glmpynet 0.5.4

A scikit-learn compatible wrapper for glmnet-based logistic regression.

glmpynet

High-Performance Logistic Regression for Scikit-Learn

glmpynet is a Python package that provides a scikit-learn-compatible LogisticRegression API powered by the high-performance glmnetpp C++ library.

This project bridges the computational speed of glmnetpp with the ease-of-use of the Python data science ecosystem, acting as a drop-in replacement for sklearn.linear_model.LogisticRegression for regularized logistic regression.

Key Features

• High Performance: Designed to leverage the optimized glmnetpp C++ backend for fitting models, suitable for large datasets. (Note: Currently uses a mock backend for API development).
• Fully Scikit-learn Compatible: Implements the full estimator API, including fit, predict, and predict_proba, enabling seamless integration with sklearn tools like Pipeline and GridSearchCV.
• User-Friendly Hybrid API: Accepts both standard Scikit-learn parameters (e.g., C, penalty) for ease of use and glmnet-native parameters (e.g., alpha, nlambda) for advanced control.
• Robust Development: Built with Bazel and Conda for reproducible builds, with a comprehensive test suite to ensure reliability.

Installation

Once released, install glmpynet via pip:

pip install glmpynet

Quick Start

Using glmpynet is as simple as any Scikit-learn estimator.

from glmpynet.logistic_regression import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score

# 1. Generate synthetic data
X, y = make_classification(n_samples=1000, n_features=50, n_informative=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 2. Instantiate and fit the model using familiar sklearn parameters
model = LogisticRegression(penalty='l1', C=0.5)
model.fit(X_train, y_train)

# 3. Make predictions
y_pred = model.predict(X_test)

# 4. Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

Project Status

The Python API for glmpynet.LogisticRegression is now complete and fully tested against a mock backend. The next major phase of development is to implement the real C++ binding that connects this API to the glmnetpp engine.

See the ROADMAP.md for the full development plan.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines on reporting bugs, suggesting features, or submitting pull requests.

License

This project is distributed under the GNU General Public License version 2.