name: data-cleaning-openenv version: 1.0.0 description: > An OpenEnv-compliant environment where AI agents learn to clean messy real-world datasets step by step. Tasks include duplicate removal, missing value imputation, dtype fixing, outlier detection, and schema validation. author: thorodin103 license: mit tags: - openenv - data-cleaning - real-world - pandas - tabular - reinforcement-learning - agent api: base_url: https://thorodin103-data-cleaning-openenv.hf.space reset: POST /reset/{task_id} step: POST /step/{task_id} state: GET /state/{task_id} tasks: GET /tasks validate: GET /validate health: GET /health tasks: - id: easy_dedup_rename difficulty: easy description: Remove duplicate rows and rename columns to snake_case max_steps: 10 scoring: duplicate_score: 0.5 schema_score: 0.5 - id: medium_missing_dtype difficulty: medium description: Fill missing values and fix wrong data types max_steps: 15 scoring: missing_score: 0.5 dtype_score: 0.5 - id: hard_full_pipeline difficulty: hard description: Full pipeline — duplicates, missing, dtypes, outliers, schema max_steps: 20 scoring: duplicate_score: 0.2 missing_score: 0.2 dtype_score: 0.2 outlier_score: 0.2 schema_score: 0.2 observation_space: task_id: string step: integer dataset_info: object columns: list shape: list missing_values: object dtypes: object duplicate_count: integer sample_rows: list available_operations: list task_description: string message: string action_space: operation: type: string values: - remove_duplicates - fill_missing - fix_dtype - remove_outliers - rename_columns - validate_schema - finish parameters: type: object reward_space: total: type: float range: [0.0, 1.0] components: - duplicate_score - missing_score - dtype_score - outlier_score - schema_score - penalty baseline_scores: model: gpt-4o-mini easy_dedup_rename: 1.0000 medium_missing_dtype: 0.6643 hard_full_pipeline: 0.8386 average: 0.8343 runtime: language: python version: "3.10" framework: fastapi port: 7860