glmpynet 0.5.8

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

glmpynet

Logistic Regression using glmnet 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.

License

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