oneclass-elasticnet 0.0.1

A package for anomaly detection using Elastic Net and Convex Hull.

ocelastic: Elastic Net One-Class Classifier for Anomaly Detection

ocelastic is a Python package for one-class classification and anomaly detection. It leverages Elastic Net and convex hull optimization to identify anomalies in data. Designed for flexibility, it supports various kernel methods and is compatible with scikit-learn tools like GridSearchCV and Pipeline.

Key Features

• Elastic Net Regularization: Combines L1 and L2 penalties for robust optimization.
• Kernel Support: Includes rbf, linear, poly, and sigmoid kernels.
• Custom Threshold: Automatically find the best decision boundary using ThresholdFinder.
• Scikit-learn Compatibility: Fully compatible with scikit-learn tools like Pipeline and GridSearchCV.
• Anomaly Detection: Designed specifically for one-class classification tasks.

Installation

Install the package using pip:

pip install oneclass-elasticnet

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Quick Start

from ocelastic import EnetConvexHull, ThresholdFinder
import numpy as np

# Generate synthetic data
X = np.random.rand(100, 2)  # 100 samples, 2 features

# Initialize and fit the model
model = EnetConvexHull(landa1=0.5, metric='rbf', solver='cvxopt')
model.fit(X)

# Use ThresholdFinder to calculate the optimal threshold
finder = ThresholdFinder(model=model, X=X)
z_values, max_z = finder.find()

# Make predictions
predictions = model.predict(X)

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Parameters

• landa1: The weight for L1 regularization (default: 0.5).
• metric: Kernel function for pairwise similarity (rbf, linear, poly, sigmoid).
• thr: Decision threshold for anomaly detection (default: 1.0).
• kernel_params: Additional kernel parameters like gamma, degree, and coef0.
• solver: Quadratic programming solver (e.g., cvxopt).

Results

Below is an example of the decision boundary generated by the model:

Requirements

• Python >= 3.8
• scikit-learn
• numpy
• matplotlib
• tqdm
• qpsolvers

Contributing

We welcome contributions! To get started:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Commit your changes (git commit -m 'Add a new feature').
4. Push to the branch (git push origin feature/your-feature-name).
5. Create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For questions or feedback, please email [[e-mail]] or open an issue in the repository.