Getting Started

This guide provides the essential steps to install glmpynet and run your first regularized logistic regression model. It is designed to get you up and running in under five minutes.

For a complete reference of all commands and options, please see the Usage Guide.

Installation

glmpynet is distributed via the Python Package Index (PyPI) and can be installed easily using pip. Ensure you have Python 3.8 or higher installed.

pip install glmpynet

This command will install glmpynet and its necessary dependencies, such as NumPy and Scikit-learn. For a full development setup, which includes the C++ toolchain, please see the Environment Setup guide.

Quick Start

Using glmpynet is as straightforward as any Scikit-learn estimator. The main class, LogisticRegression, follows the standard .fit() and .predict() API and accepts familiar parameters like penalty and C.

The following example trains a model on a synthetic dataset.

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

# 1. Generate a synthetic binary classification dataset
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 on the test set
y_pred = model.predict(X_test)

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