)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777432847818,"user_tz":-420,"elapsed":391657,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/","height":532},"outputId":"064fdb1e-ab04-4efc-c386-ff0d969b1689"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Using 50000 samples for KNN (Price) evaluation.\n","Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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2+yb3PTV5J6fd23uwpeSS09Nxh6VHHFeMmTLqoT4ZshLQJHISUDRyEtAkchJQNHIS0DRyEt0pK5UKpVqHQQUyfTp0zN+/Pi28bRp0zJu3LgaRgRQEIvmJOfuUOso3pwBw97o3rfZzrWOBqD7PXNXctUnklce6/rc/kOTQ75WzqF1dZWPDQAAAAAAoMBqXVNU321PAgCole32S47/efKZR5JDv6nID9hwbbNP8pHbk3d9NqnvYoP3JfOTqz+VTDo6eeWJ6sQHAAAAAADAGin0AwB6p4HDk30/lXzi3uS0q5Pd3p/07V/rqABqr6ExOehLyT/+KRm5V9fnP31bcv7E5Pb/TJa3VD4+AAAAAAAAVtPFFg4AAAW3/YHJ3qclY45M+vardTQAxbXF+OTDNyV3n5/c8o1kWVPn5y5rTm76ajLtd8kxP0xG7lGtKAEAAAAAAIhCP4DCa21tTXNzc9u4sbEx9fUaskI7gzZP9jw52euUZNj2tY6mV5OToJep75NM/Hgy9sjkmk8nT97atfkvTk1++u7yGvuflfQbWI0o10leAopETgKKRl4CikROAopGXgKKRl6iIwr9AAquubk5U6dObRtPmDAhAwd2/w/RoXjqkh3enex9ejLm8KRPQ60D2iDISdBLDdsuOeWK5IFLkuu/kDTP6/zc0vLkju8nD12VHPODZLv9qhXlGslLQJHISUDRyEtAkchJQNHIS0DRyEt0RKEfANCzDN6i3Llvz1OSTbapdTQAvUddXbLHh5IdD06uOzOZ/ruuzX/1qWTS0clepybv+XoyYOOqhAkAAAAAALAhUugHAPQAdclO7yl379vp0KSPT2EAqmbw5snfXZjs9nfJ5M8kC57v2vx7L04e/UNyxHnJrsdUJ0YAAAAAAIANjJ+SAwDFts8nk7f/Y7Lx1rWOBGDDMvaIZNt9k5vOSe75edfmLnwpufSUZJejk8PPTYZsWZ0YAQAAAAAANhD1tQ4AAGCd3vkpRX4AtdI4NDnqe8nfX5dsumPX5z98dfKjtydTLkpKpYqHBwAAAAAAsKFQ6AcAAMC6bTMx+cgdybs+m9R3sTH8kvnJ1Z9KJh2dvPJEdeIDAAAAAADo5RT6AQAA0LGGxuSgLyX/eGsycs+uz3/6tuT8icnt/5ksX1bx8AAAAAAAAHozhX4AAAB03ha7JWfclBzyzaTvgK7NXdac3PTV5KcHJi88UJXwAAAAAAAAeiOFfgAAAHRNn77JxI8nH7sr2f6Ars9/cWpywYHJjV9OWpoqHh4AAAAAAEBvo9APAACA9TNsu+SUK5Jjf5w0bty1uaXlyR3fLx/n+9Rt1YgOAAAAAACg11DoBwAAwPqrq0v2PCn5+N+Sce/t+vy5TyaTjkqu+kTSNK/i4QEAAAAAAPQGCv0AAAB48wZvnvzdRcmJlyQbbdn1+fdenPzo7cnDV1c8NAAAAAAAgJ6ub60DAGDdGhsbM2HChHZjgFqRk4AOjT0i2Xbf5KavJvf8omtzF76Y/N/JyS5HJ0ecl2y0RYdT5CWgSOQkoGjkJaBI5CSgaOQloGjkJTqi0A+g4Orr6zNw4MBahwGQRE4COqlxaHLUfybj359c/cnklce7Nv/hq5Mn/5wc8vVkr1PLxwOvhbwEFImcBBSNvAQUiZwEFI28BBSNvERHHN0LAABAdWy7b/KRO5J3fSap7+K/M1syv1wkOOno5JUnqhMfAAAAAABAD6HQDwAAgOppaEwO+nLyj7cmI/fs+vynb0vOn5jc/l/J8mWVjg4AAAAAAKBHUOgHAABA9W2xW3LGTckh30z6Duja3GXNyU1fSX56YPLCA9WJDwAAAAAAoMC6eHYSAN2tpaUlL730Utt4xIgRaWhoqGFEwIZMTgLelD59k4kfT8YemVzz6eTJW7s2/8WpyQUHJhM/kRxwVtIwQF4CCkVOAopGXgKKRE4CikZeAopGXqIjCv0ACq6lpSWzZs1qGw8bNsxf5tTG7IdrHQEFICcBFTFsu+SUK5L7f5P84YtJ87zOzy0tT+74r+Thq5Kjf5CWEXvLS0Bh+FwJKBp5CSgSOQkoGnkJKBp5iY4o9AMAOra8Jbnuc7WOAoDepK4u2fOkZMeDk+s+nzx0Rdfmz30ymXRU+k04KX02e1+WNwyuSpjABqy1NWma27U5TYvTd+m8N8aL5ySlgV1bY8CwpL6+a3MAAAAAgF5PoR8A0LHbvtu5jn5v/XBywBcq++wBwyq7HgDFstGI5IRJySOTk8mfSRa80KXpfaf+Orv3vy5P7frxvDpi3yoFCWyQmuYm5+7QpSkDk7xl5RduWY/nfu6JZNDw9ZgIAAAAAPRmCv0AgHV7YWry53M7vm/4zskh30gaBlQ/JgB6n7FHJtu+M7nxK8mUC7s0td+SuRlz39fyyoh9k+1/lAzcrkpBAgAAAAAA1IZzQACAtVu2NLnio0nrsnXfV1efHHe+Ij8A3pzGocnR/5Wcfm2y6Y5dnr7pS3dkwM/3T+69OCmVKh8fAAAAAABAjSj0AwDW7s/nJi9N6/i+iZ9MRr2l4/sAoDO23Tf5yB3Juz6T1HetEX3dkvnJVZ9IJh2dvPJElQIEAAAAAADoXgr9AIA1e/6+5LbvdnzfZmOTA75Q/XgA2LA0NCYHfTn5x1uTLffo+vynb0vOn5jc8f1keQedaQEAAAAAAApOoR8AsLplS5LffzQpLV/3fXV9kuN+XC7GAIBq2GK35MM3J4d8I+nbxSPilzUnN345+dm7kxemVic+AAAAAACAbqDQDwBY3a3fTl5+uOP73vkvyVZ7Vz8eADZsffomEz+RfOzOZLv9uz7/hQeSCw5Ibvpq0tJU6egAAAAAAACqTqEfANDerCnJHf/V8X2bj0v2/3zVwwGANsO2T069Mjn2R0nj0K7NLS1Pbv/P5Px9k6dvr058AAAAAAAAVaLQDwB4Q0tzcsVHk1Lruu+r71s+srdv/+6JCwBWqKtL9jw5+ee/Jbse1/X5c59ILjoyufpTSdO8SkcHAAAAAABQFX1rHQAA61ZfX58BAwa0G0PV3PqtZM6Mju9712eSkXtUPRyKR04CCmOjEckJk7J06u9Td+1n09A8p2vzp1yUzLg+OfK7yS5HVSVEAIBa8zUcUCRyElA08hJQNPISHVHoB1BwjY2N2X333WsdBhuCZ/+a3PnDju8bsVvyrs9WPx4KSU4CiqbfhPcmO787ufEryZQLuzZ54YvJ/52U7HJMcsR55eJBAIBexNdwQJHISUDRyEtA0chLdETpJwCQtDR1/sje956f9O3XPXEBQGc0Dk2O/q/k9MnJsB26Pv/hq5IfvTW59+KkVKp4eEAP0tqaLHgxefZvycNX1zoaAAAAAIA2OvoBAMkt30heebzj+/Y/M9lit+rHAwDrY9t3Jh+9M/nTd5I7vp+Ulnd+bvP85KpPJA9elhz9/WTY9tWLE6id1uXJgheSeTOTec+W/zt/5hvj+bOS5UtqHSUAAAAAwGoU+gHAhm7m3cldP+r4vi13T975L9WPBwDejIbG5OCvJOPfl1z58eSF+7s2/6k/Jz+emBz4heQd/5z08WUz9CjLW8rFevOfXal4b8WvZyavPZe0Lqt1lAAAAAAAXeYnFgCwIVu6uHxkbzo4prC+ITnuf5I+Dd0SFgC8aVvslnz45uTuHyd//FayrKnzc5c1JTd+OZn2u+SYHyZbTqhenEDXtDSXC/nmPfN6Ad+Krnyv/3fBC0mptdZRvjmTP1MuVt7h3Un/jWodDQAAAABQEAr9AAquubk5M2bMaBuPGTMmjY2NNYyIXuXmryVzn+z4vgPOSkbsWv14KDw5CSiadealPn2TfT+Z7HJUcvWnyt36uuKF+5MLDiivsf+ZScOAisUNrMWShW8U8K18pO6KYr6FL9U6wup76Iry1adfst3+ydgjkjFHJBttUevIgB7I13BAkchJQNHIS0DRyEt0RKEfQMG1tramqamp3Rgq4unbk7+c3/F9I/dM9v101cOhZ5CTgKLpVF4atn1y6lXJfb9Kbvi3pHl+5x9QWp7c/p/JQ1clx/wg2fadFYgaNmBN89bQie+ZN8ZNc2sdYXEsX5o8fmP5uuZfkq32Lhf8jT0y2WxsUldX6wiBHsDXcECRyElA0chLQNHIS3REoR8AbIiWLEyu/OeO7+vT7/Uje33KAEAPV1eX7HVKstMhyXWfSx66smvz5z6RXHRksvfpyXu+ljQOrUqY0KOVSsniuWvuxLdivKQLhba099yU8nXL15NNtnu96O+IZOt3+HwdAAAAADYAvgsIABuim76avPp0x/cd+G/J5mOrHQ0AdJ+NRiQnXJw8fE0y+TPJwhe7Nn/KRcmM65Mjv1s+Ehg2JKVSsnD268V7KxXzrVzI17Ko1lFuGF59Krn7R+VrwLBk50PLhX87vDvpP7jW0QEAAAAAVaDQDwA2NE/9OfnbTzu+b9Rbk4mfqH48AFALuxxVPob3pq8mUy7s2tyFLyb/d1Ky67HJ4eeWiwehN2hdnix4YaVOfKt25Xs2Wb6k1lGyqqa5yQOXlK8+/ZPt9y8X/Y05PNloi1pHBwAAAABUiEI/ANiQLFnQuSN7+zYmx52f1PepfkwAUCsDNk6O/q9kt/cnV32yfDxvVzx0ZfLkrckh30z2PLl8PDAU2fKW5LXn1tCJ7/XrteeS1mW1jrJ7DRyebLx1svHoZOjWycbbvDFuGJj8YI9aR9g1y5ckj91Qvq75dLLVW8rH+445MtlsjDwFAAAAAD2YQj8A2JDc+OXyD3E78u6zk+E7VT8eACiCbd+ZfPSO5E//kdzx/aS0vPNzm+cnV308efDS5OjvJ8O2r16c0JGW5mT+rDV04nt9vOD5pNRa6yi71+AtykV7ayrmGzoq6Tdo7XMXzem+OKvluXvK181fK+enMUckY49Mtn67f9QDAAAAAD2MQj8A2FA8cUtyzy86vm/rtyfv+Fj14wGAImkYkBz8lWTce8uFey880LX5T/05+fHE5MAvlv8e7ePLbapg6aKVOvE9s3ox38KXah1h96qrT4Zs9Xrx3qrFfKPLH2torHWUxTH3yeSu/y5fA4YlOx9W7va3w7vXXfAIAAAAABSCnzwAwIag+bXkyk90fF/fAcmxP9bdA4AN15YTkg/fktz94+SP30qWNXV+7rKm5MYvJdN+mxzzw/Ja0BXN89fQiW/mG+PFr9Q6wu5V35AM3er14r3RbxTztRXyjUz6NNQ6ysr76F3Js39JZlxXPh58+ZLKP6NpbvLAb8pX38Zk+wPK3f7GHJ4M3rzyzwMAAAAA3jSFfgCwIbjh35LXZnV838FfSYbvWP14AKDI+vRN9v1ksstRydWfKnfr64oX7k8uOCDZ91PJ/mfqKEZZqZQ0vbqGTnwrfj2zXOi3Ienb+HrR3krFeyuuoVsnG22xYf4DlMGbJ2/5+/K1ZGG5M/eMa5NHry//Hqq0Zc3ltR+9Prm6Lhn11nLB39gjk+E7J3V1lX8mAAAAANBlCv0AoLd7/Kbk3os7vm/0xORt/1T9eACgpxi2fXLqVcl9vyoXzXelCKu0PLn9e8nDVyVH/yDZdt/qxUkxlErJwtntj9Vt68r3+n9bFtU6yu7VMGjNR+quuAZtpoisI/0HJ7seU76WL0uevTt55NpkxuTk1aer8MBSMuuv5evmc5JhO5SP9x1zZLL12zbMwksAAAAAKAiFfgDQmzXN69yRvQ0Dk+N+lNTXVz0kAOhR6uqSvU5JdnpPcu3nyoV7XfHK48lFRyR7/33ynnOSxqHViZPqa12eLHhxzUfqzpuZzJ9V7oy2IWkc2v5I3VWL+QZsopCvkvr0TbZ9Z/k69JvJy48kj0wud/t7bkp1njn3ieTOH5avgcOTnQ8rF/5tf2DSb2B1ngkAAAAArJFCP4CCa2hoyKhRo9qNodP+8G/Jguc7vu/gc8pdi6ADchJQNN2WlzbaIvnAL5OHr04mfzZZ+GLX5k+5sHws5pHfLR+HSfEsb0lee7598d6817vzzX82mf9c0tpS6yi718BN19yJb8VxuwpXa6euLtl8l/K132eT115IHr2u3O3vqT8ly5dW/pmL5yT3/6p89W0sF/uNPSLZ+fBk8GaVfx5QEb6GA4pETgKKRl4CikZeoiN1pVKpVOsgoEimT5+e8ePHt42nTZuWcePG1TAigPX06B+S35zQ8X3bvqt8LKFufgDQOU3zkpu+kky5aP3m73JMcsAXu78wZsCwDfvv+2VLyl33Vu3Et+JY3QXPJ6XWWkfZvQZvseZOfCsK+foNqnWEtbVoTnLuDt3/3M89kQwavv7zlyxInrilXPT36PVJ87yKhbZmdeVjfcccUS5kHr5TlZ8HAAAAALVR65oihX6wilr/oQSoiKZXkx+9o+NuQw2Dko/dmWyybbeEBQC9ytO3J1d9sny0ZU/wZouHim7p4rUfqzvv2a53Yezp6uqTjUau4VjdrZONt0mGbJU0NNY6ymJrbU2a5nb/cytZlLt8WTLzrvLxvo9MLneorLZNd3yj6G/UW5P6PtV/JgAAAAB0g1rXFDm6FwB6o+vO6twPsw/5uiI/AFhf274z+egdyZ++k9zxg6S0vNYR9W7Nr61SvLdKId/iObWOsHvV902Gjnq9eG+bcgFfW1e+rcuFfH0c7fGm1Nf3/OLYPn2T7d5Vvg79VjL7oXKnvxmTk+fvq84zX3k8ufMH5Wvg8GTMYcmYI5PtD0j6DazOMwEAAABgA6DQDwB6m0euTab+b8f3bX9A8pb/V/VwAKBXaxiQHPzVZNx7k6s+kbzwQK0j6plKpXJH4jUdqTv/9aK+5vm1jrJ79em/evHextu8Md5oC53S6Jq6umTEuPK1/+eS155PZlxX7vb31J+T5Usr/8zFc5L7flW++g5Idnh3MvaIZOfDen4RJQAAAAB0M4V+AAXX2tqa5ubmtnFjY2PqK3WME73P4rnJNZ/u+L5+GyXH/LD8wz7oAjkJKJrC5KUtd08+fEty94+SP34rWdbc8ZwNSamULHr59eK9Z9ZQzPdssnRhraPsXg2DVjlSd5VivkGbVe74VrpNYXJSZwwZmbz1jPLV/FryxM3lfzT02B+qU1i7rKncSXDG5CR1ydZvLxf9jTkyGb5j5Z8HJOlheQno9eQkoGjkJaBo5CU6otAPoOCam5szderUtvGECRMycKDjjliL6z6fLHyp4/sO/Ub5h8nQRXISUDSFykt9+ib7fioZe1Ry9aeSp2+rTRy10NqaLHxxpeK9VYr55j+74RU/9h+6UvHeKsV8Q0cnA4f5Rxe9UKFyUlc0Dil3Jh333mR5S/LMna93+5tc/nNccaXk2bvL141fTobvnIw5Ihl7ZLLVWxS5QgX12LwE9EpyElA08hJQNPISHVHoBwC9xUNXJQ9e1vF9O7w72eu06scDABuqTXdITrs6ue+XyR/OTpb0giNnly9LXntu7cfqzn8uaW2pdZTda+CmKxXvjV6pmO/143YHbFzrCGH99GlItt+/fB3278lL08vH+z4yOXnh/uo8c86j5euO/yp3s9z5sHLR3/YHlI9IBwAAAAAU+gFAr7BoTnLNv3R8X/8hjuwFgO5QV5fsdWqy0yHJtZ9LHr6q1hGt27IlyfxZbxyju/KRuvNmJq89n5SW1zrK7jV4xJo78W08Ohk6Kuk/uNYRQvXV1SVbjC9f+3++XNQ749pyt7+n/lydAt9FL5cLpe/7ZdIwsPwPlcYcUS7+G7Rp5Z8HAAAAAD2EQj8A6A2u/WyyeE7H9x327+UfTAMA3WOjLZIP/DJ5+Opk8mfLx9vWyiuPJ8/f/0YXvpWL+Ra8mKRUu9i6W119stHIN7rvtTti9/VCvobGWkcJxTN0q+Rt/1C+ml9LHr+pXPj36A3V6V7asjh55JryVVefbP2OZMzh5W5/m+5Q+ecBAAAAQIEp9AOAnm7678tXR3Y6JNnjpOrHAwCsbpejk23fldz45eTeSbWJ4ReH1ua5tVDfNxmy1SpH6q5UzDdkq/LxpMD6axySjH9f+VrekjxzR/LIteXCv/nPVv55pdZk5p3l68YvJcPHJGOPSMYcmWy1d1JfX/lnAgAAAECBKPQDgJ5s4cvJ5M90fF/j0OTo7zuyFwBqacDGyTE/KBff/5/i+zelT/9y171VO/GtGG+0ZVLfp9ZRwoajT0Oy/QHl6/DvJC9Ne73ob3LywgPVeeacGcntM5Lb/7N81PbOh5U7/W23v46cAAAAAPRKCv0AoKcqlZLJ/5osfqXjew/7TjJkZPVjAgA6NvodtY6g+BoGrrkT34pivkGb6d4FRVVXl2yxW/k64Mxk/qxkxnXJI5OTp29LWpdV/pkLXyp3S713UtIwKNnx3cmYI8rFfwOHVf55AAAAAFADCv0AoKea9tvk4as6vm/nw5PdT6x+PAAAndV/yCrH6m690q+3KRfm6EQMvcPQUcnb/qF8Nc9PHruxfLzvYzcmS16r/PNaFiUPX12+6uqT0fuUi/7GHpEM277yzwMAAACAbqLQDwB6ogUvJdd+tuP7GjdOjv4vPygHALrXgGFvdOJb+UjdFcV8AzaudYRALTQOTXZ7f/latjR55o5y0d8j1yavzar880qt5Wc8c0dyw78lm+1SLvgbc0Qyci+dQQEAAADoURT6AUBPUyol1/xL0vRqx/cecV6y0RbVjwkA2LAMHrHmTnwbb13+df/BtY4QKLq+/ZIdDixfh/9H8uLUcsHfjMnJiw9W55kvP1y+bvtuMniLZMxhyZgjk+32Sxoaq/NMAAAAAKgQhX4A0NM8eFn5h18dGXtUuVMGAECX1CVDRr5evDd69WK+oaMUxACVVVeXbLl7+TrwC8m8mcmM68tf9zx9e9K6rPLPXPhiMuWi8tUwKNnxoGTskclOh5SPDwcAAACAglHoBwA9yWsvJNd+ruP7BgxLjvpPR/YCAKur65MM3er1or01FPMN2arcaQugVjYenbz9H8tX07zk8ZuSRyaX/7vktco/r2VR8vBV5auuTzJ6nzeO+B22XeWfBwAAAADroa5UKpVqHQQUyfTp0zN+/Pi28bRp0zJu3LgaRsSGrrW1Nc3NzW3jxsbG1NfX1zAiaqZUSi45MXn0+o7vff8vkvHHVz8mNjhyElA0PTIvLZqTnLtD9z/3g5ckI3ZLNtoy6ePf/UE19Mic1JMsW5o8fVsy47pkxrXJa89V/5mb71ou+Bt7RLLlnon3kx5GXgKKRE4CikZeAopGXiq+WtcU+c4+QMHV19dn4MCBtQ6DInjgks4V+e16bDLufdWPhw2SnAQUjbzUBaPelgwaXusooFeTk6qsb7/yEbs7HpQccW7ywgPlgr9Hrk1eerA6z5z9UPm67bxyofTOh5WP+N1uv6Rv/+o8EypIXgKKRE4CikZeAopGXqIjCv0AoCeY/1xy3Vkd3zdweHLk9xzZCwAA9G51dcnIPcrXgV9MXn3mjU5/z9yRtC6r/DMXvJBMubB89RtcLjgcc2Sy8yHJgE0q/zwAAAAAWIlCPwAoulIpufqTyZL5Hd971Pd06gEAADY8m2yTvOMj5avp1eSxm5IZk8v/Xbqg8s9bujB56MryVdcn2WZiudPfmCPKsQAAAABAhSn0A4Ciu+9XyeM3dXzf+OPLx/YCAABsyAZskkz4u/K1bEny9G3l431nXJcseL7yzystLz/j6duS689KRoxPxhxeLvobuaeO6wAAAABUhEI/gIJraWnJSy+91DYeMWJEGhoaahgR3Wres8kfvtjxfYM2T444r/rxsMGTk4CikZeAIpGTCqhv/2THg8vXkd9Nnr+vfLzvjOuSl6ZV55kvTStffz432Whkuehv7BHJtu8qxwPdSF4CikROAopGXgKKRl6iIwr9AAqupaUls2bNahsPGzbMX+YbilIpueoTyZLXOr73qP9MBg6rfkxs8OQkoGjkJaBI5KSCq6tLttqrfL377OTVp8sFf49MTp65s9yZr9IWPJ/c8/Py1W+jZKeDy53+dnpPufMgVJm8BBSJnAQUjbwEFI28REcU+gFAUU25KHnyjx3ft9sJyS5HVT0cAACAXmWTbZN3fLR8LZ6bPHZjMmNy8vjNydKFlX/e0gXJ9N+Xr/q+yTYTkzFHlrv9bTy68s8DAAAAoFdR6AcARfTqM8kNZ3d83+ARyeHfqX48AAAAvdnAYcnuHyhfy5YkT91WLvqbcV2y4IXKP691WfLUn8vX9WcmI3YrF/yNOTzZco9y90EAAAAAWIlCPwAomtbW5KqPd66DxNHfd2QvAABAJfXtXz5id6eDkyO+m7xwX/LItcmMa5PZD1XnmS89WL7+9J1kyFblgr8xRyTbvivp2686zwQAAACgR1HoBwBFM+UX5a4OHdn9Q+Uf/gAAAFAd9fXJVnuXr4O+lMx9qtzlb8a1yTN3JqXllX/ma88lf/tZ+eo/JNnx4GTskeX/Dti48s8DAAAAoEdQ6AcARTL3qeSGL3d830ZbJof9e/XjAQAA4A3Dtkv2+Vj5Wjw3eeyG5JHJyeM3Jy2LKv+8Ja8l039Xvur7JtvsWy76G3NEsvHWlX8eAAAAAIWl0A8AiqK1Nbny45374dAxP9TJAQAAoJYGDkt2P7F8tTSXO7PPuLbc8W/hi5V/Xuuy5Kk/la/rPp9ssVsy5shk7BHJFhOSurrKPxMAAACAwlDoBwBF8befJs/c3vF9e56c7PSe6scDAFTHgGHJ556ozXMBqI6GxmTnQ8rXkd9Lnr8vmTE5eeTa5OWHq/PMFx8sX3/6djJ062TM4eVOf9vsm/TtV51nAgAAAFAzCv0AoAheeSK58Ssd3zdkq+TQb1U/HgCgeurrk0HDax0FANVSX5+M2rt8HfTlZO6T5YK/GdclM+9MSq2Vf+b8Z5O/XlC++g9Ndjq4XPS303uSxqGVfx4AAAAA3U6hXy/yxBNP5K9//WtmzZqVpUuXZpNNNsnYsWMzceLENDY21jq8LF++PFOmTMlDDz2U2bNnp6WlJYMHD86oUaOyyy67ZOzYsamvr691mADdr7U1ufKfk2VNHd97zA/9kAYAAKAnGbZ9MvHj5Wvx3OTRP5S7/T1+S9KyqPLPWzI/mfbb8lXfkGz7zmTskeWOf0NHVf55AAAAAHQLhX69wBVXXJGvf/3ruffee9f48cGDB+f000/PV77ylQwf3v1dI5566qmce+65ueSSSzJv3ry13jdkyJAceOCB+cd//MccccQR3RcgQK395X+SmXd1fN/epyc7HlT1cAAAAKiSgcOSPT5Yvlqak6f+lDwyudztb9Hsyj+vtSV58o/l69rPJlvuXu70N+aIZIvdkrq6yj8TAAAAgKpQ6NeDLVmyJGeccUZ+/etfr/O+hQsX5r//+7/zf//3f7n88suz3377dUt8ra2t+c53vpNzzjknS5Ys6fD+1157LVdeeWUaGxsV+sFK6uvrM2DAgHZjepE5jyc3n9PxfUNHJ4d8o/rxQAfkJKBo5CWgSOQkuqShMdn50PLV2po8N6Xc6W/GdcnLj1TnmS88UL5u/ffy15ljDk/GHpFss2/Sp6E6z6Sm5CWgSOQkoGjkJaBo5CU6UlcqlUq1DoKua21tzfve975ceeWV7V7v06dPRo8enaFDh+app57K/Pnz23184MCBuemmm7LPPvtUNb6WlpacdNJJueyyy1b72NChQ7PllltmyJAhWbBgQZ555pksXry47eMf+MAH8r//+79VjW9dpk+fnvHjx7eNp02blnHjxtUsHqAXa12e/OKwZNZfO7731CuT7Q+oekgAAAAUwCtPJDOuTR65Nnn27qTUWt3nNQ5NdjqkXPi343uSxiHVfR4AAABAD1TrmiId/Xqoc889d7Uiv4985CP50pe+lJEjRyYpFwNeeeWV+fSnP52ZM2cmSRYvXpwTTjgh06ZNy9ChQ6sW3xlnnNGuyK9v3775p3/6p5x22ml5y1vekrqVjgVpbW3No48+mj/84Q+59NJL230MoFe760edK/J7yxmK/AAAADYkm+6QTPxE+Vo0J3n0D+XCvyduSVoWdzy/q5rnJw9eVr7qG5Lt3vXGEb9Dt6r88wAAAADoMh39eqBXXnkl2223XRYsWND22r//+7/nrLPOWuP9zz33XN75znfm6aefbnvty1/+cs45pxNHRa6HX/3qVznllFPaxiNHjsx1112XCRMmdGr+q6++mk022aQqsXVGratvgQ3EyzOS/3lXsryDo803Hp189K6k/+DuiQsAAIDiamlKnvzT60f8Xp8sml39Z265RzL2yHLR34hxiX+kCwAAAGygal1TpNCvBzrzzDPzH//xH23j/fbbL7feeus6O+HdfPPNOfjgg9vGG220UZ566qlsuummFY1tzpw52WWXXTJnzpwk5WN677nnnuy4444VfU411foPJbABWL4s+cUhyXNTOr73tGvKnRQAAABgZa2tyXP3JI9MLnf7m/No9Z+58ehkzJHlI363mZj0aaj+MwEAAAAKotY1RfXd9iQqorW1NRdeeGG717761a92eNztQQcdlHe9641CkQULFuTSSy+teHzf/OY324r8kuRb3/pWjyryA+gWd/2wc0V+b/snRX4AAACsWX19svXbkveck3z8b8nHpyTv+Xoyep8kVeq6N29m8pfzk4uPSc7dMfntPyTTf58sWdDxXAAAAADelL61DoCuufPOO/Pyyy+3jbfffvsccMABnZp7xhln5LbbbmsbX3HFFfnoRz9asdiWLFmSiy++uG28xRZb5J/+6Z8qtj5sqJqbmzNjxoy28ZgxY9LY2FjDiHhTZj+c/PFbHd+3yXbJwV+pfjzQRXISUDTyElAkchI1NXzHZPgnk30/mSyakzx6ffLItckTtyTLmir/vOZ5yYOXlq8+/ZLt9isf7zvm8GTIyMo/j/UiLwFFIicBRSMvAUUjL9ERhX49zOTJk9uN3/Oe93TYzW/le1d26623ZtGiRRk0aFBFYvv973+fuXPnto1PPPHE9OnTpyJrw4astbU1TU1N7cb0UMtbkt9/JFm+tIMb65Ljfpz0q0x+hkqSk4CikZeAIpGTKIxBw5M9Ty5fSxcnT95aPt730euTRS93OL3Lli9NHr+pfE3+12TknuUjfscekWy+a9LJ719SefISUCRyElA08hJQNPISHVHo18Pcf//97cYTJ07s9NyRI0dm2223zdNPP50kWbp0aR566KG89a1vrUhsqxYhHnjggRVZF6DXuOO/khfu7/i+d3w02abz+R0AAADWqt/AcsHd2COS1uXJrHuSGZPL3f5eeaw6z3z+vvL1x28kG2+TjD2y3O1v9D5JH9+SBgAAAFgfvqvSwzz88MPtxrvuumuX5u+6665thX4r1qtUod/f/va3duPdd989SbJ8+fLccMMNmTRpUu67777MmjUrDQ0N2WyzzbLnnnvm8MMPzwc+8IEMHDiwInEAFNKL05Jbv9PxfcN2SN79perHAwAAwIanvk8y+u3l6z1fS+Y8ljwyudzt79m/JilV/pnznknu/nH5atw42fnQctHfjgcl/Td68+u3tiZNczu+r9IGDEvq67v/uQAAAMAGS6FfD9LU1JSZM2e2e23rrbfu0hqr3r/y2d5vxvz58/Poo4+2jfv06ZNtttkmTz75ZE4++eTcdddda5zz+OOP57LLLsvZZ5+db3/72znllFMqEg9AoSxvSa74aNLa0sGNdclx55e7LQAAAEC1Dd8peeeny9fC2eWjfWdclzxxS7KsufLPa56XTP2/8tWnX7Ld/uVOgzsfngzZcv3WbJqbnLtDRcPslM89UT4iGQAAAKCbKPTrQebMmZNS6Y1/VdvQ0JDNN9+8S2tstdVW7cazZ8+uSGxPPvlku9g22mijPPTQQ5k4cWLmz5/f4fznn38+p556aqZPn55vf/vbFYkpKe/v5Zdf7tKcxx9/vN24qakpixcv7nBefX19GhsbV3u9ubm5S+emNzQ0pKGhod1rra2taW7u2jdXGxsbU7/KvypuaWlJS0tHhUZvsKe16849NTU1Zfny5e3GK+uJe+pIb9tT3zu+l34vTu3wvpa3fiQtw3dL1pFzirKnpPe9T4k9rcua9lQqlVbLSR0p+p564/tkT2X2tGa9YU+rfq60ZMmSNXYL70l7WlVveJ9WZU9rZ09r1xP21NHXb6vqCXvqje+TPa1lT/03ScvY9ydj35+0LE6fp/+cPo//IX0evyF11eiYt3xp8viN5Sv/kmy1dzLm8GTMkWkdPibNS5Z0bp2mxanFP5db3LQ4qVvz1+9F+r23ZC3/Hwv1e683/nmyJ3tahw15Tx19rtQT99QRe1oze1o7e1q7au5pRW7q7Pe7e8KeeuP7ZE/2tC69ZU8rf7606n6SnrmnlfWG96mrPxutNIV+PcjChQvbjQcOHJi6urourTFo0KB1rrm+5s2b125cV1eXo446qq3Ib+DAgfnQhz6U/fbbL5tuumleeeWV/OlPf8pvfvObdn8IvvOd72SrrbbKJz7xiYrE9eMf/zjnnHPOm1rj0UcfzbJlyzq8b8CAAW3HFa9sxowZXfqDPmrUqIwaNarda83NzZk6teMioZVNmDBhtR9wvvTSS5k1a1an17CntevOPS1fvjyvvfZa2/iRRx5Jnz592sY9cU8d6U17GvjaExl/1/c6vK9p0NaZuvHhKXUQWxH2tEJvep9WsKe1W9OeWltbV8tJHSn6nnrj+2RPZfa0Zr1hT6t+rvTkk09m7733Xu2+nrSnVfWG92lV9rR29rR2PWFPjzzyyDq/fltVT9hTb3yf7KmzexqZbPX3ychTs9G8h7PJS3dlk9l3ZcDi57r0rE57bkr5uuUbycbbZv7QvTJ3xD5ZsPG48pHDa9F36by8pToRrdNDDz2UZf02XuPHivR7b9UfWqxQ7N9769Yz/zytmz2tnT2tXU/cU0ff6+6Je+qIPa2ZPa2dPa1dNfe0aNGiLF++vNPf7+4Je+qN75M92dO69JY9rfz50pAhQ1b7eE/c08p6w/v05JNPdvoZ1aDQrwdZtShvTRWqHRkwYMA611xfqxb6vfrqq3n11VeTJHvvvXd+97vfZfTo0e3uOeWUU3L22Wfn2GOPbfcH5XOf+1wOPfTQ7LzzzhWJDaBW6lpbssOD56a+tHyd95VSnyd2+2xKffp3U2QAAADQSXV9smCT8VmwyfjMHPsPaVw4M8Nm35VN5/wlg+Y+lKTU4RJdVT/v6Ww57+ls+czv0tIwJPM2e1vmbr5P5g/fO619B3S8AAAAAEAvpNCvB1m1PWS/fv26vEb//u2LSCrVUnJtBYOjRo3KjTfemE022WSNH992221z8803Z7fddsuLL76YpHycxHnnnZcLLrigIrEB1MpWT/wmgxY81eF9z2/3/izceGw3RAQAAABvTvPg0Xl+8Oi8Ou707L7DlsmM65IZ1yZP3pos69qRPZ3R0PJaNnv+pmz2/E1prW/I/E33zKub75NXN3t7Who3rfjzAAAAAIqqrlQqVf6fXFIVf/vb3/K2t72tbTxixIi24rjOOv/88/Oxj32sbXzkkUfmmmuuedOxXXbZZTnhhBNWe/2SSy7JiSee2OH8SZMm5fTTT28bNzY2Zu7cuat1IOyq2bNn5+WXX+7SnMcffzzHHXdc2/hvf/tbdt111w7n1foc8FX1hrPNV7Wh7qmpqSmPPPJI23js2LHt/mz0xD11pDfsqe7FB9L4yyNT10E3v9bhY9J86vVJ3851afU+rZ09rVml97R48eK2TrylUik777xzl/6+LuKeVtZb3qeV2VOZPa1Zb9jTqp8r7brrrmv8h0Y9aU+r6g3v06rsae3sae16wp5effXVdX79tqqesKfe+D7ZU5X2tHRR8sQfs+yhq9Pn8RtS1zS3SzGtj+Vb7pXl27wr/e7+ftWftarFH38wGTh8jR8r0vu0ZMmSzJgxo2284pikXvV773X2ZE/rYk9r1t176uh73T1xTx2xpzWzp7Wzp7Wrxp5WfL97+fLyz3I6+hpuhSLvaYXe9D6tYE/2tC69ZU8rf75UX1+f3Xffvd1Rtz1xTyvrDe/TQw89lLe+9a1t42nTpmXcuHGdfu6bpaNfDzJ48OB2467+gUlW7+C36prra03rDBs2LMcff3yn5n/gAx/Ipz71qcyfPz9JeW9//etfs//++7+puDbffPNsvvnmb2qNAQMGrHZGeFeszxHLq6qvr39TMaywpkS1Puxpzaq5pz59+rT9urO/J4u+p/XRY/a0bEly3b8kHRT5pa5P6t97fgYOGfamYvE+rZ09rV0l9lRXV/em/55MirWn3vg+2dPa2dPa9bQ9rfy50qpdzFfoaXvqDHtaM3taO3tau0rtacCAAev19dvKiran3vg+2dOavek99RuU7HJU+u5yVNK6PHn2L8kjk8vd/uY++abjW5M+L9ybPi/cW5W1OzJwwMCkC//fC/M+va5X/d57nT2tmT2tnT2tXTX31NXPlXrCnrrKntbOntbMntauUntakZvezPe7i7an3vg+2dOa2dPa9dQ9rfz50qp66p7Wpaft6c02LHuz6ju+haJYtZhu8eLF6WpDxkWLFq1zzfW1pnX22WefTv9hbGxsbNetMEnuueeeisQG0O3+9J3k5Yc7vu+d/5JstXf14wEAAIDuVN8n2WZicug3k0/cm3zsL8lBX0lGvbXjuQAAAACskY5+Pcjw4cNTV1fXVtzX0tKS2bNnZ8SIEZ1e47nnnms3frPd7lZYUww777xzl9YYM2ZMbrzxxrbx7Nmz33Rc0Bs0NDRk1KhR7cYU2HNTktv/s+P7Nh+X7P/56scDFSYnAUUjLwFFIifBGtTVJZuPLV/v+tdkwUvJo9clj1ybPHlrsnxJrSPs1eQloEjkJKBo5CWgaOQlOqLQrwcZMGBARo8enWeeeabttZkzZ3ap0G/mzJntxmPHjq1IbDvssEP69euXpUuXtr02ZMiQLq2x6v2vvvpqRWKDnm7Vv8wpsJbm5PcfTUqt676vrk9y3I+Tvms+WhCKTE4CikZeAopEToJO2GhEsvfp5WvJwuSJW8rH+z56fdLk+4GVJi8BRSInAUUjLwFFIy/REUf39jCrFuY99NBDXZr/8MPtj5KsVKFfnz59Vuvgt2RJ1/41bnNzc7txJc7yBuhWt34rmTOj4/v2+2wyco+qhwMAAACF1n9wsusxyXv/J/ns48np1yb7fDzZZLtaRwYAAABQODr69TB77LFH/vCHP7SN77zzzpx22mmdmvvCCy/k6aefbhs3NDRk1113rVhse+21V6ZNm9Y2fumll7o0f9WjejfddNOKxAXQLZ79a3LnDzu+b8Ruybs+W/14AAAAoCfp0zfZdt/ydcg3kpcfSR6ZXO7299yUWke3ugsOTAZukjQOTfoPKf+37ddDVnptyOof79uv1tEDAAAAPZBCvx7mqKOOyne+85228U033ZRSqZS6uroO595www3txgceeGAGDx5csdiOOeaYXHzxxW3jKVO69g24Ve8fM2ZMReICqLqWpuSKThzZW983ee/5vqEPAAAA61JXl2y+S/na77PJay8kj16XzLguefJPyfKunSRSFfNnlq/10XfAOooBhyT9X//vyoWDqxYR1vep7H4AAACAwlPo18NMnDgxw4cPz5w5c5IkTz75ZG699dYceOCBHc79+c9/3m587LHHVjS2ww47LI2NjW1H8E6dOjWPPfZYdtpppw7nTp8+fbVjhQ844ICKxgc9VWtra7ujrRsbG1Nf7+T1QrnlG8krj3d83/5nJlvsVv14oIrkJKBo5CWgSOQkqJIhWyZv+X/la8nC5Imbk0euTR77Q9L0aq2j67plTcnCpmRh105EaaffRh0XAzYOTWv/jbK0rlxYWOo/JP2HDE/9gE2SfoPKBZXQXVpbk6a53f/cAcMSfxcXhs+VgKKRl4CikZfoiEK/Hqa+vj6nn356zjvvvLbXzjnnnBxwwAHr7Op3880357bbbmsbb7TRRjnhhBMqGtugQYNy8skn52c/+1nba9/4xjcyadKkDud+7Wtfazfef//9s/nmm1c0PuipmpubM3Xq1LbxhAkTMnDgwBpGRDsz707u+lHH9225e/LOf6l+PFBlchJQNPISUCRyEnSD/oOTXY8tX8uXJY9en/zfSbWOqvstXVC+XntunbfVJ2lc0wfq+iT9N1qli+DK3QXXVES4SqfBhjWuDGvWNDc5d4fuf+7nnkgGDe/+57JGPlcCikZeAopGXqIjCv16oDPPPDP/8z//k4ULFyZJ/vSnP+U73/lOzjrrrDXe/9xzz+XDH/5wu9c+9alPZfjwdX9xu2rh4B//+McOu+x95Stfya9+9au2CuOLL744+++/f/7f//t/a53z4x//OJdeemm7177whS+s8zkAhbB0cfnI3pTWfV99Q3Lc+Umfhm4JCwAAADYIffomo99R6yh6ptLypHle+VpfffqtvaNg48ardRds/+vX7+3jRxQAAADQWb6K7oGGDx+eL37xi/niF7/Y9toXvvCFzJw5M2effXZGjhyZpNzS86qrrsqnPvWpzJw5s+3ekSNH5jOf+UxVYhs1alTOPPPMnHPOOW2vffjDH869996bM888M1tvvXXb6zNnzsy///u/5yc/+Um7NT74wQ/m0EMPrUp8ABV189eSuU92fN8BZyUjxlU/HgAAAIDusnxpsujl8rW+GgatoxhwDYWBqxYU9tvIsawAAABsMBT69VBnnnlm7rzzzlxzzTVtr51//vm54IILss0222To0KF56qmnMm/evHbzBgwYkEsvvTQbb7xx1WL70pe+lClTprTFViqV8qMf/Sg//vGPs91222XTTTfNK6+8kiefXL04Zq+99soFF1xQtdgAKubp25O/nN/xfSP3TPb9dNXDAQAAAOhxWhaVrwUvrOcCdes4anhNHQVXPaJ4aNIwIFnldBsAAAAoIoV+PVR9fX0uu+yy/P3f/33+93//t+315cuXr7GALkk23XTTXH755dl3332rGlufPn1y+eWX55/+6Z8yadKkttdLpVKefPLJtcZ3zDHH5Ne//nUGDx5c1fgA3rSli5Ir/7nj+/r0S477H8fQAAAAAFRFKVkyv3zNf3b9lqjv27Uugm2/Xumevv0quy0AAABYA5UHPVhjY2MuueSSvP/97883vvGN3H///Wu8b9CgQTnttNPyla98JZtvvnm3xNa/f/9cdNFFOfHEE/ONb3wjd9xxxxrvq6ury9ve9racffbZOeqoo7olNoA37aavJq8+3fF9B/5bsvnYakcDAAAAdLd9P520Lkua5ydLXkuaX1v918uX1DpKOqN1WdI0t3ytr76NHXQU3LjjI4rr+1RsSwAAAPROCv16geOPPz7HH398Hn/88fzlL3/Jc889l6VLl2bjjTfOLrvskn333TeNjY1dXrdUKr3p2A477LAcdthhee6553LXXXflmWeeSXNzczbZZJNsueWW2Xfffbut+BCgIp76c/LXThwxPuqtycRPVD8eAAAAoPtN/EQyaPi671m2ZKUCwPlrLgZc8eslr4/bXnv9/tLy7tkPb86y5mRhc7LwpfVfo99GaykGXLWj4MZrfr3fYEcQAwAA9HIK/XqRHXfcMTvuuGOtw1ijrbbaKu9///trHQbAm7NkQSeP7O2fHPtj/xIbAAAANmR9+yeDNytf66NUSloWv1H0t7ZiwHX9eslrld0T1bN0QfnKc+s3v65+lQLAtXQObPfrjdu/3tD1hgEAAAB0H4V+ANBZN345mTez4/sO+lKy2c7VjwcAAADoverqkn6DyteQkeu1xOKFCzL9vr+kz7JF6btsUXYePSKNWdr5ToPN85NlTRXeGFVRak2a55Wv9dWn31q6CK5UOLi2I4pXdBrs48dOAAAA1eIrLgDojCduSe75Rcf3bf325B0fq348AAAAAB2p75PlDYOzvGFwliZp3XpCMnBg19ZY3vJ60d+8NRw1vOqv56/59daWauyOSlu+NFk8p3ytr4aBa+4i2Kdf5eIEAADYQCn0A4CONL+WXPmJ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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","KNN (Price) 0.9764 0.001952\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["# prompt: Save model best_logreg_classifier\n","import joblib\n","\n","# Assuming 'best_logreg_classifier' is your trained model\n","# joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/tags_specifications/knn_tags_specifications.pkl')\n","joblib.dump(grid_search, '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/price_patterns/knn_price_patterns.pkl')\n","print(\"Model saved to best_logreg_classifier.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1734869567483,"user_tz":-420,"elapsed":11523,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b5e5fa97-9fbe-4063-f4c7-43d12fd24f8b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to best_logreg_classifier.pkl\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Parameter Sensitivity/IMPACT-P Logistic Regression.ipynb b/Source Code/Parameter Sensitivity/IMPACT-P Logistic Regression.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..11ccc03b08c9013b116bc901e7d249a85cac6954 --- /dev/null +++ b/Source Code/Parameter Sensitivity/IMPACT-P Logistic Regression.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1RfBBMApu-iF","executionInfo":{"status":"ok","timestamp":1777427567302,"user_tz":-420,"elapsed":24057,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"a5a1d00c-36c3-4f18-b70d-e169f36b334d"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777427611674,"user_tz":-420,"elapsed":8362,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"bd50abdf-8d66-46b2-83e7-c22e537b97f2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/149.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777427641079,"user_tz":-420,"elapsed":49,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777427655304,"user_tz":-420,"elapsed":10658,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","# 1. SETUP DATA\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1],\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear'] # 'newton-cg' can be slow, removed for efficiency\n","}\n","\n","logreg = LogisticRegression(max_iter=1000, random_state=42)\n","grid_search = GridSearchCV(logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS (Convergence Proof)\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE DATA TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv('logreg_learning_curve.csv', index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Logistic Regression (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","\n","plt.savefig(os.path.join(dir_path, 'logreg_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. OUTPUT STABILITY TABLE\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Logistic Regression\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(summary_table.to_string(index=False))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":471},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777428032173,"user_tz":-420,"elapsed":203045,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"7779d4fd-5a28-4f47-9be1-81f8bf0dd4f7"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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Model Mean Accuracy Std Dev\n","Logistic Regression 0.863909 0.001346\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777428106784,"user_tz":-420,"elapsed":11064,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import os\n","import joblib\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","# 1. SETUP\n","X = user_price_features\n","y = user_price_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. HYPERPARAMETER TUNING\n","param_grid = {\n"," 'C': [0.001, 0.01, 0.1],\n"," 'penalty': ['l2'],\n"," 'solver': ['lbfgs', 'liblinear']\n","}\n","\n","logreg = LogisticRegression(max_iter=1000, random_state=42)\n","grid_search = GridSearchCV(logreg, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS (Convergence Proof)\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10)\n",")\n","\n","# 4. SAVE TO CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/logreg_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'logreg_price_learning_curve.csv'), index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Logistic Regression (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'logreg_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. OUTPUT STABILITY TABLE\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","summary_table = pd.DataFrame({\n"," \"Model\": [\"Logistic Regression (Price)\"],\n"," \"Mean Accuracy\": [np.mean(fold_scores)],\n"," \"Std Dev\": [np.std(fold_scores)]\n","})\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(summary_table.to_string(index=False))"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777429133050,"user_tz":-420,"elapsed":696921,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/","height":506},"outputId":"36c4c2eb-3479-4bc8-d57b-413c9d68d85f"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"]},{"output_type":"display_data","data":{"text/plain":["
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STABILITY SUMMARY ---\n"," Model Mean Accuracy Std Dev\n","Logistic Regression (Price) 0.899441 0.00027\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Parameter Sensitivity/IMPACT-P Naive Bayes.ipynb b/Source Code/Parameter Sensitivity/IMPACT-P Naive Bayes.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e23c170cb4ff5d8de287d068e7368895b62582c8 --- /dev/null +++ b/Source Code/Parameter Sensitivity/IMPACT-P Naive Bayes.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEp-GngME9rE","executionInfo":{"status":"ok","timestamp":1777524815259,"user_tz":-420,"elapsed":8832,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"f6e32b31-acaf-4adb-d862-a3b07e496019"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ijson\n"," Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (23 kB)\n","Downloading ijson-3.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (149 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: ijson\n","Successfully installed ijson-3.5.0\n"]}],"source":["!pip install ijson"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W2dzyUI5m-pc","executionInfo":{"status":"ok","timestamp":1777524839611,"user_tz":-420,"elapsed":24340,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"33eb3d7e-3d79-4b14-cf6f-a9af85cd946e"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast"],"metadata":{"id":"AeFl8khUG2Wz","executionInfo":{"status":"ok","timestamp":1777524839637,"user_tz":-420,"elapsed":24,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"NRnsh1W3oa_Q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Recommendation"],"metadata":{"id":"-s3p5tlhopND"}},{"cell_type":"markdown","source":["#### With tags and specifications"],"metadata":{"id":"0HqJQd9bY3fi"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_tags_and_specs_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_features.npy')\n","user_tags_and_specs_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_tags_and_specs_label.npy')"],"metadata":{"id":"IHUkfBsWY6Tn","executionInfo":{"status":"ok","timestamp":1777524856631,"user_tz":-420,"elapsed":12117,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","import os\n","\n","# 1. SETUP\n","X = user_tags_and_specs_features\n","y = user_tags_and_specs_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. GRID SEARCH (Hyperparameter Tuning)\n","param_grid = {'alpha': [0.1, 0.5, 1.0, 2.0, 5.0], 'binarize': [0.0, 0.5, 1.0]}\n","bnb = BernoulliNB()\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# --- BAGIAN PENCATATAN SENSITIVITAS PARAMETER ---\n","# Mengambil hasil lengkap dari grid search\n","sensitivity_df = pd.DataFrame(grid_search.cv_results_)\n","\n","# Memilih kolom yang relevan saja untuk disertasi\n","relevant_columns = [\n"," 'param_alpha',\n"," 'param_binarize',\n"," 'mean_test_score',\n"," 'std_test_score',\n"," 'rank_test_score'\n","]\n","sensitivity_results = sensitivity_df[relevant_columns].sort_values(by='rank_test_score')\n","\n","# Simpan Sensitivitas ke CSV\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/nb_model/'\n","os.makedirs(dir_path, exist_ok=True)\n","sensitivity_results.to_csv(os.path.join(dir_path, 'nb_tags_parameter_sensitivity.csv'), index=False)\n","print(f\"Parameter sensitivity data saved to {dir_path}\")\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10),\n"," scoring='accuracy'\n",")\n","\n","# 4. SAVE LEARNING CURVE TO CSV\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'nb_tags_learning_curve.csv'), index=False)\n","\n","# 5. HIGH QUALITY PLOTTING (HD/Vector)\n","plt.figure(figsize=(10, 6), dpi=300)\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","plt.title('Learning Curve: Naive Bayes (Tags & Specs)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","plt.savefig(os.path.join(dir_path, 'nb_tags_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. STABILITY SUMMARY (Berdasarkan Best Model folds)\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","print(\"\\n--- STABILITY SUMMARY ---\")\n","print(f\"Mean Accuracy: {np.mean(fold_scores):.4f}\")\n","print(f\"Std Dev: {np.std(fold_scores):.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":750},"id":"woA26xzuZACL","executionInfo":{"status":"ok","timestamp":1777525298030,"user_tz":-420,"elapsed":71438,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"c5170ede-a5b8-4380-e967-cd34916fb45c"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","Parameter sensitivity data saved to /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/nb_model/\n"]},{"output_type":"display_data","data":{"text/plain":["
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wrNnz5CXlwdzc3PY2dnB3t4e5cqVg6enJzw9PeHh4QGZTKazeJVRWFiIP/74Q1TZvn37wtzcXMMR6R+5XI64uLiicSE1NbXYuGBhYQFra2s4OTnB3d0dVapUQZ06dWBnZ6fr0A1WTEwMwsLCEBMTg+Tk5KJtkS0sLGBvbw87Ozs4OzvD09MT1apVg5ubm65DVkl+fr6k8k2aNFGqHy8vL6xZs0apuvriwoULksqvXr0aH330kVJ9yWQytGjRAi1atMCiRYsQERGBn3/+GZs3b8bTp0+VatMUcf6smsLCQkRERCA8PBxxcXFISUkpOmeytrYuOh+pUKECqlWrBk9PTzg6Omo0poEDB4pKXgwMDMTdu3fRoEEDjcZDRESax28iiYiIiEyUXC7HmTNncPjwYfz999+4e/euqK1dnJyc0LZtWwwaNAiDBw/W2I9oSUlJuHTpEv777z8EBwcjODgY9+7dU7hlREk8PDzQpk0bdOrUCYMHD0aVKlU0ELFqCgoKiv49AgMD8fDhQ6SlpSErKwtmZmZwd3fHmjVriq7O1oScnBwcP34cx44dw61bt/Do0SOkp6fD3Nwc7u7uqFChApo2bYpevXqhS5cuGv+iSp8EBgbi8OHDuHTpEkJDQ5GYmIjc3Fy4urqiQoUKqFGjBrp3745evXqhZs2aGo8nJycHR44cwfHjx3Hr1i2EhYUV/VuVK1cONWvWhI+PD3r06IEePXrA1tb2tTby8/Nx79490X06OzvrzXvn4sWL2Llzp1J133vvPaxbt04tSQhOTk74/PPPMXHiRHz55ZdckU0Jd+7cwb59+3D16lXcvXsXycnJyMjIgCAIKF++PMaMGYNvv/1W12FqXGZmJo4cOYLDhw/j/PnziIiIEFWvcuXK6NSpE4YMGYLevXvDyspKs4GauPz8fMybNw87duzQdSivkcvluHPnDq5fv1503hQcHIzY2FjJq1JZWlqicePGaN++Pfr27YvOnTubZCKJrun7ubohql69Ovr374+AgADRdfLy8pCamqr062hqcxpdS05OxuHDh/Hnn3/iwoULiI2NFVWvRo0a6Nq1K4YOHYouXbpoNAE4JSUFe/fuxbFjx3Dx4kXRMb5gZWWF+vXrw9fXFz4+PmjatClatWpV4vm+vjhx4gTi4+NFlR04cKBmg9ED4eHhuHLlCoKCgorGhbCwMMlb7cpkMtSqVQvt2rVD9+7d0b9/f9Er5anKEOejsbGx2LNnD06cOIHLly8jKSlJUn07Ozs0bNiw6L3n5+cHPz8/WFpaKh2TNr1Y9U+sJ0+eoHbt2hqKRr9JWXWxYsWKal31zcvLC0uXLsWCBQtw4MAByf9u6vb06VMcPnwY//zzD4KDgxEdHY1nz57BwsICzs7O8Pb2RuvWrdGvXz+0bdtWa3EZ8vw5Pz8fly9fxvXr13Hr1i2Eh4cjOjoaycnJyMnJQX5+flGyoKurK2rVqoXatWvDx8cH7dq1U9uFdP/99x/27t2LU6dOITAwEFlZWZLqu7q6okmTJvDx8YGvry9atmyJ+vXrqyU2AOjXrx/Mzc1FfTZu374dy5YtU1vfRESkIwIRERERSVK9enUBgOjbmDFjdB1yMZmZmcLKlSsFLy8vScdR0s3Ozk54//33hZiYGLXEFhcXJ0yYMEGoV6+eyrGVdpPJZELPnj2F06dPqyXmMWPGSOr/VYWFhcLGjRtF/V198803Jcbg7+8vuv/q1au/Vj89PV2YO3eu4OrqKrodBwcHYfHixUJWVpZKr9/p06clvX5btmwR3faWLVsktV3S38S+ffuE5s2bS2qnX79+QmhoqEqvS2kyMjKEBQsWSPq3cnNzE+bPny9kZmYWays8PFzScenTWNanTx+l3v9vvfWWIJfLNRbX2bNnhcjISIXlNPl3r40+1PHe+vvvv4U2bdoorDtgwAChoKBAqFSpkuj+evfuLfn1Ksns2bNF9ymTyYSHDx9K7uPx48fChx9+KDg5OSn1N/3yrXLlysLKlSuF7OxstRz/C1Ji8Pf3V2vf6iD171XRzczMTLhz547o/qV8RgMQwsPDJR3fr7/+KnTv3l1wdHRU63G+fPPw8BAWLVokpKSkSHvxSyD1s2f+/PmltjV06FDR7Tg7Owu5ubkqx3/ixAlJ8f/yyy+S+9Dnc3VN08Y8a+XKlZJfx8ePH0vqQ1/nNIGBgcL8+fNF3x49eiTx1S3Zhg0bRPe5bt06pfq4e/euMGbMGMHGxkbl17Z27drCxo0bhcLCQrUc/wuPHz8WJk2aJFhbW6v978HKykp44403hLlz5wr37t1Ta9zqMHr0aNHH8eqcRdO09f3OpUuXhCFDhkg6p5V6s7W1FSZNmiQ8ePBAvS/SSwxxPnr37l1h6NChgrm5udpfczs7O6Fr167CkiVLhOjoaDW8wpqzefNmSce2aNEiXYesM1LOMfv376/rcEskZWwraQ738OFDYeTIkYKlpaXodho2bCjs2bNHo8dlCPPnkhQWFgpHjx4Vhg4dqvK8rWLFisLEiROFo0ePCnl5eZJjOXr0qNC6dWu1j4cABHd3d2HIkCHC999/r1Rsr2rbtq2ofkv6rpuIiAwPkxeJiIiIJDLk5MXffvtNI1+WOzk5Cd99953K8QUGBmrky5PSboMHDxYSEhJUilmV5MUnT55ISmrQRPLiwYMHBQ8PD6Vfw+rVqwt3795V+vXT1wSrJ0+eCF27dlX6dbGwsBDWrl2r9OtSkn///VeoVq2aSv9W58+fL2rPUJMX7927J8hkMsnHX61aNSE1NVXX4QuCYNrJizk5OcLUqVNF1x0wYIAgCILwxRdfSHr/qTq2y+VySYlDnTp1ktR+bm6uMGfOHLUkWbx6q1WrlnDq1CmVjv9lUvo2heRFQNqPlJpOXhwwYIDaj6+0m7u7u7Bz506J/wLFqTN58dixY5LaOnDggEqxC4K08z5HR0chIyNDUvv6fq6uadqYZ23cuFHy65ecnCypD32d08TFxQlWVlai250+fbrk1/dVSUlJgoWFheg+Z8yYIan9tLQ04d1339VIUlLTpk2FmzdvqvwaCIIg7Nq1S3BwcNDK38OqVavUErO6yOVyoUKFCqJib9u2rdbj09b3O998843WxgQrKyth7ty5Qn5+vlpfK0Ocj37zzTeSxj1VbppO2lJVVFSUpONxdXUV4uPjdR22TvTo0UP06zR8+HBdh1siZZMX5XK5sGLFCpUS7Xv27Kn2vx1Dmj+/TC6XCzt27BC8vb01Mu5IOW/Kzc0VRo0apZXxEIDK38kIgiDMmjVLdH9BQUEq90dERLqluf0PiIiIiEhvZGZmYvjw4Rg1apTkbanESE9Px7Rp0zBo0CBkZGSovX1N2bt3L/z8/BAcHKz1vh8+fIi2bdvi7NmzWu8bAARBwGeffYYBAwYgLi5O6XYiIyPRoUMHBAYGqjE63Tp79ix8fHxw6tQppdsoKCjA9OnTsXjxYrXE9Msvv6BTp06IiopSuo3IyEh06tQJW7duVUtMurJ161bJW6ACwLZt21CuXDkNRERiZWVloW/fvli/fr3kuuPHjxddtqCgALt375bcx8suXrwoeuspAJgwYYLosg8ePEDLli2xdOlS5OTkKBFd2R49eoTu3burbfyh1x06dAiXL1/WdRhaFx8fj7feeguffPKJrkMBAHTr1g2enp6iy6u63XdOTg727dsnuvzw4cNhb28vqizP1bUnLS1NUnlra2u9P38QO6dxd3fH8OHDRbf722+/ITc3V6XY9u3bh4KCAtHlx40bJ7rs1atX4ePjg59++knydrtiBAYGok2bNvjll19UamfDhg0YMWKEyb73bty4gYSEBFFl/f39NRyNacjLy8OSJUvQrVs3PHv2TC1tGuJ8dO7cuZgxYwby8vK00p++8/T0ROfOnUWXT0pKwtSpUzUYkf6S8pliTN+F5ebmYujQofj8889V+vw/duwY/Pz8EBoaqpa4DHX+/ODBA/j7++Ptt9/G/fv31dr2C3K5XFS5zMxM9OvXD7/99ptG4tCUDh06iC577NgxDUZCRETawORFIiIiIiMXFxcHf39/lRM5xNi/fz+6deuGlJQUjfelLlFRUejWrRsiIyO11md8fDy6dOmi0hf/qigsLMSoUaOwatUqtbSXmJiI/v37Iz09XS3t6dKBAwfQo0cPJCUlqaW9efPmSUp0KMnPP/+MiRMnquVH2fz8fIwfP96gExj37t0ruU6XLl3QsWNH9QdDosnlcgwdOlTppOA6depI+uJa1SSl33//XXTZ8uXLY/DgwaLKXr58GW3btsXt27eVDU0UuVyOefPmYerUqUol+5qyihUriio3Z84cDUeiv77++mvMnj1b12HAzMxMUqLT4cOHVTpXOXz4sKQkELFJzTxX165r165JKt+4cWOYmen/V+hi5zQffvih6DaTkpJUPo/ds2eP6LJ+fn5o3LixqLKHDh1Cx44dJV1ooIzc3FxMnDgRy5YtU6r++fPn8d5774lOLjBGR48eFV2WyYvqdebMGfTr1w/5+fkqtWOI89EdO3Zg6dKlGu3DEM2bN09S+YCAAKxcuVJD0egvZ2dn0WXv379v0N+vvJCXl4c333xTqe9bSvL48WN06NABjx49UqkdQ50/Hzp0CM2aNcO5c+fUEJ3qPvroI5w4cULXYUjWrl07mJubiyor5XyDiIj0k/5/80JERERESktLS0P37t1x48YNrfV5+fJl9O/fX+VVOrQpNjYWI0eO1MiKHa8qLCzE4MGDdZa4CACTJ0+WlJgjxpMnT/RmJSZlnTp1CsOHD1f73+7UqVORnJysVN0TJ07gvffeU2s8giBg0qRJOH36tFrb1Yb79+8rdcW6lB/qSTPmzJmDv/76S6U2pKxuePHiRYSHhyvVj9SVG0eOHAkbGxuF5W7duoWePXsiMTFRqbiU8eOPP+Kzzz7TWn/GYMyYMahatarCcv/8849KK/QauuXLl+vFD0Tjxo2DTCYTVVbqyomvkpIU3bBhQ7Ru3VphOZ6ra1d0dDQOHTokqU6PHj00FI36iZnTtGjRAq1atRLd5qZNm5SOJykpCf/884/o8mKTkY8fP44hQ4YgOztb2dAkmz17tuSVo/Pz8/Huu++a/EUEZ86cEV22RYsWmgvERJ09exZz585Vur4hzkdTU1Px8ccfa6RtQ+fv7y/pwg8AmDVrlsqJ7IbG1dVVUvlJkyZh8eLFyMzM1FBEmvfuu++q/dw+ISEBvXr1UvriIUOdP2/evBlvvvmm3qy4fOHCBZVXkdYVR0dH1KtXT1TZCxcuqJysT0REusXkRSIiIiIjJQgChg0bhjt37mi97/Pnzxvc9jIXL17Eli1bNN7PmjVrcP78eY33U5rIyEhs3rxZI23//PPPCAkJ0Ujb2rB06VKNbCkVFxen1GoFcXFxeOeddzSSVFtQUGBw71Hg+ftUqqpVq6Jfv34aiIbEunz5slpW7BgyZAicnJxEl1c2Sfv48eOSfiCZOHGiwjIJCQno3bu35O1K1WH16tXYvn271vs1VDY2NqJXpdGH1Qd1RRAEfPDBBzrfitHLywtdunQRXV7ZVVlTUlIk/aArJtma5+ralZSUhCFDhkjabtDc3BxjxozRYFTqJ2ZOI+WijtOnTyu9atL+/ftFbxltbW2NkSNHKix37949DB48WCc/Tk+bNk1SIt5ff/2lcCtvYycIgujk7MqVK8PFxUXDEZmmr7/+Gvfu3ZNcz1Dno9u2bUN8fLxG2jYGkyZNklReLpdj5MiROHv2rIYi0j/e3t6SyhcUFGDevHmoWrUqpkyZgr/++kttW7Zrw+XLlzX2feiDBw+USoA21PlzQEAAJk2apFcrLq9evdqgL6Ro0qSJqHI5OTkICgrScDRERKRJTF4kIiIiMlKrV6/W6ZYQmzdvxsGDB3XWvzKWLFmi8S+Y5s+fr9H2dU2V1VmM2datW0X/ePvCxx9/rNEfXaT8eK8vrly5IrlOx44dDWK7R2O2aNEitYytdnZ2GDFihOjyyiYpSUl6bNq0KZo2baqw3Lhx4xAbG6tUPOrwwQcf6HTFX0Mzbtw41KlTR2G5a9euYf/+/VqISD+FhYXpRWKslFVZ//nnH6XeiwEBAaITNa2srPDOO+8oLMdzdc0SBAHp6em4efMmli1bhkaNGuHy5cuS2hg/fryosUDfKJrTDB06FB4eHqLaEgRB6dV6pKxiPGDAAIXbdObn5+Ott97S2cpWhYWFGDNmjOgVpA4cOKDZgAzAgwcPRCeeiN0ynKQrLCzEkiVLJNcz1Pko33ul27hxo6SLPl7Izc3FgAEDNL51r77o2LGjUvVSU1OxceNG9OnTB87OzmjatCkmT56Mn3/+GYGBgXq7KpymV+P+/fffcfz4cUl1DHH+HBoainHjxulV4mJ2drZBbhf9MinnB9evX9dgJEREpGkWug6AiIiIiNQvOjoaCxYskFzPxcUFU6ZMQe/evVG3bl2UL18eSUlJuH79On799Vfs2bNHUnvTpk1Dz549YW1tLTmWF2QyGXx9feHn54f69eujfv36qF69OhwdHeHo6AgHBwcUFhYiOzsbcXFxiIiIwJUrV/DXX39JTnSKjIzE8ePH0atXL6XjVUSb25tJ8WJ7w4oVKyIlJQUXLlxQ6ovp7du3Y/ny5bC0tNRAlNpXvnx5dO/eHdWqVYOZmRlCQ0Nx7NgxyStNxcXF4c8//8TAgQNFlb927ZrSq8bZ2tqia9euqFWrFuzt7fH06VOcP39eqdU29M3du3cl12nTpo0GIiEp1DnuTZgwARs2bBBVNiQkBIGBgaKSC1/IzMyUlMwjJmlq//79OHLkiOg2X6hbty7effdddO7cGdWqVYOtrS2ePn2KM2fO4IcffsC1a9dEt5Weno5PP/0Uu3btkhyHKbKwsMCiRYtEJct++eWXGDBggF4lSZcvXx4dOnRAgwYNUL9+fdSrVw+urq5wcHCAo6MjrK2tkZ2djYyMDERGRuLevXs4c+YM9u7dK3lbt40bN0pKHtSEN998Ey4uLkhOTlZYVi6XY+fOnZgxY4akPqQkQ/fv3x9ubm5lljGmc3Vd2rZtG7Zt26aRtuvVq4evv/5abe3p05zGysoKU6ZMwaJFi0S1t2XLFixatAgWFuJ/SkhMTJS0JayYbUzXrVuHW7duiW7zhebNm2PSpEno0KEDqlSpAgsLCzx58gSnTp3C2rVrJZ0jR0VFYenSpVixYoXCsn///bfodl1cXDB+/Hh07doV3t7e8PDwgLW1NTIzM5GWlobU1FSEhYXhv//+Q1BQEG7cuIGHDx+Kbl9XxK66CACNGjXSYCT6ycrKCm3btkWTJk2KxoWKFSsWjQt2dnbIy8tDVlYWnjx5gkePHuH8+fPYv38/IiIiJPUVEBCA7777TmGS8AuGOh/Ny8uTtNNF1apVMX78eHTq1Am1a9eGq6srrKys8OzZs6L33v379xEUFISgoCBcvXoVT5480eARaEZubi7Gjx+v9L8pAKSlpaFXr164ePEivLy8JNXNycmBra2tqLJ3795F/fr1lYhQfXx9feHh4YG4uDil2ygsLMStW7dw69Yt/PzzzwCerzLs6+uL1q1bo127dujcubPkLaq1xczMDO3atUPDhg1RoUIFJCYmIjQ0FP/++69Sq7HOmDEDQUFBouZMhjh/FgQBY8aMUWmraGdnZ7Rv3x5VqlSBm5sbcnNzkZiYiCdPnuDSpUtKtX3p0iVkZWWJLu/n54cxY8agdevW8PLygpOTE8zNzYvGw6SkJNy9e7doTLx8+bLGV8eUcn5w48YNyavLEhGRHhGIiIiISJLq1asLAETfxowZo/UYJ02aJClGAMLQoUOFtLS0Mts9fvy44OjoKKndb775RnTcgYGBAgBBJpMJvXv3FjZt2iTExMQo/TqcO3dOqFGjhqR4J02aJKmPMWPGSH6tlb2V9lr6+/sr3Wbjxo2Fy5cvl9jukSNHBDc3N8lt3rx5U/Trd/r0aUltb9myRXTbW7ZsUfp1sbOzE77//nshJyfntXZjYmKErl27Sm5z2rRpomMfOHCg5PbNzc2FL774Qnj27FmJbZ47d05o0KCB0q+JLsayV0kdfwEIN27c0HXYr9Hk3702+lDlvSX1NmDAgNf6b9y4sej6M2fOlPS67dixQ3TbNjY2QkpKSpntyeVyoW7dupKO2czMTFi2bJlQWFhYZrvLly+X1K5MJpM0Pr8gpQ9/f3/J7Wua1L/X+fPnC4Lw/DX28fERVWfbtm2l9i/1Mzo8PFzS8Q0YMEAAIFSuXFn4/PPPhbNnzwr5+flKvVbPnj0TPvroI8nv0ydPnojuIzw8XKl/D0U+/PBD0W36+flJel0eP34smJmZiW7/6NGjCts01HN1TVPmc14Tt/r16wuPHz9W+jgMYU4TExMjWFpaim5v//79kmLeuHGj6LarVq1a5meeIDwfn5ydnSW9BjY2NgrPb/Ly8iSNHwAEW1tbhf+ecrlcsLCwENVeo0aNhLi4OEmvryA8H083btwoDBkyRLCzsytqb9WqVZLb0pQFCxaIfl2/++47ncSore93vvnmGwF4Ps8cP368sH///lLnbYoUFhYKmzZtEuzt7SXFvmPHDtF9GOp89PHjx6Lb69y5s1L/Bnfv3hXWrl0r9OnTp9g4umfPHsltaUNSUpLwxhtvKP26v3qrU6eOEB8fLymGmJgY0e2np6dr6JWQZsmSJWp7zUq7mZmZCa1atRJWrVql0nlHSVQ5pxo8eHCp8Tx9+lR45513lGp33759CuM21Pnz5s2blX69u3fvLvz7779lxp+fny9cuXJFmDp1arFz/enTp5cZ1/bt20XH8fnnnwtyuVzU8b4c14ULF4QFCxYILVu2LNZeQkKCpLZKc/v2bdHH0KlTJ7X0SUREusHkRSIiIiKJ9D15MTExUbCxsZEU4+jRo0W3f+bMGUk/4NasWVP0lx8hISHCBx98IDx48EDZw39NRESEpB+6atSoIal9VZMXZTKZ0KdPH2Hjxo1CcHCwkJiYKOTn5wvJycnCtWvXhA0bNgiDBg0SbG1t1Z682KVLlxKT81525coVwcrKSlK7P//8s+jXTx8TrBwdHRV+QZmbm/vaF3OKbu3atRMVd2xsrGBubi6pbXNzc2Hv3r0K287MzBTatm2r1Oui6+TFwsJC0T8Cv3xLSkrSadwlYfIihKZNmwrLli0Trl27JsTExAh5eXlCWlqaEBISIuzcuVOYOHGi4O7uXmLy4rfffiu6n8qVKytMiHhZ7969Rbf99ttvK2zvzz//lPza/PLLL6LjXbx4saS2x44dK7rtF6S0b0zJi4Ig/t+vRo0aQl5eXon9azp5ccGCBcJvv/1Wav/KmDFjhqSYy0refJWmkhdv3bolqd3Q0FDRMa9cuVJ0u56engrHHEM+V9c0XScvmpubC1OnTlU6oegFQ5nTvPXWW6Lb69Onj6R4pVzoM3v2bIXtrVu3TvK/5fHjx0XHO3HiREntL1iwoMz2EhMTRbf1559/io6zNM+ePRO2bNkidOzYUfj6669Vbk9dxo0bJ/p1EDOX0QRtfb+zc+dOYfny5UJycrLaYj948KCk2MeNGyeqXUOej16/fl10e0FBQaJej7IkJiYK33//vdC8eXMhICBA5fbULSUlRWjSpIlSr3dZt+bNm0v6rLx7966odp2cnDT4akiTmpoqlC9fXu2vXVnvob59+wrnz59XS/zKnlN9+eWXotqXcn784ta1a1eF7Rri/LmwsFDw9vaWHLednZ1S40ZaWpqwaNEiwdbWVmHy4urVq0XF4uHhIRQUFEiO5VX37t0T5syZI1SvXl1tyYtSzqlq1qyplj6JiEg3mLxIREREJJG+Jy9+//33kuKrXbu2wgS2V02fPl1SH3///beGjlaczz77TFK8Un5QUCV5sXHjxsKlS5dE9ZOcnCw8fPiwxOeUSV6sUaOGwtV7XpgwYYKktt99913Rr58+JlgdPnxYVPvnzp2T1K69vb2oJKq1a9dKjnnhwoWiX5fExESlVtTUdfJienq65JhlMpmkxDVtMeXkRWdnZ+G3334TlSiTl5cn3Lp167XHExMTJSVVi/0MSkhIkJQg+88//yhsc/DgwZJen1GjRomK9YXCwkKhWbNmotu3tbUVMjIyJPUhJX5jS14UBEFo166dqHrr1q0rsX9NJy9qQnJysmBtbS06ZikrC2sqeVEQBMHPz090u2J/mBUEQfQKnACEefPmKWyP5+ql02XyooODg1qSyDRJ3XOaCxcuiG7L3Nxc9KpQUj9P79+/r7BNKe9vAMLcuXNFxfpCZmamULlyZdHtV6tWrcz2nj59KrotKcnUYqgj+UBdOnfuLPp1uHLlik5i1PfvdxSRckFdkyZNRLVpyPPRy5cvi25P6merIvr03hME1RJFxdy6desm5Obmiorl7Nmzotps1qyZhl8Vafbv3y/IZDKNvYal3fr27SuEhYWpFLsy51QDBw6U1MfIkSMltW9mZibExsaW2aYhzp+PHj0q+bW2sbEpdRccscLDwxWuZil2tcnWrVurFMur5HK5Wi+OEnvhl6WlpV5+B0hEROKYgYiIiIiMyv79+yWVX7RoEaytrSXVmTlzJszMxJ9KHjp0SFL76taqVStJ5YODgzUUyf9r06YN/v33X7Ru3VpUeWdnZ9SqVUtt/a9cuRJOTk6iyvbv319S20+ePFEmJL3Qp08f9O3bV1TZ9u3bw8XFRXTbmZmZSElJUVju6NGjotsEgGrVquGzzz4TXd7V1RWLFi2S1Ic+yM7OllzHyclJ0lhFmuXh4YGzZ8/i7bffhkwmU1je0tISPj4+rz3u6uqKgQMHiu53x44dosrt3r0bBQUFosrWrFkTHTt2LLNMTk4Ojh07Jqo9ALCwsMBXX30lujwAmJmZYebMmaLLZ2dn4+TJk5L6MHVLly4VXS4rK0vD0WiHs7MzvL29RZfXxnmTGBMmTBBd9vfffxdV7u7du7h9+7aosjKZDOPGjVNYjufq+ikjIwN9+/aFv78/rl69qutwSqTuOU3btm3RrFkzUW0VFhZiy5Ytosru27dP9Odp+/btUadOnTLLPH78GDdu3BDVHvD8POHzzz8XXR4A7Ozs8P7774suHxUVhVu3bpX6vJQ5wpUrV0SXFcPc3Fyt7alCyrzQw8NDg5EYLynjQkhICORyucJyhjwfdXV1FV3WmN97APD+++/j4sWLGmv/5MmTGDNmDARBUFj25s2botps1KiRqmGp1cCBAzFv3jyt9/vnn3/Cx8cHe/bs0VqfVlZW+PrrryXVWb58OWxsbESXl8vlOHHiRKnPG+r8effu3ZJiAIBvv/1W8nndq7y8vPDmm2+WWUbsmBgSEoL09HSV4nmZTCYT9Z2PWO7u7qLK5efnIz4+Xm39EhGRdvFXHCIiIiIjkpeXhwsXLoguX758eQwaNEhyP56enmjSpIno8rpOlJDy4xEAREdHayiS51xdXbF//36UL19eo/2UpmbNmhgyZIjo8lK/QE5LS5Makt6Q8qMLADRs2FBSeUWvjSAIkt7DADBu3DhJXxgDwOjRo2Fvby+pjq7l5uZKruPo6KiBSEhZ27dvR+PGjdXSlpQkpX379on6+xGb5AgA48ePV/hl/JUrV5CZmSm6za5du8LT01N0+Rd69eol6YcBXX8mGxp/f3/06NFDYbnY2Fh8//33WohIO6ScO2n6vEmskSNHwtbWVlTZR48eiUpakDIudOnSBV5eXmWW4bm6/ntxcdFnn30mKslHmzQxp/nwww9Ft7d582ZRSSpSfsQXk/D7zz//iG4PAIYOHQoHBwdJdYDnFzFJUdb7xtLSUvS/10cffYT9+/eLem0NzbNnz0SXtbOz02AkxkvKuJCfn4+EhIQyyxj6fFRsggsAvPPOOzh79qzaY9AHu3fvxtatWyXVGTRokOQL/3bu3IkZM2YoLCc2UVRdc0V1WrBgAdatW6f15NRnz55h+PDh2LZtm1b669OnD2rWrCmpjqenJ/r16yepzunTp0t9zlDnz3/99Zek/n19fTFlyhRJdZQl9sKAtLQ0DBw4EGFhYRqOSDlSzhGknHsQEZF+YfIiERERkRG5c+eOpASfrl27Sl7J5QWxq3QAQGhoqKQvoEpy9+5dbNu2DTNnzsSgQYPQvHlz1KhRA+XLl4etrS3Mzc2Lrux89dapUydJfWn6Ks2VK1fqdGWJ4cOHSypfpUoVSeUNNXmxSpUqeOONNyTXkULRaxMWFib5izYpiagv2Nvbo3fv3pLr6ZKVlZXkOhkZGRqIhJTxzjvvoFu3bmprr2vXrqhWrZqosqmpqThy5EiZZSIiInDp0iVR7Zmbm4tKtrh27Zqo9l4Qu+rrq5ydnVGjRg3R5a9fvy6pfUEQRN/OnDkjMXrDsHTpUlE/cK1YsUJvPgPT09Nx/PhxrFixAuPGjUPnzp3RoEEDVKxYEfb29rCysir1vEkmk0n6MV9fVrcoV64cBg8eLLq8mMTEP/74Q3R7EydOVFjGmM/VjYkgCFi1ahWGDx+u9gRGfZvTvPXWW3BzcxPVXkREhMIf8BMSEkR/Ftjb22PYsGEKy2nr87Rx48awsLAQXV7R52mbNm1EtZOSkoJBgwahWrVqGDt2LNauXYsjR47g4cOHKCwsFB2PPpKyIrHY5HNjk5+fj0uXLuGHH37A1KlT0bt3bzRp0gSenp5wcnKCtbU1zMzMSh0XFi5cKKk/ReOCoc9HnZycRF9gGBUVhY4dO8Lb2xtTpkzBDz/8gBMnTiAiIsKgk4lzcnLw8ccfiy4vk8nw448/Yu/evUpdiLN27VosW7aszDJi51lix01te//99/HPP/9ofWVIQRAwZcoUrawIrcz7WJl6gYGBpT5niPPnsLAwxMXFSepfyvtTVa1btxadqHn69GnUqVMHbdq0wSeffIItW7bg3LlzejHXk3KOYCy7IRARmSLxs3EiIiIi0nuhoaGSyvv5+Sndl9gfuYDnW4PcvXsXLVq0kNTH5cuXsW3bNhw4cABPnz6VGqLSNPlFh4uLC95++22NtS+Gv7+/pPLW1tawtrYW/WO7oV7l+sYbb0je1qRcuXKSyit6be7fvy+pPTs7OzRo0EBSnReaN2+u1W2IVKXMaixpaWmQy+XcOloPSFnZSQwzMzOMGzdO9A+2O3bsKHP1st9//130j5Q9e/ZE5cqVFZbT9mey2FUSgoKClO7HVPn5+WHQoEHYu3dvmeVSUlKwatUqLFmyREuRFZeeno4dO3bgjz/+wKVLl0Rv26oqffqBaMKECfjtt99Eld21axe++eabUlfSuXjxIsLDw0W15eLiImo7e2M7Vzd2AQEB+Pzzz7Fq1SqV2tHnOY2NjQ0mTZqkMPHkhZ9//hndu3cv9fl9+/aJTrgbMmSIqBUStfW+MTMzg4uLi+gf6RV9nnbv3l3hxRMve/LkCbZt21ZslS1LS0vUrFkT3t7eaNiwIXx8fNCyZUvJq2PpipSkaFNKXiwsLMTBgwfx22+/4eTJk1q94ErRuGAM89Hu3bsjODhYdPkHDx7gwYMHxR6zsbFB7dq1UadOHTRu3Bg+Pj5o1aqV5IsXdeGnn36StCr2smXL8O677wIApk6diidPnoj+THhh9uzZ8PDwwPjx41977ty5c4iMjFTYhp2dnV6fh3To0AG3bt3Czz//jOXLl4s6JnXIzc3F22+/jdDQUI2u/qhs4qjUrY9DQ0MhCEKJ370Z4vz59u3bkvo1MzPDgAEDJNVRRYUKFeDr61tm0ujL5HI5Ll++jMuXLxd7vHz58qhTpw7q1q2Lxo0bw9fXF61atZL8naiypHwfyAuyiIgMF5MXiYiIiIyI1C/PZs2ahVmzZmkomuKioqJEfxF548YNzJgxA+fOndNwVCVTZntasYYMGaL0CjrqImUbwRfs7e1Fvy76tsWeWMq+LlIoem1iYmIktVe/fn2lE/P0cUumstjb28Pc3FzSCjSCICAtLQ3Ozs4ajIwU8fb21sgPUePGjcOiRYtEJR0eOXIEaWlppX65/vvvv4vuV+yW1VI/k7W10khWVhaSkpLg6uqqlf6MxeLFi3HgwAGFY9DatWsxbdo0SdsWqio3NxcrVqzAmjVrdLLyoybPm6Ty9/dHrVq18OjRI4Vl4+PjcfLkSfTs2bPE56VsGT1q1ChR53fGcq6uL5o2bVpmYnpubi7S0tJw7949XLt2DSkpKZL7WL16Nd588020bdtWcl1DmdO89957WLlypahzrEOHDiEhIQEVKlQo8XkpiUhiVjEGpL9vKlWqJKm8sqKiosp8fvTo0Zg/fz5SU1OV7iM/Px/37t3DvXv3cPjw4aLHK1asiG7duqF///7o27ev5C179ZEhr3Qnxc6dOzFnzhydbc2paFwwhvno1KlT8f3336t0EUdOTg6CgoIQFBSE/fv3Fz3u5eWF7t27Y+DAgejWrZuk1Vq1ZePGjaLLdurUCZ9//nmxx7766itER0fj119/ldTv5MmTUaFChde2ERYbT/v27ZXabUGbzM3N8e6772Ly5Mk4ceIENm3ahL/++gvZ2dka7ffhw4fYtWsXRo4cqZH2HRwc4OXlpVTdatWqwcHBQXQSdm5uLhISEkqcKxni/FnshU4vNGnSBE5OTuoKTZRp06aJPucqTWpqKq5du1ZsdUwzMzP4+PigZ8+eGDJkiKRV36UylXMEIiJTx+UniIiIiIxIYmKirkMoldhVRr766iu0bNlSZz/yAZpNvtP1NjiWlpZK/aBnaWmpgWj0S/Xq1SXXUffrIvVHdSmrKqmzri6YmZkptdKFtlZEoNJpatyrXr06unbtKqpsbm4uAgICSnzu9u3boldn8fDwEL09lTF8JtP/q1+/Pt555x2F5TIyMrB06VItRPTcw4cP4efnh/nz5+tsy2p9+jFJJpOVuOpPaUpLUCwoKJCUhCU2qZnjgno1adIEc+fOLfW2ePFifPfddzh+/DiePn2K33//XalzCWW29zOkOY2np6eolUMBIC8vr9jKgC+TsmV0zZo10aFDB1Fl9fV9k5GRUebqPuXLl8fs2bM10vfTp0+xfft2DB06FJUqVcKnn36KhIQEjfSlCikrJWk6+UfXMjIyMGTIEIwYMUJniYuA4nHBGOajtWvXxuTJkzXSdkREBDZu3IjevXvD09MTS5Ys0erKmYrcuXMHISEhosuvXLmyxMc3bdqEHj16SOq7sLAQw4cPx4ULF4oei4mJKXUO9qqyLkbQN2ZmZujZsycCAgKQkpKCU6dO4YsvvkDLli019t3Z2rVrNdIuAFSuXFnyLiQvyGQyUbsSvKy0bZb19fMeKP08OTY2VlI79evXV0c4krzzzjsaSRaXy+UIDAzEsmXL4OfnBx8fH2zfvl0j36lLOUdQZtcWIiLSD0xeJCIiIjIi+rRt36vEbNvw8ccfY86cOQa7cp8YmrwSVQxlr/A1heRFZV4bdb8uOTk5ksqrcsW2o6Oj0nV1RZkE01e3uyHt0+S4JzZhCCg9SUnK6mqjR48W/b439M9ket2CBQtErQizYcMGhStyqcODBw/Qvn17SVsjmoKxY8eK3lbvwIEDJb5XT5w4IToRqEWLFqJXb+a4oDtWVlYYMWIEbt++LXmL0ytXrojebg8wzDnNhx9+KLrsL7/8UuLje/fuFb1C9tixY0UnShjy++aTTz7R2EpZL6SmpmL16tWoU6eOpHMabWDy4nNZWVno1asX9u7dq+tQFDKW+eiaNWvQsWNHjbUPPE9m+vLLL+Ht7Y2TJ09qtC+xxCaQA4CPjw+aN29e4nOWlpYICAiQPI/Lzs5Gv379is5NZ8yYIepvytzcHG+++aakvvSFtbU1unTpgmXLluHKlStIT0/H+fPnsWrVKgwaNAgeHh5q6ef69esau1BJ1feig4ODpPKlfa4b4ue91ORlXewKYm5ujkOHDml8Zeo7d+5g9OjRaNOmjdqT9KX8bUjdIYeIiPQHkxeJiIiIjIgq2+JomqIvLLdv345vvvlGS9Hojja3kSyJsl/iKHsVtiFR5rXR9euiSv/Kbu+lS1KTDQDg0qVLGohE/+nTSmiaHPcGDhwIFxcXUWXPnj2L6OjoYo8JgoCdO3eK7k/Kim6G/JlMJatevTqmTJmisFxubi4WLFig0VhycnLw5ptvlrpyiSmrXLlyqVtBvyojIwMHDx587XFNbCUPcFzQB66urtixY4fkcyixfxOGOqfx9/cXvSpPaGhoiStKil2t1MzMDGPGjBEdmyG/b2QyGbZu3Srqs0NVaWlpGDVqlOStXjVJSkKLMSdQf/jhhzh//ryuw9AIfZ2PWltb4+DBg6JXlVVFbGws+vbti7///lvjfSly8+ZN0WU7d+5c5vMODg7466+/UKNGDUkxpKSkoEePHti0aRN2794tqk6fPn10/l2ZutjY2KBdu3b45JNPsHfvXjx9+hT//fcfVq1ahZYtWyrdrlwu19hqzra2tirVl7rSXV5eXomPG+LnfW5urqR2dHURsZeXF/755x+lvlOT6urVq+jYseNr372oQkryotRkWiIi0h+G92sVEREREZXKxsZG1yGUqqyVR9LT05Xajs0QlStXTqf9K/vjgiEmukmlzGuj7tdF6nv42bNnSvelq+1FVdGqVSvJdU6fPm1QKx+VRJn49Wn7Mk2Oe9bW1hg1apSosnK5HH/88Uexx/799188fvxYVP127dqhXr16omMz1M9kKtucOXNEJbv/+uuvuHfvnsbiWLNmDVdcLIMqq7JmZmbiwIEDoura2dlhxIgRovviuKAffH194e/vL6mOmIshDH1OI2X1xU2bNhW7Hx8fj7Nnz4qq27lzZ1SrVk10X4b+vrG0tMRPP/2Es2fPit4qWxUTJ07EjRs3NN6PGFK2aTfWZPyLFy9i8+bNug5DNGOajzo5OWH//v3Yv38/fH19NdpXXl4e3nzzTTx58kSj/SgiZbWzWrVqKSzj4eGBY8eOSd7iOzo6GpMmTRJdftq0aZLaNzSNGjXCJ598gitXruD69evo1KmTUu1oam6h6sq3UldMLG0le0P8vLe2tpbUjipjpqrq1auHwMBAfP3116hYsaJG+3r8+DEGDBiglotqBUEQfY5gYWFhNInQRESmyPh/gSQiIiIyIYZ6deEvv/yCxMRESXWsra0xduxY7NmzB6GhoUhLS0NBQQEEQXjtdvr0aQ1FLp2qVzSTcRO7gtwLycnJSvcl9T2nD9q2bSu5zuPHj3HkyBENRKM9+fn5kuukp6drIBLlaHrcUyVJScr2ilL6AQz3M5nK5uHhgenTpyssV1hYiC+//FIjMeTl5Sm1slvjxo2xYsUKnD9/Hk+fPkVWVlaJ502CIEhO7NI3ffv2Ff3D1fHjx4t9Jh48eFD0CmBDhgyRtGUmxwX98cYbb0gqf+vWLYVlDH1O8/bbb4veznDPnj1ITU0tur9v3z7RW0aPGzdOUlzG8r7p0KEDzp49izt37uDzzz/X2OpH+fn5mDNnjkbalqp69eqiy6pzhSZ9smLFCsl1PDw88Omnn+L48eOIjIxERkYG5HJ5iePC/Pnz1RqvMc5HBw4ciMDAQFy8eBHTpk2TvJKgWM+ePcOSJUs00rZYL4/LilhaWooq5+3tjcOHD0teXU+spk2bokuXLhppWx/5+fnh77//lrSi/wuqvN/KompCndQLJ0v7WzLEz3upMaekpGgoEnGsrKzw8ccfIyoqCvv378eIESMkj/ti3bhxA7t27VK5ncTExFJX63xV1apVYW5urnKfRESkG0xeJCIiIjIiUlY20CdStuYDgObNm+P+/fvYsmULhgwZgrp168LJyanULyiUSfwh0gWp7+HQ0FCl+woKClK6rq7UrVsXderUkVzv+++/10A02iN1KyIAolcTNAZNmjRB8+bNRZW9desW7t69C+B5AlhAQICoeo6Ojhg2bJikuAz1M5kU+/TTT0Ul+AQEBKg0Tpfm5MmTkn7wNzc3x/fff4/bt2/js88+Q7t27eDh4VFmYrGhnztZWlpi9OjRosoWFBQU29ZQynnpxIkTJcXFcUF/VKpUSVL5zMxMhSsLGfqcxs7OTnSifnZ2drELAMRuDVq+fHkMGjRIUlzG9r5p3Lgxli9fjuDgYMTFxWH//v2YN28eBg8ejCZNmqgleeP48eMa+fyRysvLS3RZY0xeTE1NxV9//SWpztixYxEWFoaVK1eie/fuqFatGuzt7UvdJUDd44Ixz0fbtGmDtWvXIiwsDJGRkdi1axe++OIL9O/fHw0aNFDLqm+bN2/W6UVkUrZfl7LaaevWrbFz506NJAUtX75c7W3qO5lMhu+++07yipZJSUkaiScmJkbpFfIEQUBMTIykOh4eHiU+boif91LPJ0NCQjQUiTSWlpYYOHAgfv/9dyQmJuLOnTvYtGkTPvjgA3Tr1g01atRQy/v922+/VbkNKecHUi6aICIi/cPkRSIiIiIjIvUK8j/++KPUFXfUfVuwYEGJMaSnp+PmzZuiY3Z1dcWRI0ckbTVmiCvMkWny9vaWVD4tLQ3h4eFK9XXt2jWl6una4MGDJdc5deoU/v33Xw1EoxypW5RL+RHqhdu3b0uuY8iUWX3x6NGjolc+eOutt0RtFfwyqZ/JsbGxWvtM7tixo6TYqLjy5cvj008/VVhOyhZXUkhdfW327Nn44IMPJI09xnDupMy4kJSUhBMnToiq4+3tLXn1PkM8VzdWYlcJfFlZK1oZy5xm6tSpMDMT95PBi62j4+LiRJ9nvfXWW5IThKS8b+zs7FBYWKi1942U5LySuLu7Y+DAgVi4cCECAgJw+/ZtPHv2DPHx8bh48SK2bNmC999/X6mLd06dOqVSbOogJe7IyEgNRqIb58+fR0FBgejyHTp0wObNmyWtcKfuccFU5qPVqlXDsGHDsGzZMhw8eBDBwcHIyspCTEwMzp49iw0bNmDixImoWrWqpHbz8/N1Ou+UsuK91OTRfv36Yf369VJDKlOPHj3QvXt3tbZpKOzt7SUfu7IJhopkZGQgIiJCqbpRUVGSVl60srJChQoVSnzOEOfPUmO+c+eOXu2SATz/fqpx48aYMGECvv/+e5w4cQJhYWHIycnBo0ePcPToUXz99deSV5wHgOvXryMtLU2l+KScHyhzvkRERPqDyYtERERERsTX11dS+Rs3bmgmEAlCQkIgl8tFlx8xYoTobQBf+O+//6SGRaQTXl5eKFeunKQ6Bw8elNxPZmam5FVA9MWYMWMkJ/8JgoAxY8bozZfEUlfUkbL9F/D8eC9cuCCpjqEbMWKE6B/r/vjjDwDSVsiSumU0YJifySTe9OnTUbFiRZ30HRwcLLqsTCbDhx9+KKn9jIwMpRMR9Em9evXQtm1bUWUvXryIiIgI7N69W/QqVsps98dxQX/ExsZKrlNWUp+xzGlq1KiBvn37iip769YtXL9+XaNbRgPS3jdZWVl6s6qRKipUqIA2bdpg7NixWLduHe7fv4+jR49K2tpRH84FmzVrJrqsIa4Kr4iUz2sAmDZtmuR5jrrHBVOej8pkMlSqVAkdOnTA5MmT8fPPPyMqKgrbt2+XlHSty/deaUlhJTl27JjkFf4nT56ML7/8UmpYJXJ0dMSGDRvU0pa6Sfk8V4XUlQY1tXU3AFy+fFmpeleuXJFUvn79+qWOc4Z4ntykSRNJ5eVyuVJjpi5YWFigZs2a6NmzJz7++GPs2bMHsbGxoi7ie6GwsFDpv60XpHzO+fn5qdQXERHpFpMXiYiIiIyIp6cnKleuLLp8QECA1r6UK43U7aEaNGgguY8///xTch0iXZDJZJJXcdq6davkK/C3bdum1Gp++qBevXro2bOn5HoRERGYOnWqBiL6f+fOnUNUVJTCclKvVpc6Tp47d86kto0GgHLlymHIkCGiyoaHh+PYsWM4fPiwqPKNGjVCq1atJMfUunVrSeXFbrmpbTKZTPTNlFZ0tLOzw5w5c3TSt5QxoUKFCpJ+yAaAEydOGPy20S9ISTz+/fffi22DWxYLCwuMGTNGcjyGeK5urM6cOSO5jqOjY6nPGdOcRkrC86ZNm0R/fjVo0AAtW7aUHI+xfJ6qqmfPnli1apXo8sok6KpbvXr1RF+0Y4wXHGp6XIiPj8fVq1cl1VGE89HiZDIZRo0aJSlZR5fvvdq1a4sum56ejh9//FFyH4sWLVLqAo5XffPNN3q7xeuBAwcwbNgwjSfDS90GWpMXTgUEBChVb+/evZLKl5WgaIif97Vq1ZJ8McqaNWs0FI3m2dnZYeXKlZI+J1QdE6WcHzRv3lylvoiISLeYvEhERERkZPr06SO6bEREBHbt2qWxWHJzcxX+MJiVlSWpTak/ph85csQofwgh49W7d29J5W/fvo1t27aJLp+UlIT58+dLDUuvzJ49W6l6O3bswLRp09SeCJKUlISpU6fC398fycnJCstLXc3kzp07ksqvXr1aUnljISVJadKkScjOzlZ7uy+rU6eOpG2Ldu/erdHV7hISEiRtaUqKTZ48WeUtQ5Uh5dxJmSTE5cuXS66jr4YNGyY6cWb9+vW4ePGiqLJ9+vRR+gdkQztXN0ZXr14V/W/9gpubG+zt7Ut93pjmNF27dkX9+vVFld2xY4foLVKVWXUReL6VblmJo6/asGGD5FWrpYiKikJoaKjG2i+L2NVkAelJMZpgZmaGpk2biiobGxurFzGrk6bHhVWrVole9VQKzkdfZyjvvRYtWkgqv3jxYlEX371qw4YNkv9OXjZlyhSl51jaIJfLsWfPHjRq1AjDhg1Te5Lwiz5OnjwpqY4m5x1//vmn5K2jo6OjRV8Q+EKnTp1Kfc5Q58+9evWS1O6tW7f0dtVRsbQ5Joo9/7WyskLjxo1V6ouIiHSLyYtERERERmbo0KGSyk+bNg2RkZFqjSEhIQHLly9HzZo1sWDBgjLLlvUjYEmk/NCYmJiI999/X1L7RLo2ZMgQWFpaSqrz4YcfitquJysrC/3790diYqKy4emF9u3bY9iwYUrV/f7779G/f3/Ex8erHMezZ8+wYsUK1KlTBz/++KPoFUdcXV1Fb3EMPN9GLywsTFTZI0eOSP4BwVh06NBB9GojT548EVXOysoKo0aNUjomKZ/JOTk5GDNmDHJycpTuryQhISH48MMP4eXlhUOHDqm1bVNnZWWl8DxHE6ScO6WkpEhKslm1ahWuXbumTFh6ycHBAcOHDxdVNjo6WvQ4rsoP7oZ2rm5s4uPjlRrXFf0Yamxzmg8++EBUuYyMDFEXhVhYWOCdd95RKhZra2v069dPdPm4uDi8++67ar9Y5dq1axg9ejRq1aolagvEn376CVu2bBF9sYQYcXFxosuq+3xCWVJWZTamzx9As+PChQsXsHbtWqkhiWLo89GvvvoKe/fuRUFBgdraNJT3XteuXSWVT05ORt++fZGWliapnoWFBXbv3o1atWpJqgcA5ubm+OyzzyTX04UXSYytWrVC27ZtsWXLFrWtGLp06VLJuyWITQZXRl5eHj755BNJdb744gtJn3FmZmbo3r17mWUMcf4sdq7xso8++kjyltuvioqKErXy5ZQpU3Dp0iWV+nqVtsbEtLQ03Lt3T1TZ1q1bw9raWum+iIhI95i8SERERKRh27Ztk7Tlotjb2LFjS+yva9eu8Pb2Fh1fYmIi2rdvjxs3bqh0nHl5eTh8+DDeeusteHp6YtasWYiJiVFYT+r2Gnv37hV15WtCQgL69u2r9h97iTStQoUKePPNNyXVycjIQJcuXfDtt98iLy+vxDLnz59HixYtJK80pK9Wr16N8uXLK1X3yJEjqF+/PlatWoVnz55Jrn/r1i189NFHqFatGr744gukpKRIqm9ubg4fHx/R5QVBwHvvvafwR/jAwECMHDlSUizGRCaTqWULs5cNGDAAbm5uStefMmUKzM3NRZc/d+4cevXqJenHgJKkpKRg06ZN6NixIxo0aIB169ZJXv2HxHnnnXdEr1CmLlLPnebMmSMqKW/z5s2YNWuWsmHpLXWv7FOpUiWVVhsytHN1Y5Gbm4vt27fDx8cHDx48kFxfUQKWsc1pRo8eDScnJ7W116tXL3h4eChdf+rUqZLK79q1CyNHjkR6errSfQLA06dPsXbtWvj5+aFly5bYvn276ISooKAgjB8/HlWrVsUnn3yCGzduSN5a92WCIEjaalLqSt+a0rNnT9Flz549q8FItE/quLBy5UpRSWTXr1/HoEGDlFpdWQxDn49evHgRQ4YMgZeXFxYuXIi7d++q1F5eXh7WrVsnurwu33s1a9aUtCIa8HxVszZt2kj6bMzIyMC0adPw6NEjqSGisLAQ/fv3lzyH1rVLly5h/Pjx8PDwwPDhw7Fnzx6ljuHZs2f4+OOPMW/ePEn1PDw8JJ0/KmPv3r1YtGiRqLJr1qzBb7/9Jqn9Tp06oVKlSmWWMcT5c48ePSRt2Q48T+jr3Lkz9u3bJznWnJwcfPfdd2jYsCHOnTunsPyOHTvQtm1bNGvWDBs2bFB5G+eIiAhJcasyJp4/f170xShSV8AkIiL9w+RFIiIiIiMjk8kkX8X85MkTtGzZEhMmTBB95WdWVhYuXbqEH374AYMHD4abmxv69++PXbt2ITc3V3TfUrc9KSgoQM+ePUtdWUwQBOzatQvNmzdX+SpWIl2ZPXs2ZDKZpDqZmZmYMWMGKlSogCFDhmDmzJn48ssvMXHiRNSrVw9vvPGGyj/c6BNPT09s2rRJ6frJycn47LPPULFiRQwfPhwbNmzA1atXER8fj9zcXMjlcmRkZCAqKgrnz5/Hhg0bMHHiRFSvXh1NmzbF2rVrVdqS0M/PT1L5EydOYNCgQSWuUpKbm4t169ahffv2Kv9Ib+jGjBkj6ccORVRNeqpWrRpGjBghqc6ZM2fg7e2NhQsXit4GKykpCSdPnsSyZcvQsWNHuLu7Y9KkSUaXBKCPzMzMsGTJEq32WaNGDUnl9+3bh3feeafUVY6io6Mxbtw4TJgwQSPbT+pamzZt1Jpgquo4Y2jn6vrszp07WLJkSam3efPmYdq0aejRowc8PDwwevRoPH36VKm+3nrrrTKfN7Y5jYODg9LbPJdE1bbatWuH9u3bS6qza9cueHt7Y82aNaL/3WNjY3HkyBHMnz8frVq1QpUqVfDRRx+JSjQtTXJyMr7++ms0b94clStXxvjx47Fr1y7cu3dP9JgbEhKCgQMHSlpBuWbNmsqGrFatWrWCs7OzqLJityA3FFI/r8PDw9G5c2cEBQWV+HxWVha++uor+Pv7q2UV+bIYw3w0OjoaCxYsQMOGDVGjRg28//77OHjwIMLDw0UnEl+9ehWdO3fG9evXRfer6/feRx99JLlOSEgIfH19MXv2bCQkJJRaLjExEcuXL0etWrWwefNmpWMMDg5G//799WaFWCkyMzOxe/duDBs2DG5ubmjevDmmTp2KjRs34syZMwgLC0N6ejry8/ORn5+P1NRUBAcHIyAgAFOmTIGXlxe++eYbyf0OHDhQ/QdTgvnz52P48OGIjo4u8fn4+HiMGzcOM2fOlNz2e++9p7CMIc6fzczM8MUXX0iul5WVhcGDB6NXr14Kk/Tkcjlu376NOXPmwNPTE9OnT0dGRoak/gIDA/Huu++iSpUq8PPzw7x58/D333+LTvwsLCxEQEAA3njjDUnfO6kyJko5L5BysQQREeknC10HQERERETqN27cOKxfv17SjyxyuRybN2/G5s2b4eHhAV9fX9SqVQtOTk6wtrZGamoqUlJSkJKSgrCwMISGhqrlB+4qVaqgQYMGkr7ETkhIQP/+/VGnTh106NABlStXRl5eHh4/fizpixcifeXj44OxY8diy5Ytkuump6eL2jrGGAwePBiffPIJVq9erXQbWVlZ2L17N3bv3q3GyBTr3LkzfvjhB0l1Dh48iOPHj6NXr17w9vZGYWEhoqKicOLECZUSKY1J5cqV0atXL/z5558qt1WtWjV069ZN5XaWL1+O/fv3S9piLD09HQsWLMCCBQtQq1Yt+Pr6okqVKkWrYL34PE5OTkZISAhXGdaxQYMGoXnz5pJ+2FZFt27d8PPPP0uqs2PHDgQEBKBz585o2LAh7O3tkZSUhFu3buHSpUtGmbT4sgkTJkjeCq+stlRlSOfq+iwwMBCBgYEa7+fF525ZjHFO8/777+O7775TabVAAHBzc0Pfvn1Vjmft2rVo0aKFpO2g4+LiMHPmTHz66aeoW7cufHx8ULFiRTg5OaGgoKDoPZOYmIigoCClk1vFevr0KbZs2VJ0jm9tbY26deuiVq1aKFeuHJycnODk5ARzc3NkZmbi8ePHCAwMxP379yX31bp1a3WHrxRzc3P07NkTf/zxh8Ky169fR1ZWFuzs7LQQmeZ16dIFMplM0nvo5s2baNKkCVq1aoUWLVrAxcUFz549w4MHD/D3339rbSVtY5uPRkREYP369Vi/fj0AwM7ODg0aNICXl1fR++7FeXZGRgYiIyNx7do1REVFSe5L1++9IUOGoFmzZpKTrrOysrBs2TKsWLECrVq1QuPGjeHu7o7c3FwkJCTg1q1buH37tsqfCS+cP38eI0eOREBAAMzMDHOdHblcjhs3bqi8OrYYkydP1ngfL+zevRsBAQFo3749GjVqBFdX16J559mzZ5U6v6xbt67oFV0Ncf48btw4/Pjjj0r9LRw7dgzHjh2Di4sL2rdvjypVqsDNzQ25ublITExEbGwsLl++rLbVSgVBwM2bN3Hz5k0sXrwYAODq6oqGDRuiUqVKReOho6Nj0blSWFgYLl26JPl7JzMzM7Rs2VLpWMUmk1auXFnS7iZERKSfmLxIREREZITMzMywdetWtGrVCtnZ2ZLrx8XF4fjx4xqIrGR9+/ZV6gr8Bw8eKLXtG5EhWLlyJY4dO6byli6lsbCwEL3tnT5buXIlEhMTsXXrVl2HIkm/fv1QoUKFMle2KElOTg7279+voaiMw4QJE9SSvDhu3Di1/JBWpUoVfPvtt5g0aZJS9R89eqTUlmykXUuXLkWPHj200le3bt1gZWVV6raMpcnNzcXRo0dx9OhRDUWmv9555x3MmjVL5S02/f39JW8LVxJDO1c3ZVZWVlixYoWossY2p6lTpw569uyp8pgxatQoWFpaqhxPs2bNMGvWLCxdulRyXblcjpCQEISEhKgchzrl5ubizp07uHPnjlrblclkWlulS4y33npLVPJiXl4eTp06hf79+2shKs3z8PBAixYtcPXqVUn1BEHA5cuXcfnyZQ1FJo4xz0ezsrJw/fp1tV944uDgoJaLn1Qhk8nwyy+/oGXLlkqd98jlcly6dAmXLl3SQHTF7d+/Hx988EFRUimVrHfv3mjWrJlW+5TL5fj333/VtiLumjVrRM+tDXH+bGZmhm3btqFly5ZKJ5knJydLWmFZnZKSkjSy+nH79u3h5uamVN34+Hhcu3ZNVNkRI0ZIXi2YiIj0j2FezkJERERECjVu3Bg//fSTrsMQZdq0abC1tdVY+4MGDdJY20Sa4ubmhh07dsDCQv3XnJmZmWHNmjVqb1cXZDIZNm3apPQX27piaWmJ0aNH6zoMo9S3b194eHio1IZMJlPrdpkTJ07E+PHj1dYe6Z/u3bujY8eOWumrfPnymDJlisbar1GjBpo2baqx9nXB3d0d/fr1U7kdday6+IIhnaubshUrVqBx48aiyhrjnObDDz9UuQ11fp4uXLhQ54lBhqBfv34637r2Zb169YKrq6uosgcOHNBsMFr2+eefa6xtS0tLtaxqWhrOR6UbP348HB0ddR0GfH19sW7dOl2HIcqPP/6IJUuW6DoMvWVra4vvvvtOo32o4wKDsgwfPhy9e/eWVMcQ588NGzbEL7/8wiS6lyizjf0Lhw4dEr3a9qhRo5Tuh4iI9AeTF4mIiIiM2OjRo/Htt9/qOgyFqlSpgmnTpmmk7T59+qjlRzciXejUqZPkrUHF+Pbbb9WSxKEvzM3NsXHjRixbtsygtpz64osvUKFCBbW3a2lpiU8//VTt7RoKCwsLlRNDu3btiurVq6spouc2btyIIUOGqLVN0i9fffWV1vqaO3du0TZo6mRhYYHffvtNI23rmqqJh+XKlVP7e9hQztVN1axZsyT96GqMc5qePXuiTp06Stdv1qwZmjRporZ4zM3NsX//frRr105tbRobe3t7rF69WtdhFGNpaYnhw4eLKvvnn39K2hpc3w0aNEhj2wgvXboUfn5+Gmn7Bc5HxatUqRLmzZun6zCKTJ48GQsWLNB1GKJ8+eWX2Lx5s67D0EubN29GrVq1NNpH27Zt8fbbb2uk7Vq1amHDhg1K1TXE+fNbb72Fn376iQmMAHr06CF6q/CSiL2YoVGjRvD19VW6HyIi0h+G86sOERERESll+vTp2L59O6ytrXUdSpkWLVqEN954Q61t+vj44Pfff1drm0TaNnbsWGzZskUtK16Ym5tj/fr1+PDDDyVv0aXpq/HV4YsvvsDZs2dRo0YNXYciipubm9pXxLCyssKOHTskr2xgbFRdpWHixIlqiuT/mZubY+fOnUyoN2Jt2rTR6ApIL3N3d8fvv/+u9oTtTZs2oW3btmptU1/06NEDVapUUbr+yJEjNbKqnqGcq5sSBwcHbN68WamEZGOb08hkMrz//vtK11fnqosv2Nvb4+TJkwaX0KAN5ubm+PXXX1VKONUUsedmCQkJOHfunIaj0a6dO3fC3d1drW2OGzdOaxcrcT6qmL29Pfbu3St6hVFtmT9/Pr799luYm5trrU8nJycMHTpUcr0pU6bgyJEjGojIMMlkMqxfvx5vvfWWVvrbtGkTunbtqtY23dzccPToUZQrV06p+oY6f548eTL27t0Le3t7XYeiM97e3ti+fbvS9dPT0/H333+LKquJc00iItINJi8SERERmYBRo0bhypUr8PHx0Wq/9vb2on+8s7Kywv79+9GoUSO19O3r64tjx44Z5cpBZHrGjh2L06dPo1q1akq3UaVKFZw8eRLvvfceACA5OVlSfTs7O6X71qb27dvjzp07mDt3rtZj7tatGypXriypzrBhwzB//ny19O/s7Iy//vpLqR+LjE29evWUXpXJ1dUVAwcOVG9A/2Nubo7vvvsOu3fv1siqm2WpVKmS1s8DTNHSpUu1ttJGnz59sG7dOrUkMJqZmeGHH37AmDFj1BCZfjI3N8fYsWOVrq/OLaNfZQjn6qbA3NwcY8aMwd27d5X+IdQY5zTjxo2Dg4OD5HrW1tYaW8nJ1tYWu3fvxrp167SeHFCzZk29TA50cnLCvn37dLK9uBh+fn6iz822bdum4Wi0q3r16jh06BDKly+vlvZGjRqFjRs3qqUtsTgfLV2lSpVw8uRJtGnTRtehlGj69Ok4deqU2leVf5WZmRlGjRqF0NBQ7N69G1OnTpVUv6CgAMOGDcOVK1c0FKF4NWvWVOuqwVJVqFABR44cKXqvaIONjQ0OHz6M/v37q6W9KlWq4OzZsyp/Xhrq/PnNN9/EjRs3jPaisLK0adMGZ86cUenfa/fu3cjJyVFYzsHBQaNzNCIi0i4mLxIRERGZCB8fH1y7dg3r16+XnFwjhZmZGbp06YJt27bh6dOnWLx4sei6rq6uuHTpEoYNG6ZSDMOHD8e///6LihUrqtQOkT5p3749QkJCsHDhQkkrOjg7O2P27NkIDQ1Fp06dih6X+mORun5s0wYHBwcsXrwYDx8+xGeffQY3NzeN9WVtbY3x48fjv//+w4kTJ5RaVWXBggVYu3atSquZdOzYEbdu3UKXLl2UbsPYKPsl9qhRo2BlZaXmaIobOnQoQkND8cknn2hkJbcX7OzsMHLkSBw7dgyPHz9WadsmEqdJkyZaWyEFAN577z0cOXIELi4uSrfh7u6OgwcPSv6R2RCNHz9eqeRSHx8fjW/NaQjn6sZIJpPB19cXixcvRnh4OLZu3QpPT0+V2jS2OY2TkxNGjx4tud6AAQPg7OysgYiee7EqZGhoKCZOnKiWVeFKU758eUyePBnnz5/Ho0ePRCXh+fv7w8/PT+0r5JbkzTffxJ07d9SWdKIpYrdh37NnDzIzMzUbjJa1atUK169fVykhytLSEkuXLsWvv/6q0b/30hjKfLRXr15o2LChpLaVYW5ujgkTJuDOnTt6m7j4QseOHREcHIw5c+YolYxeFktLS4wYMQK3bt3C9u3bUalSJQDA999/L/mCsKysLPTt2xf3799Xa4xSNWvWDLdv38aDBw+wfPlytGjRQiv92tjYYNq0aQgNDUWvXr200uer/R84cABLlixRaT7crVs33LhxAw0aNFBbbIY4f65bty7OnTuHbdu2aWzrbzHnGG+//bZG5xYvlCtXDitWrMDZs2eLxgFlbdmyRVS5sWPHKr2yJxER6SGBiIiIiCSpXr26AEDntzFjxih9DLm5ucKuXbuEXr16CTY2NirHUr16dWH8+PHC77//LsTFxanldT5w4IDg5+cnKQ4/Pz/h0KFDr7V19uxZwdraWvRt8eLFouMcM2aMpBg1wd/fX9K/lTKk/N1L6eP06dOSXr8tW7aIbnvLli2S2j59+rTk12X+/Pka76Mk2dnZwp49e4QJEyYIfn5+gouLi2BhYSFYW1sLFSpUEFq1aiVMnjxZCAgIEDIzM0tsY+nSpZJi/+WXX9QSuy7k5OQIu3fvFkaOHCk4OzurPOaVL19eGDFihLBz504hLS1NbXHeu3dPGDBggKRYfH19hd27d7/WlqG/t9QhIyNDcHR0lPzve+fOHa3GmZSUJHz77bdCixYtBJlMptLfpkwmE3x9fYWZM2cKf/31V6nvfymk9O/v76/6C6JmUv9e58+fr3KfDx48ECwsLJT6NwwPD1eqz9jYWOGjjz4S7OzsRPdlZ2cnTJ8+XUhISHitvW7dukk6dxIrPDxc6/8er+rUqZPkf5fvvvtO7XGUxRDO1TVBE/MsMzMzwcbGRnBxcRHq1KkjtG3bVhg5cqQwf/584cCBA8LTp081ekyGMqdR5O7du5Jf+6NHj6qtfzGePHkiLF68WGjYsKHKfzcWFhZC69athblz5wqnT58WcnNzlY4rMTFR2LlzpzBp0iTB19dXsLS0VMvftoeHh/D+++9r/bxFFQUFBaLf51u3btVKTFLHHVW+hxGE5/O4b7/9VqhcubLoPmUymTBw4EDhv//+e629xYsXSxoXzp49q1L8Lx+Hvs9Ho6OjhS1btgijR48W6tevL5iZmanlvVe9enXh888/F8LCwtTxUmpdYmKisGrVKqFRo0ZKvwYymUxo0aKFsGrVKiEmJqbUvrKysoS2bdtKbt/Ly6vMdnUhNjZW2LNnjzB9+nTBz89P6XP9km7NmzcXVqxYUeI5ubKkjG0lzeFCQ0OFYcOGSTrOBg0aCDt37lTbMZRG3+fPJSkoKBD+/PNPYfDgwYKDg4NKMbu5uQljx44Vjhw5IuTl5YmO4b///hO+/vprYeDAgYKnp6da/nZlMpng5+cnfPPNN0JqaqpaXqv79++L7vvBgwdq6ZOIiPSDTBAEAURERERksrKysnDu3DncuHEDd+7cQUREBKKjo5GWloacnBwIggAHBwc4OjrC0dERbm5uqFOnDurVq4d69eqhUaNG8PLy0lh8ly9fxsmTJ3HmzBlERkYiMTERGRkZsLe3h6urK+rXr4+WLVuiX79+aNasmcbiIDI2vXv3xtGjR0WXP3/+vNLb8OqTwsJCBAUF4fr167h9+zYiIiIQFRWF+Ph4ZGZmIjs7G4IgwNHREeXKlYOTkxMqVaqERo0aoXHjxmjcuDGaNGmi0dVOwsPDcfToURw/fhxhYWFISEhAcnIyLC0t4erqinr16qFNmzbo27ev1laBIO2Ij4/H2bNnERgYiODgYERFRSE2NhYZGRnIycmBubk5HB0d4eTkBEdHR1SsWBF169Yt+kz28fGRtBIOGZ+kpCQcO3YMp06dQmBgIOLj45GUlASZTAYnJydUr14djRs3RteuXdG7d2+DWlXXVOn7uTqJYwxzmm7duuHUqVOiylapUgWRkZEwNzfXcFQli4qKwrlz5xAYGIiQkBA8fvwYT58+RWZmJnJzc2FpaVn0nnFyckLlypWL3jP16tWDr68vHB0dNRJbbm4ubt++jTt37iA8PBwRERGIiIhAXFwcMjMzkZmZiaysLADPtyF3dHSEq6srPD09UatWLTRp0gRt2rRBkyZNlFpJVtfWrVuHDz/8UGG5du3a4fz581qISDdyc3Nx6tQp/P3337h48SJiY2ORmJiI/Px8ODg4oFKlSmjYsCHat2+PAQMGaHzLX13Q9nw0MzMTgYGB+O+//4red5GRkUhISCj23pPJZLC2toaTkxPc3NxQrVo11K5dG76+vmjbti3q1q2rdAz6JiIiAn///Tdu3rxZNFYmJCQgKysLhYWFsLa2hqOjIypVqoTq1aujUaNGaNasGfz9/TW6u4EhyMzMRHBwMB4+fIhHjx7h4cOHCA8PR3JyMjIzM5GRkYGMjIyizxwbGxu4uLjAw8MD1atXR926ddGsWTO0a9dOI9she3l5ITIyUlRZf39/nDlzpsTnYmJicOjQIZw5cwbBwcF48uQJMjIyYG5uDmdnZ3h7e6N169bo27cv3njjDTUegTiGOH/Oy8vDpUuXcO3aNdy+fRvh4eGIjo5GcnIycnJyUFBQADs7u6JzxBo1aqBmzZrw8fFBu3btUK9ePbXEER8fjxs3biAkJASRkZFF342lpKQUjYk5OTmwsLCAtbU1nJ2d4e7uXuzv94033oCHh4da4nnh888/x8qVKxWWGzJkCPbs2aPWvomISLeYvEhERERERGRiIiMjUbt2bRQUFIgqb2VlhbS0NNjY2Gg4MiIiIiLSRzt27MCoUaNElZ01axa++uorDUdEhig3Nxe1a9fGkydPFJa9du0amjdvroWoSNs4HyXSLHUlLxJpU2ZmJjw9PZGSklJmOTMzM9y5cwcNGzbUUmRERKQNZroOgIiIiIiIiLTrq6++Ev1DEQC0adOGPxQRERERmbA7d+6ILjt27FjNBUIGzdraGnPmzBFV9ptvvtFwNKQrnI8SEdGrtm7dqjBxEQCGDx/OxEUiIiPE5EUiIiIiIiI9VVhYiNGjR+P+/ftqa/O3337Dxo0bJdUZPny42vonIiIiIsMil8vxxx9/iCrbrl07eHt7azgiMmQTJkwQtQ3ynj17EB0drYWIqDScjxIRkTYIgoDvvvtOYTlzc3PMnz9fCxEREZG2MXmRiIiIiIhITwmCgO3bt6NBgwZ4++23cenSJaXbksvlWL58OcaPHy+pnpOTE0aMGKF0v0RERERk2LZv347Hjx+LKjtlyhQNR0OGztLSEkuXLlVYLj8/H19//bUWIqLScD5KRETasH//flGJ8hMmTEDdunW1EBEREWmbTBAEQddBEBERERER0esKCgpgaWlZ7LFatWrh7bffRs+ePdG0aVOF22elpKRg165d+PHHHyVt9/fCrFmz8NVXX0muR0RERESGLygoCB06dBC1jZ+7uzuioqJgbW2thcjI0LVt21ZhMpytrS3CwsJQsWJFLUVFL+N8lMgweXl5ITIyUlRZf39/nDlzRrMBEZVBEAT4+voq/IwoX7487t+/jwoVKmgpMiIi0iYmLxIREREREempkn4sepmlpSV8fHxQo0YNuLi4wMXFBYWFhUhLS0NSUhJu3bqFR48eQdlpX7Vq1XD37l3Y29srewhEREREZEAKCgqQmpqKkJAQHD58GOvXr0dmZqaounPnzsXixYs1HCEZi+vXr6Nly5YK5yrTpk3D2rVrtRQVvYzzUSLDxORFMiR79+7FkCFDFJZbs2YNZsyYoYWIiIhIF5i8SEREREREpKcU/VikSRYWFjh58iQ6duyok/6JiIiISPPGjh2Lbdu2qdyOs7MzHj16BGdnZzVERaZi/Pjx2LJlS5llbGxs8PDhQ1SpUkVLUdELnI8SGSYmL5KhEAQBPj4++O+//8osV7duXfz33386+0wiIiLNM9N1AERERERERKRfZDIZfvjhB/5QRERERESizJo1i4mLJNnmzZshCEKZt+zsbCYumhjOR4mITINMJsOdO3cUnguEhoYycZGIyMgxeZGIiIiIiIiKmJubY+PGjZg8ebKuQyEiIiIiA9C4cWNMmzZN12EQkRHgfJSIiIiIyPRY6DoAIiIiIiIi0g+VK1fGH3/8gQ4dOug6FCIiIiIyALa2tti5cyesra11HQoRGTjOR4mIiIiITBNXXiQiIiIiIjJxVlZWmDFjBkJCQvhDERERERGJYmVlhZ07d6JBgwa6DoWIDBjno0REREREpo3Ji0RERERERHrK3Nwcy5YtQ9u2bWFmpv7pW6VKlfD5558jPDwca9asgZOTk9r7ICIiIiLj4+LiggMHDqB///66DoWINITzUSIiIiIi0gaZIAiCroMgIiIiIiKisiUkJODvv//G5cuXceXKFdy6dQs5OTmS2rC3t4evry/8/f3RvXt3vPHGGxr5EYqIiIiIDMPYsWOxbds20eWtra0xYsQIrFixAu7u7hqMjIj0CeejRIbDy8sLkZGRosr6+/vjzJkzmg2IiIiISAEmLxIRERERERkguVyO2NhYREZGIioqCmlpacjKykJWVhYEQYC1tTVsbW3h5uaGSpUqwcvLC9WqVYNMJtN16ERERESkJ8pKXrSyskK5cuXg7u6Opk2bom3bthg2bBhcXV21HCUR6RvOR4mIiIiISF2YvEhEREREREREREREREREREREREREWsX12ImIiIiIiIiIiIiIiIiIiIiIiIhIq5i8SERERERERERERERERERERERERERaxeRFIiIiIiIiIiIiIiIiIiIiIiIiItIqJi8SERERERERERERERERERERERERkVYxeZGIiIiIiIiIiIiIiIiIiIiIiIiItMpC1wGQ+jx69AhXr17FkydPkJeXB2dnZ9SrVw9t27aFjY2NzuJKTU3FtWvXEB4ejtTUVMjlcpQrVw5Vq1ZFixYtULFiRbX08d9//+H+/ftISUlBfn4+nJ2dUbFiRbRq1QqVKlVSw5EA+fn5uHfvHoKDgxEXF4dnz57BwcEBrq6uaNKkCRo1agQzM+YEExERERERERERERERERERERERlYXJi0bgwIEDWLx4MW7evFni8w4ODhg7dizmz58PNzc3rcW1b98+rFu3DmfOnIEgCKWWa9q0Kd59912MHz8eFhbi/iTlcjnOnDmDw4cP49SpUwgKCiqzfIMGDfD+++9j7NixsLOzk3Qc4eHhCAgIwMmTJ3H+/HlkZ2eXWrZcuXIYNWoUpk+fjjp16kjqR5NSU1Nx9uzZovuenp6wtrbWYURERERERERERERERERERERERKRLubm5ePz4cdF9f39/lC9fXmv9y4SysspIr+Xm5mLChAnYsWOHqPIVKlRAQEAAOnTooNG4kpKSMHr0aPz111+S6vn5+WHnzp2oXbt2meWOHz+OcePGITY2VnJsderUwY4dO9CiRQuFZXNzc+Hv748rV65I7sfKygpLly7FzJkzIZPJJNdXt4MHD2LgwIG6DoOIiIiIiIiIiIiIiIiIiIiIiPTUgQMHMGDAAK31x/1tDZRcLsfw4cNfS1w0NzdHjRo14Ovri3LlyhV7LiEhAb169cKlS5c0Fld6ejq6d+9eYuJihQoV0KxZM/j5+ZW4VfSNGzfQqVMnRERElNnHgwcPSk1cdHFxQYMGDdCiRQtUq1atxLr+/v74999/FR5Lfn5+qYmLNjY2qFGjBlq0aIEGDRrAysqq2PN5eXn49NNP8cEHHyjsh4iIiIiIiIiIiIiIiIiIiIiIyNQwedFArVq1CgcPHiz22LvvvouoqCiEhYUhMDAQycnJ2LdvX7EkvqysLAwbNgxpaWkaiWv27NmvbV/dv39/3Lx5E/Hx8bhx4wauX7+O2NhY3L17F2+//Xaxsk+ePMHkyZNF9yeTydCtWzf88ssvePToEZKSkhAcHIyrV68iMjISkZGRmDlzJszNzYvqZGdno1+/foiJiZF0bDVq1MCCBQtw4cIFpKenIywsDFevXkVwcDBSU1Oxfft2VK9evVid9evXY926dZL6ISIiIiIiIiIiIiIiIiIiIiIiMnbcNtoAJSUloUaNGnj27FnRY8uWLcMXX3xRYvno6Gi0b9++2IqG8+bNw8KFC9UaV3x8PCpXrozCwsKix9577z2sX7++zHqLFy/GvHnzij128eJFtGnTpsTy69atw8yZMzFlyhR8/PHH8PLyUhjb33//jT59+iA3N7fosdGjR2Pbtm2l1snIyICjoyPatWuHefPmoVu3bgq3gE5JSUGPHj1w7dq1osfKly+PR48ewcXFRWGcmnLz5k34+fkV3T9w4IDC7bmJDF12djbu379fdN/b2xu2trY6jIiISPM49hGRqeL4R0SmiGMfEZkijn1EZIo49hGRKeLYR6Q9Dx8+xMCBA4vu37hxA82aNdNa/xZa64nUZuXKlcUSFzt06IDPP/+81PJVqlTBpk2b0LVr16LHvvnmG0ybNg2urq5qi+vPP/8slrhYoUIFrF69WmG9OXPm4I8//kBISEjRY4cPHy41ebFt27a4f//+a6sclqVLly5YuXIlpk+fXvTYrl278MMPP8DBwaHEOlZWVvjzzz/Rp08f0f04OzvjwIED8Pb2RmZmJgAgNTUVe/fuxaRJk0S3o27W1tbF7teuXRsNGzbUUTRE2pGVlYWCgoKi+w0aNICdnZ0OIyIi0jyOfURkqjj+EZEp4thHRKaIYx8RmSKOfURkijj2EenOqzlGmsZtow2MXC7Hli1bij22YMEChSsCdunSBW+88UbR/WfPnmH37t1qje3evXvF7vfo0UPUh4eZmRnefPPNYo89fPiw1PLNmjWTlLj4wnvvvYdy5coV3c/NzcWZM2dKLW9lZSUpcfGFypUrY8yYMcUeO378uOR2iIiIiIiIiIiIiIiIiIiIiIiIjBWTFw3MxYsXkZCQUHS/Zs2a6Nixo6i6EyZMKHb/wIEDaowMSE5OLnbf09NTdN1q1aoVu5+amqqOkIqxtLRE69atiz0WFRWl9n4AFEsU1WQ/REREREREREREREREREREREREhojJiwbmyJEjxe5369ZN4aqLL5d92ZkzZ4q2NlaHl1c1BIDs7GzRdV8t6+bmppaYXuXs7FzsflpamkH3Q0REREREREREREREREREREREZIiYvGhgbt26Vex+27ZtRdetXLkyvLy8iu7n5eXh7t27aooM8PX1LXb/2rVroutevXq12P2WLVuqI6TXREdHF7vv6upq0P0QEREREREREREREREREREREREZIgtdB0DShISEFLvfoEEDSfUbNGiAiIiIYu21aNFCHaGhb9++sLe3L1rN8cKFC7h06RLatGlTZr2HDx9i7969RfdtbGwwcuRItcT0sszMTNy8ebPYY97e3mrvBwDOnTunlX6IqHSWlpaoWrVqsftERMaOYx8RmSqOf0Rkijj2EZEp4thHRKaIYx8RmSKOfUSmg8mLBiQ7OxtRUVHFHvP09JTUxqvl7927p3JcL5QvXx6zZ8/GnDlzih4bPHgwDh48WGqCZEhICAYMGIC8vLyix5YsWQJ3d3e1xfXC7t27i22T7eTkhHbt2qm9n/T0dAQEBBR7rHfv3mrvh4jK9uoJLRGRKeDYR0SmiuMfEZkijn1EZIo49hGRKeLYR0SmiGMfkelg8qIBSUxMhCAIRfctLS0lJ/lVqVKl2P34+Hi1xPbCF198geDgYPz+++8AgNjYWLRp0wZ9+vRB9+7dUb16dchkMkRHR+Off/7Bvn37kJ+fX6z+zJkz1RoTAGRlZWHRokXFHnv77bc1kp2/ZMkSZGRkFN13c3ND37591dZ+fHw8EhISJNV5+PBhsfvZ2dnIyspSWM/MzAw2NjavPZ6TkwO5XC66f0tLy9dea7lcjpycHNFtAM9X5TQzK77bfX5+frG/IUV4TKXjMZWOx1QyHlPpeEyl4zGVjMdUOh5T6XhMJeMxlY7HVDoeU8l4TKXjMZWOx1QyHlPpeEyl4zGVjMdUOh5T6XhMJeMxlY7HVDoeU8l4TKXjMZWOx1QyHlPpeEyl4zGVjMdUOh7T/8vOzhbdhyYwedGAvJwQBwB2dnaQyWSS2rC3ty+zTVWZmZnht99+Q9u2bbFw4UIkJCSgsLAQhw4dwqFDh0qt165dOyxcuBBdunRRazwvfPLJJ8W2y7a3t8fcuXPV3s/FixexZs2aYo/NnTsXdnZ2autj/fr1WLhwoUpt3L9/HwUFBQrL2drawsfH57XH7927J2nwqlq16mtXReTk5ODOnTui2wCAJk2avPZaxsXF4cmTJ6Lb4DGVjsdUOh5TyXhMpeMxlY7HVDIeU+l4TKXjMZWMx1Q6HlPpeEwl4zGVjsdUOh5TyXhMpeMxlY7HVDIeU+l4TKXjMZWMx1Q6HlPpeEwl4zGVjsdUOh5TyXhMpeMxlY7HVDIeU+l4TP8vLCxMdB+aYKa4COmLVxMNS8q6VcTW1rbMNtVBJpPh/fffx82bN0WtONiuXTvMnDkTnTp1UnssAPDHH3/gxx9/LPbYsmXLULlyZbX2Ex8fj7feeguFhYVFj7Vo0QIffPCBWvshIiIiIiIiIiIiIiIiIiIiIiIydExeNCCvLuNpZWUluQ1ra+ti9zWx9GdmZiY+/vhjeHt7488//1RY/sKFCxg0aBAaNmyIy5cvqzWWq1evYsKECcUe6927t9oTCnNzc/Hmm2/i8ePHRY85Ojri999/h7m5uVr7IiLxcnNzkZWVhaysLEnLKRMRGSq5XF407nHsIyJT8vL4p+stLoiItOXF2JednV3sYloiImMml8uLxj2OfURkKuRyedG4x7GPiEwJxz4i0yATBEHQdRAkzrVr19CyZcui+x4eHnj69KmkNn788UdMnTq16H6fPn1EJRiKFRMTgy5duiA0NLTosbp162L69Ono3LkzqlatCjMzM8TGxuLcuXP4/vvvcePGjaKyFhYW2LNnDwYOHKhyLA8fPkS7du0QHx9f9Fi9evVw4cIFuLi4qNz+C3K5HMOHD0dAQEDRY+bm5ti/fz/69euntn5eiI+PR0JCgqQ6Dx8+LPaaXrt2DQ0aNFBYz8zMrMQVPnNyciQlQ1haWsLS0rLYY3K5/LWEXEVsbGxgZlY85zo/Px/5+fmi2+Axlc7Yjik7Oxv37t0riuXFcs2GfEyA8f07ATymsvCYSsZjKl1+fj5CQkKK7jdp0gQ2NjYGfUzG+O/EYyodj6lkPKbSvTimrKysoq0vBEGAt7f3azsPlEUfj+llxvLv9DIe03M8ppLxmEr38jG9GPsEQYBcLke9evVEj336ekwvGNO/0ws8pud4TCXjMZXu1WPKysrC7du3i9oQO/bp8zEBxvfvBPCYXuAxlYzHVLqSjikjIwO3b98uui9m7NP3YzLGfyce03M8ppLxmEpX2jGlpKQUyztRNPYZwjEZ478Tj8k4junu3bto0aJF0f2goCA0bNhQdL+qYvKiAQkJCSmWcFauXDmkpqZKamPNmjWYOXNm0f3hw4dj586daokvJycHzZs3R3BwcNFjEydOxA8//FDqKpGCIODLL7/E0qVLix6zsbHBzZs3Ub9+faVjiYmJQfv27REeHl70mKenJy5cuABPT0+l2y3Ju+++iw0bNhTdl8lk+OWXXzBu3Di19qOK4OBgNGrUqOi+tgcaIl14+Qds4P+TF4mIjBnHPiIyVRz/iMgUcewjIlPEsY+ITBHHPiIyRRz7iLRH1zlF3DbagDg4OBS7n5WVBam5p5mZmWW2qYoVK1YUS1zs3LkzNmzYUOb21jKZDEuWLME777xT9FhOTk6xBEupkpOT0b1792KJixUqVMDJkyfVnrg4a9asYomLAPD111/rVeIiERERERERERERERERERERERGRvmHyogFxc3ODTCYrup+fn19sS2QxoqOji913d3dXS2yFhYVYt25dsceWLFny2tKopVm6dGmxsseOHcPjx48lx/Hs2TP07NmzWBJluXLlcPz4cdStW1dye2VZvnw5li9fXuyxefPmYcaMGWrth4iIiIiIiIiIiIiIiIiIiIiIyNgwedGA2Nraolq1asUei4qKktTGq+Xr1aunclwAcOfOHSQmJhbdd3NzQ+vWrUXX9/T0hI+PT9F9QRBw/vx5STFkZ2ejb9++uHbtWtFjdnZ2OHLkCJo2bSqpLUV++OEHzJo1q9hj06dPx8KFC9XaDxERERERERERERERERERERERkTFi8qKBeTXZ8O7du5Lqh4SElNmesl7eohkAvLy8iq0SKUaNGjWK3X91lciy5OXlYfDgwfj333+LHrO2tsaBAwfQrl07SXEo8uuvv+LDDz8s9tj48ePxzTffqLUfIiIiIiIiIiIiIiIiIiIiIiIiY8XkRQPj6+tb7P7FixdF142NjUVERETRfUtLSzRo0EAtceXm5ha7b2FhIbkNS0vLYvcLCwtF1SssLMTIkSNx9OjRYv3v3LkT3bp1kxxHWfbu3Yvx48dDEISix4YNG4aff/5ZcrImERERERERERERERERERERERGRqWLyooHp27dvsfunTp0qlkhXlhMnThS736lTJzg4OKglLldX12L3Y2JiJLfx6kqLFSpUUFhHEASMHz8ee/fuLXrMzMwMW7ZswcCBAyXHUJajR49i5MiRxZIq+/Tpg99++w1mZnwrERERERERERERERERERERERERicWMKwPTtm1buLm5Fd0PCwvDmTNnRNX95Zdfit0fMGCA2uLy8vIqdj8qKgqPHj0SXf/Zs2e4du1ascdq1aqlsN4HH3yAX3/9tdhj69evx6hRo0T3LcbZs2cxePBg5OXlFT3WqVMnBAQEvLZiJBEREREREREREREREREREREREZWNyYsGxszMDGPHji322MKFCxWuvvj333/j3LlzRfcdHR0xbNgwtcXl7e2NqlWrFnts9erVouuvWbOm2NbTdnZ2aN26dZl1Zs2ahfXr17/W55QpU0T3K8b169fRr18/ZGdnFz3WunVrHDp0CDY2Nmrti4iIiIiIiIiIiIiIiIiIiIiIyBQwedEAff7558W2ez579ixWrFhRavno6GhMnDix2GPTp08vtoJjSWQyWbGbohUeX13tcMOGDa+tiliSw4cPY8mSJcUee+utt2BtbV1qnRUrVmD58uXFHps/fz5mzpypsD8pgoOD0bNnTzx79qzoMV9fXxw9elRtW24TERERERERERERERERERERERGZGgtdB0DSubm5Yfbs2Zg9e3bRY7NmzUJUVBTmzp2LypUrAwDkcjkOHTqE6dOnIyoqqqhs5cqV1Z7kBwCfffYZNm7ciOTkZACAIAgYM2YMTp8+jU8++QQNGzYsVv7hw4dYu3Yt1q9fD7lcXvS4nZ0d5s2bV2o/27ZtwxdffFHssfbt26N9+/Y4deqUpJgrV66MBg0alPhcbGwsunfvjqSkpKLH7O3t8dlnn+H69euS+gGArl27Sq5DRERERERERERERERERERERERkjGSCov2GSS/J5XIMGDAAf/75Z7HHzc3NUb16dZQrVw7h4eFITU0t9rytrS1OnjyJdu3aKexDJpMVu3/69Gl07NixzDr//vsvunfvXmwL6Bfc3d1RtWpVyGQyxMTEIDY29rUyZmZm2LdvHwYMGFBqH2PHjsW2bdsUxi/GmDFjsHXr1hKfO3PmDDp16qSWfgAo3Npbk4KDg9GoUaOi+0FBQa8lkxIZG7lcjpycnKL7NjY2MDPjgsNEZNw49hGRqeL4R0SmiGMfEZkijn1EZIo49hGRKeLYR6Q9us4p4sqLBsrMzAx79uzBuHHjsHPnzqLHCwsLERYWVmIdV1dXBAQEiEpcVFaHDh1w6tQpjBo1CpGRkcWei4+PR3x8fKl1PTw8sHnzZvTu3Vtj8RGR6TAzM4OdnZ2uwyAi0iqOfURkqjj+EZEp4thHRKaIYx8RmSKOfURkijj2EZkOpiUbMBsbG/zxxx8ICAiAr69vqeXs7e0xdepU3L17V+HKierQvn17/Pfff/jmm29Qr149heW9vLywZMkSBAcHM3GRiIiIiIiIiIiIiIiIiIiIiIjIBHDbaCPy8OFDXLlyBdHR0cjLy0P58uVRv359tGvXDjY2NjqL6+nTp7h27RpiYmKQmpoKQRBQrlw5eHh4oHnz5qhWrZrOYjMVul7ilYiIiIiIiIiIiIiIiIiIiIiI9Iuuc4q4bbQRqV27NmrXrq3rMF5TsWJF9OvXT9dhEBERERERERERERERERERERERkZ5g8iIRERml/Px8xMXFFd338PCApaWlDiMiItI8jn1EZKo4/hGRKeLYR0SmiGMfEZkijn1EZIo49hGZDiYvEhGRUcrPz8eTJ0+K7ru4uPCEloiMHsc+IjJVHP+IyBRx7CMiU8Sxj4hMEcc+IjJFHPuITIeZrgMgIiIiIiIiIiIiIiIiIiIiIiIiItPC5EUiIiIiIiIiIiIiIiIiIiIiIiIi0iomLxIRERERERERERERERERERERERGRVjF5kYiIiIiIiIiIiIiIiIiIiIiIiIi0ismLRERERERERERERERERERERERERKRVTF4kIiIiIiIiIiIiIiIiIiIiIiIiIq1i8iIRERERERERERERERERERERERERaRWTF4mIiIiIiIiIiIiIiIiIiIiIiIhIq5i8SERERERERERERERERERERERERERaxeRFIiIiIiIiIiIiIiIiIiIiIiIiItIqC10HQEREpAlmZmawtbUtdp+IyNhx7CMiU8Xxj4hMEcc+IjJFHPuIyBRx7CMiU8Sxj8h0yARBEHQdBBFpVnBwMBo1alR0PygoCA0bNtRhREREREREREREREREREREREREpEu6ziliajIRERERERERERERERERERERERERaRWTF4mIiIiIiIiIiIiIiIiIiIiIiIhIq5i8SERERERERERERERERERERERERERaZaHrAIiIiIiIiIiIiIiIiIiIiIiIiMi4CIKAjNwC5BcKsDSXwcHaAjKZTNdhkR5h8iIRERmlnJwc3Lt3r+h+3bp1YWNjo8OIiIg0j2MfEZkqjn9EZIo49hGRKeLYR0SmiGMfEZkijn2GLfRpOg7disHtJ6kIik5HWnZ+0XPlbC3RqIoTfKqWxwDfKqhb0VGHkZI+YPIiEREZJblcjuzs7GL3iYiMHcc+IjJVHP+IyBRx7DNicjmQnaz9fm1dADMz7fdLJAHHPiIyRRz7iMgUcewzTP+ExuGnM2G4GlH6nDYtOx8XHibhwsMkrD/zCC29XPBex1roVM9di5GSPmHyIhERERERERERERGRvshOBlbV0n6/nz4C7N203y8REREREREZtJTMPMw/FIxDt2Mk170akYyrW5MxwLcyFvRrCGd7Kw1ESPqMl1ESERERERERERERERERERERERGRJCGx6ei59l+lEhdfdvBWDHqu/RehT9PVFBkZCiYvEhERERERERERERERERERERERkWghsel4a+NlxKXnqqW9uPRcDN9wmQmMJobJi0RERERERERERERERERERERERCRKSmYexm65irTsfLW2m5adjzGbryIlM0+t7ZL+YvIiERERERERERERERERERERERERiTL/ULDaVlx8VVx6LhYcDtZI26R/mLxIRERERERERERERERERERERERECv0TGodDt2M02sfBWzH4JzROo32QfmDyIhERERERERERERERERERERERESn005kw7fRzVjv9kG4xeZGIiIiIiIiIiIiIiIiIiIiIiIjKFPo0HVcjkrXS19XwZNx7+kwrfZHuWOg6ACIiIiIiIiIiIiIiIiIiIrWTy4Fs7SRYFGPrAphxHSEiIjI+h25pdrvo1/q7HY1PK9bTap+kXUxeJCIiIiIiIiIiIiIiIiIi45OdDKyqpf1+P30E2Ltpv18iIiINu/0kVbv9PU7Tan+kfUxeJCIio2RpaYmqVasWu09EZOw49hGRqeL4R0SmiGMfEZkijn1EZIo49hGRKeLYp58EQUBQdLpW+/wvOg2CIEAmk2m1X9IeJi8SEZFRevWElojIFHDsIyJTxfGPiEwRxz4iMkUc+4jIFHHsIyJTxLFPP2XkFiAtO1+rfaZl5yMzrxAO1kxxM1b8lyUiIiIiIiIiIiIiMnW73gbcGwCutZ/fXGoBztUBc65wQkREREREREB+oaCTfvMK5IC1TromLWDyIhERERERERERERGRrgkCkPQIuHdUN/1HXX5+e5nMHHD2+l9CY63/3f6X3OhYGTAz00moREREREREpH2W5rrZutnKgnNPY8bkRSIiIiIiIiIiIiIibRMEIOkhEHEOiLgARJwHMp7qOqrihEIg+dHz24NXnrOweb46Y1FC40uJjXaugEw3P2oRERERkQGSy4HsZO33a+vCC3KIJHCwtkA5W0utbh1dztYS9lbmWuuPtI/Ji0REZJTkcjlycnKK7tvY2MCMkw8iMnIc+4jIVHH8IyJTxLHPAAkCkPjgf8mK54HIC0BGnK6jUl5BDhAf/Pz2Kpty/7/1dLHExlqAtaP2YyWjwbGPiEwRxz4yCdnJwKpa2u/300eAvZv2+yWFOPbpJ5lMhkZVnHDhYZLW+mxcpRxkvDjOqDF5kYiIjFJOTg7u3LlTdL9Jkyaws7PTYURERJrHsY+ITBXHPyIyRRz7DIAgAIn3/z9ZMeICkBmv66i0IycNiL7x/PYqB4//T2R0eWm1RpcagIW19mMlg8Kxj4hMEcc+IjJFHPv0l0/V8lpNXvTxLKe1vkg3mLxIRERERERERERERKQqQQAS7hVfWTEzQddR6Z+MuOe3yAuvPCEDynv+fzJj0cqNtYDy1QAzbhNGRERERESkay1ruGD9mUda66+/TxWt9UW6weRFIiIiIiIiIiIiIiKpBAFICP3fqornnq+smJWo66gMmACkRj2/Pfqn+FPmVoBzjf9tP12reHKjY0WAW4gRERERERFp3K2oZCzZfQ4uyNNKf02rlUddx1xAbg9w23CjxeRFIiIiIiIiIiIiIiJF5PLiyYqRF4As7W2VZdIK84DEe89vr7JyAFxq/v9W1EUrN9YCbJ21HysREREA/NwZsLQDLKyf38yt/vdfa8DC6pX/vvT8a2WsYV4IlI+PgWBmCbmZJcxi8gF7p/+v+2odc0sm9hMRkdodC4rFwl3/4pL5ZMBGS53GA1gF4NNHgL2bljolbWPyIhERERERERERERHRq+RyICGk+MqK2cm6jkpzanUF0p8AyWHPkwUNRV4G8PTO89urbF2KJzO++K9LTcDKXvuxEhGR5uRmAOkxQHr0/27/+//kcN3EkxqptqasAdR7+YGrIiq9mhT5WnJkCc+VmGApMfnS3AqwsHn9MSZTEhEZLEEQ8PO5MCw7GgpnQQ6Y6zoiMjZMXiQiIiIiIiIiIiIiksuB+ODnSYovVlbMTtF1VNozaMPzlSzkhUDaYyDpIZAU9r///u+W9hgQ5LqOVLzsZODJ1ee3VzlV+V8i48urNdYGnKs/X7GKiIj0R+6z/09GTIsuOUkxJ03XUeqXwtznN31h/mrC40v/tbAp/TmxyZFlJVa+tjKlFbceJSISqaBQjnmHgvH7lShdh0JGjMmLRERERERERERERGR65HIgLuh5kmLEed0mK9qUA6q3B7zaAR6NgF/76yYOADAzB5y9nt9qv/JcQS6QElE8ofFFgmPGU+3HqooXSS/h/xZ/XGb+PIHxRTJj0ZbUtZ8nPDLZgYhIvXLS/5eA+OR//40B0l76//RoIDdd11GSqgrznt/0ZXFnM8syEiBLW5myrARLGxFJl9avJGy+9BjPL4hIDz3LyccHvwfi7P0EXYdCRo7Ji0RERERERERERERk/OSFz5MVI15KVsxJ1U0sNuUBr/ZA9XbP/+vR8HnSIABkJuomJjEsrIEKdZ/fXpX7DEh6BCQ/ev7flxMcDWk1LKHw+dbZyWHAgxPFn7Ow+d9KjTWLr9boUuv5qpXcEpOI6P8JwvOkw/SY/62WGF1CkmI0kPdM15GSKZLnA3n5uo7i/5lZKFh1UpXkSJFbfr+cvGnGPWGJTF1MajbGb72G0Kf8nCbNY/IiERERERERERERERkfeSHw9L//T1SMvKC7JDpb5/8lKr7xfHVF94bGt8KOtSP+j707j4+qvNs/fs1MJpnsEBISsrEKIWyiBBBc675U26f91a6K2NZa29rW2qpVq1XrU/VR21q1i4JtrdrFVsVdK9aVXbawbyEsCRCykGSSycz5/THZJiRhMszkzMz5vF+vaThnzn2f7yH4ZSgX9638E/2v7gxDaqrpCjLWdAQb20OObc1mVBuaNrd/a/Hq9Ue/l5Tp34Z62LjAr1ljJVfG4NcKAJFkGP7fU3tu31zXYyvn1iNmVwrEBl+b/+VpNLsSP5tjAFtyhxCO7C1YGS3PDkBrK+t09VPLVN3QYnYpsAjCiwAAAAAAAACA2OfzSvvX+MOKOz+Qdn0otZgVVszyhxRHneYPLQ4vjb+wYrBsNil1mP9VPCvwPZ9PatjbbaXGbV0Bx8M7/X+JHyta6qS9K/2vnlKHHx1qHDZOGjpacroGv1YA6I9h+Fcm7i2M2D2kSNAIiF+GV/I0+V/x7u2fS4lp/tUm7Q7/Kpi29q92e4/j3q5pP9fncV/XJEg2e4/jbvP3PMcK3xgkb5ZX6XvPrFKzx2t2KbAQwosAAAAAAAAAgNjjbesKK+7qCCvWm1NLyrDAlRVzJlo3rDgQdruUWeh/jTkj8D2vR6qtCAw1dvy4vtKcekPVWO1/VXzY4w2bNKSofSvqbttQDxsjZRZLDv4KB0CYGYbUfDgwkFjXSzjRCoElAJCklU+ZXUFwbD2DlD2PezvXR/jymMHKXoKWAwpfdpu/57kBBENtLa1Katovw2aXYXNIjQckpfdxX/7sdbwMw9CCD3bqrpfLZRhmVwOr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STABILITY SUMMARY ---\n","Mean Accuracy: 0.8210\n","Std Dev: 0.0021\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# 1. Atur font agar terlihat profesional dan ukuran figure yang lebih besar\n","plt.rcParams.update({'font.size': 12, 'font.family': 'serif'})\n","\n","# 2. Gunakan figsize (10, 6) dan DPI 300 (standar HD untuk paper)\n","plt.figure(figsize=(10, 6), dpi=300)\n","\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","\n","plt.title('Learning Curve (Naive Bayes)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","\n","# 3. SIMPAN SEBAGAI PDF ATAU SVG (PENTING!)\n","# Simpan sebagai PDF agar tidak pecah saat di-zoom atau dimasukkan ke MS Word/LaTeX\n","plt.savefig('learning_curve_nb.pdf', bbox_inches='tight', dpi=300)\n","plt.savefig('learning_curve_nb.png', bbox_inches='tight', dpi=300) # Tetap simpan PNG sebagai cadangan\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":600},"id":"1Ji8VkFHovQE","executionInfo":{"status":"ok","timestamp":1777422503249,"user_tz":-420,"elapsed":4778,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"ce275028-e30f-45d9-eb7a-5f2ac0aa5f10"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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joblib\n","import os\n","\n","# Ambil model terbaik\n","best_bnb_tags = grid_search.best_estimator_\n","\n","# Pastikan direktori tujuan ada\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/retrain_tags_specifications/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan model\n","joblib.dump(best_bnb_tags, os.path.join(dir_path, 'nb_tags_specifications.pkl'))\n","print(\"Model Tags & Specifications berhasil disimpan!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zlSBDwGdsfU6","executionInfo":{"status":"ok","timestamp":1777339663796,"user_tz":-420,"elapsed":67,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"d0b57519-8107-4c0e-8314-70fd13f30106"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Tags & Specifications berhasil disimpan!\n"]}]},{"cell_type":"markdown","source":["#### With price patterns"],"metadata":{"id":"VGlYJLbyY2Yw"}},{"cell_type":"code","source":["import numpy as np\n","\n","user_price_features = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_features.npy')\n","user_price_label = np.load('/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/Processing Data/user_price_label.npy')"],"metadata":{"id":"jFE65xXVXe6r","executionInfo":{"status":"ok","timestamp":1777528639608,"user_tz":-420,"elapsed":16724,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n","from sklearn.naive_bayes import BernoulliNB\n","from sklearn.metrics import classification_report, accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","import os\n","\n","# 1. SETUP DATA\n","X = user_price_features\n","y = user_price_label\n","scaler = MinMaxScaler()\n","X = scaler.fit_transform(X)\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# 2. GRID SEARCH (Parameter Sensitivity Analysis)\n","param_grid = {'alpha': [0.1, 0.5, 1.0, 2.0, 5.0], 'binarize': [0.0, 0.5, 1.0]}\n","bnb = BernoulliNB()\n","grid_search = GridSearchCV(bnb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n","grid_search.fit(X_train, y_train)\n","\n","# --- BAGIAN PENCATATAN SENSITIVITAS PARAMETER ---\n","sensitivity_df = pd.DataFrame(grid_search.cv_results_)\n","relevant_columns = [\n"," 'param_alpha',\n"," 'param_binarize',\n"," 'mean_test_score',\n"," 'std_test_score',\n"," 'rank_test_score'\n","]\n","sensitivity_results = sensitivity_df[relevant_columns].sort_values(by='rank_test_score')\n","\n","# Setup Path Penyimpanan\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/nb_price/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan Sensitivitas ke CSV\n","sensitivity_results.to_csv(os.path.join(dir_path, 'nb_price_parameter_sensitivity.csv'), index=False)\n","print(f\"Parameter sensitivity data saved to {dir_path}\")\n","\n","best_model = grid_search.best_estimator_\n","\n","# 3. LEARNING CURVE ANALYSIS\n","train_sizes, train_scores, test_scores = learning_curve(\n"," best_model, X, y, cv=5, n_jobs=-1,\n"," train_sizes=np.linspace(0.1, 1.0, 10),\n"," scoring='accuracy'\n",")\n","\n","# 4. SAVE LEARNING CURVE DATA TO CSV\n","lc_df = pd.DataFrame({\n"," 'train_size': train_sizes,\n"," 'train_score_mean': np.mean(train_scores, axis=1),\n"," 'test_score_mean': np.mean(test_scores, axis=1)\n","})\n","lc_df.to_csv(os.path.join(dir_path, 'nb_price_learning_curve.csv'), index=False)\n","\n","# 5. PROFESSIONAL HD PLOTTING\n","plt.rcParams.update({'font.size': 12, 'font.family': 'serif'})\n","plt.figure(figsize=(10, 6), dpi=300)\n","\n","plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o', linewidth=2)\n","plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Cross-validation Accuracy', marker='s', linewidth=2)\n","\n","plt.title('Learning Curve: Naive Bayes (Price Patterns)', fontsize=16, fontweight='bold')\n","plt.xlabel('Training Set Size', fontsize=14)\n","plt.ylabel('Accuracy', fontsize=14)\n","plt.legend(fontsize=12)\n","plt.grid(True, linestyle='--', alpha=0.7)\n","\n","# Simpan Grafik (PDF untuk kualitas vektor, PNG untuk pratinjau cepat)\n","plt.savefig(os.path.join(dir_path, 'nb_price_learning_curve.pdf'), bbox_inches='tight', dpi=300)\n","plt.savefig(os.path.join(dir_path, 'nb_price_learning_curve.png'), bbox_inches='tight', dpi=300)\n","plt.show()\n","\n","# 6. STABILITY SUMMARY\n","fold_scores = [grid_search.cv_results_[f'split{i}_test_score'][grid_search.best_index_] for i in range(5)]\n","print(\"\\n--- STABILITY SUMMARY (PRICE PATTERNS) ---\")\n","print(f\"Mean Accuracy: {np.mean(fold_scores):.4f}\")\n","print(f\"Std Dev: {np.std(fold_scores):.4f}\")"],"metadata":{"id":"bb9ivsa2n4cq","executionInfo":{"status":"ok","timestamp":1777528806064,"user_tz":-420,"elapsed":165086,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"3d4bc543-7ab2-4415-8b62-c47a57bedf81"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 15 candidates, totalling 75 fits\n","Parameter sensitivity data saved to /content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/nb_price/\n"]},{"output_type":"display_data","data":{"text/plain":["
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tHcUZfqIpqqD3dk+uzlNFde1VctWV61in3rqKbc6N1qtVj311FNasGBBg+v985//1FVXXdWoMrs5drtdTz75pB5++GHl5eXVH+/cubOOO+44JSQkqFOnTjpw4IA2b96sH3/8UVu2bNFtt92m2267TWPHjtX999+vE044wa3xvP28eXt8AGjKuYNCXRZlStKn6/boummJ6hXZ8t9ZAIAj81zaVq3eY7yVRWSQRTcfFaFAiqcAuCk1OdZlUWa53amtRbVK7sIdeQAAgJaqtDu1u6xWOSV/LMCsU0GFQ16svXTJ7nAqVLy3BAAAAOD7LE6ns72+98IfnHLKKfryyy8bHHv55Zd15ZVXtuh6xxxzjH766acGxz788EOdddZZLc6xOQMGDND27dsbHR8+fLjWrVvn0bUuvfRS/fe//21w7KmnntINN9xgeG5ubq5OPvlkrVmzpv5YRESEHnzwQV199dUKCgpqdE5ZWZkeffRRPfDAAw22xL700kv10ksvyWZzfYPoww8/1DnnnNPg2DHHHKPFixcb5tuUl19+WX/6058aHDvllFMadBD9oxtuuEHPPPNMg2P33HOP7r///haNf9FFF+mtt95qcOyRRx7Rbbfd1qLrHS4/P18FBc3fmGtKVlaWTj/99PrHK1eu1ODBgw3Ps1qtCgkJaXS8qqpKDodxN6hDbDZbo58Dh8Phshi5KSEhIbJarQ2O2e122e2uC9f+iDk1jzk1z1tzmvPOeqVl7nd53ZnDuunpC0b5zJya4uvPU1OYU/OYU9OYU/Pa85x+ztqni15ZbtglxSLppdkjNDGhi6T2PadD/Ol5OoQ5MSdX2uOcsvLLNH1emsvY66b00w1T+zf5vfY4pz/yl+fpj5jTQcypacypecypecypacypecypedXOAG37bdvxzPzfvvJKtbfYs+u0ByvvnKzw4EC/fJ6YU9OYU/OYU/OYU9OYU/OYU/OYU9OYU/OYU/OYU9O8PadNmzZp7Nix9Y83bNigIUOGuD3ukaBTpo+IjIxsdOyPRYKeaurcpsZoDaGhoU0eP/vssz2+1tlnn92oKHP+/PmGRZmFhYWaNGmStm3bVn+sc+fO+v777zVqVPPb40ZEROhvf/ubjj76aM2aNUs1NTWSpNdff135+fn64osvGv0C+iNvP2/eHt9TcXFxiouLO6JrhIaGut05tSlNLQaeslqtR5TDIU0tKi3BnJrGnJpnxpzuOHGQ0jKXuDz/q19ztXlaqYb06HTEufA8Ne9I53T4i92mXpy7q73MSfK/50liTq4wp4bySqp007u/uLVt3S3TkzV9WC+XMe1hTn/kL8/THzGnpnWUObVkLfb2nBJiw9UzOlS7i5rfeeHn7UX680z3nz9vz+mPOsrPXkswp+Yxp6Yxp+Yxp+Z5Y07Nrce+PKfmMKfmtfWc9pdVK+u3osuD/1uqzLwy5ZdWH/G47UGnUJtioiNlsTTdKdNXnidPMKfmMaemMafmBQQENCgQaenfqdvTnPzxeWJOzWNOTWNOzWNOzWNOTWvrOXn6d2qz5tRczZoZKMr0EREREY2OlZeXt/h6ZWVlbo3RGpor2jvqqKM8vtaECRMaHVu2bJnsdrvLX2RXXHFFg4JMSXrhhRdcFmT+0fHHH6/7779fd955Z/2x//3vf/rnP/+pv//9782e5+3nzdvjA0BzhvbspBOHdNPXG3Ndxj22IEMvXzrWZQy8q6qqSuvXr69/PHz48FZ5QQ/AO+x1Dt3w9hrtK6sxjJ2SHKsbj000ISsArvjiWmyxWJSaEqu3l+9sNmZdTpEOlNeoc3jjXS0AAGhvfHE9RvvkdDpVUFpd3+0y8w9FmIXlxu/TfNng7lHNFmQCgBHWYgAAvIu1uDGKMn1EVFRUo2OlpaUtvl5T57ZVp8zmrjto0CCPrxUbG6vY2NgGW1xXVlZq06ZNGjFiRJPnLF26VJ9++mmDY6NHj260rbiRW265RfPmzVNeXl79sX//+9+aM2eO4uPjmzzH28+bt8cHAFfmzkjWN5ty5XTRie27zflau6tII3tHm5YXAHRkD3+TrpXZBwzjuncK0ePnjZTVyg0zAC0zJcl1UabTKS3J2qdZI3qYmBUAAIA5nE6n9hZX1RdfZv1h2/GSqpbvduXLft1drM/X7dEpw7tTnAkAAADA51GU6SP69+/f6Ni+fftafL3Dz7VYLE2O0Ro6dWp629nOnTu36HpxcXENijIl1/8tXnjhhUbHLrnkEo/HDQoK0gUXXKDHH3+8/lhlZaVeffVV3X333U2e069fv0bHjuR5O3zekjRgwIBm41v758bT8QHAlZRukZo1vIc+W7fHZdy8BRn67xXjTMoKADqurzfk6sVF2wzjAq0WPTN7tLrQvQ7AEZiY2FWBVotqHc1/Qicto4CiTAAA4NMcDqdyDlQqM/8PhZf5ZcrKK1V5TZ2302tXyqprdeM7v+i/S7N13ylDNKxX0/eWAAAAAMAXUJTpI4YMGdLo2J49rotYmlNcXKyKiooGx/r06dNm21AnJja9pWFLx2uqM2NhYWGz8QsXLmx0bNKkSS0ae+LEiQ2KMiXpu+++a7YoMzIyUn369NHOnb93/ygrK1NpaWmLOkzu3bu30bGmfjZcfa+lPzdNjW+z2ZScnNzi6wHAzdOT9MX6PXJxL16LMgq0MrtQY/t1MS8xAOhgduwv1x0frHMr9q8nD9LoPi37gBUAHBIVYtPovp21Ynvz7+cXZRTI6XTSKQkAALR7tXUO7SysqN9q/NDW41sLylRld3g7PY9FBgcqMT5CuwortK/M3G3TV2Yf0KnPLNE5Y3rp9hNSFBcZYur4AAAAANAaKMr0EYMHD250LDs7u0XXauq8pq7fWoYNG9bk8ZqaGoWGhnp8vaZuxjib2fu2qqpKu3btanS8qQ6W7mjqvC1btrg8Z/DgwQ2KMqWDz0Fz/11c8fS569evn8LCwhoU4bb056a4uFhFRUUNjiUmJspms7XoegAgSQmxETpjVC99tCbHZdyj36br3auPNikrAOhYqux1mvPmGpVWG2+Rd/Kw7rpsYr+2TwpAh5CaHOuyKDO/tFpbcks1qHuUiVkBAAA0r6bWoR37y3/barysvgPmtoJy1dT5XvFlp1CbkuMjlBgXqaS4CCXFRygpLlLxUcGyWCxauCVPV7y2yvS8nE7p/VU5+urXXN1wbKIun9RPwYEBpucBAAAAAC1FUaaPiI+PV7du3ZSbm1t/LD09vUUdIzZt2tTo2MiRI480xWY1V3xYVlbWoqLM0tLSRsdiY2ObjG2ug2Z0dLTH4zZ3ntF24CNHjtTXX3/d4NjmzZs9Lsp0OBxKT09vcKx79+6Ki4tr9hyLxaLhw4dr2bJl9ccKCwuVn5/v8rymmP1zA6DjuPm4JH26drfLrSuXbSvUz1n7NDExxsTMAKBj+PtnG7Vpb4lh3ICYcD101jA61gFoNanJsXr4m3SXMWkZBRRlAgAA01XZ67StoFyZ+aXa+odtx7P3lbv8G1Z7FRMRrKS4CCX+VniZGHew+DImIsjle7xjB8br1BE99Nm6lu/AdSTKqmv10P+26J0VO/XXmYM0Y3A870kBAAAA+ASKMn3IrFmz9NJLL9U/Li8vV2ZmpsfbR//yyy9NXrutDBo0SJ06dVJxcXGD43v27Gm2mNKVgoKCRseaKzAMCwtr8nhVVVWLtk+vqqpqdMyosHTWrFl66KGHGhxbu3atzj33XI/G3rJliyorKxtd28isWbMaFGUeGv/444/3aHyzf24AdBx9uobpnKN6650VO13GPfJtuj5K6MofXgGgFX2wapfeXdm4s/zhQmxWPXfRGEWG0CUdQOsZ3D1KMRFBLrfETEsv0LWpCSZmBQAAOpKKmlptzT9YfHmo+2VWfql2FlbIB2sv1S0qpEHRZVJ8hBJjI9Q5PKjF1/zHqUO0fPt+5ZVUt2Kmntmxv0JXv7FakxK76r5ThiilW6TXcgEAAAAAd1CU6UPOOOOMBkWZkrR48WKPizKXLFnS4HH37t01YcKEI86vOTabTaeddpr++9//Nji+ceNGjRgxwqNr7d+/X3l5eQ2OderUSQMHDmwyPjo6WjabTXa7vdF1WlKU2VRXTKOOkxMmTGjU5XTx4sUej3348yYd/JkwcsYZZ+ivf/1rg2OLFy/2uCjz8PGDgoJ08skne3QNAGjOjccm6qPVOS63eVqzs0g/ZhRoWopnnX4BAE3bvLdE9366wa3Y/ztjGDe9ALQ6q9WiyUmx+viX3c3GrNpRqPLqWoUH8ycsAADQcqVVdmX91u0yK79MmXkHizBzDlQan9wO9YwO/W2r8YPFl4m/FWJGtcEH6TqHB+n1K8bpvBeWqbjSbnxCG/opa79OemKRZo/vq1tnJB9RsSkAAAAAtCX+ou1DjjvuOEVHR6uoqKj+2FdffaUrr7zS7WsUFBRo+fLlDY6deeaZbd517LzzzmtUlPnzzz/rwgsv9Og6K1eubHRs6tSpCggIaPaclJQUbdjQ8GbzmjVr1LdvX4/GPnTe4ZKSklyeY7VadeaZZ+rZZ5+tP7Zs2TIVFhaqS5cubo/9xRdfNHjcuXNnHXfccYbnDRo0SIMHD26w/fhXX32l+++/3+2xa2tr9c033zQ4NmPGDEVFsYUcgNbRIzpUF47vo9d+znYZN+/bDE1NjqVbJgAcoZIqu657a42q7M0Xwx9ywbg+OnN0LxOyAtARpSa7Lsq01zm1dOt+TR8cb2JWAADAVxVV1NR3vMzML/2tALNMuSWNd8Fq7ywWqW+XMCX+1vHyUAHmgNhw0z+wMrBblN67ZoIufXVFq3TMjI8K1rlH9dZby3eqsLz5rulNcTilN5bt0Gfr9uiW6Um6aEJf2QKsR5wTAAAAALQmijJ9SFBQkObOnau//e1v9ce+/PJL7d+/X127dnXrGm+//bYcjt9vvNpsNt16661u57Bs2TKlpaUpNjZW5557rtvdJmfMmKG+fftqx44d9cc+/vhjPfnkk7Ja3X+z/MEHHzQ6Nnv2bJfnnHjiiY2KMr/++mu3ukwe7vDCxEPXN3LrrbfqpZdequ/YWVtbq3feeUfXX3+9W+MWFBQ0Gnvu3Lmy2dz71Oudd96pSy+9tP7xmjVrtHHjRg0ZMsSt87/88ksVFhY2uiYAtKbrpibonRU7VV3bfIHQr7uL9e2mPJ0wpJuJmQGAf3E6nbrzw/Xavq/cMHZIjyj9bdZgE7IC0FFNToqRxSI5XWwPmpZRQFEmAACo53Q6tb+8pn6r8d+LMMu0r8x7W2y3VIDVon5dw37fbvwPxZchtuYbUphtYLcofX3zFP398436dO2eFl/ntJE99PdZQ9Q5PEhXTR6gJ7/P1Os/Z6vWw/3iiyvt+sfnm/TW8p2695TBSk2ObXFOAAAAANDaLE6nqz97o70pKyvTgAEDVFBQUH/svvvu0z/+8Q/Dc+12u5KSkhoURs6ZM6dBB0dX7r//ft133331jxMSEvTTTz8pPt69GyPvvfeezj///AbH/vvf/+riiy926/y9e/cqKSlJ5eW/3zweOnSo1q9f77Jj2urVq3XUUUc1OBYeHq7s7GzFxMS4NbZ0cLv1YcOG6Y//ZIKDg5WVlaVevYw7B82ZM0fPP/98/eOEhARt2bJFgYHGtdF/+ctf9OCDD9Y/jo2N1bZt29wuinU4HBo+fLg2btxYf+ySSy7R66+/7tb5kyZN0s8//1z/+KSTTtJXX33l1rltbePGjRo6dGj94w0bNrhdbAqg/fnXl5v00uLtLmMGdovUVzdNltVKt8z2oKKiQuvXr69/PHz4cIWFhXkxIwBGXlmyXfd/sckwLjIkUF/eOFl9uvJvGmjP/GEtPvXpJVqfU9zs9/t2DVPaHdNMzAgAAM/4w3rcHjmdTuWXVtd3vczML1PWb///QIV3t9FuCVuARQNiIpT4h66XSfER6tc1XEGBvtXpceGWPD2ftk0rthcaB/9mXP8umpOaoGkD4xp9b2tBmR74YpN+SC9o4kz3HDcwTn89eZAGxLp33wSAf2EtBgDAu9rrWuzNmiI6ZfqYiIgI/eMf/9B1111Xf+zhhx/W7NmzlZyc7PLcf/3rXw0KMqOjo3Xvvfe6Ne7mzZsbFX5u3bpVf/7zn90u7DvvvPP05JNPNiju+8tf/qITTjhBcXGN34Qfbu7cuQ0KMgMCAvTEE08YbmE7ZswYnXvuuXr//ffrj5WXl+vqq6/W/Pnz3crdbrfr8ssv1+E1zDfccINbBZnSweLZd999t377+a1bt+qhhx7SPffc4/K8zZs367HHHmtw7J///KfbBZnSwS3UH374Yc2cObP+2BtvvKErr7xSU6ZMcXnua6+91uA5s9lseuihh9weGwA8cW1qgt5avlMVNXXNxmzJLdVXG/bqlOE9TMwMAPzD6h2FevCrzW7Fzjt3JAWZAEwxJSnWZVHmjv0Vyt5Xrn4x4SZmBQAAzOJwOLWnuLJB0WVmfpmy8stUWlXr7fQ8Fhxo/a3bZYSS4iOVGHew+2XfLmEK9JNtto8dGK9jB8YrPbdUn63brXW7ivXr7mIVV/5eLNsp1KZhPTtpRO9OOnVET6V0i2z2egmxEfrP5eP0Q3q+Hvhik7YWGO/scLjvt+RrUWaBLpvYTzcel6SoEPd2GgMAAACAtkCnTB915pln6uOPP65/nJycrO+++069e/duMv6NN97QZZddVr91ucVi0fz583X66ae7Nd6zzz7b5Fbb8fHxys3NdTvvbdu2aezYsQ22wh47dqw+++wzdevW9Fa0tbW1uuOOO/T44483OP7oo4+6vfX6nj17dPTRR2vnzp0Njl988cV68cUXFRIS0uy5RUVFOuecc/Tdd981OD5ixAgtWrRIUVFRbuUgSZ988onOPPPM+uJOq9WqN954QxdeeGGT8Tt27ND06dOVlZVVf+yMM85wu5j0cHPnzm3w3zEuLk4LFizQ8OHDm4z//vvvNWvWLFVWVtYfe+KJJ3TTTTe1aPy2QKdMwP88/M0WPfPDVpcxCbHh+nZuqgLolul1DodDVVVV9Y9DQkJktfrHDQbA3+wvq9bJTy5RbkmVYew1qQN090mDTMgKwJHyh7V4ZXahznl+qcuYf5w6RJdO7GdOQgAAeMgf1mMz1DmcyjlQUb/VeGZ+qbJ+K7509QHd9iosKEBJcRFK/K3j5aHulz07h3bIv1k5nU6V19SpptahoECrwoMCDJtqNMVe59AbS3fo8e8yVNLCotyu4UG67fgUnTe2d4d8LoCOiLUYAADvaq9rsTdriijK9FGVlZU65ZRTtHDhwvpjXbp00T333KNzzz1XPXv2VF1dndasWaOnnnpKb7zxRn2cxWLRM888ozlz5rg93jPPPKMbbrih0fG4uDjl5eV5lPvKlSt1wgkn6MCBAw1yv+2223T22Werf//+CggIUH5+vr799ls9/vjj+uWXX+pjrVarHnjgAd19990ejZuRkaFjjz1Wu3fvbnC8d+/euvnmm3XSSScpISFBwcHBqqys1ObNm/X555/rySefbFBEKkmDBg3SDz/84PbW7X/03HPP6frrr2/QdfOSSy7RDTfcoNGjRysgIEA5OTn64IMP9MADDzQYe9q0afryyy8VGhrq8bjSwV+Cl1xyid566636Y2FhYbrjjjt08cUXKyEhQU6nUxs3btRLL72kZ599VrW1v//R46677mqwjXp7QFEm4H+KKmo0+f/9oNJq1390nXfuCJ052r1uxQDQ0dU5nLrsPyu0OHOfYey4/l309lXj/aaDC4D2r7bOoVH3L3DZCevYgXF69bKxJmYFAABayl7n0I79Fb8VXB7sepmZV6atBWWqrnV4Oz2PRYYENthuPPG3Dpjdo0JkpeCvzRSW12jegnS9vXynHC28kzmoe5T+NmuwJgzo2rrJAQAAAPAJFGWiRWpqanTrrbfq2WefbbStdlBQkGpra+s7Yx4SExOjV155RaeeeqpHY23YsEEjRoxodL2LLrqoQcGnu7Zt26YLL7xQy5cvb/Q9i8WigICABsWAh/Tt21fPPvtsg224PXHgwAHddNNNevPNN5uNCQ4OVnV1dZPfs1qtuuGGG/TQQw+1uDBSkj777DNdddVVKigoaHT9wMBA1dTUNDhusVh03XXXad68eQoKCmrxuNLBT6s+8MADuv/++2W32xt8LzAwUE6nU3V1DT+VHB4erscee0x/+tOfjmjstkBRJuCfnvguU499l+Eypm/XMH13a6psFA0BgKHHFmToie8zDeNiIoL11U3HKC6q+U7yANAWrn1jtb7e2PxOHKG2AK392wwFBwaYmBUAAHClurZO2fsqDm43nnew42Vmfqm27yuXvc73bj11DrMpKT7ytwLMiPr/HxsZ3KKOj2gdm/eW6P4vNunnrftbfI2Zw7rp7pMGqXeXsFbMDAAAAEB7R1EmjsiKFSv04IMP6quvvmpUzHdIfHy8rrzySt16663q2rVlnwh84IEHdN9999UXgPbr109Lly5tdttxI06nU++9956efvpp/fzzz40KSw8JCAjQmDFjdMUVV+iKK66QzWZr0Xh/tHnzZr3wwgv66KOPlJOTYxg/YMAAnXfeebr66qvVr1+/Ix5fkgoLC/Xoo4/qlVdeabbbaFBQkGbOnKm77rpL48ePb5VxD9myZYsefPBBffjhh6qoqGgyJjo6WrNnz9add96p3r17t+r4rYWiTMA/lVbZNfnfP6iowu4y7qEzh+n8cX1MygoAfNOP6fm6/LWVMnrnZ7VIb101QUcn0EEEgPneWbFTd8//1WXMW1eN16TEGJMyAgAAh1TZ67S14Leiy7yDhZeZ+WXasb9CdS1tYehFsZHB9YWXiX8owuwaEezt1NAMp9Opbzfl6V9fbtbOwqbvZxgJCrTq6skDNGdqgsKDA1s5QwAAAADtEUWZaBUlJSVavny5MjIyVFRUpMDAQMXHx2v48OEaOXKkrNYj7yS2atUqLV68WFFRUTr33HMVGRnZCplL+/bt08qVK5WVlaWSkhIFBQUpNjZW3bp104QJExQdHd0q4zRl7969Wr16tfLz81VUVKSKigpFREQoOjpa3bt315gxYxQT03Y3fRwOh9auXav169crLy9PtbW1io6OVnJyssaPH6+oqKg2G1uSKisrtXz5cm3ZsqV+S/nY2FgNGTJEY8eOVWBg+/7jBEWZgP969scs/fvrdJcxPaNDtfD2VDomAUAzdhdV6pQnF+uAQZG7JP35xBRdNzXRhKwAoLHdRZWa9NBClzFXTxmgv8wcZFJGAAB0POXVtb91uyxrsPX4zsIKww95tUfdO4Uc3Gr8t23Hk+IObj0eHXZku1HBe6rsdXr1p+16ZmGWymvqjE9oQnxUsO48caBOH9mT7ecBAAAAP0dRJgC0EEWZgP+qqKnVlH//oH1lTXeBPuT+04bo4qP7mZMUGrHb7Q06PsfHx7dKV2sAR66m1qFzX1iqtbuKDGOPGxinly45ihtSgA/yp7V4+rw0ZeWXNfv9gd0i9fUtU0zMCAAA9/jaelxcaf+96DLv9yLM3UWV3k6tRXp3CT1YePlb0WVSfKQSYsMVGdJ+nwMcmfySKj38Tbo+WG28E1pzRvaO1n2zBmt0n86tmBkAb/G1tRgAAH/TXtdib9YUte8WeAAAoMMKCwrUtakJeuDLzS7jnlqYpXOO6q0QG90yvcFutysn5/c/gHfp0qVdvMAGIP3fV5vdKsjs1TlUj547goJMwEf501qcmhzrsihzS26pcour1K1TiIlZAQBgrL2uxwfKa5SZ/9t243m/bT+eX6q8kmpvp+Yxq0Xq2zX8t86XEb91vozUgNhwhQVxq6ujiYsK0cPnjNDFR/fVPz7fpNU7Dnh8jbW7inTmsz/rjFE9deeJA3mNCfi49roWAwDQUbAWN8Y7VQAA0G5dNKGvXlq8zeXNgvzSar25bIeumjzAxMwAoH37Yv0evfZztmFcUIBVz84ezfZ9ANqF1ORYvbJku8uYRRkFOndsb5MyAgCg/XM6nSooq1bWHzpeZuaXKiu/zHD3kfYo0GpR/5jfiy8T4w92wOwfE84HctHI8F7R+vDao/X5+r168KvN2ltc5fE1Pv5lt77ekKvrpiboT1MG8HMGAAAAoFVQlAkAANqtEFuAbpiWqHs/3egy7vm0rbpgXB+FB/PSBgC2FpTpzg/XuxX7t1MHa3iv6LZNCADcNK5/FwUHWlVd62g2Ji2TokwAQMfkdDqVW1L1h+3Gf996vLjS7u30PBYUYNWA2HAl/VZ0eaj7Zd+u4bIFWL2dHnyIxWLRqSN6aMageD2ftlUvLNqqKnvzryebUmmv06MLMvTuyl36y8xBmjmsmywWdpMAAAAA0HJULgAAgHbt3LG99XzaNu0uqmw2Zl9ZjV5fmq3rpiaamBkAtD8VNbWa8+ZqldfUGcaeMaqnLhzXx4SsAMA9IbYATRjQVWkZBc3GLMncp9o6hwIp1gCADsPpdKqsulb2OqdsARZFBAf6dbGUw+HU7qLK+o6XmX/ogFlWXevt9DwWYrP+1vUy8g9bj0eqd+dQ1nO0qtCgAM2dkazzxvbWQ//bos/W7fH4GruLKnX922s0rn8X3XfKYA3t2akNMgUAAADQEVCUCQAA2rXgwADdeGyi7pr/q8u4Fxdt08UT+ioyxGZSZgBgPlc3pJ1Op+75eIMy8soMr5McH6F/nTHUr29mA/BNqcmxLosyiyvtWpdTrDF9O5uYFQDAbFtyS/TZ2j1al1OkDbtLGnSC7BRq09CeURrRK1qnjeyplG6RXsy05eocTu0srFBmXml90eWhr0q78Yes2puI4MA/FF1G1Bdi9owOldXK+w6Yp0d0qJ68YJQuPrqv/vn5Jv26u9jja6zYXqhZTy/ReUf11u0npCgmIrgNMgUAAADgzyjKBAAA7d5ZY3rpubSt2rG/otmYogq7Xl2SrZunJ5mYGQC0PXdvSAcGWDX/l92G1wsLCtCzs8coLIi3gwDanynJsYYxizIKKMoEAD+1cEuenv9xm1ZkFzYbU1xp109Z+/VT1n49++NWjevXRXOmJmjawDgTM3Wfvc6hHfvL6zteZuaXKTOvVNv2laum1rMtltuDqJBAJcdH/lZ4GVlfhNktKoQPfaFdGduviz69fpI+XJOjf3+drn1l1R6d73RK767cpS/X79VNxyXp0on9FBRId1cAAAAA7uEuHAAAaPdsAVbdfFySbn1/ncu4lxdv06UT+yo6LMikzACg7Xh6Q9pdD501XIlxEa2RIgC0uoTYcPWMDtXuospmY9IyCjR3RrKJWQEA2tqB8hr97bONLdpueEV2oVa8VqjTRvbQ32cNUedw7/xNwF7n1N6yOuWU1imtcLt2HKhSZl6Ztu8rV63D6ZWcjkTX8KCD3S7jD3a8TIqLUGJ8hGIjgim+hM+wWi0696jeOmloNz3zw1a9umS7auo8K4Yura7Vv77arLdX7NRfZw7ScYPi+DcAAAAAwBBFmQAAwCecNrKnnvkhS1sLypuNKa2u1UuLt+mOEwaamBkAtK4juSFt5NKj++rUET1a/boA0FosFotSU2L19vKdzcasyynSgfIarxXdAABa1+a9JbrsPyuUV+JZF7vDfbp2j5Zt26/Xrxingd2iWim7xipr6rS1oEyZ+aX13S8zcku0s7BSv5delrXZ+K0tPipYSXGRDQowE+Mi1IV1Fn4kMsSmu04aqAvG9da/vtysbzfleXyN7fvKddV/V2lyUozuO2WwkuIj2yBTAAAAAP6CokwAAOATAqwWzZ2RrBve/sVl3H9+ytYVk/qra0SwSZkBQOtprRvSTRnRO1p/OXlQq18XAFpbarLrokynU1qStU+zKDIHAJ+3eW+Jzn9xmYor7a1yvbySap33wjK9d82EIy7MLKuuVdZvW41nHdp2PL9UOQcq5fS9xpfqGR16sPDyt+LLxN+KLzuF2rydGmCavl3D9eIlR+mnrH365+eblJ5X6vE1Fmfu04lPLNbFE/rqlulJ7NgDAAAAoEkUZQIAAJ8xc2h3DeyWpS25zf/BtKKmTs+nbdVfTx5sYmYAcORa+4b0H0WH2fTs7NEKDgxo9WsDQGubmNBVgVaLy61e0zIKKMoEAB93oLxGl/1nRau//i2utOvSV1fo65unuNVVubjCrsz8PxZelikrr1R7iqtaNS8zWCxS785h9VuNH9p2PCEuQhHB3A4CDpmUGKMvbzpG76zcpXnfputAhWe/h+ocTr32c7Y+Wbtbt85I1oXj+igwwNpG2QIAAADwRbwLBwAAPsP6W7fMa95Y7TLuv0t36E+TByguKsSkzADgyLTVDelDHjhtqHpGh7bJtQGgtUWG2DS6b2et2F7YbExaRoGcTqcsFouJmQEAWtPfPtvYJh3ipYMdM//++UY9cf6o+mP7y6obFF0e+v8FpW2TQ1sKsFrUt+vB4sukuEglxUcoIfbgV2gQH8QC3BEYYNXFE/rq1OE99Pj3GXpj6Q6XHwpqSlGFXfd9ulFvLtuhe08ZrMlJsW2ULQAAAABfQ1EmAADwKccPjtewnp306+7iZmOqax169set+vupQ0zMrGOyWq0KDQ1t8BiA59ryhrQkLdicp1PoKAf4JX9di1OTY10WZRaUVmvz3lIN7nFkW9MCALxj4ZY8fbZuT5uO8enaPSqusKvCXqes/DIVlte06XhtwRZgUf+YcCX9ttV40m/dL/vFhNEFH2glncJs+tusIZo9vo/u/2Kz0jIKPL5GRl6ZLn5lhaYPitc9Jw9Sv5jwNsgUgCv++t4YAABfwVrcmMXpdHr2sS8AaEc2btyooUOH1j/esGGDhgyhCAvwdz+k5+vy/6x0GRMUYNUPd0ylMxyAdm/hljxd8dqqNh/n1cuO0rED49t8HABoDRt2F+uUp5a4jLnrpIG6NjXBpIwAAK3p3OeXakV288X3HU1woFUDYiN+63x5sPgyMS5SfbuGycaWyICpftiSr/u/2KRt+8pbdL4twKIrJvXXDccmKjLE1srZAQAAAPCEN2uK6JQJAAB8ztTkWI3uE601O4uajampc+jphVl68Mxh5iUGAC3w/I/bzBknbRtFmQB8xuDuUYqJCNK+sua7mqWlF1CUCQA+aEtuSYctyAy1BfxWcPnbtuO/FWD26hymAKvF2+kBkDRtYJwmJcbov0uz9cT3mSqtqvXofHudUy8s2qaP1uTojhNSdPaY3vz7BgAAADogijIBAIDPsVgsuv34FF348nKXcR+s2qU5qQnq0zXMpMwAwDNm3pBesb1Q6bmlSukWacp4AHAkrFaLpiTFav4vu5uNWbWjUOXVtQoP5s9bAOBLPlvbttuWtweRwYFKjD/U+TKy/v/36BQqK8VZQLsXFGjVVZMH6IxRPfXoggy9u2KnHB7uO7ivrEZ3fvSr/rt0h/42a4jG9e/SNskCAAAAaJf4qzUAAPBJExNjNGFAFy3b1nwxU63DqSe+z9Sj544wMTMAcJ/ZN6Q/W7dbd3QbaOqYANBSU5JdF2Xa65xaunW/pg+mCzAA+JJ1OUXeTqHVRIfZlBwXqYQ/bDueFBep+KhgWSwUXwK+rmtEsP7vjGG6aHxf/fOLjS7/DtmcjXtKdO4LS3Xy8O66+6SB6tWZD48DAAAAHQFFmQAAwGfddnyKznl+qcuYj3/J0XXTEpQQG2FSVgDgPrNvSK/bVWzqeABwJCYnxchikZwuuhKlZRRQlAkAPsTpdGrD7hJvp+GxmIjgPxRdRigxLlJJ8RHqGh5E8SXQAQzuEaV3/jRBX2/I1b++2qycA5UeX+PL9Xv13aY8XTNlgK6dmqCwIG7RAgAAAP6MV/wAAMBnje3XRVOSY7Uoo6DZGIdTeuK7TD15wSgTM+s4qqqqlJ6eXv84JSVFISEhXswI8B3euCH96+5iOZ1ObhwDfsSf1+KuEcEa1rOT1uc0X1Ce5uJ1IACg/SmrrlVxpd3baTSrW1SIkuIjlPjbtuNJ8RFKjI1Q5/Agl+f583oM4CCLxaKThnXXtIFxemXJdj3zQ5Yqauo8ukZ1rUNPLszS+6tydNdJA3XayB68PwdaCWsxAADexVrcGEWZAADAp906I9llUaYkfb5+j66flqiUbpEmZdVxOBwOVVZWNngMwD3euCFdXGlXeU2dIoJ5Kwj4C39fi6ckxbosytxZWKHsfeXqFxNuYlYAgJay17lof2yiXp1Df+t8GanE2Agl/laIGRVia9H1/H09BvC7EFuArp+WqLPH9NK/v07XR2tyPL5GbkmVbnlvrV5fmq2/zRqikb2jWz9RoINhLQYAwLtYixvjThwAAPBpI3tHa/qgOH23Ob/ZGKdTemxBhp6/eIyJmQGAa966IV1T65CCvTI0AHgsNSVWT/+Q5TImLaOAokwA8BG2AO90hLvymP4a0iNKSXGRGhAbrnA+pATgCMVHhejRc0fo4qP76h+fb9QvO4s8vsYvO4t0+jM/6czRPXXniQMVH9WxOwkBAAAA/sTq7QQAAACO1NwZyYYxX2/M1YbdzXdZAgCzeeuGdFAgbwMB+I5RvaMVGeK6cIYtzAHAd0QEB6pTaMu6UbZUp1Cb7jl5kM4c3UvDenWiIBNAqxrZO1rz50zU4+eNVLcWFlXOX7Nb0x75Uc/8kKUqu2dbogMAAABon7gbBwAAfN6QHp00c1g3w7h5CzJMyAYA3OOtG9LhQQGmjgkARyIwwKpJCTEuY5Zu3a/qWm5eA4AvsFgsGtozytQxh/XsJIvFOx+IAtAxWCwWnT6qpxbenqqbjktScAs+DFlRU6eHv0nX9Hlp+t+ve+V0emd3DQAAAACtg6JMAADgF26ZniyjeywLt+Rrzc4D5iQEAAYsFouG9uCGNAAYSU2Jdfn9SnudVmXzGg8AfMWIXtHmjte7k6njAei4woICdeuMZH1/W6pOGd69RdfIOVCpOW+t0QUvLdOmPSWtnCEAAAAAs1CUCQAA/EJyfKROG9HDMO4xumUCaCdyDlRo54EKU8fkhjQAXzQl2XVRpsQW5gDgS04dafzevVXHG9HT1PEAoFfnMD194Wi9f83RGtLCD2Mu21aoU55arL98/Kv2l1W3coYAAAAA2hpFmQAAwG/cPD1ZAVbXHeAWZ+7T8m37TcoIABpzOJz679JsnfDYIu0qrDR1bG5IA/BFPaNDlRQX4TJmEUWZAOAzBnaL0rh+XUwZa1z/LkrpFmnKWABwuHH9u+izG47R/ztrmGIigjw+3+GU3l6+U1Mf+VEvL96mmlpHG2QJAAAAoC1QlAkAAPxG/5hwnTnKuODo0QUZcjqdJmQEAA1tKyjT+S8u032fblR5TZ2pY3NDGoAvM+qWuSW3VLnFVSZlAwA4UtdOHWDKOHNSE0wZBwCaE2C16LyxffTD7VN1zZQBsgW4/kB5U0qravXAl5t14uOL9MOW/DbIEgAAAEBroygTAAD4lZuOSzL84+aK7YX6KYtumQDMU1vn0AtpW3XSE4u1IrvQKzlwQxqAL0t1YwtzumUCgO84dmC8Th3RttuYnzayh6YNjGvTMQDAXZEhNt09c5C+nZuq6YPiW3SNbfvKdflrK3XpqyuUlV/ayhkCAAAAaE0UZQIAAL/Su0uYzj2qt2HcI9+m0y0TgCm25JbozOd+1oP/26JqL201xg1pAL5uXP8uCrG5/jNWGkWZAOBT/nHqEMVHBbfJteOjgvX3WUPa5NoAcCT6x4Tr5UuP0htXjlNyfESLrpGWUaATHl+sf3y+UcUV9lbOEAAAAEBroCgTAAD4nRuOTVRQoOuXOWt3FemHdLb7AdB2amodemxBhmY9tUTrc4q9lgc3pAH4gxBbgMb37+oyZknWPtXWeaf4HQDguc7hQXr9inEKCwpo1et2CrXp9SvGqXN4UKteFwBa0+SkWH1102T987Qhig6zeXx+ncOp//yUramP/KA3lu3gdTAAAADQzgR6OwEAAIDW1r1TqGaP76P//JTtMu7RbzM0LSVOFovr7c7RPJvNpl69ejV4DEBat6tIf/5wvdLzvLudGDekAf/Xkdbi1ORYl90wiyvtWpdTrDF9O5uYFQDgSKTER6pn51Bl5pW1yvXio4L1+hXjNLBbVKtcz10daT0G0HoCA6y65Oh+OnVEDz3+XabeWLZDdQ7PdvY5UGHXvZ9s0JtLd+i+WYM1KTGmjbIF2jfWYgAAvIu1uDGKMgEAgF+aMzVB76zYqSp7858S37inRN9szNWJQ7ubmJl/OfwFNtDRVdnrNG9Bhl5evE0e3keRJM0YHK+1u4pUUFp9xLl464Y0AHN1pLU4NSVW+sJ1TFpGAUWZAOBDVu040GoFmaeN7KG/zxrilQ8kdaT1GEDriw4L0t9PHaLZ4/von19s0uLMfR5fIz2vVLNfXq7jB8frrycPUt+u4W2QKdB+sRYDAOBdrMWNsX05AADwS3GRIbr06H6GcY8tyJSjJZVTAHCY5dv268THF+nFRZ4XZPaMDtXrV4zTS5ccpW9vmaLTRvY4olxOG9lDX988hYJMAH5lQEy4ekaHuoxZ5KKTJgCg/Xl1yfYjvsa4/l30n8vG6onzR9EhHoBPS4qP1H+vGKdXLj1K/WNaVlT57aY8zZi3SA/9b4vKqmtbOUMAAAAA7qJTJgAA8FvXpCbozWU7VF5T12xMel6pvvh1r04dcWQFUAA6rrLqWj30v816c9nOFp1/8YS+uvOkgYoIPvj2rHN4kJ44f5ROG9lDz6dt04rthW5fa1z/LpqTmqBpA+NalAsAtGcWi0WpKbF6e3nzv2/X5RTpQHkNRTkA4AN2FVbom425LmMCLBaFhwSopPL3wqJOoTYN69lJI3p30qkjeiqlW2RbpwoAprFYLDpuULwmJ8Xq9Z+z9eT3mSr1sLiyps6h59O26sPVOfrziSk6e3QvWa2WNsoYAAAAQFMoygQAAH6rS3iQrjimv55amOUy7vEFGZo5tJsCA2giDsAzP6bn6y/zf9We4iqPz+3XNUz/76zhGj+ga5PfP3ZgvI4dGK/03FJ9tm631u0q1q+7i1Vcaa+P4YY0gI4mNdl1UabTKS3O2scHbgDAB7z2c7Zhh/nLJ/XTX08epPKaOtXUOhQUaFV4UIAsFoqLAPi3oECr/jRlgM4Y3VOPfpuud1fuktPDXTn2lVXrzx+u1xtLd+hvswbrqH5d2iZZAAAAAI1QlAkAAPzaVccM0Gs/Z6u0qvlPlG/bV65P1u7R2WN6mZiZf3A4HKqq+r0YLSQkRFYrxa3wf0UVNbr/i836aE2Ox+daLdKfJg/Q3BnJCrEFGMandIvUHd0GSpKcTic3pAE00NHW4okJXRVotajWRRXPoowCijIBoJ0rrbLrvZW7XMZYLdKlE/vJYrEc7CofbFJyLdDR1mMA5omJCNaDZw7X7PF99c8vNnm0m8Yhv+4u1tnPL9WsET1010kD1TM6tA0yBbyLtRgAAO9iLW6MokwAAODXOoXZdPXkAXp0QYbLuCe+z9BpI3vIRrdMj1RVVWn9+vX1j4cPH66wsDAvZgS0va837NU9n2zUvrJqj89NiY/Uv88erhG9o1s0ti/ckAZgro62FkeG2DS6b2eXN6PTMgrkdDopWgeAduyDVTkqM9iO94Qh3dS7i2+saR1tPQZgvqE9O+m9qyfofxty9a8vN2t3UaXH1/h83R4t2JSra6Yk6NrUBIUGGX9QFPAVrMUAAHgXa3FjVB0AAAC/d/kx/dU5zOYyZldhpT5Y5XnHOwAdR0Fpta57a7WufXONxwWZtgCLbpmepM9vPKbFBZkAgINSk2Ndfr+gtFqb95aalA0AwFN1Dqf+8/N2w7grj+lvQjYA4DssFotmDuuu729L1W0zkhXqxu4bh6uyO/TE95k67tEf9ena3XJ6uic6AAAAALdQlAkAAPxeRHCgrklNMIx7emGmqmvrTMgIgC9xOp2avyZHMx5L01e/5np8/vBenfT5jcfolunJCgrkLRgAHCmjokxJWpRZYEImAICWWLApT7sKXXd4G96rk8b07WxSRgDgW0JsAbrxuCT9cPtUnTmqZ4uusae4Sje/u1bnPL9U63OKWjdBAAAAABRlAgCAjuGSo/sqJsL1fr97iqv07opdJmUEwBfsKarU5a+t1K3vr1NRhd2jc4MDrfrLzIGaP2eiBnaLaqMMAaDjGdw9SjERQS5j0tIpygSA9urVn9zrkmmxWEzIBgB8V7dOIZp33kjNv26iRrZwV45VOw7otGd+0h0frFN+aVXrJggAAAB0YBRlAgCADiEsKFDXTXWjW+YPWaqsoVsm0NE5HE69uWyHjn9skX5sQWHPuH5d9PUtU3T1lAQFBvC2CwBak9Vq0ZQk190yV+0oVHl1rUkZAQDctWF3sVZsL3QZ0y0qRDOHdTcpIwDwfaP7dNb8ORM179wRio9y/aH0pjid0gerczTt4R/13I9b2UkIAAAAaAXcHQQAAB3GheP7qFtUiMuYgtJqvblsh0kZAWiPsveV64KXlumeTzaozMOCnvCgAN1/2hC9e/UE9Y8Jb6MMAQCpKa6LMu11Ti3dut+kbAAA7npliXGXzEsm9pWNDzYBgEesVovOHN1LC2+bqhumJSoo0PPfo+U1dfp/X2/RjHmL9PWGXDmdzjbIFAAAAOgY+MsGAADoMEJsAbrh2ETDuOfSttJZCeiA6hxOvbRom058YpGWG3TvacqU5Fh9M3eKLj66n6xWtloEgLZ0TGKMjHa1TctgC3MAaE/ySqr0xfo9LmNCbFZdOK6PSRkBgP8JDw7U7Sek6PtbU3VyC7sO7yys0LVvrtbsl5drS25JK2cIAAAAdAwUZQIAgA7l3KN6q1fnUJcxheU1eu3nbHMSAtAuZOSV6qznfta/vtqsKrvDo3M7hdr0yDkj9PrlY9Wrc1gbZQgA+KOuEcEa1rOTy5gfM/Lp7gMA7cgbS3fIXuf69/JZo3spOizIpIwAwH/17hKmZ2aP1rtXT9Cg7lEtusbPW/dr5hOLdc8nv6qwvKaVMwQAAAD8G0WZAACgQwkKtOqmY5MM415ctE0lVXYTMgLgTfY6h578PlMnP7lYa3cVeXz+iUO6acGtU3T2mF6yGLVsAwC0qtRk11uY7yqsVPb+CpOyAQC4UmWv01vLdxjGXT6pvwnZAEDHMWFAV31x4zF68Mxh6hruedG7wym9uWynpj78g15dsl32Os8+yAoAAAB0VBRlAgCADufM0T3Vr6vrbnbFlXa9sni7SRkB8IZfc4o166klmrcgw7Bjz+FiIoL07OzRev7iMYqLDGmjDAEArkwxKMqUpEVsYQ4A7cLHv+zWgQrXH3ycmhKrxLgIkzICgI4jwGrRBeP66Ic7pupPk/vLFuD5h0pLqmr1zy826cTHF+nH9Pw2yBIAAADwLxRlAgCADicwwKpbpicbxr2yZLsOsDUP4Heq7HV66H9bdPqzP2lLbqnH558xqqcWzE3VzGHd2yA7AIC7RvWOVmRIoMuYNIoyAcDrnE6nXl1i/KHHK4+hSyYAtKWoEJv+evJgfXPLFB03MK5F19haUK7L/rNSl/9nhbYWlLVyhgAAAID/oCgTAAB0SLNG9FCSQQeOsupavbh4m0kZATDDyuxCzXxisZ5P26o6h2fdMbt3CtF/Lhurx84bqc4t2PILANC6AgOsOiYxxmXM0q37VV1bZ1JGAICmLMrcp8x814U7yfERhr/TAQCtY0BshF65bKxev2JcizsU/5BeoBMeW6T7v9ik4krXnZABAACAjsh1OwEAAAA/FWC1aO6MZF331hqXca/9lK0rj+mvmIhgkzLzLSEhIRo+fHiDx0B7VF5dq39/vUX/XbZDTs9qMSVJF47vo7tPGqjIEFvrJwcAR6Cjr8VTkmP1vw25zX6/0l6nVdkHNIlCHwDwmlfc6JJ5xaT+slg83063vejo6zEA35SaHKuJN0/WW8t26LHvMj0urqx1OPXKku36+Jfduu34ZJ0/to8CrL77uxy+jbUYAADvYi1ujE6ZAACgwzpxSDcN6h7lMqbSXqfnftxqUka+x2q1KiwsrP7LauXlJdqfxZkFOv6xRXp9qecFmX27humdP03Q/50xjIJMAO1SR1+LpyTHGsawhTkAeE9mXqkWGfwe7hIepNNH9TQpo7bR0ddjAL7LFmDVZZP668fbp+qSo/u2qKiysLxGf/14g05+crF+3rqvDbIEjLEWAwDgXazFjfFfAAAAdFhWq0W3zkg2jHtz2Q7llVSZkBGA1lRcYdcdH6zTxa+s0O6iSo/OtVqkq47pr69vnqKjE7q2UYYAgCPVMzpUSQZbLqalU5QJAN7y6k/ZhjEXje+jEFtA2ycDAGhW5/Ag/fO0ofrqpsk6poVd5rfklurCl5br2jdWa+f+ilbOEAAAAPAtFGUCAIAObfqgOI3o1cllTHWtQ8/8kGVSRgBaw7cbczXjsTR9sDrH43OT4iL00ZyJuueUwQoN4uYwALR3Rt0y0/NKlVvMB2wAwGyF5TWav8b163FbgEUXTehrUkYAACMp3SL1xpXj9NIlR6lv17AWXePrjbma/lia/v31FpVV17ZyhgAAAIBvoCgTAAB0aBaLRbcen2IY986Knco5wCe8gfZuf1m1bnh7ja5+Y7XyS6s9OjfQatFNxybqi5uO0ag+ndsoQwBAa0t1Ywtzo61zAQCt750VO1Vd63AZM2tED8VFhZiUEQDAHRaLRTMGx+vbuVN090kDFREc6PE1amodevbHrTr2kR/14eocORzONsgUAAAAaL8oygQAAB3elKQYHdXXdQGWvc6ppxfSLfNwdrtdOTk59V92u93bKaGDcjqd+nTtbk2fl6Yv1u/1+PyhPaP02Q3H6NbjUxQcSHdMAL6DtVga17+LQmyu/8SVRlEmAJiqptah13/ONoy78pj+bZ+MCViPAfij4MAAXZOaoIW3p+q8o3rLYvH8Gvml1br9g3U649mftHrHgdZPEvgNazEAAN7FWtwYRZkAAKDDs1gsus2NbpkfrM5R9r5yEzLyHbzARnuQW1ylq15fpZvfXasDFZ79DAYFWnXniQP1yXWTNLhHVBtlCABth7VYCrEFaHz/ri5jlmTtU22d625tAIDW8+Wveww7108Y0EVDenQyKaO2xXoMwJ/FRYbo/509XJ/fcIzG9mvZziLrcop11nM/65Z3f9He4spWzhBgLQYAwNtYixujKBMAAEDS0QldNTHB9c38OodTT36faVJGAIw4nU69s2KnZsxL0/db8j0+/6i+nfW/mydrztQEBQbw1ggAfJnRFubFlXatyyk2KRsA6NicTqdeWbLdMO6KSf7RJRMAOoqhPTvp/WuO1lMXjFLP6NAWXeOTtXt07CNpeuK7TFXW1LVyhgAAAED7wZ1HAACA39x2fLJhzCdrdysrv8yEbAC4snN/hWa/vFx3z/9VpdW1Hp0bFhSgv88arPevOVoJsRFtlCEAwEypKa6LMiW2MAcAs6zMPqANu0tcxvTtGqbjBsWblBEAoLVYLBbNGtFD39+WqltnJCvE5vmt5kp7nR77LkPT56Xp83V75HQ62yBTAAAAwLsoygQAAPjNmL5dNNXghr7DKT3+XYZJGQE4XJ3jYNedEx5fpJ+37vf4/GMSY/TNLVN02aT+slotbZAhAMAbBsSEq1dn1916KMoEAHO8smSbYczlE/spgNfjAOCzQmwBuum4JP1w+1SdPrJHi66xu6hSN77zi859Yak27KarPQAAAPwLRZkAAAB/cOsM426ZX6zfq817XXf9AND6svJLdc7zP+v+Lzap0u7ZFleRIYH691nD9caV49S7S1gbZQgA8BaLxaIpBluYr88p0oHyGpMyAoCOaef+Cn27Kc9lTGRwoM4+qrdJGQEA2lL3TqF6/PxR+mjORI3o1alF11iZfUCznl6iOz9cr4LS6lbOEAAAAPAOijIBAAD+YHivaM0YbLyF2mML6JYJmMVe59AzP2Rp5hNLtGZnkcfnzxgcr+9uTdW5Y3vLYqEbDwD4q1SDokynU1qctc+kbACgY3rt52wZ7UJ7/rjeiggONCchAIApxvTtrI+vm6RHzhmhuMhgj893OqX3Vu3StEd+1AtpW1Vd69mHcQEAAID2hqJMAACAw7jTLfPbTXn6NYdtdYC2tmF3sU57+ic9/E26auocHp3bNTxIT10wSi9ePEbxUSFtlCEAoL2YmNBVgQZb4aals4U5ALSV0iq73l+1y2WM1SJdOrGfOQkBAExltVp09phe+uH2qbp+WoKCAj2/DV1WXasH/7dFxz+2SN9uzJXTqNIfAAAAaKcoygQAADjMoO5ROnl4d8O4Rxekm5AN0DFV2ev08DdbdNozP2nT3hKPzz9tZA8tuDVVs0b0oDsmAHQQkSE2je7b2WXMoswCbuwCQBt5f1WOyqprXcacOLSbenUOMykjAIA3hAcH6o4TBuq7uak6cUi3Fl1jx/4KXf3Gal38ygql55a2coYAAABA26MoEwAAoAlzpyfJoNGSfkwv0OodB8xJCOhAVu84oJOfXKxnftiqOodnhTPxUcF6+ZKj9MT5o9QlPKiNMgQAtFdGW5gXlFZr815u6gJAa6tzOPXaz9sN4648pr8J2QAA2oM+XcP0/MVj9Pafxmtgt8gWXWNJ1j7NfHKx7vt0gw6U17RyhgAAAEDboSgTAACgCYlxkTp9ZE/DuHl0ywRaTUVNrf7x+Uad/fzP2lpQ7vH554/trW/npmr64Pg2yA4A4AuMijIlKS2DLcwBoLUt2JSrXYWVLmNG9I7W6D6uOxoDAPzPxIQYfXnTZP3rjKEt+gBtncOp/y7doamP/KjXftoue52jDbIEAAAAWhdFmQAAAM246bgkBRi0y/wpa7+Wbt1vUkaA//opa59OeHyR/vNTtjzdVbZ3l1C9ddV4PXTWcHUKtbVNggAAnzC4e5RiIlzf6F1EUSYAtLpXlhh3ybxiUj9ZLAZbUgAA/FKA1aLZ4/vqh9un6spj+ivQaIuiJhRX2vX3zzfppCcW80ErAAAAtHsUZQIAADSjX0y4zh7dyzBu3oJ0OT2tIvMTVqtVoaGh9V9WKy8v4ZmSKrvunr9es19ebthZ53AWi3T5pH765pYpmpQY00YZAkD7xlrckNVq0ZQk190yV+0oVFl1rUkZAYD/W59TpJXZB1zGdO8UopnDupuUkflYjwHAPZ1Cbbr3lMH6Zu4UTUsx7nLflKz8Ml366gpd+dpKbSsoa+UM4atYiwEA8C7W4sYCvZ0AAABAe3bjcYma/0uO7HXNF12uzD6gxZn7NMWN7TL9TUhIiEaMGOHtNOCjvt+cp79+vEG5JVUen5sQG65/nz1cY/p2aYPMAMB3sBY3lpoSq/m/7G72+/Y6p5Zu3a8Zg+NNzAoA/NerbnTJvOTofrIF+O8NGdZjAPBMQmyE/nP5OP2Qnq8HvtikrQXlHl/j+y35WpRZoMsm9tONxyUpKoTdUzoy1mIAALyLtbgx//0rCAAAQCvo1TlM54/tYxj36Lcdt1sm4KnC8hrd/O4vuvL1VR4XZAZYLbp+WoK+vGkyBZkAgCYdkxgjo91x2cIcAFpHbnGVvli/12VMqC1AF4zrbVJGAABfMi0lTl/fMkX3nTJYUSGe9xKy1zn10uLtmvbwj3pnxU7VOfj7LAAAANoHijIBAAAMXD8tUUGBrl82rcsp1veb803KCPBNTqdTn6/boxnz0vTp2j0enz+4e5Q+vX6S7jhhoEJsAW2QIQDAH3SNCNawnp1cxvyYkc8HagCgFbyxLFu1BgUwZ43pqeiwIJMyAgD4GluAVVcc018/3jFNF03oI6vBB6yasr+8RnfP/1Wznlqi5dv2t36SAAAAgIcoygQAADDQrVOILp7Q1zBu3oIMOfg0NtCkvJIqXf3Gat34zi/aX17j0blBAVbdcUKKPr1hkoYaFNkAACBJqcmxLr+/q7BS2fsrTMoGAPxTZU2d3lq+0zDu8kn9TcgGAODruoQH6YHTh+mrmydrYkLXFl1j094SnffiMl331mrtKuT1PgAAALyHokwAAAA3zJmaoFCDznyb9pbo6425JmUE+Aan06n3V+7S9HlpWrApz+PzR/eJ1lc3H6PrpyXKFsDbFwCAe4yKMiW2MAeAIzX/lxwVVdhdxhw7ME4JsREmZQQA8AcDu0XpravG64WLx6hPl7AWXeOrX3N13Lw0PfJNusqra1s5QwAAAMBYoLcTAAAA8AUxEcG6dGI/PZ+21WXcYwsydMKQbgpoyT47Pqiqqkrp6en1j1NSUhQSEuLFjNCe7Cqs0F8+/lWLM/d5fG6oLUC3n5Ciyyb26zD/ngCgJViLmzayd7QiQwJVWtX8Ddi0jAJdOrGfeUkBgB9xOJx6dcl2w7grOkiXTNZjAGhdFotFJwzppqkpsXp1SbaeXpip8po6j65RU+vQ0z9k6YPVu3TniQN1+siesvI3Jr/FWgwAgHexFjdGqxkAAAA3XTNlgCKCXX+mJTO/TJ+v22NSRt7ncDhUWVlZ/+VwOLydEtoBh8Op137arhMeX9SigsyJCV31zS1TdOUx/SnIBAADrMVNCwyw6pjEGJcxS7fuV5Xdsxu7AICDFmUWaGtBucuYgd0iNSmxZdvP+hrWYwBoG8GBAZozNUE/3D5V54zp1aJr5JVU6/+zd99hUZ1pH8d/MzAUqSKIUgRUsPfewJJqjKZqejFNsyUbk81uNu+2bHazm2x6Nj2mrZuY3mMSk4jYey+gYgGVIgiC9Jn3DxMSAsxQZs4w8P1cV66LMzznee4j0Wc4c5/7XvD2Vl3y7CptPlzo5AjRVrAXAwDgXuzF9ZGUCQAA0ESdA3w0d6LjKh9PfJOh6hreaKJj2p9XojkvrNZfPtml082sYBDk660HLxmkRTePUY8uLWtPBQDADxy1MC+rqtGGg3woCwAt8XITq2SaTDxkBQBova7Bfnr48iH6+JcTNCKuc4vm2HLkpC5+ZpXuXLxFx4vKnRwhAAAAUBdJmQAAAM1w08QEBfvZr5aZmV+q9zdnGxQR0DZU11j17LL9Ov+JNK1vQYLLtL5d9dWCZF05ugcf3AIAnCLZQVKmdKbSGwCgedJzTjmsiN8lwEczh0YZFBEAoKMYHBOqd+eN05NXDlP3kJa1w/xgc7am/HuZnvomg8r5AAAAcBmSMgEAAJohxN+i21J6ORz3xNIMVVZTLRMdw66jxbr4mVX615I9zf7/vnMni564Yqheun6kuof4uyhCAEBHFBXqr8SugXbHpO4lKRMAmuuVlY6rZF49Nk5+Fi8DogEAdDQmk0kzh0Tp27sm645pifKzNP/j7rKqGj3ydbqmPZKqz7cfk81mc0GkAAAA6MhIygQAAGimG8bHKyzAx+6Y7JNlenvDEYMiAtyjorpGj361VzOfXqHt2UXNPn/G4O76ekGKZg2NpjomAMAlHLUw35tzSseKygyKBgA8X0Fppd7fZL8zhI+XWdeM7WFQRACAjsrfx0t3np2kb++arJlDWladOftkmW5ftElzXlijnUebf28LAAAAaAxJmQAAAM0U4OuteSk9HY57+tt9tMBBu7X5cKFmPLlCT367T9XW5lUT6Brkq+evHaGnrxqu8EBfF0UIAEDTWpinpdtvwQsA+NGiNYdU4aA6/oVDotQ1qGUtZQEAaK6oUH89eeUwvTtvnAZFh7RojnWZBZrx1Ard+/425ZdUODlCAAAAdEQkZQIAALTAtWPjFRFkP5nseHG5/rf2sEERAcYoq6zRA5/u0qXPrlJGbkmzz589MkZf35micwd0c0F0AADUNTohzGE7w9R0WpgDQFNUVlv1+ppDDsfdNDHBgGgAAKhrZHyYPvrFBD102eAWPQRss0lvrjuiKQ8v04vLD6jSwUMIAAAAgD0kZQIAALSAv4+XfjG5l8Nxzyzbr7JKqmWifVi9/4TOe2K5XlqRqWYWx1R0qL9enztaD102RCGdLK4JEACAn/GzeGlszy52x6Rl5Km6hg9cAcCRT7cdVd4p+9XDxvXsov5RwQZFBABAXWazSbNHxuq7u1M0L6WXfLya/1H4qYpq/f3z3Tr38eX6ZneObLZm3gQDAAAARFImAABAi10xuoe6h9hvyZZfUqHXVx80JiDARU6VV+kPH2zXlS+u0aETp5t9/vXj4vTVnclNaiELAICzJSfa33+Ky6u1NavIoGgAwDPZbDa9vCLT4bi5VMkEALQBQX4W/f78vvp6QbLOHRDZojky80t102sbdN3CdcrIOeXkCAEAANDekZQJAADQQn4WL/1qaqLDcc+l7ldJRbUBEQHO992eXJ3z2HL9b+3hZp/bMzxA78wbp7/OGqgAX28XRAcAgGMpfRw/FEALcwCwb11mgXYeLbY7Jr5LJ03r29WgiAAAcCyuS4Cev3akFt08Rn0ig1o0R1pGvs57Ik1/+XinTp6udHKEAAAAaK9IygQAAGiFy0fGKDbM3+6YwtNVeqUJFUWAtqSwtFILFm/Rja+u17Gi8mad62U2aV5KL31+xySNig9zUYQAADRNz/AAxXS2/36NpEwAsK8pVTJvnJAgs9lkQDQAADTPhN7h+uzXE/W3iwaqcydLs8+vsdr06qqDmvzvZXp99UFV11hdECUAAADaE8rVAAAAtILFy6xfT03Ub9/dZnfcC2kHdN24eIW04KZfW2axWBQTE1PnGJ7v8+3H9KePdii/pPlP//ftFqSHLxuiQTEhLogMAPBz7MWOmUwmJSdF2K36vC3rpApLK9U5wMfAyADAMxw6Uaqvd+fYHRPk563LRsTYHdOesR8DQNvn7WXWtWPjNHNwlB7/Jl1vrD6kaqutWXOcPF2lP320U/9dc0h/mjFAExPDXRQtmou9GAAA92Ivro+kTAAAgFa6eFi0nl22XwfySxsdc6q8Wi+tOKC7zuljYGSu9/M32PBsuafK9acPd2rJzuPNPtfiZdKvpiZqXkov+XhTkB8AjMJe3DQpDpIybTYpbV++Zg6JMjAqAPAMr646KJuDnJUrR/dQgG/H/biB/RgAPEdIJ4v+fOEAXT2mh/726e4WVc1PzynRNS+v1Vn9IvV/F/RTfHiACyJFc7AXAwDgXuzF9fFpKQAAQCt5e5l1x1mJDsctXJGpgtLmVx4EXM1ms+ndjVk6+9HlLUrIHBIbqk9/NUm/npZIQiYAoE0a36uLvB201E3dSwtzAPi54vIqvb3+iN0xXmaTrh8fb0xAAAA4Se+uQXpt7mi9csMo9WxhUuXS3Tk6+7FUPfj5bp0qr3JyhAAAAPBkfGIKAADgBBcOjlJSZKDdMaWVNXp++X6DIgKaJvtkmW54Zb3ufmerisqad/PYz2LWfdP76f3549WnW5CLIgQAoPWC/CwaEdfZ7pjlGXmyOSoFBwAdzNvrj6i0ssbumPMGdlN0qL9BEQEA4FxT+nbVkt8k6/8u6Kcgv+ZXfa6qsen55Qc05d/LtHj9YdU0syU6AAAA2ieSMgEAAJzAbDZpwdlJDse9tuqgck+VGxARYJ/VatMbqw/qnEdTW9SmaUxCmJbckaxbknvKy0HlMQAA2oLkpAi73887VaHdx04ZFA0AtH3VNVa9svKgw3FzJyS4PhgAAFzIx9usmyf11LK7J+uqMT3Ukltd+SWV+t172zXrPyu0LrPA+UECAADAo5CUCQAA4CTnDuimAVHBdseUV1n17LL2Uy3TarXq9OnTtf9ZrVZ3h4QmyMwv1RUvrtEfP9rpsOrNzwX6euuBiwbqzVvGKr6FrZ0AAM7DXtx0KQ6SMiW16EEFAGivvt6Vo+yTZXbHDI0NdViJuCNgPwaA9qFLoK/+cfEgffqrSRrbM6xFc+zILtbs51frl//b5HAfhfOwFwMA4F7sxfU1vwY7AAAAGmQynamWedNrG+yOW7T2sG5N7qnuIZ7f3q28vFzbtm2rPR48eLA6derkxohgT3WNVQtXZuqRr9JVUd38X4Ym94nQPy4epChaEwJAm8Fe3HT9uwcrPNBX+SUVjY5JTc/V/Mm9DIwKANqul1dkOhxz00SqZErsxwDQ3vSPCtabt4zVlzuP64HPdiursPnJlZ9uO6avd+XotpRempfSU518+FjeldiLAQBwL/bi+qiUCQAA4ERT+3bV0NhQu2Mqq616+tt9xgQEfG/v8VO69NlV+sfne5qdkBnayaJHZw/RKzeMIiETAOCxzGaTkhPD7Y7ZeKhQJRXVBkUEAG3X1iMnteFQod0x3UP8dN7AbgZFBACAsUwmk84b2F1LF6Tot+f2UScfr2bPUVFt1ZPfZGjqv1P14eZs2Ww2F0QKAACAtoikTAAAACcymUy665wkh+Pe3nBERwpOGxAROrrKaqseX5quGU+laWtWUbPPnz6om76+M0WXDI+RyWRyQYQAABgnpY/9FuZVNTat3n/CoGgAoO1auNJxlczrx8fL4sVHDACA9s3P4qVfTOmt7+6erEuHx7RojuPF5frN4i269NlV2nrkpHMDBAAAQJvEHRMAAAAnm9g7XKPjw+yOqaqx6alvMwyKCB3V1iMnNfPpFXp8aYaqapr3JH54oK+eu2a4nrl6hCKCfF0UIQAAxprYO1yOnjFITc81JhgAaKOOF5Xrs23H7I7xt3jpylE9DIoIAAD3iwz20yOzh+jDX0zQsB6hLZpj0+GTmvWflbrr7a3KKS53boAAAABoU0jKBAAAcLKmVst8b1O2MvNLDYgIHU15VY0e/Hy3Ln5mpfYcP9Xs8y8ZHq2lC5J13sDuLogOAAD36RLoq0HRIXbHpKbn0VYQQIf22uqDqrba/3fwshExCulkMSgiAADajqGxoXp//ng9PmeougX7tWiO9zZlacq/l+k/3+1TeVWNkyMEAABAW0BSJgAAgAuM6dlFE3uH2x1TY7XpiaXpBkWEjmJdZoHOfyJNzy8/IAefo9YTFeKnV24cpUdnD1VoJx/XBAgAgJulJNlvYX6koEwHT5w2KBoAaFvKKmv0v7WHHY67cUK864MBAKCNMplMumhYtL69O0W/npYoX+/mf+R+urJGD3+5V2c/lqovth/jwTAAAIB2hqRMAAAAF1nQhGqZH209qoyc5lcyBH6upKJaf/poh2Y/v7pFFVivHRunrxakaEqfri6IDgCAtsNRUqYkpe6lhTmAjum9TVkqKquyO2Za367qGRFoUEQAALRdnXy8teDsJH1zV4pmDG5Zx5kjBWWav2iTrnxxjXYdLXZyhAAAAHAXkjIBAABcZHiPzpra136Cm80mPUa1TLRSanqezn1suV5ffajZ58Z36aS3bh2rv100UIG+3i6IDgCAtmVobKiC/Ozvecsz8g2KBgDaDqvVpoUrMx2Ou2liggHRAADgOWI6d9LTVw3X27eN04Co4BbNseZAgWY8laY/fLBdJ0oqnBwhAAAAjEZSJgAAgAstONtxtczPtx/XzqNFBkSD9ubk6Urd9fZWXb9wnbJPljXrXLNJujW5p764I1lje3ZxUYQAALQ93l5mTewdbnfM6v0nVF5VY1BEANA2pGbk6UCe/ar7fbsFaVwvfn8AAKAhoxPC9PEvJ+pflw5SeKBPs8+32qT/rT2syf9eppfSDqiy2uqCKAEAAGAEkjIBAABcaGB0iM4dEOlw3GNfZxgQDdqTJTuO6+zHluu9TVnNPrdPZJDev32C/jC9n/x9vFwQHQAAbZujFuZlVTXacLDQoGgAoG1YuMJxlcy5ExNkMpkMiAYAAM/kZTZpzqge+u7uybotuacsXs3fN0+VV+uBz3brvMeX67s9uS6IEgAAAK5GUiYAAICL3Xl2khx9ZrV0d462HDlpSDzwbHmnKvSLRZs0778blXeqea2MvM0m3TEtUZ/8aqKGxoa6JkAAADxAsoOkTElanpFnQCQA0DbsPX5KaRn5dseEB/po5pAogyICAMCzBflZdO/0fvrqzhSd1c/xQ/sNOZBfqhtfXa/rF67TvtxTTo4QAAAArkRSJgAAgIv17RasGYMdf3D16NfpBkQDT2Wz2fTB5iyd/ViqPtt+rNnnD44J0ae/nqg7z06Sjze/BgAAOraoUH8ldg20OyZ1L0mZADqOV1Y6rpJ59Zg4+VmotA8AQHMkhAfopetH6o2bRisp0v7vII1JTc/TeY+n6a+f7FTR6SonRwgAAABX8HZ3AAAAAB3Bb85K1Gfbjspqa3zM8vQ8rT9YoFHxYcYF1kp+fn4aPHhwnWM439GTZbrvg+36rgXJIb7eZi04O0k3TUyQtxfJmADQ3rAXt1xKUoQycksa/f7enFM6VlSm7iH+BkYFAMY7UVKh9zdn2x3j42XWNWPjDIrI87AfAwAcmZQYoc9/PUn/W3dYj36drpPNTK6sttr0ysqD+nBzthac00dXjopt0b0+m82mkopqVdXYZPEyKdDXWyZHbZ48AHsxAADuxV5cH0mZAAAABugVEaiLh8XovU1Zdsc98tVevXXrOIOiaj2z2axOnTq5O4x2y2q16c31h/Xg53tUUlHd7PNHx4fpn5cOUs+Ilj2FDwBo+9iLWy6lT4ReWmG/Mlxaer5mj4o1KCIAcI9Faw+rstpqd8ysoVGKCPI1KCLPw34MAGgKby+zrhsXr5lDovT40gy9seaQauw9xd+AwtNV+uOHO7RozSH9aUZ/je8d7vCcPceL9fGWo9qadVI7sotVVPZjQmiIv0UDo4M1JCZUs4ZGq0+3oGZfV1vAXgwAgHuxF9dHUiYAAIBB7piWqI+2ZKvazo22NQcKtGpffpNupqF9O3SiVL97b5vWHCho9rkBPl76/fl9dfWYOJnNnv+kOwAArjAqPkx+FrPKqxpPREpNzyMpE0C7VlFdo9dXH3I47sYJCQZEAwBAxxDayUd/mTlAV4/pofs/3aW0jPxmz7Hn+Cld9dJandM/Uvdd0E9xXQLqjfl2T46eW3ZA6w42fn+xqKxKK/ed0Mp9J/TMsv0aHR+m+ZN7aUrfrs2OCQAAAD+ifyEAAIBBenTppMtHxjgc98jX6bLZmveENNqPGqtNL6Ud0LmPL29RQuakxHB9eWeyrh0XT0ImAAB2+Fm8NLZnF7tj0jLyVF1jv3ocAHiyT7ceU35Jhd0x43t1Uf+oYIMiAgCg40iMDNLrc0fr5etHKiG8flJlU3y1K0dnP7pc//zix047haWV+vWbmzX31Q12EzIbsu5ggW58db3ueGuzCksrWxQTAAAASMoEAAAw1C+nJsrHy/5bsI2HCrUsPc+giNCWZOSc0mXPrdIDn+22W7WrIcF+3nr4ssF6fe5oxXSmPQAAAE2RkhRh9/vF5dXamnXSmGAAwGA2m00vr8h0OO6miVTJBADAVUwmk6b1i9SXv0nWfdP7Kci3+Y0uK2usei51v6b8e5meWJqu855Yro+3Hm1VXB9tOarznliuPceLWzUPAABAR0X7cgAAAANFh/rrytGxes1Be7jHvk7X5KQImUxtu9JhVVWVcnJyao8jIyNlsVjcGJFnqqqx6rll+/XUt/tU2YJqXOcOiNTfZg1U12A/F0QHAGjL2ItbJ9lBUqYkpabna0RcmAHRAICx1hwo0K5j9hMtEsIDNKUP7UsdYT8GALSWj7dZtyT31MXDo/XIV3v11vojam4zpbxTFXpsaYbTYsoprtCc59do8W1j1bdb266azV4MAIB7sRfXR6VMAAAAg/1iSm/5ett/G7Ytq0hf78qxO6YtqKqqUlZWVu1/VVVV7g7J4+zILtLMp1fqka/Tm52QGR7oo/9cNVzPXTOChEwA6KDYi1unZ3iAYjr72x2TSgVzAO3UwpWOq2TeOCFeZnPbfliwLWA/BgA4S3igrx68ZLA++eVEjU5w/8NhRWVVun7hujbfypy9GAAA92Ivro+kTAAAAIN1DfbTdePiHI579Ot0Wa3NfBwaHqO8qkb/WrJHs/6zUrsdVKdpyMXDovX1nSm6YHD3Nl9RFQCAtspkMjlsYb4t66QK2vgHkADQXAfzS7V0t/0HAYP9vHXp8BiDIgIAAD81MDpEi28dq2euHq7oUPsPkrlaTnGF/vLJTrfGAAAA4GlIygQAAHCDeSm91MnHy+6YPcdP6fMdxwyKCEbacLBA059M07PL9qummYm33YL9tPCGkXpszlB1DvBxUYQAAHQcjlqY22zSin35BkUDAMZ4ddVBhy1RrxzTQwG+3sYEBAAA6jGZTJo+qLu+uStFd5+TJH+L/fvJrvTRlqP6dk/b7+wEAADQVpCUCQAA4AZdAn11w/h4h+Me+zq92Ul7aLtKK6r1l4936vLnV+tAXmmzz79ydA99tSBZU/tGuiA6AAA6pvG9usjbQWve1L20MAfQfhSVVentDUfsjvEym3T9uHhjAgIAAHb5Wbz0y6mJ+u7uybpkWLTb4ngu9YDb1gYAAPA0JGUCAAC4ya3JPRXkoOrI/rxSfbQl26CI4EorMvJ17uPLm1SR5ud6hHXS/24ZowcvGaRgP4trAgQAoIMK8rNoRFxnu2OWZ+TJ1twNHADaqLfXH9Hpyhq7Y84f2E1Rbm6VCgAA6uoW4qdH5wzV+7eP19DYUMPXX5dZoL3HTxm+LgAAgCciKRMAAMBNQjv56KZJCQ7HPfFNhqpqrAZEBFcoKqvS797dpmteXquswrJmnWsySTdNTNCS30zS+F7hLooQAAA4amGed6pCu4/x4SMAz1ddY9Wrqw46HHfTRMe/qwIAAPcY3qOz3p8/Xo/OHqJOPsa2NP94KwUEAAAAmoKkTAAAADeaOzFBIf72Kx8eOnFa72/KMigiONPXu3J0zmOpWuygNWBDencN1Hvzx+uPM/qrk4/9iqoAAKB1UhwkZUpSajotzAF4vq925Sj7pP2HxYb1CNWwHvYrCAMAAPcym026ZHiMBseEGLru1iNFhq4HAADgqUjKBAAAcKNgP4tuS+npcNyT3+xTRbX99nJoO06UVOhXb27WLa9vUE5xRbPO9Tab9KupvfXZrydqOB+EAgBgiP7dgxUe6Gt3TGp6rkHRAIDrvLwi0+EYqmQCAOAZbDab4RX9t2cXyWazGbomAACAJyIpEwAAwM2uHxevLgE+dsdknyzT2+ubX20RxrLZbPpoS7bOfmy5Ptl6tNnnD4wO1ke/nKC7zukjX29jWw8BANCRmc0mJSeG2x2z8VChSiqqDYoIAJxvy5GT2nio0O6YqBA/nTegm0ERAQCA1iipqFZRWZWhaxaVVenjrUdVXG7sugAAAJ6GpEwAAAA3C/D11vzJvRyOe/q7fSqvolpmW3W8qFy3vL5Bd7y1RQWllc0618fbrHvO66MPb5+gAVHGthwCAABnpPSx38K8qsam1ftPGBQNADhfU6pkXj8+Xt5efGwAAIAnqKpxT8XKO97aomH3f63Lnl2lJ5ZmaNPhQlXXWN0SCwAAQFvl7e4AAAAAIF0zNk4vLD+g3FONt7rOKa7Qf9cc0s2THLc7h3FsNpsWrz+iv3++W6fKm189a0RcZ/3r0sHq3TXQBdEBAICmmtg7XCaTZK8TX2p6rs7uH2lcUADgJMeKyvT59mN2x3Ty8dIVo3sYFBEAAGgti5fJbWvXWG3acKhQGw4V6rGl6Qr289b4XuGalBSu5MQIxYZ1cltsAAAAbQFJmQAAAG2An8VLv5zaW3/6aKfdcc+l7tdVY3qok0/beBtnNpvl7+9f57gjOVJwWr9/f5tW7mt+1Sx/i5fuOa+PrhsXLy+z+26gAgA8W0ffi52pS6CvBkeHaGtWUaNjUtPzZLPZZDKxdwPwLK+tOqQaq/1qWpePiFGIv8WgiNoX9mMAgDsE+norxN9ieAvzhhSXV2vJzuNasvO4JCm+SydNSozQpMRwjevVRUF+rn2PwV4MAIB7sRfX1zY+zQcAAIDmjIrVc8v262hReaNj8ksq9dqqQ01qd24EPz8/DRkyxN1hGK7GatNrqw7q4S/3qqwFLeUn9g7Xg5cM4olxAECrddS92FWSkyLsJmUeKSjTwROnlRAeYGBUANA6pyur9ea6w3bHmEzSDRMSDIqo/WE/BgC4g8lk0sDo4BY9MO5qB0+c1sETh/TGmkPyMps0vEdobZLm4JhQpz+kzl4MAIB7sRfXR1oqAABAG+Hr7aVfT0t0OO755ft1qtz9Tz93VPtySzT7+dW6/9NdzU7IDPLz1r8uHaQ3bhpNQiYAAG1QSlKEwzGpe3MNiAQAnOe9TdkOK2hN69uVhHMAADzQkJhQd4fgUI3VpvUHC/Xo1+m6+JlVGnb/V5r/343639rDOlJw2t3hAQAAuARJmQAAAG3IpSNi1MNBst7J01VauOKgMQGhVlWNVf/5bp+mP5mmjYcKm33+Wf0itXRBiuaM6kHLUwAA2qihsaEK8rPfWCY1Pc+gaACg9axWm15Zkelw3NyJVMkEAMATzRwa5e4Qmq24vFpf7DiuP3ywXZMe+k5T/r1Mf/poh77elUMxAgAA0G7QvhwAAKANsXiZdce0RN31zla7415KO6Drx8cptJOPQZF1bDuPFumed7dp59HiZp8bFuCjv8wcoAsHdycZEwCANs7by6yJvcP1xY7jjY5Zc6BA5VU18rN4GRgZALTMsvRcHcgvtTumX/dgjevZxaCIAACAM/XtFqzR8WFad7DA3aG0WGZ+qTLzS/X66kPyNps0zMWtzgEAAIxApUwAAIA25qJh0eoVYb9t3KmKar2YdsCgiDquiuoa/fvLvZr19MoWJWTOHBKlr+9M1swhUSRkAgDgIRy1MC+rqtGGg82vmg0A7tCULgtzJ8Tz+woAAB5s3uSehqzTMzxArs6PrP5Zq/Phf/taty/aqDfXHVZWIa3OAQCA56BSJgAAQBvjZTbpN2cl6VdvbrY77pWVBzV3QoK6BPoaFFl95eXl2rt3b+1xnz595Ofn57Z4nGnT4ULd8+427cstafa5kcG+euCiQTq7f6QLIgMA4EfteS92l2QHSZmSlJqeq4mJ4QZEAwAtt+d4sVbsy7c7JjzQ1yPbnrY17McAAHea2jdSM4dE6eOtR122xqyhUXriimE6ebpSq/afUFpGnpan5yv7ZJnL1pSkorIqfb79uD7ffqabQc/wAE1KDNfExAiN69VFgb5n0h3YiwEAcC/24vpIygQAAGiDLhjUXf/5bp/2HD/V6JjTlTV6fvkB/WF6PwMjq8tqtaqsrKzOsac7XVmtf3+ZrldWZcpma/75V4yK1b3T+ynE3+L84AAA+Jn2uBe7W1SovxK7BirDzoMZy9Pzdd8FBgYFAC2wcEWmwzHXjo2Tr7eXAdG0b+zHAAB3++vMAVqbeUI5xRVOnzsy2Fd/uXCAJCm0k4+mD+qu6YO6y2azKTO/VGkZ+UrLyNPq/SdUWlnj9PV/6kB+qQ7kl+q171udD+/RWZMSwzWqR5Csp0/L/H31b/ZiAACMxe/F9ZGUCQAA0AaZzSbdeXaSbntjo91xr606qJsnJqhrcMd+0shZVu3L1+/f367DBc1vhRPT2V//vGQwVbMAAGgHUpIi7CZl7s05pWNFZeoe4m9gVADQdPklFfpwi/1qWT7eZl09todBEQEAAFfqHOCj1+aO1pzn16iorMpp84b4W/Ta3NHqHOBT73smk0k9IwLVMyJQ14+PV1WNVZsPnzxTRTMjX9uyTrboofemqrbatO5ggdYdLJAkBVhMGhRh0eCuFnXpUa7ETp1ctzgAAIADJGUCAAC0Uef0j9Sg6BBtzy5qdExFtVXPLNuvv8wcYGBk7U9xeZUe/Hy33lx3pNnnmkzS9ePi9dtz+yjAl7fXAAC0Byl9IvSSgwpzy9PzNGcUyUwA2qZFaw6rstp+VYqLhkYpPNDXoIgAAICr9e0WrMW3jdX1C9c5pWJmZLCvXps7Wn27BTdpvMXLrNEJYRqdEKa7zumjk6crtXLfmVbnaRmub3VeWmXTmqOVWnO0Ui9sWV3b6nxSYoTG/qTVOQAAgBF45wEAANBGmUwmLTg7STe+ut7uuP+tPaxbk3sqKpRKTS3xze4c3ffBDh0vLm/2uT0jAvTQpYM1Mj7MBZEBAAB3GRUfJj+LWeVVjSc0LU/PJykTQJtUUV2jN9Yccjhu7sQEA6IBAABG6tstWEvuSNZfPtmpjxxUzbZn1tAo/eXCAQ1WyGyq0E4+umBwd10w+Eyr8wP5pVrhzlbncZ2V/H2S5sDoEHmZTS5dHwAAdGwkZQIAALRhk/tEaHiPUG06fLLRMZU1Vj317T49eMkg4wJrBwpKK3X/JzsdtvRriJfZpNuSe+rX0xLlZ/FyQXQAAMCd/CxeGtuzi5btzWt0TFpGnqprrPL2MhsYGQA49vGWo8ovsV8da0LvLk2uegUAADxL5wAfPXHFMM0aGqXnUg9oXWZBk88dnRCm+Sm9NKVvV6fGZDKZ1CsiUL2+b3VeWW3V5sOFSvs+SXNbdpHrW51nFmhdZoH+/VW6QjtZNKFX+JlKmkkRiqbgAQAAcDKSMgEAANowk8mku87po6tfWmt33Dsbjmh+Si/16NLJoMg8l81m02fbj+nPH+3UidLKZp/fr3uwHr5ssAZGh7ggOgAA0FakJEXYTcosLq/W1qyTGhFHxWwAbYfNZtPClQcdjruJKpkAALR7U/tGamrfSO09fkofb83W1iNF2p5dpKKyqtoxIf4WDYoO0ZDYEM0cEq0+3YIMic3H26wxPbtoTM8uuvvcPiosrdTK/flakZGv5el5OlrU/K5GzXHydJU+235Mn20/JulMR6TkxAhNSgzX2J5dFECrcwAA0Eq8mwAAAGjjxvfqojEJYVpr54nmaqtNT36boX9fPsTAyDxPbnG5/u/DHfpqV06zz/XxMuvX03rrtpReslARCwCAdi85KcLhmNT0fJIyAbQpqw+c0O5jxXbH9AwP0OQk51a/AgAAbVefbkH6bbe+ks48wFFaWaPKaqt8vM0K8PGSyeT+Nt6dA3w0Y3CUZgyOqm11npaep7SMfK0+cEKnXd3qPK9UB/JK9eqqg7J4mTS8R2clJ51J0hwQRatzAADQfCRlAgAAtHE/VMuc/fxqu+Pe35Sl+ZN7qVdEoEGReQ6bzaZ3NmbpgU93qbi8utnnD+sRqocuHazESGOeFAcAAO7XMzxAMZ39lVVY1uiY1PQ8LTg7ycCoAMC+hSsyHY65cUK8zCQWAADQIZlMJgX6eku+7o6kcT9tdX7DhARVVlu16XCh0jLOJGlud3Gr86oam9ZmFmhtZoEe/nLvmVbnvcOVnBiuSYkRiqLVOQAAaAKSMgEAADzA6IQwTUoMV1pGfqNjrDbpiaUZevLKYQZG1vZlFZ7Wve9vt/tn1xg/i1m/Pbevbhgfz9PQAAB0MCaTSSlJEVq09nCjY7ZlnVRBaaXCAnwMjAwAGpaZX6pv9uTaHRPib9GlI2IMiggAAKD1fLzNGtuzi8b27KLfnqvaVudp6flanpGnY0a0Ot92TJ9tO9PqvFdEgCYlRig5KVxjEmh1DgAAGsY7hHbk1KlTWrt2rdLT03Xy5ElZLBZ17dpVgwcP1tChQ9tE6XlHjh49qm3btikzM1PFxcWqqalRYGCgunbtql69eqlv374KCqJCFQCgY7rrnD4OEws/2XZUv5jSW326sV9arTa9seaQ/rVkT4va24zr2UX/vHSQ4roEuCA6AADgCRwlZdps0op9+Zo5JMrAqACgYa+uzHRYNerK0T3UyYePBQAAgOf6eavz/Xml+mbnUS3ZclC78qtU4dpO59qfV6r9jbQ6HxgVQkVyAAAgiaTMdmH9+vX65z//qU8//VSVlZUNjunWrZtuvvlm3XnnnQoLCzM4Qvvy8/P10ksv6b///a927txpd6zZbFa/fv00adIkTZ8+XdOmTVOnTp3snrNs2TJNmTLFmSHXkZKSomXLljX6/VdffVU33nijy9a//vrr9eqrrzocFx8fr0OHDjllzYcfflh33323U+YCADTd0NhQndWvq5bubrzyic0mPfZ1up67doSBkbU9B/JK9Lv3tmn9wcJmnxvo660/TO+nK0fHesRDLQAAwHXG9eoib7NJ1dbGs5xS9+aRlAnA7YrKqvTOxiy7Y7zMJl0/Ps6giAAAAFzPZDKpd9dARQXGaIh/gaqtNqUXVOu4OmvNwZOGtzrvXNvqPEITE8NpdQ4AQAdGUqYHq6qq0t13362nnnpKtp+9m7RYLKqurq59/fjx43rggQf0wgsvaOHChbrgggvcEXIdVqtVjz32mO6//34VFxfX+76Pj0+9JFOr1aqdO3dq586deu655/S3v/1N//d//2dUyAAAuN2dZyfZTcqUpCU7j2tHdpEGRoe4PB6LxaKYmJg6x+5UXWPVSysy9ejX6aqstjb7/Kl9u+rvFw9U9xBulgEAPENb24vbmyA/i0bEddbazIJGx6Sm58lqtVENBYBbLV5/2GGHgOmDuvO7jouwHwMA4F4/3Yvje0iRkZGyWCwqKK3Uyn35SsvIU1pGvstbnReertKn247p0+9bnffuGqhJieGalEircwBA+8bvxfWx63uo8vJyXXjhhVq6dGnta507d9Z9992nOXPmKCYmRtXV1dq4caOeeuopLVq0SJKUm5urmTNn6tlnn9Wtt97qrvBVXFysyy67TF9//XXta/369dM111yjGTNmqEePHgoNDVVNTY327t2rTz75RI8++qhyc+0nobhDSIjrE17a8voAAGMNiArR+QO76Ysdx+2Oe+zrdL18wyiXx/PzN9jutPtYse55d5u2Zxc1+9zOnSz684UDNGtoFNUxAQAepS3txe1VSp8Iu0mZ+SUV2n28WAOi+P0cgHtU11j12irHHXJumphgQDQdE/sxAADu1dheHBbgowuHROnCIT+0Oi/R8vQzSZprDhSorMq1vc735ZZoX26JXll5ptX5iLjOmpQYoeTECA2ICubhPgBAu8HvxfWRlOmhrr322joJmb1799bSpUsVF/dj+xlvb2+NGTNGY8aM0dlnn60bb7xRNptNVqtV8+fPV/fu3XXhhRcaHntxcbGmTp2qjRs3SjrzF/NPf/qT7r33Xnl5edUZ6+Xlpf79+6t///66+eabNX36dK1bt87wmO259tprO/T6AADj3Xl2kpbsPG637co3e3K16XChhvfobFxgblJZbdXT3+3TM9/ts9tatDEXDO6uv84coPBAXxdEBwAAPF1yYoQeWrLX7pjl6fkkZQJwmyU7jyv7ZJndMSPiOmtobKgxAQEAALRBZ1qdB6l31yDNnZigiuoabTp0sraK5o6jrm91vuZAgdYc+LHV+cTEiNpKmlQ0BwCgfTG7OwA03wsvvKB333239tjPz0+ffPJJnYTMn7v++ut133331R5brVZdf/31On7cfpUtZ7PZbLryyitrEzIlaeHChfq///u/egmZP9elSxe9//77CgoKavH6119/vWw2W6v+S05Orp0vKipKF110UZPX//Of/9yqtWtqapSQ8OMT7aNGjdLIkSOb9WeQkpLS6j+Du+++u1lrAgCcKykySDOHRDkc99jX6QZE415bjpzUjKfS9OQ3Gc1OyIwI8tXz147Qf64aTkImAABoVP/uwQ7fK6Smt73OHgA6joUrMh2OmTuBKpkAAAA/5evtpXG9uuie8/rqk19N1Ib7ztKTVw7T5SNi1C3Yz+XrF56u0idbj+qed7dp3IPf6qxHU/XXT3bquz25Ol1Z7fL1AQCAa5GU6WFKSkr0xz/+sc5rd999t/r27evw3D/+8Y/q0aNH7XFhYaEeeOABp8doz5NPPqnPP/+89njBggW65pprmnx+dHR0s8Y7265du7R8+fLa45tvvlne3sYVnF2yZIkyM3+8yTp//nzD1gYAtC13TEuUo84maRn5WnvghDEBGaysskZ//2yXLnlmpdJzSpp9/uUjYrT0zhSdO6CbC6IDAADtidlsUnJSuN0xGw4WqqSCD80AGG/z4UJtOnzS7pjoUH+dOyDSmIAAAAA8VJdAX80cEqWHLx+i1fdO1dIFyfrTjP6a0idC/hb7xYWc4Yc25ze+ul5D/vqVrnxhjZ5Ztk87sotkbUGHKAAA4F60L/cwjz/+uHJzf6y+4OvrqzvuuKNJ5/r4+Og3v/mNFixYUPvaCy+8oLvuuqtO9UVXycnJqVOtMyQkpF6CaVPMmDGjNjGya9euTouvKZ599tnar729vXXrrbe6bf2wsDBdccUVhq4PAGg7ekYE6tLhMXpnY5bdcY98na7Ft46VyeQgg7OFrFarysvLa4/9/PxkNrv2uZ81B07o9+9t08ETp5t9bnSovx68ZJCSkyJcEBkAAMZzx17cEaUkRej9TdmNfr/aatPq/Sd0dn+SngAY6+UmVMm8YXy8vL3YG1yJ/RgAAPdy9l7cUKvzjYcKlZaRr7SMPO3ILnZG2I2qqrFp9YETWn3ghB5asldhAT6a0DucVucAgDaL34vrIynTg1RVVenRRx+t89r555+v8HD71Rp+6uqrr9bdd98tq9VaZ86nnnrKqbE25B//+IdKS0trj+fPn6/Q0NBmzzN9+nRNnz7diZE1TWlpqV5//fXa45kzZyo6Otqw9Q8dOlSnyugNN9wgf3/ecANAR/braYn6YHO23bbd6zILtHLfCU1MbPr7heYoLy/Xtm3bao8HDx6sTp06uWStU+VV+teSPfrvmsMtOv+6cXG657y+CvTlLTAAoP0wci/uyCb2DpfJJNnsFCdJTc8lKROAobJPlumLHcftjunk46XZo2INiqjjYj8GAMC9XL0X+3p7aXyvcI3vFa7fnddXJ0oqtGJfvlZk5CstI1/Hi8sdT9IKBaWV+mTrUX2y9agkKbFroCYlRmhSUrjGJISpkw/3vAEA7sXvxfWxO3uQb775RoWFhXVeu+CCC5o1R9euXTV69GitWbOm9rX3339fTz75pMsqaEnSqVOntHDhwjqvXXzxxS5b76diYmJqq4mOHj26xfMsWrRIxcU/PvXU1Nbh/fv3r11/7NixLV7/+eefr02mNZlMmjdvXovnAgC0D7FhnTR7VKz+t9Z+kuIjX+/VhN5dXLrXu9p3e3N13/vbdbSo+Te3EsID9K9LB2t0QpgLIgMAAB1Bl0BfDY4O0dasokbHpKbnyWazefR7LgCe5fXVB1XjoJXl7JGxCvG3GBQRAABAx9Al0FezhkZr1tBo2Ww27cst0fLvq2iuOXBC5VVWl66fkVuijNwSLVyZKR8vs0bGd9bExHAlJ0aof/dgmc38XgoAgLuRlOlBPvjgg3qvTZo0qdnzTJo0qU5S5tGjR7V27dpWJQw6snjxYpWUlNQed+3aVaNGjXLZej/Vu3dvPf74462e56etw5OSkjRt2rQmnTd69OhWJYNKUmVlpV5++eXa47POOkuJiYmtmhMA0D78ampvvbsxS5XVjd/k2Xz4pL7bm6upfT2vctPJ05W6/5Nden9z4+1CG2M2Sbck99SdZyXJz+LlgugAAEBHkpwUYTcp80hBmQ6eOK2E8AADowLQUZVWVOtNBw/omUxnWpcDAADAdUwmkxIjg5QYGaSbfmh1frBQafuMaXVeWWPVqv0ntGr/j63OJ9a2Oo9QtxA/l64PAAAaRlKmB/nkk0/qHAcEBCgpKanZ8wwbNqzBuV2ZlPnhhx/WOR45cqRHVY5YvXq1tmzZUns8f/58Q+N/7733lJubW3t8++23G7Y2AKBt6x7ir6tG99Crqw7aHffo1+ma0qerR+2/X2w/pj9+tFP5JRXNPrdvtyA9dNlgDY4JdX5gAACgQ0pJitBT3+6zOyZ1b64SwhMMighAR/bepiwVl1fbHTOtb6TiSRQHAAAwlK+3l8b3Dtf43mdaneeXVGjlvjNtztMy8pRT3Pz73c1RUFqpj7ce1cfftzpPivy+1XliuMYkdJG/DwUMAAAwAkmZHiI3N1fHjh2r81qfPn1alFjRv3//eq/9NOHQ2aqrq/Xdd9/Vea0lyaTu9NMqmZ06ddINN9zgtvVjYmJ04YUXGro+AKBtu31KL721/rDdlig7sov15c4cnTewm4GRtUzuqXL9+aOd+mLH8Wafa/Ey6ZdTEjV/ci/5eJtdEB0AAOiohsaGKsjPW6fsJEGlpufphgkkZQJwLavVpldWHnQ47qaJ/HsEAADgbuE/a3WekVui5el5SsvI19pM17c6T88pUXpOiV5e8WOr8x+SNGl1DgCA65CU6SF27dpV77X4+PgWzdXQeQ3N7yxbtmzR6dOn67zWUOvt0tJSbdiwQceOHVNeXp4CAgIUERGhXr16NZhIapQTJ07onXfeqT2+4oorFBoaatj6O3fuVFpaWu3xrbfeKi8v5zzBZLVatW/fPu3YsUMnTpzQ6dOnFRISoi5duqhHjx4aNGiQzGYSWgCgresa5Kfrx8Xr+eUH7I577Ot0ndM/ss3eZLHZbHp/U7bu/3SXisqqmn3+kJgQPXTZEPXpFuSC6AAAQEfn7WXWpMRwfb698QdH1hwoUHlVjfwsVB4B4Drf7c1VZn6p3TH9uwdrbM8wgyICAABAU5hMJiVFBikpMkg3T+qp8qoabTpUqOXfV9HcedS4Vuf/WiJ1CfDRxO/bnE9KDFdkMK3OAQBwFpIyPcTOnTvrvda9e/cWzRUSEiJ/f3+VlZXVvnbo0CGVlpYqIMD57WwaqsIZERFR+/XSpUv1xBNPaOnSpSovL29wjujoaF144YX63e9+1+Jk1JZauHBhnbiMbh3+zDPP1H5tsVh0yy23tHrOgwcP6t///rfeeeedOm3Rf65z585KSUnR/Pnzdc4557R6XQCA69yW0kv/XXNIpZU1jY7Zm3NKn24/pplDogyMrGmyT5bpD+9vV2p6XrPP9fU2665zkjR3QoK8vXiYAAAAuE5yYoTdpMyyqhptOFioiYnhBkYFoKN5eUWmwzE3TUxoUZclAAAAGMfP8mOr89+f/2Or8+XpZ5I0c0+5ttX5idJKfbTlqD7acqbVeZ/IoO+TNGl1DgBAa5GU6SEOHKhf+So8vOU3+CMiInT48OHaY5vNpszMTA0cOLDFczZm79699V4LCAhQbm6ubrvtNn344YcO58jOztZzzz2nhQsX6o477tCDDz7otGqR9thsNj3//PO1x6NHj9aIESNcvu4PSkpK9MYbb9QeX3zxxerWrXVtZ7du3aq+ffuqosLxm/jCwkJ9+OGH+vDDDzVy5Ei9+OKLGjp0aKvWBwC4RliAj26ckKCnv9tnd9zjS9M1fWC3NpO8aLXatGjdYf3z8912E0obMzohTP+6dLASwp3/YAkAAMDPJSdFOByTmp5LUiYAl9l9rFir9p+wOyY80FczhrTsgX4AAAC4z89bnafnlCgtw7hW53tzTmlvzqnaVuejEn5sdd6vG63OAQBojrbxaTwcKi6uX6o8ODi4xfMFBdVv69nQGs5w6NCheq/l5+dr0qRJtQmZXbt21QMPPKAtW7aosLBQpaWlSk9P11NPPaU+ffrUnldZWamHH35YF1xwgU6dOuWSeH/qq6++0v79+2uP58+f7/I1f2rRokV1rtMZ6588ebI2IfP888/XK6+8oj179ujkyZM6ffq0MjMztXjxYl1++eV1nqbfsGGDxo8fXydJFADQttwyqaeC/Ow/c3Mgr1Qffv/Uq7sdzC/VlS+u0R8/3NHshMwAHy89cNFAvXXLWBIyAQCAYaJC/ZUUGWh3zPL0fIOiAdARLWxClczrxsXJ15uqRgAAAJ7MZDKpT7czbc5fmztaW/50jhbdPEa3pfRU/+4tzxNoqsoaq1buO6F/frFHFzy5QqP/sVS/eWuz3tuYpdzihrtfAgCAH1Ep00OUlJTUe601rcYbOrehNZyhofbYt912W21L8GnTpuntt99WWFhYnTGJiYlKTEzUzTffrF/84hdauHBh7fe+/PJLXXvttU2qstkaP20dHhYWpiuuuMKl6/3cs88+W/t1//79NXnyZKfMGx8fr5deeknTpk1r8Hvx8fGaPXu21q5dq0svvVTZ2dmSpLKyMl1//fXy9/fXZZdd5pRYfio3N1d5ec1rW7tvX92KcGVlZTp9+rTD88xms/z8/Oq9Xl5eLqu16U+ZWSwWWSyWOq9ZrdY6Le+bws/PT2Zz3Tz5qqoqVVVVNXkOrqlxXFPjuKaGeeo1WSTdMDZWTy2z/yHdk99kaNbQKFm8zE65JpvNVjtHWVmZwzlqrDa9ufG4Hl2a3qIneyf2CtNfZvRRTOdODT6Z29Z/TvZ46v979nBNjeOaGsc1NYxralxHvqaysjLV1NTUOf4pT7wmR9x9TeMSOis9p/F7KHtzTunA8QJ1C64/f2PcfU0/1V5+Tj/FNTWOa2pYW72mvFMVta0lG+PjZdYlgyPq3R9rq9f0U578cyorK5PNZqvXMt6Tr0lqfz8niWtqDNfUOK6pcVxTw7imxrn6muz9bvxznnJNP/85DYvqpGFRcbojJU75JZVanVmgVfsLtepAgfJKKpu1ZnPll1Tqwy1Ha4s+JHYN0PieYZrQs7NGxIXK31L3oaCO9P8e18Q1NYZrahzX1DBPvqbG7lO7+5qa8tm1q5CU6SEaqgrp7d3yH19D57qq8mRhYWG91374SzFw4EB99NFHdhNM/fz89OKLLyovL0+ffPJJ7esfffSRnnzySf361792ftCSjhw5os8++6z2+MYbb2zwHwpXWbVqlbZu3Vp77KwqnfHx8UpLS1NMTIzDsWPGjFFqaqrGjRtXmyxps9l0/fXXq0+fPho0aJBTYvrBM888o7/+9a+tmiM9PV3V1dUOx/n7+2vIkCH1Xt+7d2+z/lGOiYmp92dZXl6ubdu2NXkOSRo8eLA6depU57WcnBxlZWU1eQ6uqXFcU+O4poZ58jUNC7Aq0GJSSZWt0fkOF5zWuxuzdOXoHk65JqvVWltxe8+ePfLyarwiy5Hiaj23uVT7Cx3/W/1zARaTrh8UoEmxUv6hdJXmeu7PqTGe/P9eY7imxnFNjeOaGsY1Na4jX1NNTU2dzhc/34s98Zoccfc1RZsdf9D11rJtmhrf9HsI7r6mn2ovP6ef4poaxzU1rK1e06K1h1RZY/8DiwkxFmXt36OfX21bvaaf8uSf0w8fPP3892FPviap/f2cJK6pMVxT47imxnFNDeOaGufKa8rPz7f7u/HPecI1NeXnFCcprqd0RUKg8iotyjWHaXlGvtYeOKGKate2Os/ILVVGbqleW3NEFrPUt4tFg7taNCjCoh4hXho6ZEiH+H+Pa+Ka7OGaGsc1NcyTr6mx+9TuvqYDBw40eQ1no325h2jof0R7byQdaSgp01XZwfaykp966qkmVfw0m8166qmn6iVF3n///U2qitgSL7zwQu3NNJPJpHnz5rlkncb8tEpmQECArrvuulbNt2jRIn399ddatWpVkxIyf9CrVy899thjdV47ffq07r777lbFAwBwjU4Ws2Ym+Tsc99Q3Gaqobl7L8Naottr03p7T+v13RS1KyBwd5aNHpoUquYdvvcojAAAARurbxSIfB7dktuY2/Ql4AGiK8qoa/XfNIYfjpvcy7qFyAAAAtA0mk0lxoRbdPKmnXp87Wlv/fI7+e9MY3ZbcUwmhFscTtFKVVdqeV6VFO0/r98uKNH9Joe75YBetzgEAHRqVMj2Ev3/95Iqfln1trobObWgNZ2isFO/gwYOb1Y47Li5Os2fP1uuvv1772okTJ7Rw4UL98pe/bG2YdVRVVemll16qPT777LPVu3dvp65hT35+vt55553a46uvvlrBwcGtmnPChAktPvfqq6/W/fffr/T09NrXvvrqK61evVrjxo1rVVwAAOc7J8FPn+0rU1FF49UyjxaV6611RzTU8bMRdvn5+alv377as2ePpIYfGtlfWK3nN5focHHz37uE+Jo0d3CAxkT7ti5QAADaKS8vrzq/L7bmAU40jY+XSf3DLdqS03ji5Y68KtVYbfIy8zAJAOf4eOtR5TtoSTkowqLYYG75u4OXl5cSExNr77Eb2XEJAACcaWnK78Y/8rN4aWJiuCYmhmt6dKWOFZZqR16VtuVWaVtupU7a+ezAGYoqbPp0e44+3Z4jSerbLUiTEsPVr7NJ4TabfLz4XRkA2hvuU9dnstlsrt1x4RRXXnml3nrrrTqvPffcc7rttttaNN/YsWO1du3aOq99+eWXOuecc1ocY2MGDBigXbt21Xv9/vvv1x//+MdmzfXJJ59o5syZdV6bMmWKvv3221bF+HNvv/225syZU3v84YcfatasWU5dw56HHnpIv/vd72qPt2zZ0mA5XyP9/e9/1//93//Vee1Xv/qVnnzySaetkZubW9smvan27duniy66qPZ4/fr16t+/v8PzzGZzgzdHy8vLZbU2vZy/xWKRxVL3CTOr1Wq3QmxD/Pz8ZDbXLV5cVVXVaFJzQ7imxnFNjeOaGtYerun1NUf0z6/22Z03IshXX/1qnHy9m34DpDnXVF5Vo/+kHtQrqw/L2oJ3nLMGd9Pvzu2tUP+Gn+RtDz+nn+OaGsc1NY5rahjX1DiuqXFcU8O4pvreWJulB7/MsDtm0Y3DNSw2pEnxtIVr+kF7+jn9gGtqHNfUsLZ2Tb6+vrrgqZXac/yU3XHPXjlYKYldGvxeW7um9vhz4poaxjU1jmtqHNfUMK6pcVxT47imhnXka7LZbMrILdXKAwVatb9AGw4XubzV+U/5eJk1Mi5E43qGaULPMPWJDGi0O1ZH/jk5wjU1jmtqGNfUOK6pce3hmnbt2qVRo0bVHu/YsUMDBgxo8rqtQVKmh7jlllvqVG6UpEceeUQLFixo0XwDBw7Uzp0767y2cuVKjR8/vsUxNqahBFBJ+vzzz3X++ec3a668vDx17dq1zmv+/v4qKiqq95evNaZMmaJly5ZJkmJjY5WZmWlYFrfNZlPv3r114MABSdK4ceO0atUqQ9a2Z/ny5UpJSanzWv/+/ev9f2S0nTt3auDAgbXHRv4DCgBtWXlVjSY/vEzHHbQGuW96P92S3NPp66/LLNDv39umA/mlzT63e4if/nHJIE3p09XxYAAAADc4kFeiqY+k2h3z66m9teCcPgZFBKA9W7UvX1e9VP/+6k/1jAjQ0jtTZKZCLwAAAJqhvKpG6w8WaEVGvpZn5Gv3sWJD1w8P9NWkxHBN+r6yZ9cgKo4DAJzHnTlF9DLxEA21rj51yv6T0fY0dG5QUFCL57OnsXn79evX7LkiIiIUERFRp5piWVmZdu3a5bRKkrt3765NyJSkW2+91dCyukuWLKlNyJSk22+/3bC17Rk2bFi913bt2qWSkhIFBga6ISIAgD1+Fi/9Ympv/fHDHXbHPZu6X1eN6aEAX+e8LSypqNZDS/bo9dWHWnT+NWN76Hfn9VWQn/MetgAAAHC2hPAAxXT2V1ZhWaNjUjPyScoE4BQvr8h0OGbuhAQSMgEAANBsfhYvTUqM0KTECN0rKfdUuVbuy1da+pkkzfySCpeun19SoQ82Z+uDzdmSzrQ6T06K0KTEcI2KD5Ofhfa3AADPRFKmh0hISKj3Wn5+fovn+/m5JpOpwTWcISSk4VZdnTt3btF8Xbt2rdfiujV/Fj/37LPP1n5tsVh0yy23OG3u5q4fERGhyy+/3ND1GxMUFCR/f3+VldX9wCk3N5ekTABoo+aMjNVzy/Yr+2TjyQIFpZV6ddVB/WJK71avtzw9T/e+v93ueo2J79JJ/7x0sMb2bLjVHgAAQFtiMpmUkhShRWsPNzpmW9ZJFZRWKizAx8DIALQ3B/JK9M2eXLtjQvwtumR4tEERAQAAoD3rGuSni4fF6OJhMbLZbNpz/JTSMvKUlpGvdZkFLm91vuf4Ke05fkovLD8gX2+zRieEfV9JM0J9uwU12uocAIC2hqRMD9FQ6dSjR4+2aK6ioiKdPn26zms9evRwWWJd794NJ3m0dL2GKm8WFBS0aK6fO336tF5//fXa40suuUSRkZFOmbspDh8+rM8++6z2eO7cufL19TVsfUdCQkLqJWXm5+erZ0/nt70FALSej7dZd0xL1D3vbbM77oXlB3TN2B4ym0yqqrHJ4mVSoK93k25uVFVVKeNQtp5eeVSf7y5sdoxmk3TTxAQtOLuP/H144hUAgOaqqqpSTk5O7XFkZKQsFipOG8FRUqbNJqVl5GnWUBKlALTcq6sOOhxz1Zge6uTDrX53Yj8GAMC92Itdw2QyqV/3YPXrHqxbk3vVtjpPy8jX8vQ87Tne8s6eTVFRbVVaRr7SMvIl7VFEkK8m9Q7XpKRwTehNq3MAaEvYi+vjTo2H6N+/f73XDh482KK5GjqvofmdZdCgQQ2+XllZKX9//2bP11CCiM1ma/Y8DVm0aJGKiopqj+fPn++UeZvq+eefl9V65ukis9ms2267zdD1HWnoz5mnkQCgbbtkeLSeWbZPB0+cbnRMUVmVRv/9mzpPuIb4WzQwOlhDYkI1a2i0+nSr/1CEJH2x/aj+9NEunaxo/l6cFBmohy4boqGxoc0+FwAAnFFVVaWsrKza47CwsA5/s8so43p1kbfZpGpr4++Dlqfnk5QJoMWKTlfpnQ1Zdsd4m026blycQRGhMezHAAC4F3uxMX7a6vwP0/spt7hcK/bl1yZOurrVed6pCr2/OVvv0+ocANoc9uL6SMr0EJGRkerWrZuOHz9e+9revXtls9manRS3a9eueq8NHTq0tSE2qrGkzJKSkhYlZZ46Vf+Jm4iIiGbP05Cftg4fMGCAUlJSnDJvU1RVVenll1+uPT7vvPNc1lK+pX6asPqD8PBwN0QCAGgqby+z7jgrUXcu3mp33M9bjhSVVWnlvhNaue+Enlm2X6PjwzR/ci9N6dtVkpRfUqE/f7xTn2071vyYzCbdPqW3fjGll3y9uUkCAAA8U5CfRSPiOmttZuPdO1LT82S12mQ280AjgOZ7c/1hlVXV2B0zfVB3dQ9p/j1WAAAAoLW6BvvpkuExumR4jKzWM63OV+w70+p8bWaBKt3Q6jw5MUKTksLVJ5JW5wAA9yIp04NceOGFevHFF2uPS0tLlZGRoaSkpGbNs3nz5gbndpV+/fopJCSkXkLf0aNHW5RMmZeXV++1rl27tji+H6xdu7bOn43RVTLff//9OqV8b7/9dqfMe/jwYR0+fKadWlJSUov/rIqLi1VeXl7vdWclxAIAXGfmkGj957v92pdb0uI51h0s0LpXCzRrSJRGJ4Tp31/tVeHpqmbPMyg6RA9dNlj9uge3OBYAAIC2IqVPhN2kzPySCu0+XqwBUSEGRgWgPaiqseq1JrQuv2li23qoGwAAAB2T2WxS/6hg9Y/6sdX5uswCpWWcSdI0tNX55zrT6jwxXMmJEZrQO1wRQb4uXR8AgJ8zuzsANN3FF19c77W0tLRmz7NixYo6x927d9fYsWNbHJcjFotFs2bNqvf6zp07mz3XiRMn6iQuSlJISIj69u3b4vh+8Mwzz9R+HRgYqGuvvbbVc7Z0/fj4eJ1//vlOmXfhwoWaNGmSJk2apMWLF7d4nk2bNtV7rW/fvgoMDGxNeAAAA3iZTZo9IsYpc3209aju+3BHsxMyfbzN+v35ffXB7eNJyAQAAO1GSpLjBxWXp+cbEAmA9mbJjuM6VlT/AemfGhnXWUNiQ40JCAAAAGgGP4uXkpMidN8F/bXkN8la94dpeuTyIbp4WLTCA31cvn7eqQq9vylbv1m8RaP+vlTnP5GmBz/frRUZ+Sp3UI0eAABnICnTg0ybNk2hoaF1Xvv888+bNUdeXp7Wrl1b57VLLrnE5aW758yZU++1VatWNXue9evX13tt8uTJ8vJqXevTgoICvf3227XHV199tYKDjUsY2bVrl5YvX157fNttt8lsdv5fz+zs7Baf21AC8FlnndWacAAABtl9rFj/WbbfbeuPiu+sJXdM0ryUXvL24u0nAABoP/p1C1Z4oP1qG6npuQZFA6A9eXlFpsMxVMkEAACAp+ga7KdLR8TosTlDte4PZ+nzX0/Svef31cTe4fLxdv3nBruPFev55Qd0zctrNeSvX+m6hev0UtoB7TleLJvN5vL1AQAdD+3LPYiPj4/uvPNO/fnPf6597bPPPtOJEyfUpUuXJs3xv//9T1artfbYYrFowYIFTY5hzZo1Sk1NVUREhGbPnt3kKolnn3224uLidOjQodrXPvjgAz355JPNSj5855136r129dVXN/n8xrzyyit1WnM7q3V4Uz377LO1X/v4+Oimm25yyTo/TfxsDpvNptdee63e65dffnlrQwIAuFhhaaVueGWdisqa32q8tTr5eOl35/XVtWPjZDa79gEQAAAAdzCbTUpOCtf7mxp/CHLDwUKVVFQr0JfbcACaZtPhQm05ctLumOhQf53dP9KYgAAAAAAn+mmr89tSeqmsskbrDhYoLf1Mq/O9Oa5vdb48PU/L0/MkSV2DfDWxDbU6t9lsKqmoVlWNTRYvkwJ9vV1eZAsA4HzcDfYwCxYs0NNPP628vDNvECoqKvTkk0/qr3/9q8Nzq6qq9Nhjj9V57eabb1bPnj2btPbf/vY3/elPf6o9/sc//qGVK1cqMtLxzT+LxaJ//etfuuKKK2pfO3r0qBYtWtTkNuHHjh2r13574MCBuuyyy5p0fmNsNpuee+652uPx48dr8ODBrZqzOUpLS/X666/XHl922WWKiHDc/qwl1qxZo61bt2rIkCHNOu/111/X/v11K6xNmTJFycnJzgwPAOACf/54p3KKKwxfd1JiuP5x8SDFhnUyfG0AAAAjpSRF2E3KrLbatHr/CZKnADRZU6pk3jghnk4EAAAAaBf8fbyUkhShlKQzn5HnFJdrRUa+0jLytGJfvvJLKl26fu73rc5/+N2+f/dgTUo6k6Q5Iq6z/Cyt69rZFHuOF+vjLUe1NeukdmQX1ym0EeJv0cDoYA2JCdWsodHq0y3I5fEAAFqPuzYeJjAwsF4C5sMPP6z09HSH5/7973+vU6kyNDRUf/zjH5u07u7du+utu3//ft1zzz1NOl8608J8/PjxdV77wx/+oNzcprXxuvPOO1VaWlp77OXlpSeeeKLVT4V8/fXX2rdvX+2x0VUyFy1apOLiYkPWt9lsmjdvnioqmp6cs2/fvnrVVH19ffXQQw85OzwAgJN9uydHH289auiawX7eeuiywXp97mgSMgEAQIcwKTFCjm5N0MIcQFNlnyzTkh3H7Y4J8PHS7FGxBkUEAAAAGCvy+1bnj18xTOv+cJY++/VE/f78vprQu4shrc53HSvW86kHdPVLazX0/q90/fetzvceP+X0Vuff7snR7OdW67zH0/TMsv1aue9Evc5nRWVVWrnvhJ5Ztl/nPr5cs59bre/2cJ8BANo6KmV6oPnz5+vrr7/WBx98IEkqKyvThRdeqKVLlyo2tuGbcW+88Yb+9re/1R6bTCa98sor6t69e5PW/O6771RTU1Pv9S+//LJZsb/xxhsaNWqUCgoKJElZWVmaMWOGPv74Y3Xr1q3Bc6qrq/Xb3/62XpXMhx56SFOnTm3W+g35aevwiIgIw1ty/3T9wYMHa8KECS5db82aNZoxY4YWLVqkrl272h27bt06XXzxxbU/rx+88MILGjlypCvDBAA4wXPLDhi63qjuFv37ilGKi+xs6LoAAADuFBbgo8HRIdqaVdTomGV782Sz2Wg3BsCh11cdVI3V/ge9l4+MVbCfxaCIAAAAAPcxm00aEBWiAVEhmueGVuflVValpucpNT1P0m51DfLVpMQIJSeFa0LvcIUHtqzVeWFppf788c4WFdZYd7BA614t0KyhUfrLhQPUOcCnRTEAAFyLpEwPtWjRIs2YMUPffvutJCk9PV1Dhw7V//3f/2n27NmKjo5WTU2NNm3apKeeekpvvPFG7bkmk0n/+c9/dNFFFzV5vcae+GjukyA9e/bUkiVLdO6556qwsFCStH79eg0YMEB33XWXLrvsMiUkJMjLy0u5ubn66quv9Pjjj2vz5s21c5jNZj3wwAP1qje2RFZWlj755JPa47lz58rHx7g3LatXr9aWLVtqj+fPn2/IukuXLlViYqJuuukmXXjhhRo+fLhCQkJks9mUn5+v1atX66233tJbb71V52fs6+urxx9/XNddd50hcQIAWm7P8WKtO1jgeKCTXD3AXzN6+ysiqGU3IAAAADxZSlKE3aTMrMIyZeaXqmdEoIFRAfA0pRXV+t+6w3bHmExnWpcDAAAAHdHPW50fLyrXin3ftzrPyNeJUte3On9vU5be25QlSRoQFaxJiRGalBje5Fbnu48V64ZX1imnuOndLRvy0ZajWnPghF6bO1p9uwW3ai4AgPORlOmh/P399cUXX2jBggV65plnZLPZVFBQoAULFmjBggXy8fFRdXW1rFZrnfPCw8P18ssva+bMmc1aLyUlRWazud5855xzTrNjHzVqlDZs2KCrrrpKa9eulSQVFBTovvvu03333SeTySQvLy9VV1fXOzcuLk7PPPOMpk+f3ux1G/LCCy/UVgA1m82aN2+eU+Ztqp9WyQwODtY111zj9DXuuusudevWTc8//3ydBNDi4mI99thjeuyxxyRJ3t7estlsDVZElaQhQ4bopZdeokImAHiIj7cY27a8tEpUfgIAAB1WclKEnvx2n90xy9PzSMoEYNe7G7N0qrz+PdGfOrtfpOK6BBgUEQAAANC2dQvx02UjYnTZiBhZrTbtOlastIx8rdiXp/WZhaqssTqepBV2Hi3WzqPFei51v/wsZo1J6KJJieFKTopQYtfAep+b7D5WrCteWFOvRXlL5RRXaM7za7T4trEkZgJAG2N2dwBoOR8fHz399NNas2aNLrroojoVHisrK+skUEZGRuoPf/iD9uzZ0+yETEkaOHCg/vrXv9Z50xAfH6+HH364RbH37NlTq1ev1ptvvqkJEybUmddms9VJyPTy8tLo0aP13HPPKSMjw2kJmdXV1XrppZdqj88//3zFx8c7Ze6mOHHihN55553a42uvvVaBgc7/cCYoKEjz5s3T5s2btXXrVv3973/X+PHj61UEra6urpeQGRoaqpkzZ2rJkiXasmULCZkA4EG2Zp00dL3MIqv8/f1lNvP2EgAAI5nNZvn7+9f+x17sHkNjQxXkZ//Z5zOtzgCgYVarTa+szHQ4bu7EBAOiQXOxHwMA4F7sxZDOtDofGB2i+ZN7adHNY7X1z+fo1RtH6aaJCUqKdP1Dkj+0On/gs90657HlGvvgN7r7na36aEu2TpRUqLC0Uje8ss5pCZk/KCqr0vUL16nQxVVCAcAe9uL6TLbm9p9Gm1VcXKy1a9cqPT1dJ0+elLe3tyIjIzV48GANHTrUKf/Db9iwQWlpaQoODtbs2bMVFBTkhMil/Px8rV+/Xvv27VNxcbF8fHwUERGhbt26aezYsQoNDXXKOvhRVVWV9u7dq927d+vEiRM6efKkJKlz584KCwtTUlKSBg0a1Ob/ody5c6cGDhxYe7xjxw4NGDDAjREBgPvZbDYNvf9rp/9ib0+Iv0Vb/nQ21TIBAECHdfuijfp8+/FGv+9nMWvLn85pUiszAB3P0l05uvn1DXbHDIgK1qe/msjvXQAAAEALHC8qV1pG3veVNPNVYHASY4i/t4rK7FfGb41ZQ6P0xBXDXDY/AHgid+YU0b68HQkODtbZZ5+ts88+22VrjBw50iXVEsPDw3X++ec7fV40zmKxaODAgXX+8QEAtA8lFdWGJmRKZ57ELK2sUaAvby8BAEDHlJwYYTcps7zKqg0HCzUxMdzAqAB4ipdXOK6SedPEBBIyAQAAgBbqFuKny0fG6vKRsXVanadl5GnDQde3OndlQqYkfbTlqGYNjdLUvpEuXQcA0DR8ag4AANDOVNW4pxB6ZbVV8nXL0gAAAG6XnBThcExqei5JmQDq2Xm0SKsPnLA7JiLIVzMGRxkUEQAAANC+/dDq/Id256crq7U2s0Bp6WeSNDNyS9wdYos8l3qApEwAaCNIygQAAGhnLF7uqZzi4212y7oAAABtQVSov5IiA5We0/gHN6npebrvAgODAuARXll50OGY68bG8TsXAAAA4CKdfLw1pU9XTenTVZJ0rKhMKzLy3dbqvKXWZRZo7/FT6tMtyN2hAECHR1ImAABAOxPo660Qf4uhLcxD/C0K8PEybD0AAIC2KDkxwm5SZnpOiY4Vlal7iL+BUQFoy3JPlevjLUftjvH1NuvqsXEGRQQAAACge4h/vVbnyzPylJaerw2HCtzWsawpPt6ard926+vuMACgwyMpEwAAoJ0xmUwaGB2slfvst79zpvhgs7Zt26Y+ffrIz8/PsHUBAOjoysvLtXfv3tpj9mL3SukToZdWZNodszw9T3NG9TAoIgBt3X/XHFZljdXumEuGRysswMegiNAS7McAALgXezFc6aetzm+f3PtMq/MDBWeSNDPyta+NtTrfeqTI3SEA6IDYi+sjKRMAAKAdGhITamhSZkKIWWVlZbJa7X+YCAAAnMtqtaqsrKzOMdxnVHyY/CxmlVc1/nNIJSkTwPfKq2q0aM0hh+NunJBgQDRoDfZjAADci70YRurk460pfbtqSt8fW52n/dDqPCNPhaeN62LWkO3ZRbLZbDKZTG6NA0DHwl5cH0mZAAAA7dDMoVF6Ztl+w9abEEPVFgAAAD+Ll8b17KLv9uY1OmZFRr6qa6zy9jIbGBmAtujjLUd1orTS7phJieFKigwyKCIAAAAAzdU9xF+zR8Zq9vetznce/b7VeUaeNhwsULXBeUlFZVUqraxRoC/pQADgTtz9BQAAaIf6dgvW6PgwQ9bq18VbscH8cg8AACBJyUkRdr9fXF6trVknjQkGQJtls9m0cGWmw3E3TaRKJgAAAOApzGaTBsWE6BdTeuutW8dp2d1T3BJHeWWNW9YFAPyIpEwAAIB2at7knoasMzPR35B1AAAAPEGKg6RMSUq1U0kTQMewav8J7Tl+yu6YXhEBSk50/G8KAAAAgLYppJPFLete9MxKPf1tho4XlbtlfQAASZkAAADt1tS+kZo5JMqla1wwMFLDutG6HAAA4AcJ4QGKDbP/0EpqRr5B0QBoq15e4bhK5tyJCTKbTQZEAwAAAMAVAn29FeJvfGJmVmGZ/v1Vusb/8xvd+Mo6LdlxTJVG91EHgA6OpEwAAIB27K8zBygy2Ndj5stUAAEAAElEQVQlc0cG++q+8xJd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STABILITY SUMMARY (PRICE PATTERNS) ---\n","Mean Accuracy: 0.6753\n","Std Dev: 0.0004\n"]}]},{"cell_type":"markdown","source":["### Save"],"metadata":{"id":"h9g0gPIn7smF"}},{"cell_type":"code","source":["import joblib\n","import os\n","\n","# Ambil model terbaik\n","best_bnb_price = grid_search.best_estimator_\n","\n","# Pastikan direktori tujuan ada\n","dir_path = '/content/drive/MyDrive/Research/Disertation/Telkom University Artefak/02 Proposal/02 SOTA Bundle Matching (Feature Engineering Improve Relevance and Diversity)/models/retrain_price_pattern/'\n","os.makedirs(dir_path, exist_ok=True)\n","\n","# Simpan model\n","joblib.dump(best_bnb_price, os.path.join(dir_path, 'nb_price_patterns.pkl'))\n","print(\"Model Price Patterns berhasil disimpan!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RY4nOb_Wq72E","executionInfo":{"status":"ok","timestamp":1777339771773,"user_tz":-420,"elapsed":83,"user":{"displayName":"all Penelitian","userId":"10978130364565931227"}},"outputId":"192b8e83-2e8d-4bc2-9b1c-fff03e9cd85a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Price Patterns berhasil disimpan!\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Previous/Framework Min and Mul Matrix Factorizations.ipynb b/Source Code/Previous/Framework Min and Mul Matrix Factorizations.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e7ea1e4d8fedae82474d254fec458c808c770bc3 --- /dev/null +++ b/Source Code/Previous/Framework Min and Mul Matrix Factorizations.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"VUTSHmhQhbVe"},"source":["### Generate Group Of Bundle Based On Tags And Specs"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"sT4b3MlpQUkp","executionInfo":{"status":"ok","timestamp":1736572520354,"user_tz":-420,"elapsed":344,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1527,"status":"ok","timestamp":1736572524594,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"rrq-gfhdNyLM","outputId":"7dac07ff-2740-45c0-d64a-2a206c8b5194"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3918,"status":"ok","timestamp":1736572529971,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"I-dDe4UNQaw7","outputId":"7515b093-26e3-4413-b23a-b9bbe8b1794e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45476,"status":"ok","timestamp":1736397088801,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"NvLqTlK-SOvG","outputId":"850baec4-817f-4d67-9674-fc04379dfaaa"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 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'playtime_2weeks': 0}, {'item_id': '242760', 'item_name': 'The Forest', 'playtime_forever': 1677, 'playtime_2weeks': 0}, {'item_id': '206420', 'item_name': 'Saints Row IV', 'playtime_forever': 2402, 'playtime_2weeks': 0}, {'item_id': '213850', 'item_name': 'Magic 2014 ', 'playtime_forever': 861, 'playtime_2weeks': 0}, {'item_id': '244810', 'item_name': 'Foul Play', 'playtime_forever': 19, 'playtime_2weeks': 0}, {'item_id': '244850', 'item_name': 'Space Engineers', 'playtime_forever': 60, 'playtime_2weeks': 0}, {'item_id': '6880', 'item_name': 'Just Cause', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8190', 'item_name': 'Just Cause 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239030', 'item_name': 'Papers, Please', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '218620', 'item_name': 'PAYDAY 2', 'playtime_forever': 2741, 'playtime_2weeks': 0}, {'item_id': '209000', 'item_name': 'Batman™: Arkham Origins', 'playtime_forever': 2369, 'playtime_2weeks': 0}, {'item_id': '239820', 'item_name': 'Game Dev Tycoon', 'playtime_forever': 448, 'playtime_2weeks': 0}, {'item_id': '250320', 'item_name': 'The Wolf Among Us', 'playtime_forever': 668, 'playtime_2weeks': 0}, {'item_id': '219640', 'item_name': 'Chivalry: Medieval Warfare', 'playtime_forever': 34, 'playtime_2weeks': 0}, {'item_id': '232210', 'item_name': 'Patch testing for Chivalry', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '208610', 'item_name': 'Skullgirls ∞Endless Beta∞', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '245170', 'item_name': 'Skullgirls', 'playtime_forever': 212, 'playtime_2weeks': 0}, {'item_id': '228560', 'item_name': 'Teenage Mutant Ninja Turtles: Out of the Shadows', 'playtime_forever': 1237, 'playtime_2weeks': 0}, {'item_id': '251570', 'item_name': '7 Days to Die', 'playtime_forever': 3034, 'playtime_2weeks': 0}, {'item_id': '252350', 'item_name': 'Double Dragon Neon', 'playtime_forever': 264, 'playtime_2weeks': 0}, {'item_id': '252490', 'item_name': 'Rust', 'playtime_forever': 3483, 'playtime_2weeks': 0}, {'item_id': '252950', 'item_name': 'Rocket League', 'playtime_forever': 915, 'playtime_2weeks': 0}, {'item_id': '241540', 'item_name': 'State of Decay', 'playtime_forever': 2099, 'playtime_2weeks': 0}, {'item_id': '237890', 'item_name': 'Agarest: Generations of War', 'playtime_forever': 135, 'playtime_2weeks': 0}, {'item_id': '242050', 'item_name': \"Assassin's Creed IV Black Flag\", 'playtime_forever': 6221, 'playtime_2weeks': 0}, {'item_id': '234530', 'item_name': 'War of the Vikings', 'playtime_forever': 2509, 'playtime_2weeks': 0}, {'item_id': '257350', 'item_name': \"Baldur's Gate II: Enhanced Edition\", 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '243470', 'item_name': 'Watch_Dogs', 'playtime_forever': 2043, 'playtime_2weeks': 0}, {'item_id': '247730', 'item_name': 'Nether', 'playtime_forever': 3114, 'playtime_2weeks': 0}, {'item_id': '260730', 'item_name': 'Desperados - Wanted Dead or Alive', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261030', 'item_name': 'The Walking Dead: Season Two', 'playtime_forever': 637, 'playtime_2weeks': 0}, {'item_id': '242700', 'item_name': 'Injustice: Gods Among Us Ultimate Edition', 'playtime_forever': 368, 'playtime_2weeks': 0}, {'item_id': '262060', 'item_name': 'Darkest Dungeon', 'playtime_forever': 332, 'playtime_2weeks': 0}, {'item_id': '263440', 'item_name': 'The Stomping Land', 'playtime_forever': 142, 'playtime_2weeks': 0}, {'item_id': '108600', 'item_name': 'Project Zomboid', 'playtime_forever': 160, 'playtime_2weeks': 0}, {'item_id': '211820', 'item_name': 'Starbound', 'playtime_forever': 137, 'playtime_2weeks': 0}, {'item_id': '367540', 'item_name': 'Starbound - Unstable', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '41900', 'item_name': \"The Bard's Tale\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '240760', 'item_name': 'Wasteland 2', 'playtime_forever': 710, 'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}],"source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26678,"status":"ok","timestamp":1736572648277,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"QyPVHFFbSpOe","outputId":"4e998aef-a224-412a-c3ba-f90948587589"},"outputs":[{"output_type":"stream","name":"stdout","text":["An unexpected error occurred: name 'data' is not defined\n"]}],"source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850","executionInfo":{"status":"ok","timestamp":1736576988048,"user_tz":-420,"elapsed":323,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":51,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8","executionInfo":{"status":"ok","timestamp":1736576990089,"user_tz":-420,"elapsed":274,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":52,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PRLv0p8UhlQD"},"source":["### Generate Matrix Data"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"OSY78hII5nst","executionInfo":{"status":"ok","timestamp":1736573292866,"user_tz":-420,"elapsed":307,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"eOGfmEhPstp1","executionInfo":{"status":"ok","timestamp":1736574535166,"user_tz":-420,"elapsed":1503,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":280,"status":"ok","timestamp":1736574538110,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"CGMVGRCU4pTo","outputId":"15a8b54a-18fd-44f3-890b-f87d5b973d76"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["59305"]},"metadata":{},"execution_count":20}],"source":["len(data_reviews)"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"YHamZVi-AORP","executionInfo":{"status":"ok","timestamp":1736573316867,"user_tz":-420,"elapsed":298,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"gUF3ON79A8F0","executionInfo":{"status":"ok","timestamp":1736573712540,"user_tz":-420,"elapsed":2550,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["users_item_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," rating = 0.0\n"," if item_id in user_item_dict[user_id]:\n"," rating = user_item_dict[user_id][item_id]\n"," users_item_histories.append({ \"user_id\": user_id, \"item_id\": item_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"FMhe80ITCl4A","executionInfo":{"status":"ok","timestamp":1736576756784,"user_tz":-420,"elapsed":963,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":42,"outputs":[]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":273,"status":"ok","timestamp":1736574541597,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"L9qLjFCOB99Y","outputId":"cbd020db-82a2-4d10-993c-58abc405f05d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 23025\n"]}],"source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in data_reviews:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"]},{"cell_type":"code","source":["# prompt: distinct value of rating in data_reviews\n","\n","distinct_ratings = set()\n","for review in data_reviews:\n"," distinct_ratings.add(review['rating'])\n","print(f\"Distinct rating values: {distinct_ratings}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyIDC0pK596j","executionInfo":{"status":"ok","timestamp":1736574544222,"user_tz":-420,"elapsed":302,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"837b2de4-356b-45a0-b1c6-a481bdcce56d"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Distinct rating values: {0.0, 1.25, 2.5, 3.75, 4.05, 5.0, 4.4, 1.65, 4.5, 3.35, 3.0, 4.0, 1.0, 2.0, 2.75, 0.25, 3.5, 3.25, 2.25, 4.75, 4.25, 1.5, 0.5, 0.75, 1.75, 1.9, 1.15, 1.4, 2.4, 2.15, 2.9, 2.65, 3.4, 3.15, 3.65, 3.9, 0.45, 0.2, 4.15, 4.65, 4.9, 0.15, 2.05, 2.8, 2.3, 2.55, 3.8, 3.3, 3.55, 0.55, 3.05, 4.3, 4.8, 4.55, 1.8, 0.8, 1.05, 1.55, 1.3, 0.85, 0.4, 0.9, 0.65, 0.35, 0.6, 0.95, 0.7, 1.45, 1.95, 1.2, 1.7, 0.1, 2.95, 2.7, 2.2, 2.45, 3.45, 3.7, 3.2, 3.95, 4.2, 4.45, 4.7, 0.3, 4.35, 4.1, 4.85, 4.6, 1.35, 1.1, 1.6, 1.85, 2.85, 2.1, 2.35, 2.6, 3.6, 3.1, 3.85}\n"]}]},{"cell_type":"code","source":["len(users_item_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QErv8gqo3Dp5","executionInfo":{"status":"ok","timestamp":1736573733404,"user_tz":-420,"elapsed":272,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"829eb158-d6bc-4dc3-bf04-837a397ce36a"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1252202"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"4b8hrqoiLQ2z"},"source":["### Create matrix factorization all"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":78374,"status":"ok","timestamp":1736573846953,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"e-NW0-DW9QSS","outputId":"03554011-fa1e-4fd0-ad94-d5178d7289fa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357297 sha256=63983ab9d92a578fe16d836dcfc1781a3b0936f5cd9d0e93d8cc2027e9b8093f\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}],"source":["!pip install surprise"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ONd9cv419Bkv","executionInfo":{"status":"ok","timestamp":1736574890952,"user_tz":-420,"elapsed":331208,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ff8f36af-777b-4eaa-b327-219d98aa478f"},"outputs":[{"output_type":"stream","name":"stdout","text":["SVD Best RMSE: 1.8729777093681352\n","SVD Best MAE: 1.5771079615170036\n","SVD Best Params: {'n_factors': 100, 'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.1}\n","SVD++ Best RMSE: 1.8809173017022\n","SVD++ Best MAE: 1.5713587900616282\n","SVD++ Best Params: {'n_factors': 50, 'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.1}\n","Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n","\n"," Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std \n","RMSE (testset) 1.8690 1.8690 1.8664 1.8698 1.8781 1.8705 0.0040 \n","MAE (testset) 1.5761 1.5752 1.5784 1.5734 1.5819 1.5770 0.0029 \n","Fit time 1.40 1.38 1.37 1.78 2.08 1.60 0.28 \n","Test time 0.07 0.07 0.08 0.12 0.11 0.09 0.02 \n","Evaluating RMSE, MAE of algorithm SVDpp on 5 split(s).\n","\n"," Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std \n","RMSE (testset) 1.8703 1.8878 1.8908 1.8880 1.8991 1.8872 0.0094 \n","MAE (testset) 1.5691 1.5865 1.5837 1.5874 1.5903 1.5834 0.0074 \n","Fit time 3.02 2.22 2.19 2.64 2.92 2.60 0.35 \n","Test time 0.18 0.21 0.17 0.27 0.20 0.21 0.04 \n"]},{"output_type":"execute_result","data":{"text/plain":["{'test_rmse': array([1.87031622, 1.88784623, 1.89082617, 1.88802589, 1.89907301]),\n"," 'test_mae': array([1.56913689, 1.58646586, 1.58369071, 1.58736369, 1.59034791]),\n"," 'fit_time': (3.023115396499634,\n"," 2.216174840927124,\n"," 2.192092180252075,\n"," 2.6412436962127686,\n"," 2.9241955280303955),\n"," 'test_time': (0.17547297477722168,\n"," 0.2111523151397705,\n"," 0.1650698184967041,\n"," 0.27498769760131836,\n"," 0.20217633247375488)}"]},"metadata":{},"execution_count":23}],"source":["# prompt: From this data user_bundle_histories, apply SVD and SVD++ with Hyperparameter tuning and Gridsearch and Cross validation 5\n","\n","import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","\n","# Create a pandas DataFrame from user_bundle_histories\n","df = pd.DataFrame(data_reviews)\n","\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'item_id', 'rating']], reader)\n","\n","# Define parameter grids for SVD and SVD++\n","param_grid_svd = {'n_factors': [50, 100, 150], 'n_epochs': [20, 30], 'lr_all': [0.005, 0.01], 'reg_all': [0.02, 0.1]}\n","param_grid_svdpp = {'n_factors': [20, 50], 'n_epochs': [20, 30], 'lr_all': [0.005, 0.01], 'reg_all': [0.02, 0.1]}\n","\n","# Perform hyperparameter tuning with GridSearchCV for SVD\n","gs_svd = GridSearchCV(SVD, param_grid_svd, measures=['rmse', 'mae'], cv=5)\n","gs_svd.fit(data)\n","\n","# Print the best hyperparameters and RMSE for SVD\n","print(\"SVD Best RMSE:\", gs_svd.best_score['rmse'])\n","print(\"SVD Best MAE:\", gs_svd.best_score['mae'])\n","print(\"SVD Best Params:\", gs_svd.best_params['rmse'])\n","\n","# Perform hyperparameter tuning with GridSearchCV for SVD++\n","gs_svdpp = GridSearchCV(SVDpp, param_grid_svdpp, measures=['rmse', 'mae'], cv=5)\n","gs_svdpp.fit(data)\n","\n","# Print the best hyperparameters and RMSE for SVD++\n","print(\"SVD++ Best RMSE:\", gs_svdpp.best_score['rmse'])\n","print(\"SVD++ Best MAE:\", gs_svdpp.best_score['mae'])\n","print(\"SVD++ Best Params:\", gs_svdpp.best_params['rmse'])\n","\n","# Train and evaluate the best SVD model using cross-validation\n","best_svd = gs_svd.best_estimator['rmse']\n","cross_validate(best_svd, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)\n","\n","# Train and evaluate the best SVD++ model using cross-validation\n","best_svdpp = gs_svdpp.best_estimator['rmse']\n","cross_validate(best_svdpp, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"hsLJ7vCx-avt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736574929595,"user_tz":-420,"elapsed":300,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1ce84cf6-a00c-4bff-edc9-5795bd92c228"},"outputs":[{"output_type":"stream","name":"stdout","text":["SVD prediction for user js41637 and item 1250: 1.6412900854280326\n","SVD++ prediction for user js41637 and item 1250: 0.603159704941516\n"]}],"source":["# Example usage: predict rating for a user and bundle\n","user_id_example = df['user_id'].iloc[4]\n","item_id_example = df['item_id'].iloc[0]\n","\n","svd_prediction = best_svd.predict(user_id_example, item_id_example)\n","svdpp_prediction = best_svdpp.predict(user_id_example, item_id_example)\n","\n","\n","print(f\"SVD prediction for user {user_id_example} and item {item_id_example}: {svd_prediction.est}\")\n","print(f\"SVD++ prediction for user {user_id_example} and item {item_id_example}: {svdpp_prediction.est}\")"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"F9tuPy2R-sLY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736574937927,"user_tz":-420,"elapsed":2904,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"56186ce7-1140-4b91-ae8b-4d3b9eba26ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svdpp_model.pkl']"]},"metadata":{},"execution_count":25}],"source":["# Save the SVD model\n","import joblib\n","joblib.dump(best_svd, f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svd_model.pkl\")\n","joblib.dump(best_svdpp, f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svdpp_model.pkl\")"]},{"cell_type":"markdown","metadata":{"id":"ScY_bk_GLsxk"},"source":["### Do Recommendation"]},{"cell_type":"code","source":["# prompt: Do recommendation for each users in data_bundle_interactions, and apply predict for all item in bundle_data in each bundle. Then create a rating for bundle based on minimum value of prediction item and harmonic mean of predictions items\n","\n","import math\n","\n","def harmonic_mean(numbers):\n"," if 0 in numbers:\n"," return 0\n"," return len(numbers) / sum(1/number for number in numbers)\n","\n","def recommend_bundles(user_id, bundle_data, model):\n"," recommendations = {}\n"," for bundle_id, bundle_info in bundle_data.items():\n"," item_predictions = []\n"," for item_id in bundle_info[\"items\"]:\n"," svd_prediction = model.predict(user_id, item_id)\n"," item_predictions.append(svd_prediction.est)\n","\n"," min_svd = min(item_predictions) if item_predictions else 0\n"," hmean_svd = harmonic_mean(item_predictions)\n","\n"," recommendations[bundle_id] = {\n"," \"min\": min_svd,\n"," \"hmean\": hmean_svd,\n"," \"hybrid\": (min_svd + hmean_svd) / 2,\n"," }\n"," return recommendations\n","\n","# Example usage (assuming best_svd and best_svdpp are loaded models):\n","# Load the saved models\n","best_svd = joblib.load(f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svd_model.pkl\")\n","best_svdpp = joblib.load(f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svdpp_model.pkl\")\n","\n","# Example user\n","user_id_example = list(data_bundle_interactions.keys())[0]\n","\n","# Get recommendations for the example user\n","bundle_recommendations = recommend_bundles(user_id_example, bundle_data, best_svd)\n","\n","bundle_recommendations"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S-Xtqm3g7sj-","executionInfo":{"status":"ok","timestamp":1736575421481,"user_tz":-420,"elapsed":2113,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8ed58dc6-cdcc-487a-c267-dc94a3eab69c"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'450': {'min': 1.155686159617006,\n"," 'hmean': 1.2765725978924496,\n"," 'hybrid': 1.2161293787547278},\n"," '1473': {'min': 1.263480371067979,\n"," 'hmean': 1.263480371067979,\n"," 'hybrid': 1.263480371067979},\n"," '1474': {'min': 1.263480371067979,\n"," 'hmean': 1.263480371067979,\n"," 'hybrid': 1.263480371067979},\n"," '1437': {'min': 1.263480371067979,\n"," 'hmean': 1.2634803710679792,\n"," 'hybrid': 1.263480371067979},\n"," '1466': {'min': 1.263480371067979,\n"," 'hmean': 1.263480371067979,\n"," 'hybrid': 1.263480371067979},\n"," '1478': {'min': 1.263480371067979,\n"," 'hmean': 1.2634803710679792,\n"," 'hybrid': 1.263480371067979},\n"," '1482': {'min': 0.7904027782812227,\n"," 'hmean': 0.8993426999226619,\n"," 'hybrid': 0.8448727391019424},\n"," '1396': {'min': 1.263480371067979,\n"," 'hmean': 1.2634803710679787,\n"," 'hybrid': 1.263480371067979},\n"," '1400': {'min': 1.263480371067979,\n"," 'hmean': 1.4299222482528668,\n"," 'hybrid': 1.346701309660423},\n"," '1471': {'min': 1.263480371067979,\n"," 'hmean': 1.2634803710679787,\n"," 'hybrid': 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'hmean': 1.2634803710679792,\n"," 'hybrid': 1.263480371067979},\n"," '588': {'min': 1.263480371067979,\n"," 'hmean': 1.263480371067979,\n"," 'hybrid': 1.263480371067979},\n"," '594': {'min': 1.263480371067979,\n"," 'hmean': 1.2634803710679787,\n"," 'hybrid': 1.263480371067979}}"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","execution_count":28,"metadata":{"id":"E9RiQapKKyMi","executionInfo":{"status":"ok","timestamp":1736575956065,"user_tz":-420,"elapsed":272874,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["bundle_recommendations_svd_min = {}\n","bundle_recommendations_svd_hmean = {}\n","bundle_recommendations_svd_hybrid = {}\n","\n","bundle_recommendations_svdpp_min = {}\n","bundle_recommendations_svdpp_hmean = {}\n","bundle_recommendations_svdpp_hybrid = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," if user_id in user_item_dict:\n"," bundle_recommendations_svd_min[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svd_hmean[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svd_hybrid[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n","\n"," bundle_recommendations_svdpp_min[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svdpp_hmean[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svdpp_hybrid[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n","\n"," bundle_recommendations = recommend_bundles(user_id, bundle_data, best_svd)\n","\n"," for bundle_id, recommendation in bundle_recommendations.items():\n"," bundle_recommendations_svd_min[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"min\"]\n"," bundle_recommendations_svd_hmean[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"hmean\"]\n"," bundle_recommendations_svd_hybrid[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"hybrid\"]\n","\n"," bundle_recommendations = recommend_bundles(user_id, bundle_data, best_svdpp)\n","\n"," for bundle_id, recommendation in bundle_recommendations.items():\n"," bundle_recommendations_svdpp_min[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"min\"]\n"," bundle_recommendations_svdpp_hmean[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"hmean\"]\n"," bundle_recommendations_svdpp_hybrid[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"hybrid\"]"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":432033,"status":"ok","timestamp":1736576393218,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"kJzG-UZZU4sZ","outputId":"cc60dcc0-cca5-4da1-d7e7-4f8a0154cc69"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous/bundle_recommendations_svdpp_hybrid.pkl']"]},"metadata":{},"execution_count":29}],"source":["# prompt: save bundle_recommendations_svd and bundle_recommendations_svdpp to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous\n","\n","import joblib\n","\n","# Assuming bundle_recommendations_svd and bundle_recommendations_svdpp are defined\n","\n","# Save the dictionaries to separate files\n","output_dir = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous\"\n","joblib.dump(bundle_recommendations_svd_min, f\"{output_dir}/bundle_recommendations_svd_min.pkl\")\n","joblib.dump(bundle_recommendations_svd_hmean, f\"{output_dir}/bundle_recommendations_svd_hmean.pkl\")\n","joblib.dump(bundle_recommendations_svd_hybrid, f\"{output_dir}/bundle_recommendations_svd_hybrid.pkl\")\n","\n","joblib.dump(bundle_recommendations_svdpp_min, f\"{output_dir}/bundle_recommendations_svdpp_min.pkl\")\n","joblib.dump(bundle_recommendations_svdpp_hmean, f\"{output_dir}/bundle_recommendations_svdpp_hmean.pkl\")\n","joblib.dump(bundle_recommendations_svdpp_hybrid, f\"{output_dir}/bundle_recommendations_svdpp_hybrid.pkl\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"68yOSlAzRr0_"},"outputs":[],"source":["bundle_recommendations_svdpp_min['05041129']['rec_bundles']"]},{"cell_type":"code","execution_count":65,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":301,"status":"ok","timestamp":1736578566378,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"flYvFh9kThB6","outputId":"d6503d92-fd22-4042-c443-a1a89c515330"},"outputs":[{"output_type":"stream","name":"stdout","text":["Top 5 recommended bundles for user 05041129:\n","{'1232': 3.0707806936433846, '1100': 3.052148511725643, '236': 2.929136821005296, '836': 2.8439078751910696, '1350': 2.8264226622165856}\n","--------------------\n","Top 10 recommended bundles for user 05041129:\n","{'1232': 3.0707806936433846, '1100': 3.052148511725643, '236': 2.929136821005296, '836': 2.8439078751910696, '1350': 2.8264226622165856, '1373': 2.80300674298525, '273': 2.798784725898245, '345': 2.795599226531941, '802': 2.7898200713403627, '1147': 2.7890264943462384}\n","--------------------\n","Top 15 recommended bundles for user 05041129:\n","{'1232': 3.0707806936433846, '1100': 3.052148511725643, '236': 2.929136821005296, '836': 2.8439078751910696, '1350': 2.8264226622165856, '1373': 2.80300674298525, '273': 2.798784725898245, '345': 2.795599226531941, '802': 2.7898200713403627, '1147': 2.7890264943462384, '934': 2.776746113247566, '251': 2.7761389527509497, '512': 2.7760269525208248, '382': 2.775642696698879, '320': 2.7615210797842664}\n","--------------------\n","Top 20 recommended bundles for user 05041129:\n","{'1232': 3.0707806936433846, '1100': 3.052148511725643, '236': 2.929136821005296, '836': 2.8439078751910696, '1350': 2.8264226622165856, '1373': 2.80300674298525, '273': 2.798784725898245, '345': 2.795599226531941, '802': 2.7898200713403627, '1147': 2.7890264943462384, '934': 2.776746113247566, '251': 2.7761389527509497, '512': 2.7760269525208248, '382': 2.775642696698879, '320': 2.7615210797842664, '853': 2.7598507857991605, '346': 2.755849334232977, '234': 2.7544688461148246, '1339': 2.749671026320554, '1146': 2.746284383620326}\n","--------------------\n"]}],"source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}\n","\n","top_bundles_results = get_top_rec_bundles(bundle_recommendations_svdpp_hybrid['05041129'])\n","\n","for result in top_bundles_results:\n"," print(f\"Top {result['top_n']} recommended bundles for user {result['user_id']}:\")\n"," print(result['top_rec_bundles'])\n"," print(\"-\" * 20)"]},{"cell_type":"markdown","metadata":{"id":"fbCasxBUTgRk"},"source":["### Evaluation"]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","from surprise.model_selection import train_test_split\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"x2VXxeekBgO3","executionInfo":{"status":"ok","timestamp":1736576835051,"user_tz":-420,"elapsed":633,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"lXAq5lJTBo-W","executionInfo":{"status":"ok","timestamp":1736576838108,"user_tz":-420,"elapsed":292,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"W4OsYmbFBwm-","executionInfo":{"status":"ok","timestamp":1736576842157,"user_tz":-420,"elapsed":281,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":46,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Dy88qfjB8RC","executionInfo":{"status":"ok","timestamp":1736576854315,"user_tz":-420,"elapsed":296,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"990e75f3-6581-47bc-ab2c-fa760ee81e4d"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DfRuISneC_A3","executionInfo":{"status":"ok","timestamp":1736576862243,"user_tz":-420,"elapsed":291,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"62e31734-5749-46c6-9e90-f5192a981574"},"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","execution_count":66,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":303,"status":"ok","timestamp":1736578579455,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"hfTbC2xdTWNg","outputId":"851b55d4-77cd-4ca9-d5ff-8b078ab16bd2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{5: 0.09522076393877814,\n"," 10: 0.09706551998163583,\n"," 15: 0.10025580170081427,\n"," 20: 0.13482695804361347}"]},"metadata":{},"execution_count":66}],"source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","hit_ratios"]},{"cell_type":"code","execution_count":67,"metadata":{"id":"xUuraXZlT3f-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736578582673,"user_tz":-420,"elapsed":378,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8d878561-b90e-4907-8d4d-4d7ce00e0446"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{5: 0.8110843373493978,\n"," 10: 0.9015596330275231,\n"," 15: 0.9315555555555556,\n"," 20: 0.9371268656716418}"]},"metadata":{},"execution_count":67}],"source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","gini_simpson_result"]},{"cell_type":"code","execution_count":80,"metadata":{"id":"_CvOgzRCUDNS","executionInfo":{"status":"ok","timestamp":1736580624160,"user_tz":-420,"elapsed":407436,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","# filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous/mf_bundles.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","for user_id, bundle_recommendations in bundle_recommendations_svd_hybrid.items():\n","\n"," top_bundles_results = get_top_rec_bundles(bundle_recommendations)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[bundle_recommendations['user_id']] = hit_ratios\n"," average_diversity[bundle_recommendations['user_id']] = diversity_scores"]},{"cell_type":"code","execution_count":81,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1736580624160,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"jtl6g8BPW1hg","outputId":"9d4a1fa2-2485-4e59-d776-c49b1dc5e1af"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.033748705311428616, 10: 0.04002168468403027, 15: 0.04635710070599147, 20: 0.05281912448390756}\n","Mean Diversity: {5: 0.8795129077538518, 10: 0.9229181301305618, 15: 0.9352645897457688, 20: 0.9419351251937135}\n"]}],"source":["# prompt: calculate mean from average_hit_ratio\n","\n","import numpy as np\n","\n","# Assuming average_hit_ratio is a dictionary where keys are user IDs and values are dictionaries of hit ratios for different top_n values.\n","# Example: average_hit_ratio = {'user1': {5: 0.2, 10: 0.3}, 'user2': {5: 0.1, 10: 0.2}}\n","\n","def calculate_mean_hit_ratio(average_hit_ratio):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average_hit_ratio.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios\n","\n","# Example usage:\n","mean_hit_ratio = calculate_mean_hit_ratio(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean_hit_ratio(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"]},{"cell_type":"code","execution_count":82,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1736580624160,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"},"user_tz":-420},"id":"kpWLqx-_asIk","outputId":"5e4e11ce-c5e7-491d-9345-4e69a4fbc5d2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.033749 0.879513\n","1 10 0.040022 0.922918\n","2 15 0.046357 0.935265\n","3 20 0.052819 0.941935"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0337490.879513
1100.0400220.922918
2150.0463570.935265
3200.0528190.941935
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.008204025967768971,\n \"min\": 0.033748705311428616,\n \"max\": 0.05281912448390756,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.04002168468403027,\n 0.05281912448390756,\n 0.033748705311428616\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.028058530809174902,\n \"min\": 0.8795129077538518,\n \"max\": 0.9419351251937135,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9229181301305618,\n 0.9419351251937135,\n 0.8795129077538518\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":82}],"source":["# prompt: combine average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df and make it colomn like top_n | hit_ratio | intra_diversity | inter_diversity just create from dictionary do not using merge function of pandas\n","\n","import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)\n","\n","# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"]},{"cell_type":"code","execution_count":83,"metadata":{"id":"JImcjAVFawOX","executionInfo":{"status":"ok","timestamp":1736580624160,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["# prompt: save combined_df as /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_native.csv\n","\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svd_min.csv', index=False)\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svd_hmean.csv', index=False)\n","combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svd_hybrid.csv', index=False)\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svdpp_min.csv', index=False)\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svdpp_hmean.csv', index=False)\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svdpp_hybrid.csv', index=False)"]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"17F1Y5IruYY5C8ldCgge_YwB15Q3hlHfH","authorship_tag":"ABX9TyPisHzS+eYwX4nIp5Get85l"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Source Code/Previous/Matrix Factorizations.ipynb b/Source Code/Previous/Matrix Factorizations.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59ca07d0166ee898ca81355c5c738a7d7a63364f --- /dev/null +++ b/Source Code/Previous/Matrix Factorizations.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["### Generate Group Of Bundle Based On Tags And Specs"],"metadata":{"id":"VUTSHmhQhbVe"}},{"cell_type":"code","source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"],"metadata":{"id":"sT4b3MlpQUkp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rrq-gfhdNyLM","executionInfo":{"status":"ok","timestamp":1736565019812,"user_tz":-420,"elapsed":1346,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"dbde2bba-ce6a-43d5-c1d0-d358a801b3a2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I-dDe4UNQaw7","executionInfo":{"status":"ok","timestamp":1736565024041,"user_tz":-420,"elapsed":4230,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ab08b6be-819e-4ca1-ac91-8b4c361a32dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NvLqTlK-SOvG","executionInfo":{"status":"ok","timestamp":1736492310943,"user_tz":-420,"elapsed":42563,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"42e176aa-909f-4179-f929-9e32c65e8044"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 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'playtime_2weeks': 0}, {'item_id': '259130', 'item_name': 'Wasteland 1 - The Original Classic', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '404730', 'item_name': \"Wasteland 2: Director's Cut\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '221100', 'item_name': 'DayZ', 'playtime_forever': 131, 'playtime_2weeks': 0}, {'item_id': '202170', 'item_name': 'Sleeping Dogs™', 'playtime_forever': 1322, 'playtime_2weeks': 0}, {'item_id': '268870', 'item_name': 'Satellite Reign', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '239840', 'item_name': 'Dead State', 'playtime_forever': 718, 'playtime_2weeks': 0}, {'item_id': '271590', 'item_name': 'Grand Theft Auto V', 'playtime_forever': 6740, 'playtime_2weeks': 0}, {'item_id': '273110', 'item_name': 'Counter-Strike Nexon: Zombies', 'playtime_forever': 6, 'playtime_2weeks': 0}, {'item_id': '274310', 'item_name': 'Always Sometimes Monsters', 'playtime_forever': 501, 'playtime_2weeks': 0}, {'item_id': '213670', 'item_name': 'South Park™: The Stick of Truth™', 'playtime_forever': 1105, 'playtime_2weeks': 0}, {'item_id': '278080', 'item_name': 'DYNASTY WARRIORS 8: Xtreme Legends Complete Edition', 'playtime_forever': 671, 'playtime_2weeks': 0}, {'item_id': '48700', 'item_name': 'Mount & Blade: Warband', 'playtime_forever': 871, 'playtime_2weeks': 0}, {'item_id': '254700', 'item_name': 'resident evil 4 / biohazard 4', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '235210', 'item_name': 'Strider', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '290140', 'item_name': 'Echo of Soul', 'playtime_forever': 44, 'playtime_2weeks': 0}, {'item_id': '290300', 'item_name': 'Rebel Galaxy', 'playtime_forever': 96, 'playtime_2weeks': 0}, {'item_id': '293780', 'item_name': 'Crawl', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '295110', 'item_name': 'H1Z1: Just Survive', 'playtime_forever': 4778, 'playtime_2weeks': 0}, {'item_id': '362300', 'item_name': 'H1Z1: Just Survive Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QyPVHFFbSpOe","executionInfo":{"status":"ok","timestamp":1736565158539,"user_tz":-420,"elapsed":20192,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"cb363d92-ff3e-4524-f3b3-66feee09e7fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"amTibdP9S850"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"Fhghu_CXXEM8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bundle_specs['494']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uLJD8145apO-","executionInfo":{"status":"ok","timestamp":1736398562735,"user_tz":-420,"elapsed":508,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0aec2697-45e1-4c40-ec38-ac06f081800f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Single-player': 2,\n"," 'Steam Achievements': 2,\n"," 'Steam Trading Cards': 2,\n"," 'Steam Workshop': 2,\n"," 'Steam Cloud': 2,\n"," 'Stats': 2,\n"," 'Steam Leaderboards': 2,\n"," 'Includes level editor': 1}"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["path_list_of_bundles = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/Previous\""],"metadata":{"id":"Atn1W6diVxRm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["i = 0\n","data_bundle_temp_to_save = []\n","for tag in input_default_tags:\n"," data_bundle_temp_to_save = []\n"," for bundle_id in bundle_tags:\n"," if tag in bundle_tags[bundle_id]:\n"," data_bundle_temp_to_save.append(bundle_id)\n","\n"," np.save(f\"{path_list_of_bundles}/Tags/bundle_for_tag_{tag}.npy\", np.array(data_bundle_temp_to_save))\n","\n"," print(f\"{i} / {len(input_default_tags)}\")\n"," i += 1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qNZySb2ua8AA","executionInfo":{"status":"ok","timestamp":1736399179541,"user_tz":-420,"elapsed":2799,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"49c6916e-9d24-44d2-cebf-96f38f90d5b8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0 / 204\n","1 / 204\n","2 / 204\n","3 / 204\n","4 / 204\n","5 / 204\n","6 / 204\n","7 / 204\n","8 / 204\n","9 / 204\n","10 / 204\n","11 / 204\n","12 / 204\n","13 / 204\n","14 / 204\n","15 / 204\n","16 / 204\n","17 / 204\n","18 / 204\n","19 / 204\n","20 / 204\n","21 / 204\n","22 / 204\n","23 / 204\n","24 / 204\n","25 / 204\n","26 / 204\n","27 / 204\n","28 / 204\n","29 / 204\n","30 / 204\n","31 / 204\n","32 / 204\n","33 / 204\n","34 / 204\n","35 / 204\n","36 / 204\n","37 / 204\n","38 / 204\n","39 / 204\n","40 / 204\n","41 / 204\n","42 / 204\n","43 / 204\n","44 / 204\n","45 / 204\n","46 / 204\n","47 / 204\n","48 / 204\n","49 / 204\n","50 / 204\n","51 / 204\n","52 / 204\n","53 / 204\n","54 / 204\n","55 / 204\n","56 / 204\n","57 / 204\n","58 / 204\n","59 / 204\n","60 / 204\n","61 / 204\n","62 / 204\n","63 / 204\n","64 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1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OO8q4lfiddZA","executionInfo":{"status":"ok","timestamp":1736399604221,"user_tz":-420,"elapsed":467,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"46bfff98-0f3d-40f0-e97e-c750cb521261"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0 / 21\n","1 / 21\n","2 / 21\n","3 / 21\n","4 / 21\n","5 / 21\n","6 / 21\n","7 / 21\n","8 / 21\n","9 / 21\n","10 / 21\n","11 / 21\n","12 / 21\n","13 / 21\n","14 / 21\n","15 / 21\n","16 / 21\n","17 / 21\n","18 / 21\n","19 / 21\n","20 / 21\n"]}]},{"cell_type":"markdown","source":["### Generate Matrix Data"],"metadata":{"id":"PRLv0p8UhlQD"}},{"cell_type":"code","source":["items_dictionary['32150']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NbyzDA5Sj-of","executionInfo":{"status":"ok","timestamp":1736400968680,"user_tz":-420,"elapsed":460,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8583477d-0326-4883-872f-ecbbd26fe507"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'publisher': 'TrueThought',\n"," 'genres': ['Casual', 'Indie', 'Strategy'],\n"," 'app_name': 'Everyday Genius: SquareLogic',\n"," 'sentiment': 'Very Positive',\n"," 'title': 'Everyday Genius: SquareLogic',\n"," 'url': 'http://store.steampowered.com/app/32150/Everyday_Genius_SquareLogic/',\n"," 'release_date': '2009-10-09',\n"," 'tags': ['Casual', 'Puzzle', 'Indie', 'Strategy', 'Relaxing'],\n"," 'reviews_url': 'http://steamcommunity.com/app/32150/reviews/?browsefilter=mostrecent&p=1',\n"," 'specs': ['Single-player',\n"," 'Steam Achievements',\n"," 'Steam Cloud',\n"," 'Stats',\n"," 'Steam Leaderboards'],\n"," 'price': 4.99,\n"," 'early_access': False,\n"," 'id': '32150',\n"," 'developer': 'TrueThought'}"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews.json.gz\"\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f, gzip.open(file_path_write, 'wt', encoding='utf-8') as outfile:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," for review in data_dict[\"reviews\"]:\n"," json.dump({ \"user_id\": data_dict['user_id'], \"item_id\": review['item_id'], \"rating\": review['helpful'] }, outfile, ensure_ascii=False)\n"," outfile.write('\\n')\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n"," if i % 1000 == 0:\n"," print(f\"{i} / 7,793,069\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jtfmN1NFhrHx","executionInfo":{"status":"ok","timestamp":1736405137171,"user_tz":-420,"elapsed":6753,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0c7dc159-e35a-49a4-b317-dee040d6fdc6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0 / 7,793,069\n","1000 / 7,793,069\n","2000 / 7,793,069\n","3000 / 7,793,069\n","4000 / 7,793,069\n","5000 / 7,793,069\n","6000 / 7,793,069\n","7000 / 7,793,069\n","8000 / 7,793,069\n","9000 / 7,793,069\n","10000 / 7,793,069\n","11000 / 7,793,069\n","12000 / 7,793,069\n","13000 / 7,793,069\n","14000 / 7,793,069\n","15000 / 7,793,069\n","16000 / 7,793,069\n","17000 / 7,793,069\n","18000 / 7,793,069\n","19000 / 7,793,069\n","20000 / 7,793,069\n","21000 / 7,793,069\n","22000 / 7,793,069\n","23000 / 7,793,069\n","24000 / 7,793,069\n","25000 / 7,793,069\n"]}]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"OSY78hII5nst"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"eOGfmEhPstp1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(data_reviews)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CGMVGRCU4pTo","executionInfo":{"status":"ok","timestamp":1736406941390,"user_tz":-420,"elapsed":482,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"61a99789-6d71-4094-d191-1a9a01e1ab25"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["59305"]},"metadata":{},"execution_count":50}]},{"cell_type":"code","source":["# prompt: get unique rating from data_reviews\n","\n","unique_ratings = set()\n","for review in data_reviews:\n"," unique_ratings.add(review['rating'])\n","unique_ratings"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"1zSqCNkn40KY","executionInfo":{"status":"ok","timestamp":1736565181318,"user_tz":-420,"elapsed":297,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"ebf6a3b4-2804-47c5-d8d5-f55a16fbe6c1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0.0,\n"," 2.0,\n"," 3.0,\n"," 4.0,\n"," 5.0,\n"," 6.0,\n"," 7.0,\n"," 8.0,\n"," 9.0,\n"," 10.0,\n"," 11.0,\n"," 12.0,\n"," 13.0,\n"," 14.0,\n"," 15.0,\n"," 16.0,\n"," 17.0,\n"," 18.0,\n"," 19.0,\n"," 20.0,\n"," 21.0,\n"," 22.0,\n"," 23.0,\n"," 24.0,\n"," 25.0,\n"," 26.0,\n"," 27.0,\n"," 28.0,\n"," 29.0,\n"," 30.0,\n"," 31.0,\n"," 32.0,\n"," 33.0,\n"," 34.0,\n"," 35.0,\n"," 36.0,\n"," 37.0,\n"," 38.0,\n"," 39.0,\n"," 40.0,\n"," 41.0,\n"," 42.0,\n"," 43.0,\n"," 44.0,\n"," 45.0,\n"," 46.0,\n"," 47.0,\n"," 48.0,\n"," 49.0,\n"," 50.0,\n"," 51.0,\n"," 52.0,\n"," 53.0,\n"," 54.0,\n"," 55.0,\n"," 56.0,\n"," 57.0,\n"," 58.0,\n"," 59.0,\n"," 60.0,\n"," 61.0,\n"," 62.0,\n"," 63.0,\n"," 64.0,\n"," 65.0,\n"," 66.0,\n"," 67.0,\n"," 68.0,\n"," 69.0,\n"," 70.0,\n"," 71.0,\n"," 72.0,\n"," 73.0,\n"," 74.0,\n"," 75.0,\n"," 76.0,\n"," 77.0,\n"," 78.0,\n"," 79.0,\n"," 80.0,\n"," 81.0,\n"," 82.0,\n"," 83.0,\n"," 84.0,\n"," 85.0,\n"," 86.0,\n"," 87.0,\n"," 88.0,\n"," 89.0,\n"," 90.0,\n"," 91.0,\n"," 92.0,\n"," 93.0,\n"," 94.0,\n"," 95.0,\n"," 96.0,\n"," 97.0,\n"," 98.0,\n"," 100.0}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"2uDNR7dl7HH1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"HjtZTpXh65jv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Check how many data that has rating more than 0.0 in user_bundle_histories\n","\n","count = 0\n","for entry in user_bundle_histories:\n"," if entry[\"rating\"] > 0.0:\n"," count += 1\n","print(f\"Number of data entries with rating > 0.0: {count}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"krXSWUIP612O","executionInfo":{"status":"ok","timestamp":1736565204797,"user_tz":-420,"elapsed":324,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"27701e0e-4d61-492c-d570-b0f53bb4e1c1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of data entries with rating > 0.0: 1870\n"]}]},{"cell_type":"code","source":["len(user_bundle_histories)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Dm2308ezVjdk","executionInfo":{"status":"ok","timestamp":1736565207436,"user_tz":-420,"elapsed":317,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"dbc74f18-a603-4f8a-bccd-814c179af1ad"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["103453"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["### Create matrix factorization all"],"metadata":{"id":"4b8hrqoiLQ2z"}},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e-NW0-DW9QSS","executionInfo":{"status":"ok","timestamp":1736565315279,"user_tz":-420,"elapsed":80368,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"fb840572-1cfa-4cec-d3bb-46592127cbbe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357277 sha256=482fb4f9133c86478d975acb90e0db0203516ed3f193f2123ac5532455888650\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["# prompt: From this data user_bundle_histories, apply SVD and SVD++ with Hyperparameter tuning and Gridsearch and Cross validation 5\n","\n","import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","\n","# Create a pandas DataFrame from user_bundle_histories\n","df = pd.DataFrame(user_bundle_histories)\n","\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Define parameter grids for SVD and SVD++\n","param_grid_svd = {'n_factors': [50, 100, 150], 'n_epochs': [20, 30], 'lr_all': [0.005, 0.01], 'reg_all': [0.02, 0.1]}\n","param_grid_svdpp = {'n_factors': [20, 50], 'n_epochs': [20, 30], 'lr_all': [0.005, 0.01], 'reg_all': [0.02, 0.1]}\n","\n","# Perform hyperparameter tuning with GridSearchCV for SVD\n","gs_svd = GridSearchCV(SVD, param_grid_svd, measures=['rmse', 'mae'], cv=5)\n","gs_svd.fit(data)\n","\n","# Print the best hyperparameters and RMSE for SVD\n","print(\"SVD Best RMSE:\", gs_svd.best_score['rmse'])\n","print(\"SVD Best MAE:\", gs_svd.best_score['mae'])\n","print(\"SVD Best Params:\", gs_svd.best_params['rmse'])\n","\n","# Perform hyperparameter tuning with GridSearchCV for SVD++\n","gs_svdpp = GridSearchCV(SVDpp, param_grid_svdpp, measures=['rmse', 'mae'], cv=5)\n","gs_svdpp.fit(data)\n","\n","# Print the best hyperparameters and RMSE for SVD++\n","print(\"SVD++ Best RMSE:\", gs_svdpp.best_score['rmse'])\n","print(\"SVD++ Best MAE:\", gs_svdpp.best_score['mae'])\n","print(\"SVD++ Best Params:\", gs_svdpp.best_params['rmse'])\n","\n","# Train and evaluate the best SVD model using cross-validation\n","best_svd = gs_svd.best_estimator['rmse']\n","cross_validate(best_svd, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)\n","\n","# Train and evaluate the best SVD++ model using cross-validation\n","best_svdpp = gs_svdpp.best_estimator['rmse']\n","cross_validate(best_svdpp, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ONd9cv419Bkv","executionInfo":{"status":"ok","timestamp":1736482734943,"user_tz":-420,"elapsed":1544848,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"6819d371-c9d0-43fb-fc2d-0a309ff6e441"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["SVD Best RMSE: 0.4227881074144716\n","SVD Best MAE: 0.11731391067717534\n","SVD Best Params: {'n_factors': 150, 'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.02}\n","SVD++ Best RMSE: 0.43099673247053455\n","SVD++ Best MAE: 0.115585997101746\n","SVD++ Best Params: {'n_factors': 50, 'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.02}\n","Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n","\n"," Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std \n","RMSE (testset) 0.4125 0.4154 0.4399 0.4388 0.4486 0.4310 0.0144 \n","MAE (testset) 0.1189 0.1166 0.1197 0.1230 0.1215 0.1199 0.0022 \n","Fit time 2.58 3.69 2.88 2.62 2.63 2.88 0.42 \n","Test time 0.47 0.21 0.13 0.15 0.45 0.28 0.15 \n","Evaluating RMSE, MAE of algorithm SVDpp on 5 split(s).\n","\n"," Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std \n","RMSE (testset) 0.4212 0.4261 0.4266 0.4501 0.4262 0.4300 0.0102 \n","MAE (testset) 0.1168 0.1148 0.1166 0.1220 0.1138 0.1168 0.0028 \n","Fit time 21.13 19.60 21.21 21.66 19.67 20.65 0.85 \n","Test time 1.58 1.81 1.70 1.58 1.50 1.64 0.11 \n"]},{"output_type":"execute_result","data":{"text/plain":["{'test_rmse': array([0.42116177, 0.42608143, 0.42663834, 0.45007444, 0.42619555]),\n"," 'test_mae': array([0.11682776, 0.11483491, 0.11662314, 0.12203263, 0.11383799]),\n"," 'fit_time': (21.12747883796692,\n"," 19.59500551223755,\n"," 21.213244915008545,\n"," 21.66492986679077,\n"," 19.674121856689453),\n"," 'test_time': (1.5773656368255615,\n"," 1.8139982223510742,\n"," 1.6955671310424805,\n"," 1.5843687057495117,\n"," 1.504225254058838)}"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["# prompt: Load model using joblib from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svd_model.pkl\n","\n","import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","import joblib\n","\n","best_svd = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svd_model.pkl')\n","best_svdpp = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svdpp_model.pkl')"],"metadata":{"id":"CZZ4J7i7AuNG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","from surprise.model_selection import train_test_split\n","\n","df = pd.DataFrame(user_bundle_histories)\n","# Define the rating scale for the Surprise library\n","reader = Reader(rating_scale=(0, 5))\n","# Load the data into Surprise Dataset format\n","data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n","# Split the data into training and testing sets\n","trainset, testset = train_test_split(data, test_size=0.2)"],"metadata":{"id":"O4lvXLhDBmz-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Example usage: predict rating for a user and bundle\n","user_id_example = df['user_id'].iloc[4]\n","bundle_id_example = df['bundle_id'].iloc[4]\n","\n","svd_prediction = best_svd.predict(user_id_example, bundle_id_example)\n","svdpp_prediction = best_svdpp.predict(user_id_example, bundle_id_example)\n","\n","print(f\"SVD prediction for user {user_id_example} and bundle {bundle_id_example}: {svd_prediction.est}\")\n","print(f\"SVD++ prediction for user {user_id_example} and bundle {bundle_id_example}: {svdpp_prediction.est}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hsLJ7vCx-avt","executionInfo":{"status":"ok","timestamp":1736565408053,"user_tz":-420,"elapsed":334,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b92a4dab-17b4-4929-e8cd-fa5de4068a96"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["SVD prediction for user 76561197970982479 and bundle 237: 0\n","SVD++ prediction for user 76561197970982479 and bundle 237: 0\n"]}]},{"cell_type":"code","source":["# Save the SVD model\n","import joblib\n","joblib.dump(best_svd, f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svd_model.pkl\")\n","joblib.dump(best_svdpp, f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svdpp_model.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F9tuPy2R-sLY","executionInfo":{"status":"ok","timestamp":1736484176384,"user_tz":-420,"elapsed":6651,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3ffcc434-5d4f-4edf-d512-00de3164ad5e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svdpp_model.pkl']"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["### Do Recommendation"],"metadata":{"id":"ScY_bk_GLsxk"}},{"cell_type":"code","source":["# prompt: Convert to dictionary users bundles for variable testset\n","\n","testset = [{'user_id': entry['user_id'], 'bundle_id': entry['bundle_id'], 'rating': entry['rating']} for entry in user_bundle_histories]"],"metadata":{"id":"y_DMhiEeGIOU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: group testset by user_id as dictionary\n","\n","from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data\n","\n","# Example usage (assuming user_bundle_histories is defined as in the provided code)\n","grouped_testset = group_testset_by_user(user_bundle_histories)\n","\n","# Accessing data for a specific user:\n","# user_id = 'some_user_id'\n","# if user_id in grouped_testset:\n","# print(grouped_testset[user_id])"],"metadata":{"id":"WDEDrAgqGWob"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bundle_recommendations_svd = {}\n","bundle_recommendations_svdpp = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," if user_id in user_item_dict:\n"," bundle_recommendations_svd[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svdpp[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," for bundle_id, recommendation in bundle_data.items():\n"," svd_prediction = best_svd.predict(user_id, bundle_id)\n"," svdpp_prediction = best_svdpp.predict(user_id, bundle_id)\n"," bundle_recommendations_svd[user_id][\"rec_bundles\"][bundle_id] = svd_prediction.est\n"," bundle_recommendations_svdpp[user_id][\"rec_bundles\"][bundle_id] = svdpp_prediction.est"],"metadata":{"id":"E9RiQapKKyMi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: save bundle_recommendations_svd and bundle_recommendations_svdpp to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous\n","\n","import joblib\n","\n","# Assuming bundle_recommendations_svd and bundle_recommendations_svdpp are defined\n","\n","# Save the dictionaries to separate files\n","output_dir = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous\"\n","joblib.dump(bundle_recommendations_svd, f\"{output_dir}/bundle_recommendations_svd.pkl\")\n","joblib.dump(bundle_recommendations_svdpp, f\"{output_dir}/bundle_recommendations_svdpp.pkl\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kJzG-UZZU4sZ","executionInfo":{"status":"ok","timestamp":1736484442049,"user_tz":-420,"elapsed":105131,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"b77c33ae-4462-47ed-f0c2-d3bbb20922ca"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous/bundle_recommendations_svdpp.pkl']"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["bundle_recommendations_svd['05041129']['rec_bundles']"],"metadata":{"id":"68yOSlAzRr0_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: get top rec_bundles from data_dict with variation TOP 5 until top 20 with space 5\n","\n","def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results\n","\n","# Example usage (assuming data_dict is defined as in the provided code)\n","# Assuming data_dict contains the following:\n","# data_dict = {\"user_id\": 123, \"rec_bundles\": {\"bundle_1\": 0.9, \"bundle_2\": 0.8, \"bundle_3\": 0.7, \"bundle_4\": 0.6, \"bundle_5\": 0.5, \"bundle_6\": 0.4, \"bundle_7\": 0.3, \"bundle_8\":0.2, \"bundle_9\":0.1, \"bundle_10\": 0.0}}\n","\n","top_bundles_results = get_top_rec_bundles(bundle_recommendations_svd['05041129'])\n","\n","for result in top_bundles_results:\n"," print(f\"Top {result['top_n']} recommended bundles for user {result['user_id']}:\")\n"," print(result['top_rec_bundles'])\n"," print(\"-\" * 20)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"flYvFh9kThB6","executionInfo":{"status":"ok","timestamp":1736566037797,"user_tz":-420,"elapsed":315,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"6e27772e-c05c-4df2-f272-a7a1aa9ff86e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Top 5 recommended bundles for user 05041129:\n","{'236': 4.58394644822258, '232': 4.450175094671308, '779': 1.2538914930131972, '1373': 1.153043238103371, '273': 1.11762194663225}\n","--------------------\n","Top 10 recommended bundles for user 05041129:\n","{'236': 4.58394644822258, '232': 4.450175094671308, '779': 1.2538914930131972, '1373': 1.153043238103371, '273': 1.11762194663225, '746': 1.1054268718330793, '837': 1.0987879634869864, '778': 0.992210387729151, '1208': 0.9838027523112424, '336': 0.968144804258227}\n","--------------------\n","Top 15 recommended bundles for user 05041129:\n","{'236': 4.58394644822258, '232': 4.450175094671308, '779': 1.2538914930131972, '1373': 1.153043238103371, '273': 1.11762194663225, '746': 1.1054268718330793, '837': 1.0987879634869864, '778': 0.992210387729151, '1208': 0.9838027523112424, '336': 0.968144804258227, '340': 0.9596856608050313, '718': 0.9464258486809938, '471': 0.9388995808241061, '1352': 0.8837189877221086, '245': 0.8768343404645818}\n","--------------------\n","Top 20 recommended bundles for user 05041129:\n","{'236': 4.58394644822258, '232': 4.450175094671308, '779': 1.2538914930131972, '1373': 1.153043238103371, '273': 1.11762194663225, '746': 1.1054268718330793, '837': 1.0987879634869864, '778': 0.992210387729151, '1208': 0.9838027523112424, '336': 0.968144804258227, '340': 0.9596856608050313, '718': 0.9464258486809938, '471': 0.9388995808241061, '1352': 0.8837189877221086, '245': 0.8768343404645818, '1138': 0.865768559555006, '15': 0.8646955964450294, '1140': 0.8627168807592118, '871': 0.850707424409803, '285': 0.8168487934929798}\n","--------------------\n"]}]},{"cell_type":"code","source":["top_bundles_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZaKabLfoMT79","executionInfo":{"status":"ok","timestamp":1736495416549,"user_tz":-420,"elapsed":315,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"663072af-793e-4c2e-e2f2-b0ae843f1e0c","collapsed":true},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'user_id': '76561198039327637',\n"," 'top_rec_bundles': {'1373': 0.57700419949448,\n"," '1208': 0.46659130244267333,\n"," '285': 0.4516739555225235,\n"," '417': 0.39634801114642193,\n"," '284': 0.3711011812201989},\n"," 'top_n': 5},\n"," {'user_id': '76561198039327637',\n"," 'top_rec_bundles': {'1373': 0.57700419949448,\n"," '1208': 0.46659130244267333,\n"," '285': 0.4516739555225235,\n"," '417': 0.39634801114642193,\n"," '284': 0.3711011812201989,\n"," '274': 0.348557396310978,\n"," '1340': 0.33219701307626803,\n"," '1336': 0.3313216658302862,\n"," '1392': 0.3166953015218182,\n"," '1477': 0.30648172691321063},\n"," 'top_n': 10},\n"," {'user_id': '76561198039327637',\n"," 'top_rec_bundles': {'1373': 0.57700419949448,\n"," '1208': 0.46659130244267333,\n"," '285': 0.4516739555225235,\n"," '417': 0.39634801114642193,\n"," '284': 0.3711011812201989,\n"," '274': 0.348557396310978,\n"," '1340': 0.33219701307626803,\n"," '1336': 0.3313216658302862,\n"," '1392': 0.3166953015218182,\n"," '1477': 0.30648172691321063,\n"," '837': 0.2885112052176223,\n"," '836': 0.280917999017427,\n"," '496': 0.27426196581305284,\n"," '166': 0.2692156490220411,\n"," '516': 0.2599261998891152},\n"," 'top_n': 15},\n"," {'user_id': '76561198039327637',\n"," 'top_rec_bundles': {'1373': 0.57700419949448,\n"," '1208': 0.46659130244267333,\n"," '285': 0.4516739555225235,\n"," '417': 0.39634801114642193,\n"," '284': 0.3711011812201989,\n"," '274': 0.348557396310978,\n"," '1340': 0.33219701307626803,\n"," '1336': 0.3313216658302862,\n"," '1392': 0.3166953015218182,\n"," '1477': 0.30648172691321063,\n"," '837': 0.2885112052176223,\n"," '836': 0.280917999017427,\n"," '496': 0.27426196581305284,\n"," '166': 0.2692156490220411,\n"," '516': 0.2599261998891152,\n"," '273': 0.2580370742043224,\n"," '462': 0.24488578655997226,\n"," '1156': 0.23804384583782556,\n"," '243': 0.23242754858394987,\n"," '264': 0.22748889019619434},\n"," 'top_n': 20}]"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["grouped_testset['117arbiter']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mMbxE_HNGjRW","executionInfo":{"status":"ok","timestamp":1736494112386,"user_tz":-420,"elapsed":281,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7a4d8fd8-32a5-4953-ba0a-669650cdf8ff","collapsed":true},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'1144',\n"," '1158',\n"," '1190',\n"," '1229',\n"," '1246',\n"," '1336',\n"," '1340',\n"," '139',\n"," '1399',\n"," '1468',\n"," '231',\n"," '232',\n"," '233',\n"," '234',\n"," '241',\n"," '248',\n"," '257',\n"," '312',\n"," '362',\n"," '383',\n"," '396',\n"," '572',\n"," '604',\n"," '612',\n"," '616',\n"," '635',\n"," '646',\n"," '712',\n"," '727',\n"," '803',\n"," '813',\n"," '957'}"]},"metadata":{},"execution_count":47}]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"fbCasxBUTgRk"}},{"cell_type":"code","source":["# prompt: Calculate hit_ratio from top_bundles_results by calculating how many items from user_item_interactions is on bundle_data by bundle_id and item_id\n","\n","def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios\n","\n","# Example usage (assuming the necessary data structures are defined):\n","hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n","hit_ratios"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hfTbC2xdTWNg","executionInfo":{"status":"ok","timestamp":1736566418051,"user_tz":-420,"elapsed":297,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d132c1ac-218a-4130-9c3c-cfdc96eb3b02"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{5: 0.15767951752885323,\n"," 10: 0.15909343974655227,\n"," 15: 0.15968104269273362,\n"," 20: 0.15983777478434272}"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["# prompt: Calculate Gini-Simpson Diversity Index using top_bundles_results and grouped_testset based on varible bundle_tags bundle_specs for each bundle_id that recommended\n","\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices\n","\n","# Example usage (assuming necessary data structures are defined)\n","gini_simpson_result = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","gini_simpson_result"],"metadata":{"id":"-MwuwWdpU7ZY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1736571082901,"user_tz":-420,"elapsed":338,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"4688110a-e29d-405c-996e-856a6faede27"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{5: 0.8561702127659574,\n"," 10: 0.9006024096385543,\n"," 15: 0.9138833883388339,\n"," 20: 0.928235294117647}"]},"metadata":{},"execution_count":56}]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","# filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous/mf_bundles.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","for user_id, bundle_recommendations in bundle_recommendations_svd.items():\n","\n"," top_bundles_results = get_top_rec_bundles(bundle_recommendations)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[bundle_recommendations['user_id']] = hit_ratios\n"," average_diversity[bundle_recommendations['user_id']] = diversity_scores"],"metadata":{"id":"_CvOgzRCUDNS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","# filepath_rarank_rec_data = '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/previous/mf_bundles.json.gz'\n","\n","average_hit_ratio = {}\n","average_diversity = {}\n","count = 0\n","\n","top_bundles_results = []\n","hit_ratios = []\n","diversity_scores = []\n","\n","for user_id, bundle_recommendations in bundle_recommendations_svdpp.items():\n","\n"," top_bundles_results = get_top_rec_bundles(bundle_recommendations)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[bundle_recommendations['user_id']] = hit_ratios\n"," average_diversity[bundle_recommendations['user_id']] = diversity_scores\n"],"metadata":{"id":"tJW7duLzbMJj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: calculate mean from average_hit_ratio\n","\n","import numpy as np\n","\n","# Assuming average_hit_ratio is a dictionary where keys are user IDs and values are dictionaries of hit ratios for different top_n values.\n","# Example: average_hit_ratio = {'user1': {5: 0.2, 10: 0.3}, 'user2': {5: 0.1, 10: 0.2}}\n","\n","def calculate_mean_hit_ratio(average_hit_ratio):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average_hit_ratio.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios\n","\n","# Example usage:\n","mean_hit_ratio = calculate_mean_hit_ratio(average_hit_ratio)\n","print(f\"Mean Hit Ratio: {mean_hit_ratio}\")\n","mean_diversity = calculate_mean_hit_ratio(average_diversity)\n","print(f\"Mean Diversity: {mean_diversity}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jtl6g8BPW1hg","executionInfo":{"status":"ok","timestamp":1736572250005,"user_tz":-420,"elapsed":337,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c72fc83d-9484-4dca-cdc7-44621eb53f5f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Hit Ratio: {5: 0.018696515078579508, 10: 0.02914531265888084, 15: 0.044024473936550075, 20: 0.06400405595420844}\n","Mean Diversity: {5: 0.8563891379271561, 10: 0.91239270055999, 15: 0.9296749412996965, 20: 0.9385664697012452}\n"]}]},{"cell_type":"code","source":["# prompt: combine average_hit_ratio_df, average_intra_diversity_df, average_inter_diversity_df and make it colomn like top_n | hit_ratio | intra_diversity | inter_diversity just create from dictionary do not using merge function of pandas\n","\n","import pandas as pd\n","\n","def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = []\n"," for top_n in mean_hit_ratio:\n"," row = {\n"," 'top_n': top_n,\n"," 'hit_ratio': mean_hit_ratio[top_n],\n"," 'diversity': mean_diversity[top_n],\n"," }\n"," combined_data.append(row)\n"," return pd.DataFrame(combined_data)\n","\n","# Example usage (assuming the three dataframes are available):\n","combined_df = combine_metrics(mean_hit_ratio, mean_diversity)\n","combined_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"kpWLqx-_asIk","executionInfo":{"status":"ok","timestamp":1736572257619,"user_tz":-420,"elapsed":492,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2e9de219-d3ba-4bf1-ecde-4b7c80ea8635"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" top_n hit_ratio diversity\n","0 5 0.018697 0.856389\n","1 10 0.029145 0.912393\n","2 15 0.044024 0.929675\n","3 20 0.064004 0.938566"],"text/html":["\n","
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top_nhit_ratiodiversity
050.0186970.856389
1100.0291450.912393
2150.0440240.929675
3200.0640040.938566
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"combined_df","summary":"{\n \"name\": \"combined_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"top_n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 5,\n \"max\": 20,\n \"num_unique_values\": 4,\n \"samples\": [\n 10,\n 20,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hit_ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.019662064263107275,\n \"min\": 0.018696515078579508,\n \"max\": 0.06400405595420844,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.02914531265888084,\n 0.06400405595420844,\n 0.018696515078579508\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diversity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03688170636688508,\n \"min\": 0.8563891379271561,\n \"max\": 0.9385664697012452,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.91239270055999,\n 0.9385664697012452,\n 0.8563891379271561\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["# prompt: save combined_df as /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/tags_and_specs_native.csv\n","\n","# combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svd.csv', index=False)\n","combined_df.to_csv('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/previous/bundle_recommendation_svdpp.csv', index=False)"],"metadata":{"id":"JImcjAVFawOX"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Source Code/Uji Statistik/Calculate Wilcoxon.ipynb b/Source Code/Uji Statistik/Calculate Wilcoxon.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1999c66afae8b0b5d4c905c15e6338b1eda16beb --- /dev/null +++ b/Source Code/Uji Statistik/Calculate Wilcoxon.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1LKrEjXW5GqqEARn_W6Lrd8Sw1G2hVxXF","authorship_tag":"ABX9TyPidlW66C7R3lUf1FBzTvSb"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PlMjuBBAHAe8","executionInfo":{"status":"ok","timestamp":1737339099386,"user_tz":-420,"elapsed":264,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"230b8852-8e4a-4be4-976c-0d1b2361aee6"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'iterasi': 50, 'results': {'svd': {'hr': [0.04033885325568181, 0.04068211259062088, 0.040419835950823026, 0.03996788518295968, 0.04060381177588588, 0.04012338744234081, 0.03987734791411903, 0.04095527217207548, 0.04020828547638544, 0.04022900790577523, 0.04074740960564105, 0.039899408546939935, 0.04044162821575808, 0.04021734121681724, 0.040055047695169176, 0.04024818524161538, 0.04096431592793605, 0.040395554065377745, 0.04037107879435824, 0.0395696824220955, 0.04050134511905375, 0.04002002879495735, 0.03959103090434617, 0.04047423164301854, 0.041005993107862466, 0.03993481678951712, 0.04036252734505402, 0.04032142609349588, 0.04013481965765714, 0.040527667347842564, 0.0403663141683269, 0.03991358218161059, 0.0403281707898069, 0.04023514046273763, 0.040474147880272295, 0.04021350215971107, 0.03992018384469764, 0.04088051026522182, 0.04025531450998464, 0.03983782148859206, 0.04037125873515723, 0.040556597940103974, 0.04032792586144805, 0.03968211999575034, 0.040704159529909315, 0.04066141518319191, 0.04000489836858485, 0.03998431417436247, 0.041473859591160164, 0.04050312102992151], 'diversity': [0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427, 0.9042235634156427]}, 'svdpp': {'hr': [0.0367677257526021, 0.03704634948492646, 0.0365875515600103, 0.03607905562893876, 0.036673195252916034, 0.036535727725518816, 0.036231342132610145, 0.037441990815983706, 0.03665059570763659, 0.03648757497558593, 0.037300107538637675, 0.03605305213100466, 0.03676078516431007, 0.03617547793628163, 0.03641720361186941, 0.03664681685519482, 0.037089089502261, 0.03681770693262538, 0.03650334251729407, 0.0357300894919242, 0.03664166437941879, 0.0364148080913142, 0.03614803333686016, 0.03679650504294925, 0.03715686258527609, 0.036201024923416654, 0.03626313652802629, 0.03650576870433894, 0.03616987925589906, 0.036775153589882675, 0.036601849178475254, 0.03634986304951692, 0.03632344185677602, 0.036530913555005366, 0.03665132429894255, 0.036270727044033654, 0.03613133690005452, 0.037093493281904394, 0.03622540248892503, 0.03606275066702124, 0.036531848463975926, 0.03659766366927051, 0.03648812364245431, 0.036197327391518944, 0.03661533638645274, 0.03687998706558173, 0.03671106558353742, 0.03572873179318177, 0.0377412917905339, 0.036849804399941316], 'diversity': [0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219, 0.9092558123720219]}}}\n"]}],"source":["# prompt: Load file using joblib from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/impact_p_attr.pkl\n","\n","import joblib\n","import pandas as pd\n","\n","# Load the file using joblib\n","try:\n"," # data = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/impact_p_attr.pkl')\n"," # data = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/impact_p_patterns.pkl')\n"," # data = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/mf_all_min_mul.pkl')\n"," data = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/mf_all.pkl')\n"," # Check if it is a pandas DataFrame, if so print it, otherwise just print data\n"," if isinstance(data, pd.DataFrame):\n"," print(data)\n"," else:\n"," print(data)\n","except FileNotFoundError:\n"," print(\"Error: File not found at the specified path.\")\n","except Exception as e:\n"," print(f\"An error occurred: {e}\")"]},{"cell_type":"code","source":["import numpy as np\n","\n","modelName = \"\"\n","bestBalanceValue = 0.0\n","\n","temp = 0\n","for model, modelValue in data[\"results\"].items():\n"," temp = (np.array(modelValue[\"hr\"]) + np.array(modelValue[\"diversity\"])) / 2\n"," temp = np.sum(temp) / len(temp)\n"," if temp > bestBalanceValue:\n"," bestBalanceValue = temp\n"," modelName = model\n","\n","print(modelName)\n","print(bestBalanceValue)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3tZi0F8PHwKC","executionInfo":{"status":"ok","timestamp":1737166033784,"user_tz":-420,"elapsed":293,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9fc61974-76f5-4e72-fe3c-a54e65623254"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["svdpp\n","0.47290440518263716\n"]}]},{"cell_type":"code","source":["IMPACTP_ATTR_BEST_MODEL = data[\"results\"]"],"metadata":{"id":"PpTJS23kJgjQ","executionInfo":{"status":"ok","timestamp":1737339065625,"user_tz":-420,"elapsed":293,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["IMPACTP_PATTERNS_BEST_MODEL = data[\"results\"]"],"metadata":{"id":"P98OPHCsJ3SE","executionInfo":{"status":"ok","timestamp":1737339077451,"user_tz":-420,"elapsed":1,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["MF_MIN_MUL_BEST_MODEL = data[\"results\"]"],"metadata":{"id":"TIoEqq6yJ7PI","executionInfo":{"status":"ok","timestamp":1737339090492,"user_tz":-420,"elapsed":334,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["MF_ALL_BEST_MODEL = data[\"results\"]"],"metadata":{"id":"Jp6icgsBKRUw","executionInfo":{"status":"ok","timestamp":1737339103171,"user_tz":-420,"elapsed":293,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["## HR\n"],"metadata":{"id":"EGNnnksdaZ_c"}},{"cell_type":"code","source":["# prompt: generate new hr array from IMPACTP_ATTR_BEST_MODEL by calculate the average of it\n","import numpy as np\n","\n","hr_array = []\n","for model, modelValue in IMPACTP_ATTR_BEST_MODEL.items():\n"," hr_array.append(modelValue[\"hr\"])\n","hr_array = np.array(hr_array)\n","IMPACTP_ATTR_hr_array = np.mean(hr_array, axis=0)\n","IMPACTP_ATTR_hr_array"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-jCl1TO_XbL2","executionInfo":{"status":"ok","timestamp":1737339127918,"user_tz":-420,"elapsed":246,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1dba0782-9d46-4235-bf28-62876c6114e6"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.13029241, 0.12932162, 0.13136075, 0.13085184, 0.12719423,\n"," 0.12637225, 0.1249317 , 0.12537029, 0.13006649, 0.12874449,\n"," 0.1291569 , 0.12786016, 0.12986063, 0.12867723, 0.12875514,\n"," 0.1295843 , 0.12965259, 0.12760935, 0.12774314, 0.1248774 ,\n"," 0.12764701, 0.12551867, 0.12930279, 0.1290407 , 0.12697608,\n"," 0.12583518, 0.12448328, 0.12754017, 0.12969566, 0.12600127,\n"," 0.12652752, 0.12689407, 0.12694426, 0.12770581, 0.12778584,\n"," 0.12734386, 0.1268856 , 0.12643329, 0.12716184, 0.128646 ,\n"," 0.1269067 , 0.12798751, 0.12469573, 0.12823152, 0.12740214,\n"," 0.13145002, 0.12751038, 0.13085209, 0.12757192, 0.12498397])"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# prompt: generate new hr array from IMPACTP_ATTR_BEST_MODEL by calculate the average of it\n","\n","hr_array = []\n","for model, modelValue in IMPACTP_PATTERNS_BEST_MODEL.items():\n"," hr_array.append(modelValue[\"hr\"])\n","hr_array = np.array(hr_array)\n","IMPACTP_PATTERNS_hr_array = np.mean(hr_array, axis=0)\n","IMPACTP_PATTERNS_hr_array"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_SDswzMSYG2H","executionInfo":{"status":"ok","timestamp":1737339130393,"user_tz":-420,"elapsed":273,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"92efc2bd-b6c7-4db1-e7c4-b7f1734c5e81"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.15663861, 0.15715273, 0.15713259, 0.15322261, 0.15452331,\n"," 0.15767466, 0.15650014, 0.15308404, 0.15785579, 0.15760767,\n"," 0.15834854, 0.15300545, 0.16223219, 0.15529107, 0.16125537,\n"," 0.15462446, 0.15979035, 0.15731681, 0.15567591, 0.15777873,\n"," 0.16035863, 0.15623068, 0.15449076, 0.15651331, 0.15980453,\n"," 0.15552578, 0.16031015, 0.15581229, 0.16212336, 0.15713108,\n"," 0.15546065, 0.15410835, 0.16050913, 0.15780519, 0.15523701,\n"," 0.15459113, 0.15479235, 0.15535257, 0.15710601, 0.15546619,\n"," 0.15163792, 0.16060138, 0.15560034, 0.15894106, 0.15710302,\n"," 0.16059475, 0.15338345, 0.16227538, 0.15652658, 0.15821994])"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# prompt: generate new hr array from IMPACTP_ATTR_BEST_MODEL by calculate the average of it\n","\n","MF_ALL_MIN_hr_array = []\n","MF_ALL_MUL_hr_array = []\n","for model, modelValue in MF_MIN_MUL_BEST_MODEL.items():\n"," if \"min\" in model:\n"," MF_ALL_MIN_hr_array.append(modelValue[\"hr\"])\n"," elif \"hmean\" in model:\n"," MF_ALL_MUL_hr_array.append(modelValue[\"hr\"])\n","MF_ALL_MIN_hr_array = np.array(MF_ALL_MIN_hr_array)\n","MF_ALL_MIN_hr_array = np.mean(MF_ALL_MIN_hr_array, axis=0)\n","MF_ALL_MUL_hr_array = np.array(MF_ALL_MUL_hr_array)\n","MF_ALL_MUL_hr_array = np.mean(MF_ALL_MUL_hr_array, axis=0)"],"metadata":{"id":"V9mQy2NKYXcb","executionInfo":{"status":"ok","timestamp":1737339132124,"user_tz":-420,"elapsed":269,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["MF_ALL_MIN_hr_array"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"thoBjD9IZDjH","executionInfo":{"status":"ok","timestamp":1737339134615,"user_tz":-420,"elapsed":245,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"14214bf0-f241-4744-bc01-9c5831944827"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.03135129, 0.03558444, 0.0354742 , 0.031597 , 0.03737852,\n"," 0.03511766, 0.03570385, 0.0348259 , 0.03326845, 0.03269295,\n"," 0.03403985, 0.03360245, 0.03660781, 0.03330253, 0.03242283,\n"," 0.037427 , 0.03793722, 0.03252496, 0.03591897, 0.03422008,\n"," 0.03606653, 0.03435071, 0.03409061, 0.03478817, 0.03584337,\n"," 0.03376528, 0.03586962, 0.0360571 , 0.03576182, 0.03417696,\n"," 0.03496238, 0.03509165, 0.03507819, 0.0352324 , 0.03326405,\n"," 0.03424563, 0.0349543 , 0.03477265, 0.03675649, 0.03390816,\n"," 0.03298795, 0.03360393, 0.03672094, 0.03460901, 0.03263504,\n"," 0.03411091, 0.03545152, 0.0332523 , 0.03359327, 0.03446577])"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["MF_ALL_MUL_hr_array"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O93zNSUBZE0P","executionInfo":{"status":"ok","timestamp":1737339136905,"user_tz":-420,"elapsed":263,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"0706a12b-c7e9-47b4-98ef-951879738bcc"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.04317643, 0.04671373, 0.04732577, 0.0423985 , 0.04971917,\n"," 0.04663506, 0.04680014, 0.04591743, 0.0453428 , 0.04385832,\n"," 0.04542288, 0.04564837, 0.04759112, 0.04419236, 0.0437474 ,\n"," 0.04897384, 0.04895021, 0.04358845, 0.04810171, 0.04461418,\n"," 0.04767918, 0.04700782, 0.04585013, 0.04588988, 0.04736941,\n"," 0.04502275, 0.04811579, 0.04662867, 0.04694831, 0.04512197,\n"," 0.04673973, 0.0464956 , 0.04630946, 0.04602855, 0.04429428,\n"," 0.04616497, 0.04617844, 0.04636479, 0.04803436, 0.04477272,\n"," 0.04453659, 0.04534408, 0.04801994, 0.04550066, 0.04378365,\n"," 0.04494951, 0.04698946, 0.04453994, 0.04544261, 0.04577701])"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["# prompt: generate new hr array from IMPACTP_ATTR_BEST_MODEL by calculate the average of it\n","\n","hr_array = []\n","for model, modelValue in MF_ALL_BEST_MODEL.items():\n"," hr_array.append(modelValue[\"hr\"])\n","hr_array = np.array(hr_array)\n","MF_ALL_hr_array = np.mean(hr_array, axis=0)\n","MF_ALL_hr_array"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"761Xhqrxfv0Q","executionInfo":{"status":"ok","timestamp":1737339406529,"user_tz":-420,"elapsed":249,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"38b4af0c-f925-4f6e-efef-2bcabf2c9b5f"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.03855329, 0.03886423, 0.03850369, 0.03802347, 0.0386385 ,\n"," 0.03832956, 0.03805435, 0.03919863, 0.03842944, 0.03835829,\n"," 0.03902376, 0.03797623, 0.03860121, 0.03819641, 0.03823613,\n"," 0.0384475 , 0.0390267 , 0.03860663, 0.03843721, 0.03764989,\n"," 0.0385715 , 0.03821742, 0.03786953, 0.03863537, 0.03908143,\n"," 0.03806792, 0.03831283, 0.0384136 , 0.03815235, 0.03865141,\n"," 0.03848408, 0.03813172, 0.03832581, 0.03838303, 0.03856274,\n"," 0.03824211, 0.03802576, 0.038987 , 0.03824036, 0.03795029,\n"," 0.03845155, 0.03857713, 0.03840802, 0.03793972, 0.03865975,\n"," 0.0387707 , 0.03835798, 0.03785652, 0.03960758, 0.03867646])"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["# prompt: Apply boxplot from IMPACTP_ATTR_hr_array, IMPACTP_PATTERNS_hr_array, MF_ALL_MIN_hr_array, MF_ALL_MUL_hr_array to see the significance improvement by wicoxon\n","\n","import matplotlib.pyplot as plt\n","import scipy.stats as stats\n","\n","# Assuming IMPACTP_ATTR_hr_array, IMPACTP_PATTERNS_hr_array, MF_ALL_MIN_hr_array, and MF_ALL_MUL_hr_array are defined\n","\n","plt.figure(figsize=(10, 6))\n","plt.boxplot([IMPACTP_ATTR_hr_array, IMPACTP_PATTERNS_hr_array, MF_ALL_hr_array, MF_ALL_MIN_hr_array, MF_ALL_MUL_hr_array],\n"," labels=['IMPACTP_ATTR', 'IMPACTP_PATRN', 'MF_ALL', 'MF_ALL_MIN', 'MF_ALL_MUL'])\n","plt.title('')\n","plt.ylabel('HR Value')\n","plt.show()\n","\n","# Perform Wilcoxon signed-rank test (paired samples)\n","# Example: comparing IMPACTP_ATTR and IMPACTP_PATTERNS\n","statistic, p_value = stats.wilcoxon(IMPACTP_ATTR_hr_array, IMPACTP_PATTERNS_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_ATTR vs. IMPACTP_PATTERNS): Statistic={statistic}, p-value={p_value}\")\n","\n","# Repeat for other pairs\n","statistic, p_value = stats.wilcoxon(IMPACTP_ATTR_hr_array, MF_ALL_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_ATTR vs. MF_ALL): Statistic={statistic}, p-value={p_value}\")\n","\n","statistic, p_value = stats.wilcoxon(IMPACTP_ATTR_hr_array, MF_ALL_MUL_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_ATTR vs. MF_ALL_MUL): Statistic={statistic}, p-value={p_value}\")\n","\n","statistic, p_value = stats.wilcoxon(IMPACTP_ATTR_hr_array, MF_ALL_MIN_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_ATTR vs. MF_ALL_MIN): Statistic={statistic}, p-value={p_value}\")\n","\n","# Repeat for other pairs\n","statistic, p_value = stats.wilcoxon(IMPACTP_PATTERNS_hr_array, MF_ALL_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_PATTERNS vs. MF_ALL): Statistic={statistic}, p-value={p_value}\")\n","\n","statistic, p_value = stats.wilcoxon(IMPACTP_PATTERNS_hr_array, MF_ALL_MIN_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_PATTERNS vs. MF_ALL_MIN): Statistic={statistic}, p-value={p_value}\")\n","\n","statistic, p_value = stats.wilcoxon(IMPACTP_PATTERNS_hr_array, MF_ALL_MUL_hr_array)\n","print(f\"Wilcoxon test (IMPACTP_PATTERNS vs. MF_ALL_MUL): Statistic={statistic}, p-value={p_value}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":698},"id":"dHXc4j9De35l","executionInfo":{"status":"ok","timestamp":1737342081154,"user_tz":-420,"elapsed":1109,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"6743d367-9ce8-4646-d5f5-dd33df320c5c"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":[":9: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n"," plt.boxplot([IMPACTP_ATTR_hr_array, IMPACTP_PATTERNS_hr_array, MF_ALL_hr_array, MF_ALL_MIN_hr_array, MF_ALL_MUL_hr_array],\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Wilcoxon test (IMPACTP_ATTR vs. IMPACTP_PATTERNS): Statistic=0.0, p-value=1.7763568394002505e-15\n","Wilcoxon test (IMPACTP_ATTR vs. MF_ALL): Statistic=0.0, p-value=1.7763568394002505e-15\n","Wilcoxon test (IMPACTP_ATTR vs. MF_ALL_MUL): Statistic=0.0, p-value=1.7763568394002505e-15\n","Wilcoxon test (IMPACTP_ATTR vs. MF_ALL_MIN): Statistic=0.0, p-value=1.7763568394002505e-15\n","Wilcoxon test (IMPACTP_PATTERNS vs. MF_ALL): Statistic=0.0, p-value=1.7763568394002505e-15\n","Wilcoxon test (IMPACTP_PATTERNS vs. MF_ALL_MIN): Statistic=0.0, p-value=1.7763568394002505e-15\n","Wilcoxon test (IMPACTP_PATTERNS vs. MF_ALL_MUL): Statistic=0.0, p-value=1.7763568394002505e-15\n"]}]},{"cell_type":"code","source":["# prompt: Apply linechart from IMPACTP_ATTR_hr_array, IMPACTP_PATTERNS_hr_array, MF_ALL_hr_array, MF_ALL_MIN_hr_array, MF_ALL_MUL_hr_array\n","\n","plt.figure(figsize=(10, 6))\n","plt.plot(IMPACTP_ATTR_hr_array, label='IMPACTP_ATTR')\n","plt.plot(IMPACTP_PATTERNS_hr_array, label='IMPACTP_PATRN')\n","plt.plot(MF_ALL_hr_array, label='MF_ALL')\n","plt.plot(MF_ALL_MIN_hr_array, label='MF_ALL_MIN')\n","plt.plot(MF_ALL_MUL_hr_array, label='MF_ALL_MUL')\n","plt.xlabel('Batch')\n","plt.ylabel('HR Value')\n","plt.title('')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"X7MBtj3cgNMt","executionInfo":{"status":"ok","timestamp":1737342090605,"user_tz":-420,"elapsed":377,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a7240b2c-b681-4016-b43f-2b0c58d76151"},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["from scipy.stats import wilcoxon\n","statistic, p_value = wilcoxon(MF_ALL_MIN_hr_array, MF_ALL_MUL_hr_array)\n","p_value"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ymtfbesVZKCn","executionInfo":{"status":"ok","timestamp":1737169947437,"user_tz":-420,"elapsed":283,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"3f54cacf-3872-4929-d5ed-62cb3d14c520"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.7763568394002505e-15"]},"metadata":{},"execution_count":70}]},{"cell_type":"markdown","source":["## Diversity"],"metadata":{"id":"CXMDd8QJaeoC"}},{"cell_type":"markdown","source":["## Not used"],"metadata":{"id":"aER-HeNAac3s"}},{"cell_type":"code","source":["from scipy.stats import wilcoxon\n","\n","wilcoxon_results = {}\n","\n","for impatr_model, impatr_value in IMPACTP_PATTERNS_BEST_MODEL.items():\n"," for mf_all_model, mf_all_value in MF_ALL_BEST_MODEL.items():\n"," statistic, p_value = wilcoxon(impatr_value[\"hr\"], mf_all_value[\"hr\"])\n"," wilcoxon_results[(impatr_model, mf_all_model)] = {\"statistic\": statistic, \"p_value\": p_value}\n","\n","print(\"Wilcoxon test results: IMPACTP_PATTERN vs MF_ALL (Using Bundle Rating Only)\")\n","wilcoxon_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Q-NzteOHSkc_","executionInfo":{"status":"ok","timestamp":1737169084008,"user_tz":-420,"elapsed":300,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1efa0648-20f9-44af-d827-5d4f125bcfa7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Wilcoxon test results: IMPACTP_PATTERN vs MF_ALL (Using Bundle Rating Only)\n"]},{"output_type":"execute_result","data":{"text/plain":["{('ann', 'svd'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('ann', 'svdpp'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('dt', 'svd'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('dt', 'svdpp'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('knn', 'svd'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('knn', 'svdpp'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('lr', 'svd'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('lr', 'svdpp'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('nb', 'svd'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15},\n"," ('nb', 'svdpp'): {'statistic': 0.0, 'p_value': 1.7763568394002505e-15}}"]},"metadata":{},"execution_count":56}]},{"cell_type":"code","source":["# prompt: Plot data from data[\"results\"]\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming IMPACTP_ATTR_BEST_MODEL contains the data to plot\n","# Replace with the actual variable name if different\n","\n","try:\n"," # Access hr and diversity from the best model\n"," for item in data[\"results\"]:\n"," hr_values = data[\"results\"][item][\"hr\"]\n","\n"," # Create the plot\n"," plt.plot(hr_values, label=item)\n"," plt.xlabel(\"Scenario\") # Or a more meaningful label\n"," plt.ylabel(\"Results\")\n"," plt.title(\"Hit Rate\")\n"," plt.legend()\n"," plt.grid(True) # Add grid lines\n"," plt.show()\n","\n","except KeyError:\n"," print(\"Error: 'hr' or 'diversity' key not found in the data.\")\n","except Exception as e:\n"," print(f\"An error occurred during plotting: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"1O0pSP80OFf7","executionInfo":{"status":"ok","timestamp":1737167185720,"user_tz":-420,"elapsed":738,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"a5acc903-2249-4c23-9025-a06770b1d6c1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","try:\n"," # Access hr and diversity from the best model\n"," for item in data[\"results\"]:\n"," hr_values = data[\"results\"][item][\"diversity\"]\n","\n"," # Create the plot\n"," plt.plot(hr_values, label=item)\n"," plt.xlabel(\"Scenario\") # Or a more meaningful label\n"," plt.ylabel(\"Results\")\n"," plt.title(\"Diversity\")\n"," plt.legend()\n"," plt.grid(True) # Add grid lines\n"," plt.show()\n","\n","except KeyError:\n"," print(\"Error: 'hr' or 'diversity' key not found in the data.\")\n","except Exception as e:\n"," print(f\"An error occurred during plotting: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"fL8tHZh3PMHI","executionInfo":{"status":"ok","timestamp":1737167288516,"user_tz":-420,"elapsed":355,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"5a0fb50c-030e-43a2-f891-91a98a3f84cf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"hrknYhG8PbqA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: combine the data from IMPACTP_ATTR_BEST_MODEL, IMPACTP_PATTERNS_BEST_MODEL, MF_MIN_MUL_BEST_MODEL, MF_ALL_BEST_MODEL and apply the label \"IMPACT-P_ATTR\", \"IMPACT-P_PATTERN\", \"MF_ALL_MIN_MUL\", \"MF_ALL\"\n","\n","import pandas as pd\n","import numpy as np\n","\n","# Assuming IMPACTP_ATTR_BEST_MODEL, IMPACTP_PATTERNS_BEST_MODEL,\n","# MF_MIN_MUL_BEST_MODEL, and MF_ALL_BEST_MODEL are dictionaries\n","# with the same keys (e.g., 'hr', 'diversity')\n","\n","# Create a list of tuples to hold the data and labels\n","combined_data = []\n","\n","for key in IMPACTP_ATTR_BEST_MODEL.keys(): # Iterate through the keys of one of the dictionaries (assuming all have the same keys)\n"," combined_data.append(\n"," (\"IMPACT-P_ATTR\", key, IMPACTP_ATTR_BEST_MODEL[key])\n"," )\n"," combined_data.append(\n"," (\"IMPACT-P_PATTERN\", key, IMPACTP_PATTERNS_BEST_MODEL[key])\n"," )\n"," combined_data.append(\n"," (\"MF_ALL_MIN_MUL\", key, MF_MIN_MUL_BEST_MODEL[key])\n"," )\n"," combined_data.append(\n"," (\"MF_ALL\", key, MF_ALL_BEST_MODEL[key])\n"," )\n","\n","# Create a Pandas DataFrame from the combined data\n","df = pd.DataFrame(combined_data, columns=[\"Label\", \"Metric\", \"Value\"])\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"hiyEsuyCKiG8","executionInfo":{"status":"ok","timestamp":1737166147502,"user_tz":-420,"elapsed":304,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"7986fbcf-d7c9-4397-811d-8d29923092fe"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Label Metric \\\n","0 IMPACT-P_ATTR hr \n","1 IMPACT-P_PATTERN hr \n","2 MF_ALL_MIN_MUL hr \n","3 MF_ALL hr \n","4 IMPACT-P_ATTR diversity \n","5 IMPACT-P_PATTERN diversity \n","6 MF_ALL_MIN_MUL diversity \n","7 MF_ALL diversity \n","\n"," Value \n","0 [0.17754143546032886, 0.17739276242271698, 0.1... \n","1 [0.17113020663505207, 0.17173882162662052, 0.1... \n","2 [0.030952251658669105, 0.035559349657536043, 0... \n","3 [0.0367677257526021, 0.03704634948492646, 0.03... \n","4 [0.8231844601177403, 0.8231844601177403, 0.823... \n","5 [0.8232431932359284, 0.8232431932359284, 0.823... \n","6 [0.9614569988747128, 0.9614569988747128, 0.961... \n","7 [0.9092558123720219, 0.9092558123720219, 0.909... "],"text/html":["\n","
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LabelMetricValue
0IMPACT-P_ATTRhr[0.17754143546032886, 0.17739276242271698, 0.1...
1IMPACT-P_PATTERNhr[0.17113020663505207, 0.17173882162662052, 0.1...
2MF_ALL_MIN_MULhr[0.030952251658669105, 0.035559349657536043, 0...
3MF_ALLhr[0.0367677257526021, 0.03704634948492646, 0.03...
4IMPACT-P_ATTRdiversity[0.8231844601177403, 0.8231844601177403, 0.823...
5IMPACT-P_PATTERNdiversity[0.8232431932359284, 0.8232431932359284, 0.823...
6MF_ALL_MIN_MULdiversity[0.9614569988747128, 0.9614569988747128, 0.961...
7MF_ALLdiversity[0.9092558123720219, 0.9092558123720219, 0.909...
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"IMPACT-P_PATTERN\",\n \"MF_ALL\",\n \"IMPACT-P_ATTR\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Metric\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"diversity\",\n \"hr\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Value\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# Assuming 'df' is already defined from the previous code block\n","\n","# Filter the DataFrame for 'hr' metric\n","hr_data = df[df[\"Metric\"] == \"hr\"]\n","\n","# Plot the 'hr' metric for each label\n","plt.figure(figsize=(10, 6)) # Adjust figure size as needed\n","for label in hr_data[\"Label\"].unique():\n"," label_data = hr_data[hr_data[\"Label\"] == label]\n"," # Convert the list of values to a NumPy array and then plot\n"," plt.plot(np.array(label_data[\"Value\"].values[0]), label=label) # label_data[\"Value\"] contains a list, so we need to extract the first element which is the list of values\n","\n","plt.xlabel(\"Scenario\") # or a more descriptive x-axis label if applicable\n","plt.ylabel(\"HR Value\")\n","plt.title(\"\")\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"o3TanR7OLBa-","executionInfo":{"status":"ok","timestamp":1737168853180,"user_tz":-420,"elapsed":412,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"83ab501d-670d-4c5f-bcc6-7a3b4cc153db"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# Assuming 'df' is already defined from the previous code block\n","\n","# Filter the DataFrame for 'hr' metric\n","hr_data = df[df[\"Metric\"] == \"diversity\"]\n","\n","# Plot the 'hr' metric for each label\n","plt.figure(figsize=(10, 6)) # Adjust figure size as needed\n","for label in hr_data[\"Label\"].unique():\n"," label_data = hr_data[hr_data[\"Label\"] == label]\n"," # Convert the list of values to a NumPy array and then plot\n"," plt.plot(np.array(label_data[\"Value\"].values[0]), label=label) # label_data[\"Value\"] contains a list, so we need to extract the first element which is the list of values\n","\n","plt.xlabel(\"Scenario\") # or a more descriptive x-axis label if applicable\n","plt.ylabel(\"Diversity Value\")\n","plt.title(\"\")\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"gXcJ_MMsLroG","executionInfo":{"status":"ok","timestamp":1737168885273,"user_tz":-420,"elapsed":1391,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"8c1fa0ec-e425-4da8-9e92-9432a1759754"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# prompt: calculate wilcoxon for each pair for IMPACT-P and MF_ALL\n","\n","from scipy.stats import wilcoxon\n","import pandas as pd\n","import numpy as np\n","\n","# ... (Your existing code for loading data and creating the DataFrame 'df') ...\n","\n","# Assuming 'df' is already defined and contains the data\n","\n","# Define the model names for comparison\n","model_names = [\"IMPACT-P_ATTR\", \"IMPACT-P_PATTERN\", \"MF_ALL_MIN_MUL\", \"MF_ALL\"]\n","\n","# Create a dictionary to store the Wilcoxon test results\n","wilcoxon_results = {}\n","\n","# Iterate through each pair of model names\n","for i in range(len(model_names)):\n"," for j in range(i + 1, len(model_names)):\n"," model1_name = model_names[i]\n"," model2_name = model_names[j]\n","\n"," # Extract the data for the current model pair and 'hr' metric\n"," model1_data = df[(df[\"Label\"] == model1_name) & (df[\"Metric\"] == \"hr\")]\n"," model2_data = df[(df[\"Label\"] == model2_name) & (df[\"Metric\"] == \"hr\")]\n","\n"," # Perform Wilcoxon signed-rank test\n"," if not model1_data.empty and not model2_data.empty :\n"," statistic, p_value = wilcoxon(model1_data[\"Value\"].values[0], model2_data[\"Value\"].values[0])\n"," wilcoxon_results[(model1_name, model2_name)] = {\"statistic\": statistic, \"p_value\": p_value}\n"," else:\n"," print(f\"Warning: No data for model pair {model1_name} vs {model2_name} or 'hr' metric. Skipping comparison.\")\n","\n","# Print the Wilcoxon test results\n","for (model1, model2), result in wilcoxon_results.items():\n"," print(f\"Wilcoxon test for {model1} vs {model2} (hr metric):\")\n"," print(f\" Statistic: {result['statistic']}\")\n"," print(f\" P-value: {result['p_value']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"evGYbUQ2L8jw","executionInfo":{"status":"ok","timestamp":1737166497650,"user_tz":-420,"elapsed":2656,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2ce66f2b-1fdc-407d-e8e9-1c133f21c2e7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Wilcoxon test for IMPACT-P_ATTR vs IMPACT-P_PATTERN (hr metric):\n"," Statistic: 228.0\n"," P-value: 3.633577416550793e-05\n","Wilcoxon test for IMPACT-P_ATTR vs MF_ALL_MIN_MUL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_ATTR vs MF_ALL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_PATTERN vs MF_ALL_MIN_MUL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_PATTERN vs MF_ALL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for MF_ALL_MIN_MUL vs MF_ALL (hr metric):\n"," Statistic: 32.0\n"," P-value: 4.9116266609416925e-12\n"]}]},{"cell_type":"code","source":["# prompt: calculate wilcoxon for each pair for IMPACT-P and MF_ALL\n","\n","from scipy.stats import wilcoxon\n","import pandas as pd\n","import numpy as np\n","\n","# ... (Your existing code for loading data and creating the DataFrame 'df') ...\n","\n","# Assuming 'df' is already defined and contains the data\n","\n","# Define the model names for comparison\n","model_names = [\"IMPACT-P_ATTR\", \"IMPACT-P_PATTERN\", \"MF_ALL_MIN_MUL\", \"MF_ALL\"]\n","\n","# Create a dictionary to store the Wilcoxon test results\n","wilcoxon_results = {}\n","\n","# Iterate through each pair of model names\n","for i in range(len(model_names)):\n"," for j in range(i + 1, len(model_names)):\n"," model1_name = model_names[i]\n"," model2_name = model_names[j]\n","\n"," # Extract the data for the current model pair and 'hr' metric\n"," model1_data = df[(df[\"Label\"] == model1_name) & (df[\"Metric\"] == \"diversity\")]\n"," model2_data = df[(df[\"Label\"] == model2_name) & (df[\"Metric\"] == \"diversity\")]\n","\n"," # Perform Wilcoxon signed-rank test\n"," if not model1_data.empty and not model2_data.empty :\n"," statistic, p_value = wilcoxon(model1_data[\"Value\"].values[0], model2_data[\"Value\"].values[0])\n"," wilcoxon_results[(model1_name, model2_name)] = {\"statistic\": statistic, \"p_value\": p_value}\n"," else:\n"," print(f\"Warning: No data for model pair {model1_name} vs {model2_name} or 'diversity' metric. Skipping comparison.\")\n","\n","# Print the Wilcoxon test results\n","for (model1, model2), result in wilcoxon_results.items():\n"," print(f\"Wilcoxon test for {model1} vs {model2} (hr metric):\")\n"," print(f\" Statistic: {result['statistic']}\")\n"," print(f\" P-value: {result['p_value']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Uis3NGeFMe-h","executionInfo":{"status":"ok","timestamp":1737166592914,"user_tz":-420,"elapsed":324,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d7734f81-291c-4b23-8975-77fb4955cb9f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Wilcoxon test for IMPACT-P_ATTR vs IMPACT-P_PATTERN (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_ATTR vs MF_ALL_MIN_MUL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_ATTR vs MF_ALL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_PATTERN vs MF_ALL_MIN_MUL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for IMPACT-P_PATTERN vs MF_ALL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n","Wilcoxon test for MF_ALL_MIN_MUL vs MF_ALL (hr metric):\n"," Statistic: 0.0\n"," P-value: 1.7763568394002505e-15\n"]}]}]} \ No newline at end of file diff --git a/Source Code/Uji Statistik/Previous MF.ipynb b/Source Code/Uji Statistik/Previous MF.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..20e405d545268c6ed74d67819f9ccb98c9d96778 --- /dev/null +++ b/Source Code/Uji Statistik/Previous MF.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1JokOgCNDWe2KVbXg9o0-E6A6yVg8I8tn","authorship_tag":"ABX9TyP7+mfe4Q8+wxN1Ovz2Q3C0"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Utilization"],"metadata":{"id":"hmo8FAPWAEQE"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"en11EpRD_WJZ","executionInfo":{"status":"ok","timestamp":1736926438656,"user_tz":-420,"elapsed":320,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i7zOEQCB_oKt","executionInfo":{"status":"ok","timestamp":1736926440648,"user_tz":-420,"elapsed":1689,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"f7fcf937-664d-49cb-ea42-2cf4ac649410"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"owBWOGbO_t5V","executionInfo":{"status":"ok","timestamp":1736926446780,"user_tz":-420,"elapsed":6134,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"41e1f798-3dff-4cb1-d560-7c7b514fb86f"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"djAhyjf7_wmg","executionInfo":{"status":"ok","timestamp":1736926491988,"user_tz":-420,"elapsed":45210,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"92d2eab7-7792-4d8e-9387-1ac2db699930"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. Winterbottom', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40990', 'item_name': 'Mafia', 'playtime_forever': 21, 'playtime_2weeks': 0}, {'item_id': '50130', 'item_name': 'Mafia II', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '50310', 'item_name': 'MLB 2K11', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '57900', 'item_name': 'Duke Nukem Forever', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '65950', 'item_name': 'NBA 2K11', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '201020', 'item_name': 'NBA 2K12', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409710', 'item_name': 'BioShock Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409720', 'item_name': 'BioShock 2 Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '67370', 'item_name': 'The Darkness II', 'playtime_forever': 146, 'playtime_2weeks': 0}, {'item_id': '207610', 'item_name': 'The Walking Dead', 'playtime_forever': 457, 'playtime_2weeks': 0}, {'item_id': '97330', 'item_name': 'Magic: The Gathering - Duels of the Planeswalkers 2013', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '65800', 'item_name': 'Dungeon Defenders', 'playtime_forever': 4376, 'playtime_2weeks': 0}, {'item_id': '211420', 'item_name': 'Dark Souls: Prepare to Die Edition', 'playtime_forever': 71, 'playtime_2weeks': 0}, {'item_id': '730', 'item_name': 'Counter-Strike: Global Offensive', 'playtime_forever': 1900, 'playtime_2weeks': 0}, {'item_id': '204300', 'item_name': 'Awesomenauts', 'playtime_forever': 584, 'playtime_2weeks': 0}, {'item_id': '200170', 'item_name': 'Worms Revolution', 'playtime_forever': 423, 'playtime_2weeks': 0}, {'item_id': '217200', 'item_name': 'Worms Armageddon', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '204360', 'item_name': 'Castle Crashers', 'playtime_forever': 614, 'playtime_2weeks': 0}, {'item_id': '49520', 'item_name': 'Borderlands 2', 'playtime_forever': 3393, 'playtime_2weeks': 0}, {'item_id': '216890', 'item_name': 'Blood Bowl: Chaos Edition', 'playtime_forever': 846, 'playtime_2weeks': 0}, {'item_id': '200510', 'item_name': 'XCOM: Enemy Unknown', 'playtime_forever': 715, 'playtime_2weeks': 0}, {'item_id': '4920', 'item_name': 'Natural Selection 2', 'playtime_forever': 10, 'playtime_2weeks': 0}, {'item_id': '220240', 'item_name': 'Far Cry® 3', 'playtime_forever': 1336, 'playtime_2weeks': 0}, {'item_id': '219740', 'item_name': \"Don't Starve\", 'playtime_forever': 4806, 'playtime_2weeks': 0}, {'item_id': '322330', 'item_name': \"Don't Starve Together\", 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '220440', 'item_name': 'DmC Devil May Cry', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '226700', 'item_name': 'Infestation: Survivor Stories Classic', 'playtime_forever': 22, 'playtime_2weeks': 0}, {'item_id': '4540', 'item_name': 'Titan Quest', 'playtime_forever': 40, 'playtime_2weeks': 0}, {'item_id': '4550', 'item_name': 'Titan Quest: Immortal Throne', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4560', 'item_name': 'Company of Heroes', 'playtime_forever': 136, 'playtime_2weeks': 0}, {'item_id': '9340', 'item_name': 'Company of Heroes: Opposing Fronts', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9350', 'item_name': 'Supreme Commander', 'playtime_forever': 15, 'playtime_2weeks': 0}, {'item_id': '9420', 'item_name': 'Supreme Commander: Forged Alliance', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9480', 'item_name': 'Saints Row 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '15620', 'item_name': 'Warhammer® 40,000™: Dawn of War® II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '20530', 'item_name': 'Red Faction', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '20550', 'item_name': 'Red Faction II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '20570', 'item_name': 'Warhammer® 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'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9ubHDNhe_2Do","executionInfo":{"status":"ok","timestamp":1736926513250,"user_tz":-420,"elapsed":21264,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"2da36701-a932-4531-b197-db49e2fc6158"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"I2Gl7tHEABce","executionInfo":{"status":"ok","timestamp":1736926513251,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"tUOyZS2hACEE","executionInfo":{"status":"ok","timestamp":1736926513251,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"2YA4hpR9ALnq","executionInfo":{"status":"ok","timestamp":1736926513251,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"El34fm-IAO5W","executionInfo":{"status":"ok","timestamp":1736926623435,"user_tz":-420,"elapsed":3271,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"XRVawVsXAQyp","executionInfo":{"status":"ok","timestamp":1736926647863,"user_tz":-420,"elapsed":298,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"3JMhHuBBATNr","executionInfo":{"status":"ok","timestamp":1736926675811,"user_tz":-420,"elapsed":558,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":["## Uji Wilcoxon"],"metadata":{"id":"o65HwVCQAGdG"}},{"cell_type":"markdown","source":["### Setup Data and Function"],"metadata":{"id":"TqjzRJdsJGxr"}},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iQ28EbuQAKLV","executionInfo":{"status":"ok","timestamp":1736926614152,"user_tz":-420,"elapsed":96579,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"fa9f3b96-644e-4a5f-ef7e-8b30c921273a"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m451.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357281 sha256=e8ea9eb55e2b0f09abe65a2b0364af19347c50ea36be84046c1bdf25b6015b87\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","from surprise.model_selection import train_test_split"],"metadata":{"id":"hmiF1ZmIA315","executionInfo":{"status":"ok","timestamp":1736926614858,"user_tz":-420,"elapsed":709,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["# Load the saved models\n","import joblib\n","\n","best_svd = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svd_model.pkl')\n","best_svdpp = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svdpp_model.pkl')"],"metadata":{"id":"wEbTCz-SEP1y","executionInfo":{"status":"ok","timestamp":1736926939392,"user_tz":-420,"elapsed":16402,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results"],"metadata":{"id":"080We5IKEW02","executionInfo":{"status":"ok","timestamp":1736926963409,"user_tz":-420,"elapsed":304,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data"],"metadata":{"id":"lXQ1OZsAEeXw","executionInfo":{"status":"ok","timestamp":1736926968879,"user_tz":-420,"elapsed":309,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios"],"metadata":{"id":"oErpunCBEi7d","executionInfo":{"status":"ok","timestamp":1736926972414,"user_tz":-420,"elapsed":297,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":["\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices"],"metadata":{"id":"6gP__ahgElph","executionInfo":{"status":"ok","timestamp":1736926974421,"user_tz":-420,"elapsed":299,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","# Assuming average_hit_ratio is a dictionary where keys are user IDs and values are dictionaries of hit ratios for different top_n values.\n","# Example: average_hit_ratio = {'user1': {5: 0.2, 10: 0.3}, 'user2': {5: 0.1, 10: 0.2}}\n","\n","def calculate_mean_hit_ratio(average_hit_ratio):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average_hit_ratio.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"grP7k0GiEtoY","executionInfo":{"status":"ok","timestamp":1736926978035,"user_tz":-420,"elapsed":295,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":31,"outputs":[]},{"cell_type":"code","source":["def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = {\n"," \"hr\": 0,\n"," \"diversity\": 0,\n"," }\n"," for top_n in mean_hit_ratio:\n"," combined_data[\"hr\"] += mean_hit_ratio[top_n]\n"," combined_data[\"diversity\"] += mean_diversity[top_n]\n","\n"," combined_data[\"hr\"] = combined_data[\"hr\"] / len(mean_hit_ratio)\n"," combined_data[\"diversity\"] = combined_data[\"diversity\"] / len(mean_hit_ratio)\n"," return combined_data"],"metadata":{"id":"FGUhCjW6E6M0","executionInfo":{"status":"ok","timestamp":1736926980178,"user_tz":-420,"elapsed":383,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":32,"outputs":[]},{"cell_type":"markdown","source":["### Do Test"],"metadata":{"id":"LyEUedeJJMJN"}},{"cell_type":"code","source":["bundle_recommendations_svd_min = {}\n","bundle_recommendations_svd_hmean = {}\n","bundle_recommendations_svd_hybrid = {}\n","\n","bundle_recommendations_svdpp_min = {}\n","bundle_recommendations_svdpp_hmean = {}\n","bundle_recommendations_svdpp_hybrid = {}\n","\n","parameter_pengujian = {\n"," \"iterasi\": 50,\n"," \"results\": {\n"," \"svd\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"svdpp\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," }\n","}\n","\n","for i in range(parameter_pengujian[\"iterasi\"]):\n"," print(f\"Iterasi ke {i}\")\n","\n"," ## Generate prediction rating for all bundles\n"," bundle_recommendations_svd = {}\n"," bundle_recommendations_svdpp = {}\n","\n"," for user_id, recommendations in data_bundle_interactions.items():\n"," if user_id in user_item_dict:\n"," bundle_recommendations_svd[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svdpp[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n","\n"," for bundle_id, recommendation in bundle_data.items():\n"," svd_prediction = best_svd.predict(user_id, bundle_id)\n"," svdpp_prediction = best_svdpp.predict(user_id, bundle_id)\n"," bundle_recommendations_svd[user_id][\"rec_bundles\"][bundle_id] = svd_prediction.est\n"," bundle_recommendations_svdpp[user_id][\"rec_bundles\"][bundle_id] = svdpp_prediction.est\n","\n"," ## Train test split\n","\n"," df = pd.DataFrame(user_bundle_histories)\n"," reader = Reader(rating_scale=(0, 5))\n"," data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n"," trainset, testset = train_test_split(data, test_size=0.2)\n"," new_testset = [{'user_id': entry[0], 'bundle_id': entry[1], 'rating': entry[2]} for entry in testset]\n","\n"," grouped_testset = group_testset_by_user(new_testset)\n","\n"," ## Calculate Evaluation\n","\n"," average_hit_ratio = {}\n"," average_diversity = {}\n"," count = 0\n","\n"," top_bundles_results = []\n"," hit_ratios = []\n"," diversity_scores = []\n","\n"," bundle_recommendations_temp = [\n"," {\"branch\": \"svd\", \"model\": bundle_recommendations_svd},\n"," {\"branch\": \"svdpp\", \"model\": bundle_recommendations_svdpp},\n"," ]\n","\n"," for temp in bundle_recommendations_temp:\n","\n"," for user_id, bundle_recommendations in temp[\"model\"].items():\n","\n"," top_bundles_results = get_top_rec_bundles(bundle_recommendations)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[bundle_recommendations['user_id']] = hit_ratios\n"," average_diversity[bundle_recommendations['user_id']] = diversity_scores\n","\n"," mean_hit_ratio = calculate_mean_hit_ratio(average_hit_ratio)\n"," mean_diversity = calculate_mean_hit_ratio(average_diversity)\n","\n"," combined_data = combine_metrics(mean_hit_ratio, mean_diversity)\n","\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"hr\"].append(combined_data[\"hr\"])\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"diversity\"].append(combined_data[\"diversity\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F0_fdOqBBPAl","outputId":"8196ec05-ea40-4994-8dc0-d92697bc00b9","executionInfo":{"status":"ok","timestamp":1736945310824,"user_tz":-420,"elapsed":17790227,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["Iterasi ke 0\n","Iterasi ke 1\n","Iterasi ke 2\n","Iterasi ke 3\n","Iterasi ke 4\n","Iterasi ke 5\n","Iterasi ke 6\n","Iterasi ke 7\n","Iterasi ke 8\n","Iterasi ke 9\n","Iterasi ke 10\n","Iterasi ke 11\n","Iterasi ke 12\n","Iterasi ke 13\n","Iterasi ke 14\n","Iterasi ke 15\n","Iterasi ke 16\n","Iterasi ke 17\n","Iterasi ke 18\n","Iterasi ke 19\n","Iterasi ke 20\n","Iterasi ke 21\n","Iterasi ke 22\n","Iterasi ke 23\n","Iterasi ke 24\n","Iterasi ke 25\n","Iterasi ke 26\n","Iterasi ke 27\n","Iterasi ke 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RecSys","userId":"12384593251438087944"}},"outputId":"1508e69c-0d4d-4037-ab6c-da06276ecf08"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/mf_all_only.pkl']"]},"metadata":{},"execution_count":36}]}]} \ No newline at end of file diff --git a/Source Code/Uji Statistik/Previous.ipynb b/Source Code/Uji Statistik/Previous.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..588bb40c0d72fa91134d4d61a065a3e75121307f --- /dev/null +++ b/Source Code/Uji Statistik/Previous.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1WPmOdJNwzdueMD2rtlEqYNQ7WvrFXd4P","authorship_tag":"ABX9TyM5/GwF66DAQmfXk268KrMV"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Utilization"],"metadata":{"id":"hmo8FAPWAEQE"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"en11EpRD_WJZ","executionInfo":{"status":"ok","timestamp":1736986871244,"user_tz":-420,"elapsed":276,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i7zOEQCB_oKt","executionInfo":{"status":"ok","timestamp":1736986873325,"user_tz":-420,"elapsed":1793,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"c476e7fc-2ac3-4883-b3e3-ede01033d5f6"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"owBWOGbO_t5V","executionInfo":{"status":"ok","timestamp":1736986879104,"user_tz":-420,"elapsed":5780,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d53a060d-6eec-4921-d105-c6bae39b0d91"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"djAhyjf7_wmg","executionInfo":{"status":"ok","timestamp":1736986934861,"user_tz":-420,"elapsed":55758,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d2a62e20-c2d8-4d35-f716-1e0d33421d26"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. 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Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '433850', 'item_name': 'H1Z1: King of the Kill', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '439700', 'item_name': 'H1Z1: King of the Kill Test Server', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '222900', 'item_name': 'Dead Island: Epidemic', 'playtime_forever': 268, 'playtime_2weeks': 0}, {'item_id': '300550', 'item_name': \"Shadowrun: Dragonfall - Director's Cut\", 'playtime_forever': 1056, 'playtime_2weeks': 0}, {'item_id': '300570', 'item_name': 'Infinifactory', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '300580', 'item_name': 'GALAK-Z', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '301520', 'item_name': 'Robocraft', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '302270', 'item_name': 'Dungeon Defenders Eternity', 'playtime_forever': 551, 'playtime_2weeks': 0}, {'item_id': '304050', 'item_name': 'Trove', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '304030', 'item_name': 'ArcheAge', 'playtime_forever': 12249, 'playtime_2weeks': 0}, {'item_id': '304930', 'item_name': 'Unturned', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '244630', 'item_name': 'NEOTOKYO°', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '312990', 'item_name': 'The Expendabros', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '227940', 'item_name': 'Heroes & Generals', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '265590', 'item_name': 'The Red Solstice', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '241930', 'item_name': 'Middle-earth: Shadow of Mordor', 'playtime_forever': 1494, 'playtime_2weeks': 0}, {'item_id': '265550', 'item_name': 'Dead Rising 3', 'playtime_forever': 171, 'playtime_2weeks': 0}, {'item_id': '226720', 'item_name': 'Lost Planet 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '323370', 'item_name': 'TERA', 'playtime_forever': 1785, 'playtime_2weeks': 0}, {'item_id': '327510', 'item_name': 'WASTED', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '261640', 'item_name': 'Borderlands: The Pre-Sequel', 'playtime_forever': 2462, 'playtime_2weeks': 0}, {'item_id': '330840', 'item_name': 'Game of Thrones - A Telltale Games Series', 'playtime_forever': 719, 'playtime_2weeks': 0}, {'item_id': '17570', 'item_name': 'Pirates, Vikings, & Knights II', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '305620', 'item_name': 'The Long Dark', 'playtime_forever': 404, 'playtime_2weeks': 0}, {'item_id': '323470', 'item_name': 'DRAGON BALL XENOVERSE', 'playtime_forever': 920, 'playtime_2weeks': 0}, {'item_id': '333930', 'item_name': 'Dirty Bomb', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '249050', 'item_name': 'Dungeon of the Endless', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '236110', 'item_name': 'Dungeon Defenders II', 'playtime_forever': 88, 'playtime_2weeks': 0}, {'item_id': '337850', 'item_name': 'Avernum 2: Crystal Souls', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339120', 'item_name': \"Fork Parker's Holiday Profit Hike\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '342200', 'item_name': 'MechWarrior Online', 'playtime_forever': 699, 'playtime_2weeks': 0}, {'item_id': '314660', 'item_name': \"Oddworld: New 'n' Tasty\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '345520', 'item_name': 'Infinite Crisis™', 'playtime_forever': 406, 'playtime_2weeks': 0}, {'item_id': '346110', 'item_name': 'ARK: Survival Evolved', 'playtime_forever': 23434, 'playtime_2weeks': 0}, {'item_id': '407530', 'item_name': 'ARK: Survival Of The Fittest', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '347160', 'item_name': 'Steredenn', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '287290', 'item_name': 'Resident Evil Revelations 2 / Biohazard Revelations 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '339280', 'item_name': 'Strife', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '351570', 'item_name': 'Killing Floor: Uncovered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '232090', 'item_name': 'Killing Floor 2', 'playtime_forever': 1171, 'playtime_2weeks': 0}, {'item_id': '232150', 'item_name': 'Killing Floor 2 - SDK', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '362070', 'item_name': 'Metal Reaper Online', 'playtime_forever': 8, 'playtime_2weeks': 0}, {'item_id': '47780', 'item_name': 'Dead Space 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '362680', 'item_name': 'Fran Bow', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '329460', 'item_name': 'JumpJet Rex', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '365590', 'item_name': \"Tom Clancy's The Division\", 'playtime_forever': 10442, 'playtime_2weeks': 0}, {'item_id': '329050', 'item_name': 'Devil May Cry® 4 Special Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '370360', 'item_name': 'TIS-100', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '292030', 'item_name': 'The Witcher 3: Wild Hunt', 'playtime_forever': 2140, 'playtime_2weeks': 0}, {'item_id': '373480', 'item_name': '1993 Space Machine', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '377160', 'item_name': 'Fallout 4', 'playtime_forever': 7194, 'playtime_2weeks': 0}, {'item_id': '386070', 'item_name': 'Planetary Annihilation: TITANS', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '386360', 'item_name': 'SMITE', 'playtime_forever': 498, 'playtime_2weeks': 0}, {'item_id': '234140', 'item_name': 'Mad Max', 'playtime_forever': 1123, 'playtime_2weeks': 0}, {'item_id': '393420', 'item_name': 'Hurtworld', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '445900', 'item_name': 'Hurtworld SDK', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '394310', 'item_name': 'Punch Club', 'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9ubHDNhe_2Do","executionInfo":{"status":"ok","timestamp":1736986957057,"user_tz":-420,"elapsed":22198,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"be922c3b-dcbf-4ce8-b2cf-747f798d1f00"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Successfully loaded 9675 items from the JSON file.\n"]}]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"I2Gl7tHEABce","executionInfo":{"status":"ok","timestamp":1736986957057,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"tUOyZS2hACEE","executionInfo":{"status":"ok","timestamp":1736986957057,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"2YA4hpR9ALnq","executionInfo":{"status":"ok","timestamp":1736986957057,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent / 20\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"El34fm-IAO5W","executionInfo":{"status":"ok","timestamp":1736986959518,"user_tz":-420,"elapsed":2465,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"XRVawVsXAQyp","executionInfo":{"status":"ok","timestamp":1736986959518,"user_tz":-420,"elapsed":2,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["users_item_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," rating = 0.0\n"," if item_id in user_item_dict[user_id]:\n"," rating = user_item_dict[user_id][item_id]\n"," users_item_histories.append({ \"user_id\": user_id, \"item_id\": item_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"GyjLa5jlASgu","executionInfo":{"status":"ok","timestamp":1736986961477,"user_tz":-420,"elapsed":1961,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"3JMhHuBBATNr","executionInfo":{"status":"ok","timestamp":1736986961812,"user_tz":-420,"elapsed":336,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":["## Uji Wilcoxon"],"metadata":{"id":"o65HwVCQAGdG"}},{"cell_type":"markdown","source":["### Setup Data and Function"],"metadata":{"id":"TqjzRJdsJGxr"}},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iQ28EbuQAKLV","executionInfo":{"status":"ok","timestamp":1736987054353,"user_tz":-420,"elapsed":92543,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"81ac75a8-a941-4be9-f321-4009cf41a8c1"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl.metadata (327 bytes)\n","Collecting scikit-surprise (from surprise)\n"," Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.4.2)\n","Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.26.4)\n","Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.13.1)\n","Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357277 sha256=4ab6a517f9dfc0f5fc270326315d896690faa1c21c43c58b62739b28eb10f3fa\n"," Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.4 surprise-0.1\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","from surprise.model_selection import train_test_split"],"metadata":{"id":"hmiF1ZmIA315","executionInfo":{"status":"ok","timestamp":1736987055446,"user_tz":-420,"elapsed":1096,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["# Load the saved models\n","import joblib\n","\n","best_svd = joblib.load(f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svd_model.pkl\")\n","best_svdpp = joblib.load(f\"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/item_svdpp_model.pkl\")"],"metadata":{"id":"wEbTCz-SEP1y","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":4042,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["import math\n","\n","def harmonic_mean(numbers):\n"," if 0 in numbers:\n"," return 0\n"," return len(numbers) / sum(1/number for number in numbers)\n","\n","def recommend_bundles(user_id, bundle_data, model):\n"," recommendations = {}\n"," for bundle_id, bundle_info in bundle_data.items():\n"," item_predictions = []\n"," for item_id in bundle_info[\"items\"]:\n"," svd_prediction = model.predict(user_id, item_id)\n"," item_predictions.append(svd_prediction.est)\n","\n"," min_svd = min(item_predictions) if item_predictions else 0\n"," hmean_svd = harmonic_mean(item_predictions)\n","\n"," recommendations[bundle_id] = {\n"," \"min\": min_svd,\n"," \"hmean\": hmean_svd,\n"," \"hybrid\": (min_svd + hmean_svd) / 2,\n"," }\n"," return recommendations"],"metadata":{"id":"GdE1Bho_A3YV","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results"],"metadata":{"id":"080We5IKEW02","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data"],"metadata":{"id":"lXQ1OZsAEeXw","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":5,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios"],"metadata":{"id":"oErpunCBEi7d","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices"],"metadata":{"id":"6gP__ahgElph","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","# Assuming average_hit_ratio is a dictionary where keys are user IDs and values are dictionaries of hit ratios for different top_n values.\n","# Example: average_hit_ratio = {'user1': {5: 0.2, 10: 0.3}, 'user2': {5: 0.1, 10: 0.2}}\n","\n","def calculate_mean_hit_ratio(average_hit_ratio):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average_hit_ratio.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"grP7k0GiEtoY","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = {\n"," \"hr\": 0,\n"," \"diversity\": 0,\n"," }\n"," for top_n in mean_hit_ratio:\n"," combined_data[\"hr\"] += mean_hit_ratio[top_n]\n"," combined_data[\"diversity\"] += mean_diversity[top_n]\n","\n"," combined_data[\"hr\"] = combined_data[\"hr\"] / len(mean_hit_ratio)\n"," combined_data[\"diversity\"] = combined_data[\"diversity\"] / len(mean_hit_ratio)\n"," return combined_data"],"metadata":{"id":"FGUhCjW6E6M0","executionInfo":{"status":"ok","timestamp":1736987059487,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":["### Do Test"],"metadata":{"id":"LyEUedeJJMJN"}},{"cell_type":"code","source":["bundle_recommendations_svd_min = {}\n","bundle_recommendations_svd_hmean = {}\n","bundle_recommendations_svd_hybrid = {}\n","\n","bundle_recommendations_svdpp_min = {}\n","bundle_recommendations_svdpp_hmean = {}\n","bundle_recommendations_svdpp_hybrid = {}\n","\n","parameter_pengujian = {\n"," \"iterasi\": 50,\n"," \"results\": {\n"," \"svd_min\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"svd_hmean\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"svdpp_min\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"svdpp_hmean\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," }\n"," }\n","}\n","\n","for i in range(parameter_pengujian[\"iterasi\"]):\n"," print(f\"Iterasi ke {i}\")\n","\n"," ## Generate prediction rating for all bundles\n","\n"," bundle_recommendations_svd_min = {}\n"," bundle_recommendations_svd_hmean = {}\n","\n"," bundle_recommendations_svdpp_min = {}\n"," bundle_recommendations_svdpp_hmean = {}\n","\n"," for user_id, recommendations in data_bundle_interactions.items():\n"," if user_id in user_item_dict:\n"," bundle_recommendations_svd_min[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svd_hmean[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n","\n"," bundle_recommendations_svdpp_min[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n"," bundle_recommendations_svdpp_hmean[user_id] = {\n"," \"user_id\": user_id,\n"," \"rec_bundles\": {}\n"," }\n","\n"," bundle_recommendations = recommend_bundles(user_id, bundle_data, best_svd)\n","\n"," for bundle_id, recommendation in bundle_recommendations.items():\n"," bundle_recommendations_svd_min[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"min\"]\n"," bundle_recommendations_svd_hmean[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"hmean\"]\n","\n"," bundle_recommendations = recommend_bundles(user_id, bundle_data, best_svdpp)\n","\n"," for bundle_id, recommendation in bundle_recommendations.items():\n"," bundle_recommendations_svdpp_min[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"min\"]\n"," bundle_recommendations_svdpp_hmean[user_id][\"rec_bundles\"][bundle_id] = recommendation[\"hmean\"]\n","\n"," ## Train test split\n","\n"," df = pd.DataFrame(user_bundle_histories)\n"," reader = Reader(rating_scale=(0, 5))\n"," data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n"," trainset, testset = train_test_split(data, test_size=0.2)\n"," new_testset = [{'user_id': entry[0], 'bundle_id': entry[1], 'rating': entry[2]} for entry in testset]\n","\n"," grouped_testset = group_testset_by_user(new_testset)\n","\n"," ## Calculate Evaluation\n","\n"," average_hit_ratio = {}\n"," average_diversity = {}\n"," count = 0\n","\n"," top_bundles_results = []\n"," hit_ratios = []\n"," diversity_scores = []\n","\n"," bundle_recommendations_temp = [\n"," {\"branch\": \"svd_min\", \"model\": bundle_recommendations_svd_min},\n"," {\"branch\": \"svd_hmean\", \"model\": bundle_recommendations_svd_hmean},\n"," {\"branch\": \"svdpp_min\", \"model\": bundle_recommendations_svdpp_min},\n"," {\"branch\": \"svdpp_hmean\", \"model\": bundle_recommendations_svdpp_hmean}\n"," ]\n","\n"," for temp in bundle_recommendations_temp:\n","\n"," for user_id, bundle_recommendations in temp[\"model\"].items():\n","\n"," top_bundles_results = get_top_rec_bundles(bundle_recommendations)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[bundle_recommendations['user_id']] = hit_ratios\n"," average_diversity[bundle_recommendations['user_id']] = diversity_scores\n","\n"," mean_hit_ratio = calculate_mean_hit_ratio(average_hit_ratio)\n"," mean_diversity = calculate_mean_hit_ratio(average_diversity)\n","\n"," combined_data = combine_metrics(mean_hit_ratio, mean_diversity)\n","\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"hr\"].append(combined_data[\"hr\"])\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"diversity\"].append(combined_data[\"diversity\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F0_fdOqBBPAl","outputId":"0870d714-c1df-48e9-9a92-f6fd57ae9706","executionInfo":{"status":"ok","timestamp":1737028013809,"user_tz":-420,"elapsed":40954325,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Iterasi ke 0\n","Iterasi ke 1\n","Iterasi ke 2\n","Iterasi ke 3\n","Iterasi ke 4\n","Iterasi ke 5\n","Iterasi ke 6\n","Iterasi ke 7\n","Iterasi ke 8\n","Iterasi ke 9\n","Iterasi ke 10\n","Iterasi ke 11\n","Iterasi ke 12\n","Iterasi ke 13\n","Iterasi ke 14\n","Iterasi ke 15\n","Iterasi ke 16\n","Iterasi ke 17\n","Iterasi ke 18\n","Iterasi ke 19\n","Iterasi ke 20\n","Iterasi ke 21\n","Iterasi ke 22\n","Iterasi ke 23\n","Iterasi ke 24\n","Iterasi ke 25\n","Iterasi ke 26\n","Iterasi ke 27\n","Iterasi ke 28\n","Iterasi ke 29\n","Iterasi ke 30\n","Iterasi ke 31\n","Iterasi ke 32\n","Iterasi ke 33\n","Iterasi ke 34\n","Iterasi ke 35\n","Iterasi ke 36\n","Iterasi ke 37\n","Iterasi ke 38\n","Iterasi ke 39\n","Iterasi ke 40\n","Iterasi ke 41\n","Iterasi ke 42\n","Iterasi ke 43\n","Iterasi ke 44\n","Iterasi ke 45\n","Iterasi ke 46\n","Iterasi ke 47\n","Iterasi ke 48\n","Iterasi ke 49\n"]}]},{"cell_type":"code","source":["import joblib\n","\n","# Assuming bundle_recommendations_svd and bundle_recommendations_svdpp are defined\n","\n","# Save the dictionaries to separate files\n","output_dir = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik\"\n","joblib.dump(parameter_pengujian, f\"{output_dir}/mf_all.pkl\")"],"metadata":{"id":"atP9uxUOxC4D","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1737028014119,"user_tz":-420,"elapsed":313,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d2b34946-6812-49c8-f93c-f032a8410cce"},"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik/mf_all.pkl']"]},"metadata":{},"execution_count":25}]}]} \ No newline at end of file diff --git a/Source Code/Uji Statistik/Proposed IMPACT-P_ATTR.ipynb b/Source Code/Uji Statistik/Proposed IMPACT-P_ATTR.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c3d07a72cff15e08546723337a7f742eccd3f243 --- /dev/null +++ b/Source Code/Uji Statistik/Proposed IMPACT-P_ATTR.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1lx4k5FBTvXE4FvDjXETSHGbinZ6b8Ofr","authorship_tag":"ABX9TyMl3SUuFfkrDdXJiNslAHwj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Utilization"],"metadata":{"id":"hmo8FAPWAEQE"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"en11EpRD_WJZ","executionInfo":{"status":"ok","timestamp":1737074480595,"user_tz":-420,"elapsed":511,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i7zOEQCB_oKt","executionInfo":{"status":"ok","timestamp":1737074482361,"user_tz":-420,"elapsed":1767,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d300dbad-d04b-48cb-dbdf-f22d795f4d67"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"owBWOGbO_t5V","executionInfo":{"status":"ok","timestamp":1737074488012,"user_tz":-420,"elapsed":5652,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"92e4c6b9-db28-4a7b-d962-e70b4709ba5f"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"djAhyjf7_wmg","executionInfo":{"status":"error","timestamp":1737074502565,"user_tz":-420,"elapsed":14555,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"9edfa780-4a47-40b1-8e59-fe2097ec96ed"},"execution_count":4,"outputs":[{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mcleaned_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mdata_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mliteral_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcleaned_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"items_count\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"items\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdata_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"items_count\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.11/ast.py\u001b[0m in \u001b[0;36mliteral_eval\u001b[0;34m(node_or_string)\u001b[0m\n\u001b[1;32m 62\u001b[0m \"\"\"\n\u001b[1;32m 63\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_or_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mnode_or_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_or_string\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \\t\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'eval'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_or_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExpression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mnode_or_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode_or_string\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.11/ast.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(source, filename, mode, type_comments, feature_version)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mfeature_version\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;31m# Else it should be an int giving the minor version for 3.x.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m return compile(source, filename, mode, flags,\n\u001b[0m\u001b[1;32m 51\u001b[0m _feature_version=feature_version)\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"id":"9ubHDNhe_2Do","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"I2Gl7tHEABce","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"tUOyZS2hACEE","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"2YA4hpR9ALnq","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"El34fm-IAO5W","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"XRVawVsXAQyp","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"3JMhHuBBATNr","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Uji Wilcoxon"],"metadata":{"id":"o65HwVCQAGdG"}},{"cell_type":"markdown","source":["### Setup Data and Function"],"metadata":{"id":"TqjzRJdsJGxr"}},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"id":"iQ28EbuQAKLV","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","from surprise.model_selection import train_test_split"],"metadata":{"id":"hmiF1ZmIA315","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load the saved models\n","import joblib\n","\n","best_svd = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svd_model.pkl')\n","best_svdpp = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svdpp_model.pkl')"],"metadata":{"id":"wEbTCz-SEP1y","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results"],"metadata":{"id":"080We5IKEW02","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data"],"metadata":{"id":"lXQ1OZsAEeXw","executionInfo":{"status":"aborted","timestamp":1737074502566,"user_tz":-420,"elapsed":3,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios"],"metadata":{"id":"oErpunCBEi7d","executionInfo":{"status":"aborted","timestamp":1737074502567,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices"],"metadata":{"id":"6gP__ahgElph","executionInfo":{"status":"aborted","timestamp":1737074502567,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","# Assuming average_hit_ratio is a dictionary where keys are user IDs and values are dictionaries of hit ratios for different top_n values.\n","# Example: average_hit_ratio = {'user1': {5: 0.2, 10: 0.3}, 'user2': {5: 0.1, 10: 0.2}}\n","\n","def calculate_mean_hit_ratio(average_hit_ratio):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average_hit_ratio.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"grP7k0GiEtoY","executionInfo":{"status":"aborted","timestamp":1737074502567,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = {\n"," \"hr\": 0,\n"," \"diversity\": 0,\n"," }\n"," for top_n in mean_hit_ratio:\n"," combined_data[\"hr\"] += mean_hit_ratio[top_n]\n"," combined_data[\"diversity\"] += mean_diversity[top_n]\n","\n"," combined_data[\"hr\"] = combined_data[\"hr\"] / len(mean_hit_ratio)\n"," combined_data[\"diversity\"] = combined_data[\"diversity\"] / len(mean_hit_ratio)\n"," return combined_data"],"metadata":{"id":"FGUhCjW6E6M0","executionInfo":{"status":"aborted","timestamp":1737074502567,"user_tz":-420,"elapsed":4,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Do Test"],"metadata":{"id":"LyEUedeJJMJN"}},{"cell_type":"code","source":["parameter_pengujian = {\n"," \"iterasi\": 50,\n"," \"results\": {\n"," \"ann\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"dt\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"knn\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"lr\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"nb\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," }\n","}\n","\n","for i in range(parameter_pengujian[\"iterasi\"]):\n"," print(f\"Iterasi ke {i}\")\n","\n"," ## Train test split\n","\n"," df = pd.DataFrame(user_bundle_histories)\n"," reader = Reader(rating_scale=(0, 5))\n"," data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n"," trainset, testset = train_test_split(data, test_size=0.2)\n"," new_testset = [{'user_id': entry[0], 'bundle_id': entry[1], 'rating': entry[2]} for entry in testset]\n","\n"," grouped_testset = group_testset_by_user(new_testset)\n","\n"," ## Calculate Evaluation\n","\n"," average_hit_ratio = {}\n"," average_diversity = {}\n"," count = 0\n","\n"," top_bundles_results = []\n"," hit_ratios = []\n"," diversity_scores = []\n","\n"," bundle_recommendations_temp = [\n"," {\"branch\": \"ann\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz'},\n"," {\"branch\": \"dt\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz'},\n"," {\"branch\": \"knn\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz'},\n"," {\"branch\": \"lr\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz'},\n"," {\"branch\": \"nb\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz'},\n"," ]\n","\n"," for temp in bundle_recommendations_temp:\n","\n"," try:\n"," with gzip.open(temp[\"model\"], 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"File {temp['model']} successfully processed and saved to {temp['model']}\")\n","\n"," except FileNotFoundError:\n"," print(f\"Error: File not found at {temp['model']}\")\n"," except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n"," mean_hit_ratio = calculate_mean_hit_ratio(average_hit_ratio)\n"," mean_diversity = calculate_mean_hit_ratio(average_diversity)\n","\n"," combined_data = combine_metrics(mean_hit_ratio, mean_diversity)\n","\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"hr\"].append(combined_data[\"hr\"])\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"diversity\"].append(combined_data[\"diversity\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F0_fdOqBBPAl","outputId":"0f13d639-e765-45ba-eb12-f9340a88d6e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Iterasi ke 0\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 1\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 2\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 3\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 4\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 5\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 6\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 7\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 8\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 9\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 10\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 11\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 12\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 13\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 14\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 15\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 16\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 17\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 18\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 19\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 20\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 21\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 22\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 23\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 24\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 25\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 26\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 27\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 28\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 29\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 30\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 31\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 32\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 33\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 34\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 35\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 36\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/tags_and_specs_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 37\n"]}]},{"cell_type":"code","source":["import joblib\n","\n","# Assuming bundle_recommendations_svd and bundle_recommendations_svdpp are defined\n","\n","# Save the dictionaries to separate files\n","output_dir = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik\"\n","joblib.dump(parameter_pengujian, f\"{output_dir}/impact_p_attr.pkl\")"],"metadata":{"id":"atP9uxUOxC4D"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Source Code/Uji Statistik/Proposed IMPACT-P_PATTERNS.ipynb b/Source Code/Uji Statistik/Proposed IMPACT-P_PATTERNS.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..44b36df5ae4aa6b5b34c209f3bbf591cd752141d --- /dev/null +++ b/Source Code/Uji Statistik/Proposed IMPACT-P_PATTERNS.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"14XBrlmvQI5f95zQAdMvGqUXOMo_zzZIH","authorship_tag":"ABX9TyO0W9IBoOuqHNwHUgcCU4jB"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Utilization"],"metadata":{"id":"hmo8FAPWAEQE"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"en11EpRD_WJZ","executionInfo":{"status":"ok","timestamp":1737074485493,"user_tz":-420,"elapsed":294,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}}},"outputs":[],"source":["import re\n","import numpy as np\n","\n","def remove_currency_symbol(price_str):\n"," \"\"\"Removes currency symbols ($, £, €, etc.) from a price string.\n","\n"," Args:\n"," price_str: The price string to clean.\n","\n"," Returns:\n"," The cleaned price string without currency symbols, or None if no numeric part is found.\n"," \"\"\"\n"," # Use a regular expression to find any numeric part of the string, allowing for decimal points\n"," match = re.search(r\"[-+]?\\d*\\.?\\d+\", price_str)\n"," if match:\n"," return float(match.group(0))\n"," else:\n"," return 0.0"]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","bundle_data = {}\n","data_dict = None\n","item_dict = None\n","count = {\n"," \"bundles\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," count[\"items\"] += len(data_dict[\"items\"])\n"," count[\"bundles\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," bundle_data[data_dict['bundle_id']] = {\n"," \"bundle_id\": data_dict['bundle_id'],\n"," \"bundle_price\": remove_currency_symbol(data_dict['bundle_price']),\n"," \"bundle_name\": data_dict['bundle_name'],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of bundle {count['bundles']}\")\n","print(f\"Number of items {count['items']}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i7zOEQCB_oKt","executionInfo":{"status":"ok","timestamp":1737074488190,"user_tz":-420,"elapsed":2221,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"d0a7c3d2-4a89-408a-9bd4-85c4e7975b00"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of bundle 615\n","Number of items 3525\n"]}]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\n","\n","import json\n","import gzip\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/steam_games.json.gz\"\n","\n","# Open the gzipped file\n","items_dictionary = {}\n","data_dict = None\n","count = {\n"," \"items\": 0\n","}\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if 'id' in data_dict:\n"," items_dictionary[data_dict['id']] = data_dict\n"," count[\"items\"] += 1\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"owBWOGbO_t5V","executionInfo":{"status":"ok","timestamp":1737074493343,"user_tz":-420,"elapsed":5155,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"1584b990-027e-4c86-de72-d6aa9c9fe089"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of items 32133\n","{'app_name': 'Maze Run VR', 'sentiment': 'Positive', 'tags': ['Early Access', 'Adventure', 'Indie', 'Action', 'Simulation', 'VR'], 'url': 'http://store.steampowered.com/app/681550/Maze_Run_VR/', 'price': 4.99, 'reviews_url': 'http://steamcommunity.com/app/681550/reviews/?browsefilter=mostrecent&p=1', 'id': '681550', 'early_access': True, 'specs': ['Single-player', 'Stats', 'Steam Leaderboards', 'HTC Vive', 'Oculus Rift', 'Tracked Motion Controllers', 'Standing', 'Room-Scale']}\n"]}]},{"cell_type":"code","source":["# Open the gzipped file\n","user_item_interactions = {}\n","data_dict = None\n","count = {\n"," \"users\": 0,\n"," \"items\": 0\n","}\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_users_items.json.gz\"\n","\n","with gzip.open(f\"{file_path}\", 'rt', encoding='utf-8') as f: # 'rt' is for text mode\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," if data_dict[\"items_count\"] > 0:\n"," count[\"items\"] += data_dict[\"items_count\"]\n"," count[\"users\"] += 1\n","\n"," item_dict = set()\n"," for item in data_dict[\"items\"]:\n"," item_dict.add(item[\"item_id\"])\n","\n"," user_item_interactions[data_dict['user_id']] = {\n"," \"user_id\": data_dict['user_id'],\n"," \"items_count\": data_dict[\"items_count\"],\n"," \"items\": item_dict\n"," }\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")\n","\n"," if count[\"users\"] == 10000:\n"," break\n","\n","print(f\"Number of user {count['users']}\")\n","print(f\"Number of items {count['items']}\")\n","print(data_dict)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"djAhyjf7_wmg","executionInfo":{"status":"ok","timestamp":1737074536264,"user_tz":-420,"elapsed":42923,"user":{"displayName":"Fundamental RecSys","userId":"12384593251438087944"}},"outputId":"48b8b0a9-c0e2-4e70-9f70-2c084915a50a"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of user 10000\n","Number of items 1132592\n","{'user_id': '76561198030000787', 'items_count': 314, 'steam_id': '76561198030000787', 'user_url': 'http://steamcommunity.com/profiles/76561198030000787', 'items': [{'item_id': '4000', 'item_name': \"Garry's Mod\", 'playtime_forever': 32, 'playtime_2weeks': 0}, {'item_id': '2600', 'item_name': 'Vampire: The Masquerade - Bloodlines', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '2200', 'item_name': 'Quake III Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2310', 'item_name': 'Quake', 'playtime_forever': 26, 'playtime_2weeks': 0}, {'item_id': '2320', 'item_name': 'Quake II', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '2330', 'item_name': 'Quake II: The Reckoning', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2340', 'item_name': 'Quake II: Ground Zero', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '2350', 'item_name': 'Quake III: Team Arena', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9030', 'item_name': 'Quake Mission Pack 2: Dissolution of Eternity', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '9040', 'item_name': 'Quake Mission Pack 1: Scourge of Armagon', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '4760', 'item_name': 'Rome: Total War', 'playtime_forever': 124, 'playtime_2weeks': 0}, {'item_id': '4770', 'item_name': 'Rome: Total War - Alexander', 'playtime_forever': 5, 'playtime_2weeks': 0}, {'item_id': '18500', 'item_name': 'Defense Grid: The Awakening', 'playtime_forever': 486, 'playtime_2weeks': 0}, {'item_id': '17460', 'item_name': 'Mass Effect', 'playtime_forever': 1896, 'playtime_2weeks': 0}, {'item_id': '1250', 'item_name': 'Killing Floor', 'playtime_forever': 949, 'playtime_2weeks': 0}, {'item_id': '35420', 'item_name': 'Killing Floor Mod: Defence Alliance 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21680', 'item_name': 'Bionic Commando Rearmed', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21670', 'item_name': 'Bionic Commando', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38400', 'item_name': 'Fallout', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '38410', 'item_name': 'Fallout 2', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '38420', 'item_name': 'Fallout Tactics', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '20900', 'item_name': 'The Witcher: Enhanced Edition', 'playtime_forever': 38, 'playtime_2weeks': 0}, {'item_id': '17450', 'item_name': 'Dragon Age: Origins', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10180', 'item_name': 'Call of Duty: Modern Warfare 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '10190', 'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer', 'playtime_forever': 835, 'playtime_2weeks': 0}, {'item_id': '550', 'item_name': 'Left 4 Dead 2', 'playtime_forever': 683, 'playtime_2weeks': 0}, {'item_id': '223530', 'item_name': 'Left 4 Dead 2 Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22370', 'item_name': 'Fallout 3 - Game of the Year Edition', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '24980', 'item_name': 'Mass Effect 2', 'playtime_forever': 1837, 'playtime_2weeks': 0}, {'item_id': '33910', 'item_name': 'Arma 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '33930', 'item_name': 'Arma 2: Operation Arrowhead', 'playtime_forever': 57, 'playtime_2weeks': 0}, {'item_id': '219540', 'item_name': 'Arma 2: Operation Arrowhead Beta (Obsolete)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '22380', 'item_name': 'Fallout: New Vegas', 'playtime_forever': 5078, 'playtime_2weeks': 0}, {'item_id': '42700', 'item_name': 'Call of Duty: Black Ops', 'playtime_forever': 537, 'playtime_2weeks': 0}, {'item_id': '42710', 'item_name': 'Call of Duty: Black Ops - Multiplayer', 'playtime_forever': 2037, 'playtime_2weeks': 0}, {'item_id': '47810', 'item_name': 'Dragon Age: Origins - Ultimate Edition', 'playtime_forever': 1522, 'playtime_2weeks': 0}, {'item_id': '78000', 'item_name': 'Bejeweled 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '56400', 'item_name': 'Warhammer® 40,000™: Dawn of War® II – Retribution™', 'playtime_forever': 917, 'playtime_2weeks': 0}, {'item_id': '48720', 'item_name': 'Mount & Blade: With Fire and Sword', 'playtime_forever': 424, 'playtime_2weeks': 0}, {'item_id': '20920', 'item_name': 'The Witcher 2: Assassins of Kings Enhanced Edition', 'playtime_forever': 2141, 'playtime_2weeks': 0}, {'item_id': '105600', 'item_name': 'Terraria', 'playtime_forever': 1807, 'playtime_2weeks': 0}, {'item_id': '17430', 'item_name': 'Need for Speed: Undercover', 'playtime_forever': 2, 'playtime_2weeks': 0}, {'item_id': '24740', 'item_name': 'Burnout Paradise: The Ultimate Box', 'playtime_forever': 48, 'playtime_2weeks': 0}, {'item_id': '24870', 'item_name': 'Need for Speed: SHIFT', 'playtime_forever': 102, 'playtime_2weeks': 0}, {'item_id': '47870', 'item_name': 'Need for Speed: Hot Pursuit', 'playtime_forever': 211, 'playtime_2weeks': 0}, {'item_id': '47920', 'item_name': 'Shift 2 Unleashed', 'playtime_forever': 7, 'playtime_2weeks': 0}, {'item_id': '33440', 'item_name': 'Driver San Francisco', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '72850', 'item_name': 'The Elder Scrolls V: Skyrim', 'playtime_forever': 10060, 'playtime_2weeks': 0}, {'item_id': '91310', 'item_name': 'Dead Island', 'playtime_forever': 1853, 'playtime_2weeks': 0}, {'item_id': '111400', 'item_name': 'Bunch Of Heroes', 'playtime_forever': 167, 'playtime_2weeks': 0}, {'item_id': '45770', 'item_name': 'Dead Rising 2: Off the Record', 'playtime_forever': 206, 'playtime_2weeks': 0}, {'item_id': '102600', 'item_name': 'Orcs Must Die!', 'playtime_forever': 209, 'playtime_2weeks': 0}, {'item_id': '48220', 'item_name': 'Might & Magic: Heroes VI', 'playtime_forever': 1314, 'playtime_2weeks': 0}, {'item_id': '110800', 'item_name': 'L.A. Noire', 'playtime_forever': 243, 'playtime_2weeks': 0}, {'item_id': '16450', 'item_name': 'F.E.A.R. 2: Project Origin', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21000', 'item_name': 'LEGO Batman: The Videogame', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21010', 'item_name': 'Watchmen: The End Is Nigh', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21030', 'item_name': 'Watchmen: The End Is Nigh Part 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21070', 'item_name': 'Wanted: Weapons of Fate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21080', 'item_name': 'Terminator Salvation', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21090', 'item_name': 'F.E.A.R.', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21100', 'item_name': 'F.E.A.R. 3', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21110', 'item_name': 'F.E.A.R.: Extraction Point', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '21120', 'item_name': 'F.E.A.R.: Perseus Mandate', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '21130', 'item_name': 'Lego Harry Potter', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32800', 'item_name': 'The Lord of the Rings: War in the North', 'playtime_forever': 64, 'playtime_2weeks': 0}, {'item_id': '35140', 'item_name': 'Batman: Arkham Asylum GOTY Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '57400', 'item_name': 'Batman: Arkham City™', 'playtime_forever': 1924, 'playtime_2weeks': 0}, {'item_id': '107100', 'item_name': 'Bastion', 'playtime_forever': 36, 'playtime_2weeks': 0}, {'item_id': '200260', 'item_name': 'Batman: Arkham City GOTY', 'playtime_forever': 18, 'playtime_2weeks': 0}, {'item_id': '205790', 'item_name': 'Dota 2 Test', 'playtime_forever': 1, 'playtime_2weeks': 0}, {'item_id': '3900', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3910', 'item_name': \"Sid Meier's Civilization III: Complete\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '3920', 'item_name': \"Sid Meier's Pirates!\", 'playtime_forever': 91, 'playtime_2weeks': 0}, {'item_id': '3990', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7650', 'item_name': 'X-COM: Terror from the Deep', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7660', 'item_name': 'X-COM: Apocalypse', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7670', 'item_name': 'BioShock', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7730', 'item_name': 'X-COM: Interceptor', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '7760', 'item_name': 'X-COM: UFO Defense', 'playtime_forever': 27, 'playtime_2weeks': 0}, {'item_id': '7770', 'item_name': 'X-COM: Enforcer', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8800', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8850', 'item_name': 'BioShock 2', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8880', 'item_name': 'Freedom Force', 'playtime_forever': 98, 'playtime_2weeks': 0}, {'item_id': '8890', 'item_name': 'Freedom Force vs. the 3rd Reich', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8930', 'item_name': \"Sid Meier's Civilization V\", 'playtime_forever': 1591, 'playtime_2weeks': 0}, {'item_id': '8970', 'item_name': 'Axel & Pixel', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '8980', 'item_name': 'Borderlands', 'playtime_forever': 12, 'playtime_2weeks': 0}, {'item_id': '16810', 'item_name': \"Sid Meier's Civilization IV: Colonization\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34440', 'item_name': \"Sid Meier's Civilization IV\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34450', 'item_name': \"Sid Meier's Civilization IV: Warlords\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '34460', 'item_name': \"Sid Meier's Civilization IV: Beyond the Sword\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40920', 'item_name': 'NBA 2K10', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40930', 'item_name': 'The Misadventures of P.B. Winterbottom', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '40990', 'item_name': 'Mafia', 'playtime_forever': 21, 'playtime_2weeks': 0}, {'item_id': '50130', 'item_name': 'Mafia II', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '50310', 'item_name': 'MLB 2K11', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '57900', 'item_name': 'Duke Nukem Forever', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '65950', 'item_name': 'NBA 2K11', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '201020', 'item_name': 'NBA 2K12', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409710', 'item_name': 'BioShock Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '409720', 'item_name': 'BioShock 2 Remastered', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '67370', 'item_name': 'The Darkness II', 'playtime_forever': 146, 'playtime_2weeks': 0}, {'item_id': '207610', 'item_name': 'The Walking Dead', 'playtime_forever': 457, 'playtime_2weeks': 0}, {'item_id': '97330', 'item_name': 'Magic: The Gathering - Duels of the Planeswalkers 2013', 'playtime_forever': 300, 'playtime_2weeks': 0}, {'item_id': '65800', 'item_name': 'Dungeon Defenders', 'playtime_forever': 4376, 'playtime_2weeks': 0}, {'item_id': '211420', 'item_name': 'Dark Souls: Prepare to Die Edition', 'playtime_forever': 71, 'playtime_2weeks': 0}, {'item_id': '730', 'item_name': 'Counter-Strike: Global Offensive', 'playtime_forever': 1900, 'playtime_2weeks': 0}, {'item_id': '204300', 'item_name': 'Awesomenauts', 'playtime_forever': 584, 'playtime_2weeks': 0}, {'item_id': '200170', 'item_name': 'Worms Revolution', 'playtime_forever': 423, 'playtime_2weeks': 0}, {'item_id': '217200', 'item_name': 'Worms Armageddon', 'playtime_forever': 4, 'playtime_2weeks': 0}, {'item_id': '204360', 'item_name': 'Castle Crashers', 'playtime_forever': 614, 'playtime_2weeks': 0}, {'item_id': '49520', 'item_name': 'Borderlands 2', 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'playtime_forever': 416, 'playtime_2weeks': 0}, {'item_id': '413150', 'item_name': 'Stardew Valley', 'playtime_forever': 1321, 'playtime_2weeks': 0}, {'item_id': '268500', 'item_name': 'XCOM 2', 'playtime_forever': 620, 'playtime_2weeks': 0}, {'item_id': '444560', 'item_name': 'Agent Origins: Escape', 'playtime_forever': 9, 'playtime_2weeks': 0}, {'item_id': '275850', 'item_name': \"No Man's Sky\", 'playtime_forever': 564, 'playtime_2weeks': 0}]}\n"]}]},{"cell_type":"code","source":["# prompt: Load data using json from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\n","\n","import gzip\n","import json\n","\n","file_path = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Processing Data/user_tags_and_specs_data.json.gz\"\n","\n","try:\n"," with gzip.open(file_path, 'rt', encoding='utf-8') as gzip_file:\n"," data_bundle_interactions = json.load(gzip_file)\n"," # Now you can work with the loaded JSON data\n"," print(f\"Successfully loaded {len(data_bundle_interactions)} items from the JSON file.\")\n","\n","except FileNotFoundError:\n"," print(f\"Error: File not found at {file_path}\")\n","except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}\")\n","except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")"],"metadata":{"id":"9ubHDNhe_2Do"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["input_default_tags = {}\n","input_default_specs = {}\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," for keys in recommendation['frequent_distinct_tags'].items():\n"," if keys[0] not in input_default_tags:\n"," input_default_tags[keys[0]] = 0\n","\n"," for keys in recommendation['frequent_distinct_specs'].items():\n"," if keys[0] not in input_default_specs:\n"," input_default_specs[keys[0]] = 0\n"," break\n"," break"],"metadata":{"id":"I2Gl7tHEABce"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bundle_tags = {}\n","bundle_specs = {}\n","for bundle_id, bundle_info in bundle_data.items():\n"," bundle_tags[bundle_id] = {}\n"," bundle_specs[bundle_id] = {}\n"," for item_id in bundle_info[\"items\"]:\n"," if item_id in items_dictionary:\n"," if 'tags' in items_dictionary[item_id]:\n"," for tag in items_dictionary[item_id]['tags']:\n"," if tag in input_default_tags:\n"," if tag not in bundle_tags[bundle_id]:\n"," bundle_tags[bundle_id][tag] = 1\n"," else:\n"," bundle_tags[bundle_id][tag] += 1\n"," if 'specs' in items_dictionary[item_id]:\n"," for spec in items_dictionary[item_id]['specs']:\n"," if spec in input_default_specs:\n"," if spec not in bundle_specs[bundle_id]:\n"," bundle_specs[bundle_id][spec] = 1\n"," else:\n"," bundle_specs[bundle_id][spec] += 1"],"metadata":{"id":"tUOyZS2hACEE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Get the percent value from string on unique_ratings\n","\n","import re\n","\n","def extract_percent(rating_str):\n"," \"\"\"\n"," Extracts the numeric percentage value from a string.\n","\n"," Args:\n"," rating_str: The input string potentially containing a percentage value.\n","\n"," Returns:\n"," The numeric percentage value as a float, or None if no percentage is found.\n"," \"\"\"\n"," match = re.search(r\"(\\d+(\\.\\d+)?)%\", rating_str) # Match digits (optional decimal) followed by %\n"," if match:\n"," return float(match.group(1))\n"," return None"],"metadata":{"id":"2YA4hpR9ALnq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: Load data from /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/bundle_data.json.gz using json\n","\n","import json\n","import gzip\n","import ast\n","\n","# Open the gzipped file\n","data_dict = None\n","\n","file_path_write = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Datasets/australian_user_reviews_process.json.gz\"\n","data_reviews = []\n","with gzip.open(f\"{file_path_write}\", 'rt', encoding='utf-8') as f:\n"," for i, line in enumerate(f):\n"," # Remove the trailing newline character\n"," cleaned_string = line.strip()\n"," try:\n"," data_dict = ast.literal_eval(cleaned_string)\n"," percent = extract_percent(str(data_dict[\"rating\"])) # Convert to string before processing\n"," if percent is None:\n"," percent = 0.0\n"," data_dict[\"rating\"] = percent\n"," data_reviews.append(data_dict)\n"," except (ValueError, SyntaxError) as e:\n"," print(f\"Failed to convert string to dictionary: {e}\")"],"metadata":{"id":"El34fm-IAO5W"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# prompt: data_reviews convert to dictionary with structure user_id -> item_id\n","\n","user_item_dict = {}\n","for review in data_reviews:\n"," user_id = review['user_id']\n"," item_id = review['item_id']\n"," if user_id not in user_item_dict:\n"," user_item_dict[user_id] = {}\n"," user_item_dict[user_id][item_id] = review['rating']"],"metadata":{"id":"XRVawVsXAQyp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["user_bundle_histories = []\n","\n","for user_id, recommendations in data_bundle_interactions.items():\n"," for bundle_id, recommendation in recommendations.items():\n"," rating = []\n"," if user_id in user_item_dict:\n"," if bundle_id in bundle_data:\n"," for item_id in bundle_data[bundle_id][\"items\"]:\n"," if item_id in user_item_dict[user_id]:\n"," rating.append(user_item_dict[user_id][item_id])\n"," if len(rating) > 0:\n"," rating = sum(rating) / len(rating)\n"," else:\n"," rating = 0.0\n"," user_bundle_histories.append({ \"user_id\": user_id, \"bundle_id\": bundle_id, \"label\": recommendation[\"label\"], \"rating\": rating / 20 })"],"metadata":{"id":"3JMhHuBBATNr"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Uji Wilcoxon"],"metadata":{"id":"o65HwVCQAGdG"}},{"cell_type":"markdown","source":["### Setup Data and Function"],"metadata":{"id":"TqjzRJdsJGxr"}},{"cell_type":"code","source":["!pip install surprise"],"metadata":{"id":"iQ28EbuQAKLV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","from surprise import SVD, SVDpp\n","from surprise import Dataset, Reader\n","from surprise.model_selection import GridSearchCV, cross_validate\n","# prompt: Apply train test split # Create a pandas DataFrame from user_bundle_histories\n","from surprise.model_selection import train_test_split"],"metadata":{"id":"hmiF1ZmIA315"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load the saved models\n","import joblib\n","\n","best_svd = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svd_model.pkl')\n","best_svdpp = joblib.load('/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/models/previous/svdpp_model.pkl')"],"metadata":{"id":"wEbTCz-SEP1y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def get_top_rec_bundles(data_dict, start=5, end=20, step=5):\n"," \"\"\"\n"," Gets top recommended bundles from data_dict with variations.\n","\n"," Args:\n"," data_dict: A dictionary containing recommendation data.\n"," start: The starting rank of top bundles.\n"," end: The ending rank of top bundles.\n"," step: The increment in rank.\n","\n"," Returns:\n"," A list of dictionaries, where each dictionary contains the user ID and top recommended bundles for a specified range.\n"," Returns an empty list if 'rec_bundles' key not found in data_dict.\n"," \"\"\"\n","\n"," if 'rec_bundles' not in data_dict:\n"," return []\n","\n"," results = []\n"," for top_n in range(start, end + 1, step):\n"," top_bundles = dict(sorted(data_dict['rec_bundles'].items(), key=lambda item: item[1], reverse=True)[:top_n])\n"," results.append({\"user_id\": data_dict['user_id'], \"top_rec_bundles\": top_bundles, \"top_n\": top_n})\n"," return results"],"metadata":{"id":"080We5IKEW02"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from collections import defaultdict\n","\n","def group_testset_by_user(testset):\n"," \"\"\"Groups the testset by user_id.\n","\n"," Args:\n"," testset: A list of dictionaries, where each dictionary represents a data point\n"," and contains 'user_id', 'bundle_id', and 'rating' keys.\n","\n"," Returns:\n"," A dictionary where keys are user IDs and values are lists of dictionaries\n"," containing bundle IDs and ratings for that user.\n"," \"\"\"\n"," grouped_data = {}\n"," for entry in testset:\n"," user_id = entry['user_id']\n"," if user_id not in grouped_data:\n"," grouped_data[user_id] = set()\n"," grouped_data[user_id].add(entry['bundle_id'])\n"," return grouped_data"],"metadata":{"id":"lXQ1OZsAEeXw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def calculate_hit_ratio(top_bundles_results, grouped_testset):\n"," \"\"\"\n"," Calculates the hit ratio based on the provided data.\n","\n"," Args:\n"," top_bundles_results: A list of dictionaries, where each dictionary contains user ID and top recommended bundles.\n"," user_item_interactions: A dictionary mapping user IDs to their item interactions.\n"," bundle_data: A dictionary mapping bundle IDs to their items.\n","\n"," Returns:\n"," A list of dictionaries, each containing the user ID, top N, and hit ratio.\n"," \"\"\"\n","\n"," hit = {}\n"," for user_id2 in grouped_testset:\n"," data_test = grouped_testset[user_id2]\n"," temp_hit = {}\n"," for bundle_id_test in data_test:\n"," for result in top_bundles_results:\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in temp_hit:\n"," temp_hit[top_n] = 0\n","\n"," if bundle_id_test in top_bundles:\n"," temp_hit[top_n] += 1\n","\n"," for top_n, count in temp_hit.items():\n"," if top_n not in hit:\n"," hit[top_n] = 0\n"," hit[top_n] += (count / len(data_test))\n","\n"," hit_ratios = {}\n"," for top_n, count in hit.items():\n"," hit_ratios[top_n] = count / len(grouped_testset)\n","\n"," return hit_ratios"],"metadata":{"id":"oErpunCBEi7d"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","import pandas as pd\n","\n","def calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs):\n"," \"\"\"\n"," Calculates the Gini-Simpson diversity index for recommended bundles.\n","\n"," Args:\n"," top_bundles_results: Results from get_top_rec_bundles function.\n"," grouped_testset: User's interaction data grouped by user.\n"," bundle_tags: Dictionary of bundle tags.\n"," bundle_specs: Dictionary of bundle specs.\n","\n"," Returns:\n"," A dictionary containing Gini-Simpson index for each user and top_n.\n"," \"\"\"\n","\n"," gini_simpson_indices = {}\n","\n"," for result in top_bundles_results:\n"," user_id = result['user_id']\n"," top_n = result['top_n']\n"," top_bundles = result['top_rec_bundles']\n","\n"," if top_n not in gini_simpson_indices:\n"," gini_simpson_indices[top_n] = 0.0\n","\n"," # Combine tags and specs for each bundle\n"," combined_features = {}\n"," for bundle_id in top_bundles:\n"," combined_features[bundle_id] = {}\n"," if bundle_id in bundle_tags:\n"," combined_features[bundle_id].update(bundle_tags[bundle_id])\n"," if bundle_id in bundle_specs:\n"," combined_features[bundle_id].update(bundle_specs[bundle_id])\n","\n"," # Calculate probabilities for each feature\n"," total_features = {}\n"," for bundle_id in combined_features:\n"," total_features[bundle_id] = {}\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in total_features:\n"," total_features[bundle_id][feature] = 1\n","\n"," all_features = set()\n"," for bundle_id in combined_features:\n"," for feature, count in combined_features[bundle_id].items():\n"," if feature not in all_features:\n"," all_features.add(feature)\n","\n"," probabilities = {}\n"," for feature in all_features:\n"," probabilities[feature] = 0\n"," for bundle_id in combined_features:\n"," if feature in combined_features[bundle_id]:\n"," probabilities[feature] += 1\n","\n"," probabilities[feature] = probabilities[feature] / len(combined_features)\n","\n"," # Calculate Gini-Simpson index\n"," gini_simpson_index = 1 - (sum(p**2 for p in probabilities.values()) / len(probabilities))\n"," gini_simpson_indices[top_n] = gini_simpson_index\n","\n"," return gini_simpson_indices"],"metadata":{"id":"6gP__ahgElph"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","# Assuming average_hit_ratio is a dictionary where keys are user IDs and values are dictionaries of hit ratios for different top_n values.\n","# Example: average_hit_ratio = {'user1': {5: 0.2, 10: 0.3}, 'user2': {5: 0.1, 10: 0.2}}\n","\n","def calculate_mean_hit_ratio(average_hit_ratio):\n"," \"\"\"Calculates the mean hit ratio across all users and top_n values.\n","\n"," Args:\n"," average_hit_ratio: A dictionary containing hit ratios for different users and top_n values.\n","\n"," Returns:\n"," The mean hit ratio as a float. Returns 0 if input dictionary is empty or contains no valid hit ratios.\n"," \"\"\"\n"," all_hit_ratios = {}\n"," for user_id, hit_ratios in average_hit_ratio.items():\n"," for top_n, hit_ratio in hit_ratios.items():\n"," if top_n not in all_hit_ratios:\n"," all_hit_ratios[top_n] = []\n"," all_hit_ratios[top_n].append(hit_ratio)\n","\n"," if not all_hit_ratios:\n"," return 0\n","\n"," new_hit_ratios = {}\n"," for top_n, hit_ratios in all_hit_ratios.items():\n"," new_hit_ratios[top_n] = np.mean(hit_ratios)\n","\n"," return new_hit_ratios"],"metadata":{"id":"grP7k0GiEtoY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def combine_metrics(mean_hit_ratio, mean_diversity):\n"," \"\"\"Combines the three dataframes into a single columnar dataframe.\n"," \"\"\"\n"," combined_data = {\n"," \"hr\": 0,\n"," \"diversity\": 0,\n"," }\n"," for top_n in mean_hit_ratio:\n"," combined_data[\"hr\"] += mean_hit_ratio[top_n]\n"," combined_data[\"diversity\"] += mean_diversity[top_n]\n","\n"," combined_data[\"hr\"] = combined_data[\"hr\"] / len(mean_hit_ratio)\n"," combined_data[\"diversity\"] = combined_data[\"diversity\"] / len(mean_hit_ratio)\n"," return combined_data"],"metadata":{"id":"FGUhCjW6E6M0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Do Test"],"metadata":{"id":"LyEUedeJJMJN"}},{"cell_type":"code","source":["parameter_pengujian = {\n"," \"iterasi\": 50,\n"," \"results\": {\n"," \"ann\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"dt\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"knn\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"lr\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," \"nb\": {\n"," \"hr\": [],\n"," \"diversity\": []\n"," },\n"," }\n","}\n","\n","for i in range(parameter_pengujian[\"iterasi\"]):\n"," print(f\"Iterasi ke {i}\")\n","\n"," ## Train test split\n","\n"," df = pd.DataFrame(user_bundle_histories)\n"," reader = Reader(rating_scale=(0, 5))\n"," data = Dataset.load_from_df(df[['user_id', 'bundle_id', 'rating']], reader)\n","\n"," trainset, testset = train_test_split(data, test_size=0.2)\n"," new_testset = [{'user_id': entry[0], 'bundle_id': entry[1], 'rating': entry[2]} for entry in testset]\n","\n"," grouped_testset = group_testset_by_user(new_testset)\n","\n"," ## Calculate Evaluation\n","\n"," average_hit_ratio = {}\n"," average_diversity = {}\n"," count = 0\n","\n"," top_bundles_results = []\n"," hit_ratios = []\n"," diversity_scores = []\n","\n"," bundle_recommendations_temp = [\n"," {\"branch\": \"ann\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz'},\n"," {\"branch\": \"dt\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz'},\n"," {\"branch\": \"knn\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz'},\n"," {\"branch\": \"lr\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz'},\n"," {\"branch\": \"nb\", \"model\": '/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz'},\n"," ]\n","\n"," for temp in bundle_recommendations_temp:\n","\n"," try:\n"," with gzip.open(temp[\"model\"], 'rt', encoding='utf-8') as infile:\n"," for line in infile:\n"," try:\n"," data_dict = json.loads(line) # Use json.loads instead of ast.literal_eval\n","\n"," if len(data_dict['rec_bundles']) > 0:\n"," top_bundles_results = get_top_rec_bundles(data_dict)\n"," hit_ratios = calculate_hit_ratio(top_bundles_results, grouped_testset)\n"," diversity_scores = calculate_gini_simpson_index(top_bundles_results, grouped_testset, bundle_tags, bundle_specs)\n","\n"," average_hit_ratio[data_dict['user_id']] = hit_ratios\n"," average_diversity[data_dict['user_id']] = diversity_scores\n","\n"," except json.JSONDecodeError as e:\n"," print(f\"Error decoding JSON: {e}, skipping line: {line}\")\n"," count += 1\n"," print(f\"File {temp['model']} successfully processed and saved to {temp['model']}\")\n","\n"," except FileNotFoundError:\n"," print(f\"Error: File not found at {temp['model']}\")\n"," except Exception as e:\n"," print(f\"An unexpected error occurred: {e}\")\n","\n"," mean_hit_ratio = calculate_mean_hit_ratio(average_hit_ratio)\n"," mean_diversity = calculate_mean_hit_ratio(average_diversity)\n","\n"," combined_data = combine_metrics(mean_hit_ratio, mean_diversity)\n","\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"hr\"].append(combined_data[\"hr\"])\n"," parameter_pengujian[\"results\"][temp[\"branch\"]][\"diversity\"].append(combined_data[\"diversity\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F0_fdOqBBPAl","outputId":"944266fe-c133-488e-9e4f-f945003e853f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Iterasi ke 0\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 1\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 2\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 3\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 4\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 5\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 6\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 7\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 8\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 9\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 10\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 11\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 12\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 13\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 14\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 15\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 16\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 17\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 18\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 19\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 20\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 21\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 22\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 23\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 24\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 25\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 26\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 27\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 28\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 29\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 30\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 31\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 32\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 33\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 34\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 35\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 36\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 37\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 38\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_logreg.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_bnb.json.gz\n","Iterasi ke 39\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_ann.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_dt.json.gz\n","File /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz successfully processed and saved to /content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Recommendations/price_patterns_reranking/reranking_recommendation_data_knn.json.gz\n"]}]},{"cell_type":"code","source":["import joblib\n","\n","# Assuming bundle_recommendations_svd and bundle_recommendations_svdpp are defined\n","\n","# Save the dictionaries to separate files\n","output_dir = \"/content/drive/MyDrive/Projects/Disertation/SOTA Bundle Matching/Evaluation/Uji Statistik\"\n","joblib.dump(parameter_pengujian, f\"{output_dir}/impact_p_patterns.pkl\")"],"metadata":{"id":"atP9uxUOxC4D"},"execution_count":null,"outputs":[]}]} \ No newline at end of file