semvec/pypi-packages huggingface dataset:
towelie 0.1.4
Local code review for AI agents
Requires Python: >=3.13

# towelie

Local code review tool. Fast, easy and without clutter.

## Usage

```bash
uvx towelie
```

This starts a local server at `http://localhost:4242` and opens it in your browser.

## Development

```bash
uv run towelie --dev
```

This runs:
- FastAPI with backend auto-reload
- Bun frontend watcher for `bundle.js`
- Tailwind watcher for `output.css`

Browser reload is manual in dev mode, but every refresh will pick up the latest built JS/CSS.
automac 0.1.1
An all-in-one automated ML pipeline for feature engineering, optimization, and evaluation.
Requires Python: >=3.8

# ML-Automator ðŸš€

**ML-Automator** is a powerful, low-code machine learning utility library that automates Feature Engineering, Hyperparameter Optimization, and Model Evaluation.


## ðŸŒŸ Features

- **Automated Feature Engineering**: Handle Target Encoding, Scaling, and Imputation in one line.
- **Optuna-Powered Optimization**: Pre-configured search spaces for RandomForest, XGBoost, CatBoost, LightGBM, and more.
- **Deep Evaluation**:
    - Multi-model score comparison.
    - Interactive ROC and Calibration curves using Plotly.
    - Automated Learning Curve analysis.
    - Automatic report generation (CSV/Excel/PNG).

## ðŸ“‚ Project Structure
Your library is organized into three core modules:
1. `feature_engineering.py`: Data preprocessing and importance extraction.
2. `models_optimizer.py`: Optuna-based hyperparameter tuning.
3. `trainer.py`: Model training, cross-validation, and visualization.

## ðŸš€ Quick Start

### 1. Automation at its Best
```python
from automater.feature_engineering import FeatureEvaluation
from sklearn.ensemble import RandomForestClassifier

evaluator = FeatureEvaluation(X, y)
processed_x, importance_df = evaluator.fit_all_at_once(RandomForestClassifier())