---
title: Data Cleaning Environment
emoji: 🧹
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8000
base_path: /web
---
# Data Cleaning Environment

A real-world OpenEnv environment where an AI agent learns to clean tabular data
through the standard `step()` / `reset()` / `state()` API.

The agent receives a dirty CSV-style dataset and must apply cleaning operations
step-by-step to maximise a data-quality score (0.0 → 1.0).

---

## Tasks

| Task ID  | Difficulty | Description                                                     | Max Steps |
|----------|------------|-----------------------------------------------------------------|-----------|
| `easy`   | Easy       | Fix 3 missing values (name / age) in a 5-row table             | 10        |
| `medium` | Medium     | Remove 2 duplicate rows **and** fix 2 type errors in `age`     | 15        |
| `hard`   | Hard       | Full pipeline: missing values + duplicates + outliers + normalise text | 25  |

---

## Action Space

```json
{
  "operation": "<string>",
  "column":    "<string | null>"
}
```

| Operation           | Task(s)          | Effect                                              |
|---------------------|------------------|-----------------------------------------------------|
| `impute_mean`       | easy             | Fill numeric `None` with column mean                |
| `impute_mode`       | easy             | Fill string `None` with column mode                 |
| `drop_missing_rows` | easy / medium    | Drop all rows containing any `None`                 |
| `remove_duplicates` | medium           | Drop exact-duplicate rows                           |
| `fix_type_errors`   | medium           | Coerce non-numeric values to `float` (or `None`)    |
| `remove_outliers`   | hard             | Drop rows where `price ≤ 0` or `price ≥ 500`        |
| `normalize_text`    | hard             | Strip whitespace + title-case all string columns    |
| `fill_quantity_mean`| hard             | Fill missing `quantity` with column mean            |

---

## Observation Space

| Field             | Type    | Description                                               |
|-------------------|---------|-----------------------------------------------------------|
| `current_text`    | `str`   | Human-readable table of current rows                      |
| `is_normalized`   | `bool`  | True when no missing values, duplicates, or outliers left |
| `remaining_typos` | `int`   | Composite issue count (missing + dupes + outliers)        |
| `html_found`      | `bool`  | Always `False` (compatibility field)                      |
| `done`            | `bool`  | Episode ended (score=1.0 or step limit reached)           |
| `reward`          | `float` | Δ quality score from this step (partial progress signal)  |
| `metadata`        | `dict`  | Full row data, per-field diagnostics, valid ops list       |

---

## Reward Function

```
reward = (quality_score_after - quality_score_before) + 0.01   # if improvement
reward = (quality_score_after - quality_score_before) - 0.01   # if no improvement
```

Each task has its own grader:

- **Easy**: fraction of the 3 original missing values that are resolved
- **Medium**: average of (dedup score, type-fix score)
- **Hard**: average of 4 sub-scores (no missing, no dupes, no outliers, normalised text)

Invalid operations incur a −0.05 penalty but do **not** end the episode.

---

## Baseline Scores

Deterministic rule-based agent (reproducible, seed=42):

| Task   | Score |
|--------|-------|
| easy   | 1.0   |
| medium | 1.0   |
| hard   | 1.0   |

---

## API Endpoints

| Method | Path        | Description                                      |
|--------|-------------|--------------------------------------------------|
| POST   | `/reset`    | Start new episode. Body: `{"task":"easy"}`       |
| POST   | `/step`     | Apply action. Body: `{"action":{...}}`           |
| GET    | `/state`    | Current episode state                            |
| GET    | `/schema`   | Action + Observation JSON schemas                |
| GET    | `/tasks`    | Task list + action schema per difficulty         |
| POST   | `/grader`   | Score the current episode (0.0–1.0)              |
| POST   | `/baseline` | Run baseline agent on all 3 tasks                |
| GET    | `/health`   | Health check                                     |
| WS     | `/ws`       | WebSocket for low-latency persistent sessions    |

---

## Quick Start

### 1. Reset to a task

```bash
curl -X POST http://localhost:8000/reset \
  -H "Content-Type: application/json" \
  -d '{"task": "easy"}'
```

### 2. Apply a cleaning operation

```bash
curl -X POST http://localhost:8000/step \
  -H "Content-Type: application/json" \
  -d '{"action": {"operation": "impute_mean"}}'
```

### 3. Get current state

```bash
curl http://localhost:8000/state
```

### 4. Score the episode

```bash
curl -X POST http://localhost:8000/grader
```

### 5. Run baseline on all tasks

```bash
curl -X POST http://localhost:8000/baseline
```

---

## Setup

### Run locally

```bash
uv run server
# or
uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
```

### Run with Docker

```bash
docker build -t data-cleaning-env:latest -f server/Dockerfile .
docker run -p 8000:8000 data-cleaning-env:latest
```

### Deploy to Hugging Face Spaces

```bash
openenv push --repo-id your-username/data-cleaning-env
```

---

## Project Structure

```
data_cleaning_env/
├── README.md                          # This file
├── openenv.yaml                       # OpenEnv manifest
├── pyproject.toml                     # Project metadata & dependencies
├── uv.lock                            # Locked dependencies
├── models.py                          # Action + Observation Pydantic models
├── client.py                          # DataCleaningEnv HTTP/WS client
└── server/
    ├── app.py                         # FastAPI app + /tasks /grader /baseline
    ├── data_cleaning_env_environment.py  # Core environment logic + graders
    └── Dockerfile                     # Container image
```

---

## Dependencies

- `openenv-core[core] >= 0.2.2` — OpenEnv runtime
- Python 3.10+
- No external data dependencies — all datasets are generated synthetically at runtime