{"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 - 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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{'item_id': '216370', 'item_name': 'Nexuiz Beta', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '225760', 'item_name': 'Nexuiz STUPID Mode', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '228200', 'item_name': 'Company of Heroes (New Steam Version)', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '475150', 'item_name': 'Titan Quest Anniversary Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '228280', 'item_name': \"Baldur's Gate: Enhanced Edition\", 'playtime_forever': 481, 'playtime_2weeks': 0}, {'item_id': '11020', 'item_name': 'TrackMania Nations Forever', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '200210', 'item_name': 'Realm of the Mad God', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '205710', 'item_name': 'EverQuest Free-to-Play', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '207230', 'item_name': 'Archeblade', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '208090', 'item_name': 'Loadout', 'playtime_forever': 140, 'playtime_2weeks': 0}, {'item_id': '212500', 'item_name': 'The Lord of the Rings Online™', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '218230', 'item_name': 'PlanetSide 2', 'playtime_forever': 2808, 'playtime_2weeks': 0}, {'item_id': '230410', 'item_name': 'Warframe', 'playtime_forever': 252, 'playtime_2weeks': 0}, {'item_id': '236390', 'item_name': 'War Thunder', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '238960', 'item_name': 'Path of Exile', 'playtime_forever': 3579, 'playtime_2weeks': 0}, {'item_id': '243870', 'item_name': \"Tom Clancy's Ghost Recon Phantoms - NA\", 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '263500', 'item_name': 'Dragons and Titans', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '39120', 'item_name': 'RIFT', 'playtime_forever': 2866, 'playtime_2weeks': 0}, {'item_id': '242720', 'item_name': 'GunZ 2: The Second Duel', 'playtime_forever': 0, 'playtime_2weeks': 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'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32370', 'item_name': 'STAR WARS™: Knights of the Old Republic™', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32380', 'item_name': 'STAR WARS™ Jedi Knight: Dark Forces II', 'playtime_forever': 3, 'playtime_2weeks': 0}, {'item_id': '32390', 'item_name': 'STAR WARS™ Jedi Knight: Mysteries of the Sith™', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32400', 'item_name': 'STAR WARS™: Dark Forces', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32420', 'item_name': 'STAR WARS™: The Clone Wars - Republic Heroes™', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32430', 'item_name': 'STAR WARS™: The Force Unleashed™ Ultimate Sith Edition', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32470', 'item_name': 'STAR WARS™ Empire at War: Gold Pack', 'playtime_forever': 0, 'playtime_2weeks': 0}, {'item_id': '32500', 'item_name': 'STAR WARS™: The Force Unleashed™ II', 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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"]}]},{"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":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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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":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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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': 12.700000000000003,\n","  '261': 11.749999999999998,\n","  '250': 11.049999999999997,\n","  '575': 10.899999999999999,\n","  '612': 10.699999999999998,\n","  '153': 10.1,\n","  '307': 9.35,\n","  '1190': 9.25,\n","  '728': 9.049999999999999,\n","  '225': 8.899999999999999,\n","  '249': 8.799999999999999,\n","  '349': 8.5,\n","  '663': 7.6,\n","  '334': 7.399999999999999,\n","  '957': 7.25,\n","  '292': 6.799999999999999,\n","  '457': 6.7,\n","  '1216': 6.649999999999999,\n","  '319': 6.299999999999999,\n","  '712': 5.6,\n","  '772': 5.599999999999999,\n","  '1303': 5.3999999999999995,\n","  '274': 5.3500000000000005,\n","  '767': 5.3,\n","  '495': 5.199999999999999,\n","  '426': 5.199999999999999,\n","  '231': 5.199999999999999,\n","  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0.6,\n","  '1150': 0.6,\n","  '1328': 0.6,\n","  '851': 0.6,\n","  '267': 0.6,\n","  '1209': 0.6,\n","  '366': 0.6,\n","  '1372': 0.6,\n","  '1332': 0.6,\n","  '662': 0.6,\n","  '913': 0.6,\n","  '704': 0.6,\n","  '509': 0.6,\n","  '588': 0.6,\n","  '850': 0.5,\n","  '385': 0.5,\n","  '340': 0.5,\n","  '265': 0.5,\n","  '278': 0.5,\n","  '341': 0.5,\n","  '1282': 0.5,\n","  '308': 0.5,\n","  '962': 0.5,\n","  '1352': 0.5,\n","  '871': 0.5,\n","  '1024': 0.5,\n","  '255': 0.5,\n","  '1228': 0.5,\n","  '313': 0.5,\n","  '350': 0.5,\n","  '317': 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 /  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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 /  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 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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","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":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","  <div id=\"df-9e9f5581-f77d-42a0-8377-67cb3411f555\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>top_n</th>\n","      <th>inter_diversity</th>\n","    </tr>\n","    <tr>\n","      <th>top_n</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5</th>\n","      <td>5.0</td>\n","      <td>0.998234</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-e099b8bc-84c4-444a-b9be-f3ee8979201c\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e099b8bc-84c4-444a-b9be-f3ee8979201c')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-e099b8bc-84c4-444a-b9be-f3ee8979201c button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('average_inter_diversity_df');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\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","  <div id=\"df-a2ccd226-c061-4022-84ed-b7a015b98448\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>top_n</th>\n","      <th>hit_ratio</th>\n","      <th>intra_diversity</th>\n","      <th>inter_diversity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      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\"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":["<Figure size 800x1000 with 3 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}