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Logic:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L22", "community": 1, "norm_label": "coordinates multiple specialist agents to select the best action. logic:", "id": "agent_coordinator_rationale_22" }, { "label": "Initialize the coordinator with all specialist agents.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L34", "community": 1, "norm_label": "initialize the coordinator with all specialist agents.", "id": "agent_coordinator_rationale_34" }, { "label": "Initialize all specialist agents.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L42", "community": 1, "norm_label": "initialize all specialist agents.", "id": "agent_coordinator_rationale_42" }, { "label": "Reset coordinator and all agents for new episode.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L52", "community": 1, "norm_label": "reset coordinator and all agents for new episode.", "id": "agent_coordinator_rationale_52" }, { "label": "Select the best action from all available agents. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L63", "community": 1, "norm_label": "select the best action from all available agents. args:", "id": "agent_coordinator_rationale_63" }, { "label": "Update agent performance tracking based on received reward. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L109", "community": 1, "norm_label": "update agent performance tracking based on received reward. args:", "id": "agent_coordinator_rationale_109" }, { "label": "Get performance statistics for all agents. Returns: Dict", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L126", "community": 1, "norm_label": "get performance statistics for all agents. returns: dict", "id": "agent_coordinator_rationale_126" }, { "label": "Get recommendations from all capable agents. Returns: Li", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L160", "community": 1, "norm_label": "get recommendations from all capable agents. returns: li", "id": "agent_coordinator_rationale_160" }, { "label": "Select the best agent from recommendations. Args: recomm", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L192", "community": 1, "norm_label": "select the best agent from recommendations. args: recomm", "id": "agent_coordinator_rationale_192" }, { "label": "Calculate a composite score for agent selection. Score combines:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L229", "community": 1, "norm_label": "calculate a composite score for agent selection. score combines:", "id": "agent_coordinator_rationale_229" }, { "label": "Calculate expertise bonus based on issue types. Different agents have", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L264", "community": 1, "norm_label": "calculate expertise bonus based on issue types. different agents have", "id": "agent_coordinator_rationale_264" }, { "label": "Get skip action from SkipAgent.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L303", "community": 1, "norm_label": "get skip action from skipagent.", "id": "agent_coordinator_rationale_303" }, { "label": "Get summary of the current episode. Returns: Dict with e", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\agent_coordinator.py", "source_location": "L310", "community": 1, "norm_label": "get summary of the current episode. returns: dict with e", "id": "agent_coordinator_rationale_310" }, { "label": "base_agent.py", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_backend_ml_base_agent_py", "community": 1, "norm_label": "base_agent.py" }, { "label": "Agent", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L13", "id": "base_agent_agent", "community": 1, "norm_label": "agent" }, { "label": "ABC", "file_type": "code", "source_file": "", "source_location": "", "id": "abc", "community": 1, "norm_label": "abc" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L21", "id": "base_agent_agent_init", "community": 1, "norm_label": ".__init__()" }, { "label": "reset()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L34", "id": "base_agent_reset", "community": 1, "norm_label": "reset()" }, { "label": "get_action()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L44", "id": "base_agent_get_action", "community": 1, "norm_label": "get_action()" }, { "label": "get_confidence()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L60", "id": "base_agent_get_confidence", "community": 1, "norm_label": "get_confidence()" }, { "label": ".update_reward()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L69", "id": "base_agent_agent_update_reward", "community": 1, "norm_label": ".update_reward()" }, { "label": ".can_handle()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L79", "id": "base_agent_agent_can_handle", "community": 1, "norm_label": ".can_handle()" }, { "label": ".get_name()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L92", "id": "base_agent_agent_get_name", "community": 1, "norm_label": ".get_name()" }, { "label": ".get_last_reward()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L96", "id": "base_agent_agent_get_last_reward", "community": 1, "norm_label": ".get_last_reward()" }, { "label": "AgentFactory", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L101", "id": "base_agent_agentfactory", "community": 1, "norm_label": "agentfactory" }, { "label": "create_agent()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L105", "id": "base_agent_create_agent", "community": 1, "norm_label": "create_agent()" }, { "label": "Base Agent class for multi-agent data cleaning system. This module defines the", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L1", "community": 1, "norm_label": "base agent class for multi-agent data cleaning system. this module defines the", "id": "base_agent_rationale_1" }, { "label": "Abstract base class for all specialist data cleaning agents. All agents mus", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L14", "community": 1, "norm_label": "abstract base class for all specialist data cleaning agents. all agents mus", "id": "base_agent_rationale_14" }, { "label": "Initialize the agent with a name. Args: name: Human-readabl", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L22", "community": 1, "norm_label": "initialize the agent with a name. args: name: human-readabl", "id": "base_agent_rationale_22" }, { "label": "Reset agent state for a new episode. Args: observation: Ini", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L35", "community": 1, "norm_label": "reset agent state for a new episode. args: observation: ini", "id": "base_agent_rationale_35" }, { "label": "Select an action based on current observation. Args: observ", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L47", "community": 1, "norm_label": "select an action based on current observation. args: observ", "id": "base_agent_rationale_47" }, { "label": "Get agent's confidence in its last action. Returns: Confide", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L61", "community": 1, "norm_label": "get agent's confidence in its last action. returns: confide", "id": "base_agent_rationale_61" }, { "label": "Update agent's internal state based on received reward. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L70", "community": 1, "norm_label": "update agent's internal state based on received reward. args:", "id": "base_agent_rationale_70" }, { "label": "Check if agent can handle the current observation. Args: ob", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L80", "community": 1, "norm_label": "check if agent can handle the current observation. args: ob", "id": "base_agent_rationale_80" }, { "label": "Get the last reward received by this agent.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L97", "community": 1, "norm_label": "get the last reward received by this agent.", "id": "base_agent_rationale_97" }, { "label": "Factory class for creating agent instances.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L102", "community": 1, "norm_label": "factory class for creating agent instances.", "id": "base_agent_rationale_102" }, { "label": "Create an agent instance of the specified type. Args: agent", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\base_agent.py", "source_location": "L106", "community": 1, "norm_label": "create an agent instance of the specified type. args: agent", "id": "base_agent_rationale_106" }, { "label": "benchmark.py", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_backend_ml_benchmark_py", "community": 8, "norm_label": "benchmark.py" }, { "label": "BenchmarkResult", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L35", "id": "benchmark_benchmarkresult", "community": 8, "norm_label": "benchmarkresult" }, { "label": ".to_dict()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L46", "id": "benchmark_benchmarkresult_to_dict", "community": 8, "norm_label": ".to_dict()" }, { "label": "ComparisonSummary", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L51", "id": "benchmark_comparisonsummary", "community": 8, "norm_label": "comparisonsummary" }, { "label": ".get_best_approach()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L57", "id": "benchmark_comparisonsummary_get_best_approach", "community": 8, "norm_label": ".get_best_approach()" }, { "label": "._avg_metric()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L66", "id": "benchmark_comparisonsummary_avg_metric", "community": 8, "norm_label": "._avg_metric()" }, { "label": ".to_comparison_table()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L72", "id": "benchmark_comparisonsummary_to_comparison_table", "community": 8, "norm_label": ".to_comparison_table()" }, { "label": "DataCleaningBenchmark", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L98", "id": "benchmark_datacleaningbenchmark", "community": 8, "norm_label": "datacleaningbenchmark" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L103", "id": "benchmark_datacleaningbenchmark_init", "community": 0, "norm_label": ".__init__()" }, { "label": ".load_dqn_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L127", "id": "benchmark_datacleaningbenchmark_load_dqn_model", "community": 8, "norm_label": ".load_dqn_model()" }, { "label": ".benchmark_approach()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L146", "id": "benchmark_datacleaningbenchmark_benchmark_approach", "community": 8, "norm_label": ".benchmark_approach()" }, { "label": "._benchmark_dqn()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L179", "id": "benchmark_datacleaningbenchmark_benchmark_dqn", "community": 8, "norm_label": "._benchmark_dqn()" }, { "label": "._benchmark_specialists()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L224", "id": "benchmark_datacleaningbenchmark_benchmark_specialists", "community": 8, "norm_label": "._benchmark_specialists()" }, { "label": "._benchmark_llm()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L267", "id": "benchmark_datacleaningbenchmark_benchmark_llm", "community": 8, "norm_label": "._benchmark_llm()" }, { "label": "._get_expected_action()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L304", "id": "benchmark_datacleaningbenchmark_get_expected_action", "community": 8, "norm_label": "._get_expected_action()" }, { "label": ".run_full_benchmark()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L332", "id": "benchmark_datacleaningbenchmark_run_full_benchmark", "community": 8, "norm_label": ".run_full_benchmark()" }, { "label": ".save_results()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L386", "id": "benchmark_datacleaningbenchmark_save_results", "community": 8, "norm_label": ".save_results()" }, { "label": "main()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L418", "id": "benchmark_main", "community": 8, "norm_label": "main()" }, { "label": "Benchmarking system for comparing DQN, Specialist Agents, and LLM approaches. T", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L1", "community": 0, "norm_label": "benchmarking system for comparing dqn, specialist agents, and llm approaches. t", "id": "benchmark_rationale_1" }, { "label": "Results from a single benchmark run.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L36", "community": 0, "norm_label": "results from a single benchmark run.", "id": "benchmark_rationale_36" }, { "label": "Summary comparing all approaches.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L52", "community": 0, "norm_label": "summary comparing all approaches.", "id": "benchmark_rationale_52" }, { "label": "Determine best approach based on metric.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L58", "community": 0, "norm_label": "determine best approach based on metric.", "id": "benchmark_rationale_58" }, { "label": "Generate comparison table for documentation.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L73", "community": 0, "norm_label": "generate comparison table for documentation.", "id": "benchmark_rationale_73" }, { "label": "Comprehensive benchmarking suite for data cleaning approaches.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L99", "community": 0, "norm_label": "comprehensive benchmarking suite for data cleaning approaches.", "id": "benchmark_rationale_99" }, { "label": "Initialize benchmark suite. Args: device: Device fo", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L104", "community": 0, "norm_label": "initialize benchmark suite. args: device: device fo", "id": "benchmark_rationale_104" }, { "label": "Load trained DQN model for benchmarking. Args: mode", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L128", "community": 0, "norm_label": "load trained dqn model for benchmarking. args: mode", "id": "benchmark_rationale_128" }, { "label": "Benchmark a specific approach. Args: approach: \"DQN", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L152", "community": 0, "norm_label": "benchmark a specific approach. args: approach: \"dqn", "id": "benchmark_rationale_152" }, { "label": "Benchmark DQN approach.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L184", "community": 0, "norm_label": "benchmark dqn approach.", "id": "benchmark_rationale_184" }, { "label": "Benchmark Specialist Agents approach.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L229", "community": 0, "norm_label": "benchmark specialist agents approach.", "id": "benchmark_rationale_229" }, { "label": "Benchmark LLM approach (simulated since we don't have API access).", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L272", "community": 0, "norm_label": "benchmark llm approach (simulated since we don't have api access).", "id": "benchmark_rationale_272" }, { "label": "Determine expected correct action for a row. This is a simplifi", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L305", "community": 0, "norm_label": "determine expected correct action for a row. this is a simplifi", "id": "benchmark_rationale_305" }, { "label": "Run complete benchmark across all approaches and difficulties.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L337", "community": 0, "norm_label": "run complete benchmark across all approaches and difficulties.", "id": "benchmark_rationale_337" }, { "label": "Save benchmark results to files. Args: summary: Com", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L387", "community": 0, "norm_label": "save benchmark results to files. args: summary: com", "id": "benchmark_rationale_387" }, { "label": "Run benchmarks from command line.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\benchmark.py", "source_location": "L419", "community": 0, "norm_label": "run benchmarks from command line.", "id": "benchmark_rationale_419" }, { "label": "dqn_model.py", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_backend_ml_dqn_model_py", "community": 0, "norm_label": "dqn_model.py" }, { "label": "QNetwork", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L20", "id": 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"dqn_model_qnetwork_get_q_values", "community": 16, "norm_label": ".get_q_values()" }, { "label": "DQNAgent", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L99", "id": "dqn_model_dqnagent", "community": 0, "norm_label": "dqnagent" }, { "label": "Agent", "file_type": "code", "source_file": "", "source_location": "", "id": "agent", "community": 1, "norm_label": "agent" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L107", "id": "dqn_model_dqnagent_init", "community": 11, "norm_label": ".__init__()" }, { "label": ".reset()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L142", "id": "dqn_model_dqnagent_reset", "community": 2, "norm_label": ".reset()" }, { "label": ".get_action()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L148", "id": "dqn_model_dqnagent_get_action", "community": 2, "norm_label": ".get_action()" }, { "label": ".get_confidence()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L196", "id": "dqn_model_dqnagent_get_confidence", "community": 21, "norm_label": ".get_confidence()" }, { "label": ".update_reward()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L200", "id": "dqn_model_dqnagent_update_reward", "community": 2, "norm_label": ".update_reward()" }, { "label": "._update_action_mapping()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L205", "id": "dqn_model_dqnagent_update_action_mapping", "community": 2, "norm_label": "._update_action_mapping()" }, { "label": "._encode_observation()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L232", "id": "dqn_model_dqnagent_encode_observation", "community": 2, "norm_label": "._encode_observation()" }, { "label": "._index_to_action()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L293", "id": "dqn_model_dqnagent_index_to_action", "community": 2, "norm_label": "._index_to_action()" }, { "label": "._create_skip_action()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L300", "id": "dqn_model_dqnagent_create_skip_action", "community": 2, "norm_label": "._create_skip_action()" }, { "label": ".decay_epsilon()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L306", "id": "dqn_model_dqnagent_decay_epsilon", "community": 0, "norm_label": ".decay_epsilon()" }, { "label": ".update_target_network()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L310", "id": "dqn_model_dqnagent_update_target_network", "community": 0, "norm_label": ".update_target_network()" }, { "label": ".set_training_mode()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L314", "id": "dqn_model_dqnagent_set_training_mode", "community": 0, "norm_label": ".set_training_mode()" }, { "label": ".save_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L322", "id": "dqn_model_dqnagent_save_model", "community": 0, "norm_label": ".save_model()" }, { "label": ".load_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L333", "id": "dqn_model_dqnagent_load_model", "community": 2, "norm_label": ".load_model()" }, { "label": ".get_model_info()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L346", "id": "dqn_model_dqnagent_get_model_info", "community": 0, "norm_label": ".get_model_info()" }, { "label": "Deep Q-Network (DQN) model for data cleaning reinforcement learning. This modul", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L1", "community": 0, "norm_label": "deep q-network (dqn) model for data cleaning reinforcement learning. this modul", "id": "dqn_model_rationale_1" }, { "label": "Deep Q-Network for approximating Q-values. Architecture: - Input la", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L21", "community": 0, "norm_label": "deep q-network for approximating q-values. architecture: - input la", "id": "dqn_model_rationale_21" }, { "label": "Initialize Q-network. Args: state_dim: Dimension of", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L32", "community": 11, "norm_label": "initialize q-network. args: state_dim: dimension of", "id": "dqn_model_rationale_32" }, { "label": "Initialize network weights using Xavier initialization.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L66", "community": 11, "norm_label": "initialize network weights using xavier initialization.", "id": "dqn_model_rationale_66" }, { "label": "Forward pass through network. Args: state: Input st", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L73", "community": 16, "norm_label": "forward pass through network. args: state: input st", "id": "dqn_model_rationale_73" }, { "label": "Get Q-values as numpy array. Args: state: Input sta", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L85", "community": 16, "norm_label": "get q-values as numpy array. args: state: input sta", "id": "dqn_model_rationale_85" }, { "label": "DQN Agent that learns optimal data cleaning policies. Uses epsilon-gree", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L100", "community": 0, "norm_label": "dqn agent that learns optimal data cleaning policies. uses epsilon-gree", "id": "dqn_model_rationale_100" }, { "label": "Initialize DQN agent. Args: state_dim: Dimension of", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L108", "community": 11, "norm_label": "initialize dqn agent. args: state_dim: dimension of", "id": "dqn_model_rationale_108" }, { "label": "Reset agent state for new episode.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L143", "community": 2, "norm_label": "reset agent state for new episode.", "id": "dqn_model_rationale_143" }, { "label": "Select action using epsilon-greedy policy. Args: ob", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L149", "community": 2, "norm_label": "select action using epsilon-greedy policy. args: ob", "id": "dqn_model_rationale_149" }, { "label": "Get agent's confidence in last action.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L197", "community": 21, "norm_label": "get agent's confidence in last action.", "id": "dqn_model_rationale_197" }, { "label": "Update agent's internal state based on received reward.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L201", "community": 2, "norm_label": "update agent's internal state based on received reward.", "id": "dqn_model_rationale_201" }, { "label": "Update mapping between action indices and action dictionaries.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L206", "community": 2, "norm_label": "update mapping between action indices and action dictionaries.", "id": "dqn_model_rationale_206" }, { "label": "Encode observation to state vector. Args: observati", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L233", "community": 2, "norm_label": "encode observation to state vector. args: observati", "id": "dqn_model_rationale_233" }, { "label": "Convert action index to action dictionary.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L294", "community": 2, "norm_label": "convert action index to action dictionary.", "id": "dqn_model_rationale_294" }, { "label": "Decay epsilon for exploration.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L307", "community": 0, "norm_label": "decay epsilon for exploration.", "id": "dqn_model_rationale_307" }, { "label": "Update target network with current network weights.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L311", "community": 0, "norm_label": "update target network with current network weights.", "id": "dqn_model_rationale_311" }, { "label": "Set training/inference mode.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L315", "community": 0, "norm_label": "set training/inference mode.", "id": "dqn_model_rationale_315" }, { "label": "Get model information.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\dqn_model.py", "source_location": "L347", "community": 0, "norm_label": "get model information.", "id": "dqn_model_rationale_347" }, { "label": "experience_replay.py", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_backend_ml_experience_replay_py", "community": 0, "norm_label": "experience_replay.py" }, { "label": "ReplayBuffer", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L15", "id": "experience_replay_replaybuffer", "community": 0, "norm_label": "replaybuffer" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L23", "id": "experience_replay_replaybuffer_init", "community": 0, "norm_label": ".__init__()" }, { "label": ".add()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L40", "id": "experience_replay_replaybuffer_add", "community": 0, "norm_label": ".add()" }, { "label": ".sample()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L60", "id": "experience_replay_replaybuffer_sample", "community": 6, "norm_label": ".sample()" }, { "label": ".sample_tensors()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L82", "id": "experience_replay_replaybuffer_sample_tensors", "community": 6, "norm_label": ".sample_tensors()" }, { "label": ".is_full()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L106", "id": "experience_replay_replaybuffer_is_full", "community": 2, "norm_label": ".is_full()" }, { "label": ".size()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L110", "id": "experience_replay_replaybuffer_size", "community": 2, "norm_label": ".size()" }, { "label": ".clear()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L114", "id": "experience_replay_replaybuffer_clear", "community": 2, "norm_label": ".clear()" }, { "label": ".get_statistics()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L121", "id": "experience_replay_replaybuffer_get_statistics", "community": 0, "norm_label": ".get_statistics()" }, { "label": ".save_buffer()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L153", "id": "experience_replay_replaybuffer_save_buffer", "community": 0, "norm_label": ".save_buffer()" }, { "label": ".load_buffer()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L174", "id": "experience_replay_replaybuffer_load_buffer", "community": 0, "norm_label": ".load_buffer()" }, { "label": "PrioritizedReplayBuffer", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L194", "id": "experience_replay_prioritizedreplaybuffer", "community": 0, "norm_label": "prioritizedreplaybuffer" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L201", "id": "experience_replay_prioritizedreplaybuffer_init", "community": 0, "norm_label": ".__init__()" }, { "label": ".add()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L216", "id": "experience_replay_prioritizedreplaybuffer_add", "community": 0, "norm_label": ".add()" }, { "label": ".sample()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L233", "id": "experience_replay_prioritizedreplaybuffer_sample", "community": 6, "norm_label": ".sample()" }, { "label": ".update_priorities()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L266", "id": "experience_replay_prioritizedreplaybuffer_update_priorities", "community": 6, "norm_label": ".update_priorities()" }, { "label": ".get_statistics()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L279", "id": "experience_replay_prioritizedreplaybuffer_get_statistics", "community": 0, "norm_label": ".get_statistics()" }, { "label": "Experience Replay Buffer for DQN training. This module implements a replay buff", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L1", "community": 0, "norm_label": "experience replay buffer for dqn training. this module implements a replay buff", "id": "experience_replay_rationale_1" }, { "label": "Experience Replay Buffer for DQN training. Stores transitions (state, a", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L16", "community": 0, "norm_label": "experience replay buffer for dqn training. stores transitions (state, a", "id": "experience_replay_rationale_16" }, { "label": "Initialize replay buffer. Args: capacity: Maximum n", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L24", "community": 0, "norm_label": "initialize replay buffer. args: capacity: maximum n", "id": "experience_replay_rationale_24" }, { "label": "Add a transition to the buffer. Args: state: Curren", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L42", "community": 0, "norm_label": "add a transition to the buffer. args: state: curren", "id": "experience_replay_rationale_42" }, { "label": "Sample a batch of transitions from the buffer. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L61", "community": 6, "norm_label": "sample a batch of transitions from the buffer. args:", "id": "experience_replay_rationale_61" }, { "label": "Sample a batch and convert to tensors for training. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L83", "community": 6, "norm_label": "sample a batch and convert to tensors for training. args:", "id": "experience_replay_rationale_83" }, { "label": "Check if buffer is at capacity.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L107", "community": 2, "norm_label": "check if buffer is at capacity.", "id": "experience_replay_rationale_107" }, { "label": "Get current buffer size.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L111", "community": 2, "norm_label": "get current buffer size.", "id": "experience_replay_rationale_111" }, { "label": "Clear all transitions from buffer.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L115", "community": 2, "norm_label": "clear all transitions from buffer.", "id": "experience_replay_rationale_115" }, { "label": "Get buffer statistics. Returns: Dictionary with buf", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L122", "community": 0, "norm_label": "get buffer statistics. returns: dictionary with buf", "id": "experience_replay_rationale_122" }, { "label": "Save buffer to file. Args: filepath: Path to save b", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L154", "community": 0, "norm_label": "save buffer to file. args: filepath: path to save b", "id": "experience_replay_rationale_154" }, { "label": "Load buffer from file. Args: filepath: Path to load", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L175", "community": 0, "norm_label": "load buffer from file. args: filepath: path to load", "id": "experience_replay_rationale_175" }, { "label": "Prioritized Experience Replay Buffer. Samples transitions based on thei", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L195", "community": 0, "norm_label": "prioritized experience replay buffer. samples transitions based on thei", "id": "experience_replay_rationale_195" }, { "label": "Initialize prioritized replay buffer. Args: capacit", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L202", "community": 0, "norm_label": "initialize prioritized replay buffer. args: capacit", "id": "experience_replay_rationale_202" }, { "label": "Add a transition with maximum priority. Args: state", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L218", "community": 0, "norm_label": "add a transition with maximum priority. args: state", "id": "experience_replay_rationale_218" }, { "label": "Sample based on priorities. Args: batch_size: Numbe", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L234", "community": 6, "norm_label": "sample based on priorities. args: batch_size: numbe", "id": "experience_replay_rationale_234" }, { "label": "Update priorities based on TD-errors. Args: indices", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L267", "community": 6, "norm_label": "update priorities based on td-errors. args: indices", "id": "experience_replay_rationale_267" }, { "label": "Get buffer statistics including priority information.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\experience_replay.py", "source_location": "L280", "community": 0, "norm_label": "get buffer statistics including priority information.", "id": "experience_replay_rationale_280" }, { "label": "model_registry.py", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_backend_ml_model_registry_py", "community": 0, "norm_label": "model_registry.py" }, { "label": "ModelMetadata", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L21", "id": "model_registry_modelmetadata", "community": 0, "norm_label": "modelmetadata" }, { "label": ".to_dict()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L35", "id": "model_registry_modelmetadata_to_dict", "community": 0, "norm_label": ".to_dict()" }, { "label": "from_dict()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L40", "id": "model_registry_from_dict", "community": 0, "norm_label": "from_dict()" }, { "label": "ModelRegistry", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L45", "id": "model_registry_modelregistry", "community": 0, "norm_label": "modelregistry" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L52", "id": "model_registry_modelregistry_init", "community": 0, "norm_label": ".__init__()" }, { "label": "._load_metadata()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L73", "id": "model_registry_modelregistry_load_metadata", "community": 0, "norm_label": "._load_metadata()" }, { "label": "._save_metadata()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L90", "id": "model_registry_modelregistry_save_metadata", "community": 0, "norm_label": "._save_metadata()" }, { "label": ".register_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L102", "id": "model_registry_modelregistry_register_model", "community": 0, "norm_label": ".register_model()" }, { "label": ".load_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L161", "id": "model_registry_modelregistry_load_model", "community": 0, "norm_label": ".load_model()" }, { "label": ".get_best_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L194", "id": "model_registry_modelregistry_get_best_model", "community": 0, "norm_label": ".get_best_model()" }, { "label": ".list_models()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L226", "id": "model_registry_modelregistry_list_models", "community": 0, "norm_label": ".list_models()" }, { "label": ".delete_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L250", "id": "model_registry_modelregistry_delete_model", "community": 0, "norm_label": ".delete_model()" }, { "label": ".export_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L286", "id": "model_registry_modelregistry_export_model", "community": 0, "norm_label": ".export_model()" }, { "label": ".import_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L329", "id": "model_registry_modelregistry_import_model", "community": 0, "norm_label": ".import_model()" }, { "label": ".get_registry_stats()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L380", "id": "model_registry_modelregistry_get_registry_stats", "community": 0, "norm_label": ".get_registry_stats()" }, { "label": ".cleanup_old_models()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L412", "id": "model_registry_modelregistry_cleanup_old_models", "community": 0, "norm_label": ".cleanup_old_models()" }, { "label": ".validate_models()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L444", "id": "model_registry_modelregistry_validate_models", "community": 0, "norm_label": ".validate_models()" }, { "label": "save_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L477", "id": "model_registry_save_model", "community": 0, "norm_label": "save_model()" }, { "label": "load_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L493", "id": "model_registry_load_model", "community": 0, "norm_label": "load_model()" }, { "label": "get_best_model()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L505", "id": "model_registry_get_best_model", "community": 0, "norm_label": "get_best_model()" }, { "label": "Model Registry for DQN model management. This module handles saving, loading, a", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L1", "community": 0, "norm_label": "model registry for dqn model management. this module handles saving, loading, a", "id": "model_registry_rationale_1" }, { "label": "Metadata for a trained model.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L22", "community": 0, "norm_label": "metadata for a trained model.", "id": "model_registry_rationale_22" }, { "label": "Convert to dictionary.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L36", "community": 0, "norm_label": "convert to dictionary.", "id": "model_registry_rationale_36" }, { "label": "Create from dictionary.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L41", "community": 0, "norm_label": "create from dictionary.", "id": "model_registry_rationale_41" }, { "label": "Registry for managing trained DQN models. Handles model saving, loading", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L46", "community": 0, "norm_label": "registry for managing trained dqn models. handles model saving, loading", "id": "model_registry_rationale_46" }, { "label": "Initialize model registry. Args: registry_dir: Dire", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L53", "community": 0, "norm_label": "initialize model registry. args: registry_dir: dire", "id": "model_registry_rationale_53" }, { "label": "Load model metadata from registry file.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L74", "community": 0, "norm_label": "load model metadata from registry file.", "id": "model_registry_rationale_74" }, { "label": "Save model metadata to registry file.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L91", "community": 0, "norm_label": "save model metadata to registry file.", "id": "model_registry_rationale_91" }, { "label": "Register a new model in the registry. Args: agent:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L105", "community": 0, "norm_label": "register a new model in the registry. args: agent:", "id": "model_registry_rationale_105" }, { "label": "Load a model from the registry. Args: version: Mode", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L162", "community": 0, "norm_label": "load a model from the registry. args: version: mode", "id": "model_registry_rationale_162" }, { "label": "Get the best model version based on specified metric. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L195", "community": 0, "norm_label": "get the best model version based on specified metric. args:", "id": "model_registry_rationale_195" }, { "label": "List all models in the registry. Args: difficulty:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L227", "community": 0, "norm_label": "list all models in the registry. args: difficulty:", "id": "model_registry_rationale_227" }, { "label": "Delete a model from the registry. Args: version: Mo", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L251", "community": 0, "norm_label": "delete a model from the registry. args: version: mo", "id": "model_registry_rationale_251" }, { "label": "Export a model to a specified path. Args: version:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L287", "community": 0, "norm_label": "export a model to a specified path. args: version:", "id": "model_registry_rationale_287" }, { "label": "Import a model from a specified path. Args: import_", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L330", "community": 0, "norm_label": "import a model from a specified path. args: import_", "id": "model_registry_rationale_330" }, { "label": "Get registry statistics. Returns: Registry statisti", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L381", "community": 0, "norm_label": "get registry statistics. returns: registry statisti", "id": "model_registry_rationale_381" }, { "label": "Clean up old models, keeping only the best N models. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L413", "community": 0, "norm_label": "clean up old models, keeping only the best n models. args:", "id": "model_registry_rationale_413" }, { "label": "Validate all models in the registry. Returns: Dicti", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L445", "community": 0, "norm_label": "validate all models in the registry. returns: dicti", "id": "model_registry_rationale_445" }, { "label": "Convenience function to save a model. Returns: Model file path", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L480", "community": 0, "norm_label": "convenience function to save a model. returns: model file path", "id": "model_registry_rationale_480" }, { "label": "Convenience function to load a model. Returns: Loaded DQN agent", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L495", "community": 0, "norm_label": "convenience function to load a model. returns: loaded dqn agent", "id": "model_registry_rationale_495" }, { "label": "Convenience function to get best model version. Returns: Best m", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\model_registry.py", "source_location": "L507", "community": 0, "norm_label": "convenience function to get best model version. returns: best m", "id": "model_registry_rationale_507" }, { "label": "reward_shaper.py", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_backend_ml_reward_shaper_py", "community": 22, "norm_label": "reward_shaper.py" }, { "label": "RewardShaper", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L13", "id": "reward_shaper_rewardshaper", "community": 1, "norm_label": "rewardshaper" }, { "label": ".__init__()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L25", "id": "reward_shaper_rewardshaper_init", "community": 1, "norm_label": ".__init__()" }, { "label": ".reset_episode()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L49", "id": "reward_shaper_rewardshaper_reset_episode", "community": 2, "norm_label": ".reset_episode()" }, { "label": ".calculate_reward()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L62", "id": "reward_shaper_rewardshaper_calculate_reward", "community": 2, "norm_label": ".calculate_reward()" }, { "label": ".calculate_episode_completion_bonus()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L127", "id": "reward_shaper_rewardshaper_calculate_episode_completion_bonus", "community": 1, "norm_label": ".calculate_episode_completion_bonus()" }, { "label": "._get_base_reward()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L148", "id": "reward_shaper_rewardshaper_get_base_reward", "community": 2, "norm_label": "._get_base_reward()" }, { "label": "._calculate_progress_bonus()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L152", "id": "reward_shaper_rewardshaper_calculate_progress_bonus", "community": 2, "norm_label": "._calculate_progress_bonus()" }, { "label": "._calculate_consistency_bonus()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L175", "id": "reward_shaper_rewardshaper_calculate_consistency_bonus", "community": 2, "norm_label": "._calculate_consistency_bonus()" }, { "label": "._calculate_contradiction_penalty()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L211", "id": "reward_shaper_rewardshaper_calculate_contradiction_penalty", "community": 2, "norm_label": "._calculate_contradiction_penalty()" }, { "label": "._calculate_issue_adjustment()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L241", "id": "reward_shaper_rewardshaper_calculate_issue_adjustment", "community": 2, "norm_label": "._calculate_issue_adjustment()" }, { "label": "._calculate_skip_bonus()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L275", "id": "reward_shaper_rewardshaper_calculate_skip_bonus", "community": 2, "norm_label": "._calculate_skip_bonus()" }, { "label": "._update_tracking()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L304", "id": "reward_shaper_rewardshaper_update_tracking", "community": 2, "norm_label": "._update_tracking()" }, { "label": ".get_reward_statistics()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L337", "id": "reward_shaper_rewardshaper_get_reward_statistics", "community": 2, "norm_label": ".get_reward_statistics()" }, { "label": "._get_action_type_distribution()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L359", "id": "reward_shaper_rewardshaper_get_action_type_distribution", "community": 2, "norm_label": "._get_action_type_distribution()" }, { "label": "Reward Shaper for multi-agent data cleaning system. This module implements so", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L1", "community": 22, "norm_label": "reward shaper for multi-agent data cleaning system. this module implements so", "id": "reward_shaper_rationale_1" }, { "label": "Calculates sophisticated rewards for agent actions. Logic: - Base re", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L14", "community": 1, "norm_label": "calculates sophisticated rewards for agent actions. logic: - base re", "id": "reward_shaper_rationale_14" }, { "label": "Initialize the reward shaper.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L26", "community": 1, "norm_label": "initialize the reward shaper.", "id": "reward_shaper_rationale_26" }, { "label": "Reset reward shaper for new episode. Args: total_steps_e", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L50", "community": 2, "norm_label": "reset reward shaper for new episode. args: total_steps_e", "id": "reward_shaper_rationale_50" }, { "label": "Calculate sophisticated reward for an action. Args: acti", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L68", "community": 2, "norm_label": "calculate sophisticated reward for an action. args: acti", "id": "reward_shaper_rationale_68" }, { "label": "Calculate bonus for episode completion. Args: final_scor", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L128", "community": 1, "norm_label": "calculate bonus for episode completion. args: final_scor", "id": "reward_shaper_rationale_128" }, { "label": "Get base reward for action type.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L149", "community": 2, "norm_label": "get base reward for action type.", "id": "reward_shaper_rationale_149" }, { "label": "Calculate progress bonus based on episode completion. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L153", "community": 2, "norm_label": "calculate progress bonus based on episode completion. args:", "id": "reward_shaper_rationale_153" }, { "label": "Calculate consistency bonus for successful action patterns. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L178", "community": 2, "norm_label": "calculate consistency bonus for successful action patterns. args:", "id": "reward_shaper_rationale_178" }, { "label": "Calculate penalty for actions that contradict previous decisions. Arg", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L214", "community": 2, "norm_label": "calculate penalty for actions that contradict previous decisions. arg", "id": "reward_shaper_rationale_214" }, { "label": "Calculate issue-specific reward adjustments. Args: actio", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L244", "community": 2, "norm_label": "calculate issue-specific reward adjustments. args: actio", "id": "reward_shaper_rationale_244" }, { "label": "Calculate bonus for skip actions when appropriate. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L278", "community": 2, "norm_label": "calculate bonus for skip actions when appropriate. args:", "id": "reward_shaper_rationale_278" }, { "label": "Update internal tracking for future reward calculations. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\backend\\ml\\reward_shaper.py", "source_location": "L307", "community": 2, "norm_label": "update internal tracking for future reward calculations. args:", "id": "reward_shaper_rationale_307" }, { "label": "Get statistics about reward distribution. 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This module creates synthetic dir", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L1", "community": 0, "norm_label": "synthetic dataset generator for dqn training. this module creates synthetic dir", "id": "synthetic_datasets_rationale_1" }, { "label": "Difficulty levels for synthetic datasets.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L17", "community": 0, "norm_label": "difficulty levels for synthetic datasets.", "id": "synthetic_datasets_rationale_17" }, { "label": "Configuration for synthetic dataset generation.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L25", "community": 0, "norm_label": "configuration for synthetic dataset generation.", "id": "synthetic_datasets_rationale_25" }, { "label": "Generates synthetic datasets with controlled data quality issues. Creat", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L40", "community": 0, "norm_label": "generates synthetic datasets with controlled data quality issues. creat", "id": "synthetic_datasets_rationale_40" }, { "label": "Initialize generator. Args: seed: Random seed for r", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L48", "community": 0, "norm_label": "initialize generator. args: seed: random seed for r", "id": "synthetic_datasets_rationale_48" }, { "label": "Get predefined configuration for difficulty level. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L68", "community": 8, "norm_label": "get predefined configuration for difficulty level. args:", "id": "synthetic_datasets_rationale_68" }, { "label": "Generate clean base dataset. Args: num_rows: Number", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L111", "community": 6, "norm_label": "generate clean base dataset. args: num_rows: number", "id": "synthetic_datasets_rationale_111" }, { "label": "Generate realistic salary based on department.", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L151", "community": 6, "norm_label": "generate realistic salary based on department.", "id": "synthetic_datasets_rationale_151" }, { "label": "Inject missing values into dataset. Args: df: Clean", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L164", "community": 6, "norm_label": "inject missing values into dataset. args: df: clean", "id": "synthetic_datasets_rationale_164" }, { "label": "Inject duplicate rows into dataset. Args: df: Clean", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L194", "community": 6, "norm_label": "inject duplicate rows into dataset. args: df: clean", "id": "synthetic_datasets_rationale_194" }, { "label": "Inject outlier values into dataset. Args: df: Clean", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L226", "community": 6, "norm_label": "inject outlier values into dataset. args: df: clean", "id": "synthetic_datasets_rationale_226" }, { "label": "Inject categorical inconsistencies into dataset. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L268", "community": 6, "norm_label": "inject categorical inconsistencies into dataset. args:", "id": "synthetic_datasets_rationale_268" }, { "label": "Inject type errors into dataset. Args: df: Clean Da", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L314", "community": 6, "norm_label": "inject type errors into dataset. args: df: clean da", "id": "synthetic_datasets_rationale_314" }, { "label": "Inject formatting errors into dataset. Args: df: Cl", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L342", "community": 6, "norm_label": "inject formatting errors into dataset. args: df: cl", "id": "synthetic_datasets_rationale_342" }, { "label": "Generate complete dirty dataset with all error types. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L386", "community": 6, "norm_label": "generate complete dirty dataset with all error types. args:", "id": "synthetic_datasets_rationale_386" }, { "label": "Create training episodes from synthetic data. Args:", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L440", "community": 8, "norm_label": "create training episodes from synthetic data. args:", "id": "synthetic_datasets_rationale_440" }, { "label": "Detect issues specific to this row. Args: row: Row", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L486", "community": 8, "norm_label": "detect issues specific to this row. args: row: row", "id": "synthetic_datasets_rationale_486" }, { "label": "Save dataset to CSV file. Args: df: DataFrame to sa", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L527", "community": 0, "norm_label": "save dataset to csv file. args: df: dataframe to sa", "id": "synthetic_datasets_rationale_527" }, { "label": "Load dataset from CSV file. Args: filepath: Input f", "file_type": "rationale", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\data\\synthetic_datasets.py", "source_location": "L538", "community": 0, "norm_label": "load dataset from csv file. args: filepath: input f", "id": "synthetic_datasets_rationale_538" }, { "label": "eslint.config.js", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\eslint.config.js", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_eslint_config_js", "community": 31, "norm_label": "eslint.config.js" }, { "label": "vite.config.ts", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\vite.config.ts", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_vite_config_ts", "community": 32, "norm_label": "vite.config.ts" }, { "label": "App.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\App.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_app_tsx", "community": 23, "norm_label": "app.tsx" }, { "label": "main.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\main.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_main_tsx", "community": 23, "norm_label": "main.tsx" }, { "label": "ActionPanel.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\ActionPanel.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_actionpanel_tsx", "community": 33, "norm_label": "actionpanel.tsx" }, { "label": "AgentSuggestion.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AgentSuggestion.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_agentsuggestion_tsx", "community": 17, "norm_label": "agentsuggestion.tsx" }, { "label": "getConfidenceColor()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AgentSuggestion.tsx", "source_location": "L37", "id": "agentsuggestion_getconfidencecolor", "community": 17, "norm_label": "getconfidencecolor()" }, { "label": "getConfidenceLabel()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AgentSuggestion.tsx", "source_location": "L43", "id": "agentsuggestion_getconfidencelabel", "community": 17, "norm_label": "getconfidencelabel()" }, { "label": "getActionLabel()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AgentSuggestion.tsx", "source_location": "L49", "id": "agentsuggestion_getactionlabel", "community": 17, "norm_label": "getactionlabel()" }, { "label": "AuditLogTable.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AuditLogTable.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_auditlogtable_tsx", "community": 12, "norm_label": "auditlogtable.tsx" }, { "label": "handleChangePage()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AuditLogTable.tsx", "source_location": "L47", "id": "auditlogtable_handlechangepage", "community": 12, "norm_label": "handlechangepage()" }, { "label": "handleChangeRowsPerPage()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AuditLogTable.tsx", "source_location": "L51", "id": "auditlogtable_handlechangerowsperpage", "community": 12, "norm_label": "handlechangerowsperpage()" }, { "label": "getConfidenceColor()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AuditLogTable.tsx", "source_location": "L70", "id": "auditlogtable_getconfidencecolor", "community": 12, "norm_label": "getconfidencecolor()" }, { "label": "formatTimestamp()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AuditLogTable.tsx", "source_location": "L76", "id": "auditlogtable_formattimestamp", "community": 12, "norm_label": "formattimestamp()" }, { "label": "formatValue()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\AuditLogTable.tsx", "source_location": "L81", "id": "auditlogtable_formatvalue", "community": 12, "norm_label": "formatvalue()" }, { "label": "DataQualityChart.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DataQualityChart.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_dataqualitychart_tsx", "community": 24, "norm_label": "dataqualitychart.tsx" }, { "label": "DataQualityChart()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DataQualityChart.tsx", "source_location": "L47", "id": "dataqualitychart_dataqualitychart", "community": 24, "norm_label": "dataqualitychart()" }, { "label": "DatasetPreview.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DatasetPreview.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_datasetpreview_tsx", "community": 14, "norm_label": "datasetpreview.tsx" }, { "label": "formatDate()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DatasetPreview.tsx", "source_location": "L47", "id": "datasetpreview_formatdate", "community": 14, "norm_label": "formatdate()" }, { "label": "getScoreColor()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DatasetPreview.tsx", "source_location": "L51", "id": "datasetpreview_getscorecolor", "community": 14, "norm_label": "getscorecolor()" }, { "label": "getColumnNames()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DatasetPreview.tsx", "source_location": "L57", "id": "datasetpreview_getcolumnnames", "community": 14, "norm_label": "getcolumnnames()" }, { "label": "getQualityScore()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\DatasetPreview.tsx", "source_location": "L67", "id": "datasetpreview_getqualityscore", "community": 14, "norm_label": "getqualityscore()" }, { "label": "FileUploader.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\FileUploader.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_fileuploader_tsx", "community": 15, "norm_label": "fileuploader.tsx" }, { "label": "validateFile()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\FileUploader.tsx", "source_location": "L51", "id": "fileuploader_validatefile", "community": 15, "norm_label": "validatefile()" }, { "label": "handleUpload()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\FileUploader.tsx", "source_location": "L117", "id": "fileuploader_handleupload", "community": 15, "norm_label": "handleupload()" }, { "label": "handleCancel()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\FileUploader.tsx", "source_location": "L151", "id": "fileuploader_handlecancel", "community": 15, "norm_label": "handlecancel()" }, { "label": "formatFileSize()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\FileUploader.tsx", "source_location": "L163", "id": "fileuploader_formatfilesize", "community": 15, "norm_label": "formatfilesize()" }, { "label": "JobProgressCard.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\JobProgressCard.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_jobprogresscard_tsx", "community": 13, "norm_label": "jobprogresscard.tsx" }, { "label": "getStatusColor()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\JobProgressCard.tsx", "source_location": "L42", "id": "jobprogresscard_getstatuscolor", "community": 13, "norm_label": "getstatuscolor()" }, { "label": "getStatusIcon()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\JobProgressCard.tsx", "source_location": "L58", "id": "jobprogresscard_getstatusicon", "community": 13, "norm_label": "getstatusicon()" }, { "label": "getStatusLabel()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\JobProgressCard.tsx", "source_location": "L72", "id": "jobprogresscard_getstatuslabel", "community": 13, "norm_label": "getstatuslabel()" }, { "label": "formatElapsedTime()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\JobProgressCard.tsx", "source_location": "L88", "id": "jobprogresscard_formatelapsedtime", "community": 13, "norm_label": "formatelapsedtime()" }, { "label": "estimateRemaining()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\JobProgressCard.tsx", "source_location": "L102", "id": "jobprogresscard_estimateremaining", "community": 13, "norm_label": "estimateremaining()" }, { "label": "MetricsOverview.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\MetricsOverview.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_metricsoverview_tsx", "community": 25, "norm_label": "metricsoverview.tsx" }, { "label": "formatDataSize()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\MetricsOverview.tsx", "source_location": "L32", "id": "metricsoverview_formatdatasize", "community": 25, "norm_label": "formatdatasize()" }, { "label": "Navigation.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\Navigation.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_navigation_tsx", "community": 34, "norm_label": "navigation.tsx" }, { "label": "RowViewer.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\RowViewer.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_rowviewer_tsx", "community": 18, "norm_label": "rowviewer.tsx" }, { "label": "getValueType()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\RowViewer.tsx", "source_location": "L42", "id": "rowviewer_getvaluetype", "community": 18, "norm_label": "getvaluetype()" }, { "label": "hasIssue()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\RowViewer.tsx", "source_location": "L50", "id": "rowviewer_hasissue", "community": 18, "norm_label": "hasissue()" }, { "label": "isChanged()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\RowViewer.tsx", "source_location": "L54", "id": "rowviewer_ischanged", "community": 18, "norm_label": "ischanged()" }, { "label": "Sidebar.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\components\\Sidebar.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_components_sidebar_tsx", "community": 35, "norm_label": "sidebar.tsx" }, { "label": "Dashboard.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Dashboard.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_pages_dashboard_tsx", "community": 5, "norm_label": "dashboard.tsx" }, { "label": "fetchDashboardData()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Dashboard.tsx", "source_location": "L86", "id": "dashboard_fetchdashboarddata", "community": 5, "norm_label": "fetchdashboarddata()" }, { "label": "getStatusChip()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Dashboard.tsx", "source_location": "L167", "id": "dashboard_getstatuschip", "community": 5, "norm_label": "getstatuschip()" }, { "label": "Interactive.tsx", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L1", "id": "d_rl_project_data_cleaning_openenv_frontend_src_pages_interactive_tsx", "community": 5, "norm_label": "interactive.tsx" }, { "label": "loadData()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L98", "id": "interactive_loaddata", "community": 5, "norm_label": "loaddata()" }, { "label": "handleAccept()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L114", "id": "interactive_handleaccept", "community": 5, "norm_label": "handleaccept()" }, { "label": "handleReject()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L157", "id": "interactive_handlereject", "community": 5, "norm_label": "handlereject()" }, { "label": "moveToNextRow()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L175", "id": "interactive_movetonextrow", "community": 5, "norm_label": "movetonextrow()" }, { "label": "handlePrev()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L189", "id": "interactive_handleprev", "community": 5, "norm_label": "handleprev()" }, { "label": "handleNext()", "file_type": "code", "source_file": "D:\\RL-PROJECT\\data-cleaning-openenv\\frontend\\src\\pages\\Interactive.tsx", "source_location": "L200", "id": "interactive_handlenext", "community": 5, "norm_label": "handlenext()" }, { "label": "JobHistory.tsx", "file_type": "code", "source_file": 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