ENH - Adds support for L1 + L2 regularization in SparseLogisticRegression (#278)

Co-authored-by: mathurinm <[email protected]>

Author: AnavAgrawal

skglm/estimators.py

@@ -967,6 +967,12 @@ class SparseLogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstim
     alpha : float, default=1.0
         Regularization strength; must be a positive float.
 
+    l1_ratio : float, default=1.0
+        The ElasticNet mixing parameter, with ``0 <= l1_ratio <= 1``. For
+        ``l1_ratio = 0`` the penalty is an L2 penalty. ``For l1_ratio = 1`` it
+        is an L1 penalty.  For ``0 < l1_ratio < 1``, the penalty is a
+        combination of L1 and L2.
+
     tol : float, optional
         Stopping criterion for the optimization.
 
@@ -1003,10 +1009,11 @@ class SparseLogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstim
         Number of subproblems solved to reach the specified tolerance.
     """
 
-    def __init__(self, alpha=1.0, tol=1e-4, max_iter=20, max_epochs=1_000, verbose=0,
-                 fit_intercept=True, warm_start=False):
+    def __init__(self, alpha=1.0, l1_ratio=1.0, tol=1e-4, max_iter=20, max_epochs=1_000,
+                 verbose=0, fit_intercept=True, warm_start=False):
         super().__init__()
         self.alpha = alpha
+        self.l1_ratio = l1_ratio
         self.tol = tol
         self.max_iter = max_iter
         self.max_epochs = max_epochs
@@ -1035,7 +1042,8 @@ def fit(self, X, y):
             max_iter=self.max_iter, max_pn_iter=self.max_epochs, tol=self.tol,
             fit_intercept=self.fit_intercept, warm_start=self.warm_start,
             verbose=self.verbose)
-        return _glm_fit(X, y, self, Logistic(), L1(self.alpha), solver)
+        return _glm_fit(X, y, self, Logistic(), L1_plus_L2(self.alpha, self.l1_ratio),
+                        solver)
 
     def predict_proba(self, X):
         """Probability estimates.

skglm/tests/test_estimators.py

@@ -600,5 +600,25 @@ def test_GroupLasso_estimator_sparse_vs_dense(positive):
     np.testing.assert_allclose(coef_sparse, coef_dense, atol=1e-7, rtol=1e-5)
 
 
+@pytest.mark.parametrize("X, l1_ratio", product([X, X_sparse], [1., 0.7, 0.]))
+def test_SparseLogReg_elasticnet(X, l1_ratio):
+
+    estimator_sk = clone(dict_estimators_sk['LogisticRegression'])
+    estimator_ours = clone(dict_estimators_ours['LogisticRegression'])
+    estimator_sk.set_params(fit_intercept=True, solver='saga',
+                            penalty='elasticnet', l1_ratio=l1_ratio, max_iter=10_000)
+    estimator_ours.set_params(fit_intercept=True, l1_ratio=l1_ratio, max_iter=10_000)
+
+    estimator_sk.fit(X, y)
+    estimator_ours.fit(X, y)
+    coef_sk = estimator_sk.coef_
+    coef_ours = estimator_ours.coef_
+
+    np.testing.assert_array_less(1e-5, norm(coef_ours))
+    np.testing.assert_allclose(coef_ours, coef_sk, atol=1e-6)
+    np.testing.assert_allclose(
+        estimator_sk.intercept_, estimator_ours.intercept_, rtol=1e-4)
+
+
 if __name__ == "__main__":
     pass