README.md

metadata

title: CSV Data Cleaning Environment
emoji: 🧹
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8000
pinned: false

CSV Cleaner Environment

A real-world data cleaning OpenEnv environment where AI agents learn to clean messy CSV datasets through structured commands. Built on the OpenEnv framework.

Motivation

Data cleaning is one of the most common and time-consuming tasks in real-world data work. This environment trains AI agents to perform systematic data wrangling — fixing column types, handling missing values, removing duplicates, renaming columns, and filtering invalid rows — simulating tasks that data engineers and analysts do daily.

Environment Description

The agent receives a messy CSV dataset and a cleaning objective. Each step, the agent issues one cleaning command via MCP tools. The environment applies the command, returns the updated dataset state, and provides progressive reward based on how close the dataset is to the target clean version.

Action Space (MCP Tools)

Tool	Parameters	Description
get_dataset_info	—	View columns, types, null counts, sample values
rename_column	old_name, new_name	Rename a column
cast_column	column, dtype	Cast to int, float, str, datetime
fill_missing	column, strategy, value?	Fill nulls. Strategy: mean, median, mode, constant, zero
drop_missing	column?	Drop rows with nulls (empty = all columns)
drop_duplicates	columns?	Remove duplicate rows (comma-separated or all)
filter_rows	column, operator, value	Filter rows. Operators: ==, !=, >, <, >=, <=, contains
strip_whitespace	column	Strip leading/trailing whitespace
replace_values	column, old_value, new_value	Replace values in a column

Observation Space

Each observation includes:

• columns: List of {name, dtype, null_count, unique_count, sample_values}
• row_count: Current number of rows
• duplicate_count: Number of duplicate rows
• task_description: What needs to be cleaned
• last_action_result: Success/error message from last command
• progress: Score from 0.0 to 1.0 representing cleaning completion

Tasks

Task	Difficulty	Max Steps	Description
fix_column_types	Easy	10	Fix 4 columns with wrong types (string→int/float/datetime)
clean_missing_duplicates	Medium	15	Fill missing values with appropriate strategies + remove duplicates
full_pipeline	Hard	20	Rename columns, fix types, strip whitespace, fill missing, normalize values, filter invalid rows, remove duplicates

Reward Function

• Progressive: Each step computes similarity to target dataset (column types, null counts, duplicates, row count, column names)
• Reward = score_delta: the improvement in score since the last step
• Completion bonus: +0.1 when progress ≥ 0.95
• Score range: Final score always in [0.0, 1.0]

Setup & Usage

Install

pip install -e .

Run Server Locally

uvicorn server.app:app --host 0.0.0.0 --port 8000

Docker Build & Run

docker build -t csv-cleaner-env .
docker run -p 8000:8000 csv-cleaner-env

Run Inference

export HF_TOKEN=your_token_here
export IMAGE_NAME=csv-cleaner-env
python inference.py

Environment Variables

Variable	Required	Default	Description
API_BASE_URL	No	https://router.huggingface.co/v1	LLM API endpoint
MODEL_NAME	No	Qwen/Qwen2.5-72B-Instruct	Model identifier
HF_TOKEN	Yes	—	API key
IMAGE_NAME	Yes*	—	Docker image name (*for inference)
CSV_CLEANER_TASK	No	fix_column_types	Default task

Baseline Scores

Task	Baseline Score	Model
fix_column_types	~0.80	Qwen2.5-72B-Instruct
clean_missing_duplicates	~0.65	Qwen2.5-72B-Instruct
full_pipeline	~0.45	Qwen2.5-72B-Instruct

Project Structure

csv_cleaner_env/
├── __init__.py              # Package exports
├── models.py                # Pydantic Action/Observation models
├── client.py                # MCPToolClient wrapper
├── openenv.yaml             # OpenEnv manifest
├── pyproject.toml            # Dependencies
├── Dockerfile               # Container definition
├── inference.py             # Baseline inference script
├── README.md                # This file
└── server/
    ├── __init__.py
    ├── app.py               # FastAPI entry point
    ├── csv_cleaning_environment.py  # Core environment
    └── tasks.py             # Task definitions & graders

License

MIT