Metadata-Version: 2.4
Name: custom-pyod
Version: 0.1.0
Summary: COPOD and ECOD implementation using Polars
Project-URL: Homepage, https://github.com/yourusername/custom-pyod
Project-URL: Repository, https://github.com/yourusername/custom-pyod.git
Project-URL: Bug Tracker, https://github.com/yourusername/custom-pyod/issues
Author-email: Your Name <your.email@example.com>
License: MIT
Keywords: anomaly-detection,copod,ecod,outlier-detection,polars
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Requires-Dist: numpy>=1.21.0
Requires-Dist: polars>=0.20.0
Requires-Dist: scikit-learn>=0.24.2
Requires-Dist: scipy>=1.7.0
Description-Content-Type: text/markdown

# custom-pyod

High-performance outlier detection algorithms using Polars.

## Features

- **COPOD**: Copula-Based Outlier Detection
- **ECOD**: Empirical Cumulative Distribution Based Outlier Detection
- Built on Polars for high performance
- Compatible with scikit-learn API

## Installation

```bash
pip install custom-pyod
```

## Quick Start

```python
from custom_pyod.models import COPOD, ECOD
import polars as pl

# Load your data as a Polars DataFrame
df = pl.DataFrame({
    'feature1': [1, 2, 3, 100],
    'feature2': [1, 1, 1, 50]
})

# COPOD
copod = COPOD()
outlier_scores = copod.fit_predict(df)

# ECOD
ecod = ECOD()
outlier_scores = ecod.fit_predict(df)
```

## License

MIT License
