Sparse Linear Regression Models

:warning: this package is currently largely lacking in unit-tests. Use at your own risk!

sparse-lm includes several regularized regression estimators that are absent in the sklearn.linear_model module. The estimators in sparse-lm are designed to fit right into scikit-lean by inheriting from their base LinearModel. But the underlying optimization problem is expressed and solved by leveraging cvxpy.

Available regression models

• Ordinary Least Squares (sklearn may be a better option)
• Lasso (sklearn may be a better option)
• Group Lasso, Overlap Group Lasso & Sparse Group Lasso
• Adaptive versions of Lasso, Group Lasso, Overlap Group Lasso & Sparse Group Lasso
• Best subset selection, ridged best subset, L0, L1L0 & L2L0 (gurobi recommended for performance)
• Best group selection, ridged best group selection, grouped L0, grouped L2L0 (gurobi recommended for performance)

Installation

From pypi:

pip install sparse-lm

Usage

If you already use scikit-learn, using sparse-lm will be very easy

import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import GridSearchCV
from sparselm.model import AdaptiveLasso

X, y = make_regression(n_samples=200, n_features=5000, random_state=0)
alasso = AdaptiveLasso(fit_intercept=False)
param_grid = {'alpha': np.logsppace(-7, -2)}

cvsearch = GridSearchCV(alasso, param_grid)
cvsearch.fit(X, y)
print(cvsearch.best_params_)