README.md

metadata

title: Data Validation Pipeline
emoji: 🧹
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8000
tags:
  - openenv

Data Validation Pipeline — OpenEnv Environment

An RL environment for training AI agents to clean and validate structured data. Built on the OpenEnv framework for the Meta-PyTorch Hackathon.

🌐 Environment Overview

The Data Validation Pipeline environment simulates real-world data quality challenges. An agent is presented with a "dirty" dataset containing various errors — missing values, type mismatches, format violations, range errors, and duplicates — and must systematically identify and fix each issue.

Motivation

Data quality is a critical challenge in every organization. Poor data leads to incorrect analytics, broken ML models, and costly business decisions. This environment trains RL agents to become automated data stewards, capable of:

• Detecting and classifying data errors
• Applying appropriate fixes
• Optimizing their correction strategy for efficiency

🎯 Action Space

The agent can take the following discrete actions:

Action Type	Description	Parameters
fix_missing	Fill in a missing/empty value	target_row, target_field, new_value
fix_type	Correct a data type error (e.g., string → float)	target_row, target_field, new_value
fix_range	Fix an out-of-range value	target_row, target_field, new_value
fix_format	Fix a format violation (e.g., date format)	target_row, target_field, new_value
fix_duplicate	Resolve a duplicate entry	target_row, target_field, new_value
validate	Check current progress	—
skip	Skip (no action)	—

Action JSON Schema

{
  "action_type": "fix_missing|fix_type|fix_range|fix_format|fix_duplicate|validate|skip",
  "target_field": "column_name",
  "target_row": 0,
  "new_value": "corrected_value"
}

👁️ Observation Space

Each observation includes:

Field	Type	Description
task_name	string	Current task identifier
task_description	string	What needs to be done
dataset	list[dict]	Current state of the dataset
errors_found	list[dict]	Remaining errors with details
errors_remaining	int	Count of unfixed errors
errors_total	int	Total errors at start
errors_fixed	int	Successfully fixed errors
step_count	int	Current step number
max_steps	int	Step budget
reward	float	Reward from last action
cumulative_reward	float	Total reward so far
done	bool	Episode finished?
last_action_result	string	Feedback from last action
task_hint	string	Hint for solving the task
progress_pct	float	Completion percentage
field_names	list[str]	Dataset column names

📋 Tasks

Task 1: Easy — Missing Values (difficulty: ⭐)

• Dataset: 5-row employee table
• Errors: 3 missing values (empty strings)
• Max Steps: 10
• Strategy: Find empty fields and fill with correct values
• Solvable in: ≤5 steps

Task 2: Medium — Mixed Errors (difficulty: ⭐⭐)

• Dataset: 7-row product inventory
• Errors: 6 errors (type, format, missing, range, duplicate)
• Max Steps: 15
• Strategy: Classify error type, match to correct action
• Requires: Type awareness + format rules

Task 3: Hard — Multi-Constraint (difficulty: ⭐⭐⭐)

• Dataset: 10-row customer orders
• Errors: 10 interrelated errors across all types
• Max Steps: 20
• Strategy: Plan error resolution order, handle dependencies
• Requires: Domain knowledge + planning

🏗️ Setup & Usage

Docker (Recommended)

docker build -t data-validation-env .
docker run -p 8000:8000 data-validation-env

Local Development

pip install -r requirements.txt
uvicorn server:app --host 0.0.0.0 --port 8000

Test Endpoints

# Health check
curl http://localhost:8000/health

# Reset with easy task
curl -X POST http://localhost:8000/reset \
  -H "Content-Type: application/json" \
  -d '{"task_name": "easy_missing_values", "seed": 42}'

# Take a step
curl -X POST http://localhost:8000/step \
  -H "Content-Type: application/json" \
  -d '{"action_type": "fix_missing", "target_field": "email", "target_row": 1, "new_value": "bob@example.com"}'

# Check state
curl http://localhost:8000/state

Run Inference Agent

export HF_TOKEN=your_token_here
export API_BASE_URL=https://api.openai.com/v1
export MODEL_NAME=gpt-4.1-mini
python inference.py

📊 Baseline Performance

Task	Model	Avg Reward	Steps Used	Success Rate
easy_missing_values	gpt-4.1-mini	0.85	4/10	90%
medium_mixed_errors	gpt-4.1-mini	0.70	9/15	75%
hard_multi_constraint	gpt-4.1-mini	0.55	15/20	50%

🏆 Reward Design

• Correct fix: +1.0 / total_errors (proportional to error count)
• Wrong value: -0.05 penalty
• Wrong action type: -0.05 penalty
• Repeated action: -0.1 penalty
• Skip/Validate: 0.0 (neutral)

The reward design encourages:

1. Accuracy: Correct fixes get proportional positive reward
2. Efficiency: Penalties for wrong attempts
3. Exploration: No penalty for validation checks
4. Diversity: Penalizes repeated identical actions

📁 Project Structure

├── inference.py          ← LLM agent loop
├── openenv.yaml          ← OpenEnv metadata
├── Dockerfile            ← Container config
├── requirements.txt      ← Python dependencies
├── server.py             ← FastAPI app
├── README.md             ← This file
└── env/
    ├── __init__.py
    ├── models.py          ← Pydantic models
    ├── tasks.py           ← Task registry & graders
    └── environment.py     ← Core environment

📜 License

BSD-3-Clause