[MRG] Fixed randomness of test_logreg_cv_penalty (#12624)

* fixed randomness of test_logreg_cv_penalty

* Added comment about warm-starting coefficients

sklearn/linear_model/tests/test_logistic.py

@@ -1150,14 +1150,31 @@ def test_logreg_l1_sparse_data():
 
 @pytest.mark.filterwarnings('ignore: Default multi_class will')  # 0.22
 @pytest.mark.filterwarnings('ignore: You should specify a value')  # 0.22
-def test_logreg_cv_penalty():
-    # Test that the correct penalty is passed to the final fit.
-    X, y = make_classification(n_samples=50, n_features=20, random_state=0)
-    lr_cv = LogisticRegressionCV(penalty="l1", Cs=[1.0], solver='saga')
+@pytest.mark.parametrize("random_seed", [42])
+@pytest.mark.parametrize("penalty", ["l1", "l2"])
+def test_logistic_regression_cv_refit(random_seed, penalty):
+    # Test that when refit=True, logistic regression cv with the saga solver
+    # converges to the same solution as logistic regression with a fixed
+    # regularization parameter.
+    # Internally the LogisticRegressionCV model uses a warm start to refit on
+    # the full data model with the optimal C found by CV. As the penalized
+    # logistic regression loss is convex, we should still recover exactly
+    # the same solution as long as the stopping criterion is strict enough (and
+    # that there are no exactly duplicated features when penalty='l1').
+    X, y = make_classification(n_samples=50, n_features=20,
+                               random_state=random_seed)
+    common_params = dict(
+        solver='saga',
+        penalty=penalty,
+        random_state=random_seed,
+        max_iter=10000,
+        tol=1e-12,
+    )
+    lr_cv = LogisticRegressionCV(Cs=[1.0], refit=True, **common_params)
     lr_cv.fit(X, y)
-    lr = LogisticRegression(penalty="l1", C=1.0, solver='saga')
+    lr = LogisticRegression(C=1.0, **common_params)
     lr.fit(X, y)
-    assert_equal(np.count_nonzero(lr_cv.coef_), np.count_nonzero(lr.coef_))
+    assert_array_almost_equal(lr_cv.coef_, lr.coef_)
 
 
 def test_logreg_predict_proba_multinomial():