Fix TypeError in LogisticRegressionCV with refit, use_legacy_attribute False (scikit-learn#33902)

Author: Fazel94

doc/whats_new/upcoming_changes/sklearn.linear_model/33902.fix.rst

@@ -0,0 +1,5 @@
+- :class:`linear_model.LogisticRegressionCV` no longer raises a ``TypeError``
+  when `refit=False` and `use_legacy_attributes=False` are set together with a
+  non-elasticnet penalty like `l1_ratios=[0.0]`. Previously, `None` was stored in `l1_ratio_` instead
+  of `0.0`, which caused `float()` to fail during post-processing.
+  By :user:`Mohamad Fazeli <Fazel94>`.

sklearn/linear_model/_logistic.py

@@ -2195,7 +2195,7 @@ def fit(self, X, y, sample_weight=None, **params):
                 best_indices_l1 = best_indices[:, 1]
                 self.l1_ratio_.append(np.mean(l1_ratios_[best_indices_l1]))
             else:
-                self.l1_ratio_.append(None)
+                self.l1_ratio_.append(0.0)
 
         if is_binary:
             self.coef_ = w[:, :n_features] if w.ndim == 2 else w[:n_features][None, :]

sklearn/linear_model/tests/test_logistic.py

@@ -473,6 +473,27 @@ def test_logistic_cv(global_random_seed, use_legacy_attributes):
         assert lr_cv.scores_.shape == (n_cv, n_l1_ratios, n_Cs)
 
 
+# TODO(1.11): remove filterwarnings with change of default scoring
+@pytest.mark.filterwarnings("ignore:The default value.*scoring.*:FutureWarning")
+def test_logistic_cv_refit_false_non_elasticnet(global_random_seed):
+    """Test that non-elasticnet penalty with refit=False and
+    use_legacy_attributes=False works without error.
+
+    For non-elasticnet penalties, l1_ratio=0.0 (equivalent to pure L2).
+    Previously, None was stored, which caused float() to raise a
+    TypeError when use_legacy_attributes=False converted the value to a scalar.
+    """
+    X, y = make_classification(random_state=global_random_seed)
+    lr_cv = LogisticRegressionCV(
+        l1_ratios=[0.0],
+        refit=False,
+        use_legacy_attributes=False,
+        random_state=global_random_seed,
+    )
+    lr_cv.fit(X, y)
+    assert lr_cv.l1_ratio_ == 0.0
+
+
 def test_logistic_cv_mock_scorer():
     """Test that LogisticRegressionCV calls the scorer."""