try other setups test cv

Author: mathurinm

skglm/tests/test_cv.py

@@ -35,3 +35,29 @@ def test_elasticnet_cv_matches_sklearn(seed):
                                skglm_model.coef_.ravel(), rtol=1e-4, atol=1e-6)
     np.testing.assert_allclose(sklearn_model.best_estimator_.intercept_,
                                skglm_model.intercept_, rtol=1e-4, atol=1e-6)
+
+
+if __name__ == "__main__":
+    X, y = make_regression(n_samples=40, n_features=60, noise=2, random_state=0)
+
+    alphas = np.array([1e-4, 0.001, 0.01, 0.1])
+    l1_ratios = np.array([0.2, 0.5, 0.8])
+    cv = KFold(n_splits=5, shuffle=True, random_state=0)
+
+    sklearn_model = GridSearchCV(
+        ElasticNet(max_iter=10000, tol=1e-8),
+        {'alpha': alphas, 'l1_ratio': l1_ratios},
+        cv=cv, scoring='neg_mean_squared_error', n_jobs=1
+    ).fit(X, y)
+
+    skglm_model = GeneralizedLinearEstimatorCV(
+        Quadratic(), L1_plus_L2(0.1, 0.5), AndersonCD(max_iter=10000, tol=1e-8),
+        alphas=alphas, l1_ratio=l1_ratios, cv=5, random_state=0, n_jobs=1
+    ).fit(X, y)
+
+    assert sklearn_model.best_params_['alpha'] == skglm_model.alpha_
+    assert sklearn_model.best_params_['l1_ratio'] == skglm_model.l1_ratio_
+    np.testing.assert_allclose(sklearn_model.best_estimator_.coef_,
+                               skglm_model.coef_.ravel(), rtol=1e-4, atol=1e-6)
+    np.testing.assert_allclose(sklearn_model.best_estimator_.intercept_,
+                               skglm_model.intercept_, rtol=1e-4, atol=1e-6)