veox 0.1.19
The Incredible ML Client for DOUG - Distributed Evolution with TQDM Experience
License: MIT
Requires Python: >=3.8
Keywords: machine-learning, evolution, optimization, distributed, api-client

# Veox: The Incredible ML Client for DOUG

Veox is a standalone, pip-installable Python client for DOUG (Distributed Optimization Using GP). It delivers an "incredible" developer experience with a sklearn-style API, real-time TQDM progress bars, and rich terminal output.

## Features

- **sklearn-style API**: Familiar `fit(X, y)` interface with full pandas DataFrame support.
- **Distributed Evolution**: Seamlessly offload heavy computation to the DOUG cluster.
- **Real-time Streaming**: Beautiful TQDM progress bars and live status updates.
- **Built-in Datasets**: Instant access to curated ML datasets (Heart Disease, Titanic, etc.).
- **Code Pulling**: Extract generated pipeline code directly to your local machine.

## Installation

```bash
pip install veox
```

## Usage

### Python API

```python
from veox import Veox
from veox.datasets import load_heart_disease

# Initialize client
client = Veox(api_url="http://localhost:8090")

# Load built-in dataset
X, y = load_heart_disease()

# Run evolution
model = client.fit(X=X, y=y, task="binary", population=50, generations=10)

# Pull best pipeline code
client.pull_code(output_file="best_pipeline.py")
```

### Command Line Interface

```bash
# Run evolution on a CSV file
veox fit --csv my_data.csv --target-column target --task binary --verbose

# Pull code from the last job
veox pull-code --job-id job_abc123 --output pipeline.py
```

### Advanced Features

**Polling Mode (Robustness)**
If streaming connections are unstable, you can switch to polling mode:
```python
client.fit(..., use_polling=True, poll_interval=3.0)
```
The client also automatically falls back to polling if the stream disconnects.

**Plotting Evolution**
Visualizing the evolution process is built-in (requires `matplotlib` and `seaborn`):
```python
# During fit (auto-plot at end)
client.fit(..., plot=True)

# Or manually after the job
history = client.get_job_history(job_id, include_individuals=True)
from veox.plotting import plot_evolution
plot_evolution(history)
```

## Release Process

**CRITICAL:** Do NOT run `pip install` or `twine upload` directly on your host machine. Always use the provided Docker scripts to ensure a clean, isolated build environment.

To build and publish a new version of `veox`:

1.  **Update Version**:
    Edit `pyproject.toml` and bump the `version` field (e.g., `0.1.14` -> `0.1.15`).

2.  **Configure Credentials**: 
    Ensure you have a `.env` file in the project root directory (`~/veox_pip/.env`) containing your PyPI token.
    ```bash
    TWINE_USERNAME=__token__
    TWINE_PASSWORD=pypi-your-token-here
    ```

3.  **Run Containerized Release**:
    Execute the release script. This will spin up a clean Docker container, install build dependencies, build the wheel, and upload to PyPI.
    ```bash
    ./scripts/docker_release.sh
    ```

4.  **Verify**:
    Check [PyPI](https://pypi.org/project/veox/) to confirm the new version is live.

### Local Testing

To run the test suite purely locally (without publishing), use the Docker test script:
```bash
./scripts/docker_run_tests.sh
```
This runs all unit tests inside a container, matching the CI environment.