name: data-cleaning-benchmark
version: "1.0.0"
description: >
  A multi-task LLM agent benchmark for real-world tabular data cleaning.
  The agent receives a dirty dataset and must apply structured cleaning
  actions to fix duplicates, missing values, format issues, and outliers.

author: "Jayesh"
license: MIT

tasks:
  - id: task1_easy
    task_id: task1_easy
    name: "Basic Customer Data Cleanup"
    difficulty: easy
    max_steps: 20
    description: "Remove duplicates, fill missing values, standardise country names."
    grader: env.graders:grade_task1_easy
    grader_fn: env.graders:grade_task1_easy
    grader_path: env.graders:grade_task1_easy
  - id: task2_medium
    task_id: task2_medium
    name: "E-commerce Orders Normalisation"
    difficulty: medium
    max_steps: 20
    description: "Fix mixed date formats, convert price strings, correct category typos."
    grader: env.graders:grade_task2_medium
    grader_fn: env.graders:grade_task2_medium
    grader_path: env.graders:grade_task2_medium
  - id: task3_hard
    task_id: task3_hard
    name: "Analytics Data Deep Clean"
    difficulty: hard
    max_steps: 20
    description: "Resolve duplicate user IDs, clip session outliers, fix invalid bounce rates."
    grader: env.graders:grade_task3_hard
    grader_fn: env.graders:grade_task3_hard
    grader_path: env.graders:grade_task3_hard
  - id: task4_medium_alt
    task_id: task4_medium_alt
    name: "E-commerce Orders Cleanup (Alt)"
    difficulty: medium
    max_steps: 20
    description: "Alternative medium scenario sharing the same grading criteria as task2_medium."
    grader: env.graders:grade_task4_medium_alt
    grader_fn: env.graders:grade_task4_medium_alt
    grader_path: env.graders:grade_task4_medium_alt
  - id: task5_hard_alt
    task_id: task5_hard_alt
    name: "Analytics Deep Clean (Alt)"
    difficulty: hard
    max_steps: 20
    description: "Alternative hard scenario sharing the same grading criteria as task3_hard."
    grader: env.graders:grade_task5_hard_alt
    grader_fn: env.graders:grade_task5_hard_alt
    grader_path: env.graders:grade_task5_hard_alt

observation_space:
  type: structured_json
  fields:
    - task_id
    - task_description
    - table_preview
    - schema_info
    - valid_actions
    - step / max_steps
    - cleaning_log
    - issues_detected

action_space:
  type: structured_json
  actions:
    - name: fill_missing
      params: ["column", "strategy(mean|median|mode|constant)", "value?"]
    - name: standardize_values
      params: ["column", "mapping(dict)"]
    - name: remove_duplicates
      params: []
    - name: remove_row
      params: ["row_id(int)"]
    - name: convert_type
      params: ["column", "target_type(float|int|str|datetime)"]
    - name: clip_outliers
      params: ["column", "lower?", "upper?"]
    - name: submit
      params: []

reward:
  type: shaped
  intermediate: true
  range: [0.01, 0.99]
  description: >
    Positive rewards for correct cleaning steps; small penalties for
    invalid or wasted actions; final grader score awarded on submit().

api:
  base_path: "/"
  endpoints:
    reset: "POST /reset"
    step: "POST /step"
    state: "GET  /state"
    step_legacy: "POST /step/{session_id}"
    state_legacy: "GET  /state/{session_id}"
    health: "GET  /health"
    tasks: "GET  /tasks"

runtime:
  language: python
  version: "3.11"
  port: 7860
  framework: fastapi

tags:
  - openenv
  - data-cleaning
  - llm-benchmark
  - tabular