more old sklearn fixes

Author: Genuster

mne/decoding/base.py

@@ -381,6 +381,7 @@ class LinearModel(MetaEstimatorMixin, BaseEstimator):
         "predict",
         "predict_proba",
         "predict_log_proba",
+        "_estimator_type",  # remove after sklearn 1.6
         "decision_function",
         "score",
         "classes_",
@@ -428,14 +429,23 @@ def __getattr__(self, attr):
     def _fit_transform(self, X, y):
         return self.fit(X, y).transform(X)
 
-    def _validate_params(self):
-        is_predictor = is_regressor(self._orig_model) or is_classifier(self._orig_model)
+    def _validate_params(self, X):
+        model = self._orig_model
+        if isinstance(model, MetaEstimatorMixin):
+            model = model.estimator
+        is_predictor = is_regressor(model) or is_classifier(model)
         if not is_predictor:
             raise ValueError(
                 "Linear model should be a supervised predictor "
                 "(classifier or regressor)"
             )
 
+        # For sklearn < 1.6
+        try:
+            self._check_n_features(X, reset=True)
+        except AttributeError:
+            pass
+
     def fit(self, X, y, **fit_params):
         """Estimate the coefficients of the linear model.
 
@@ -456,7 +466,7 @@ def fit(self, X, y, **fit_params):
         self : instance of LinearModel
             Returns the modified instance.
         """
-        self._validate_params()
+        self._validate_params(X)
         X, y = validate_data(self, X, y, multi_output=True)
 
         # fit the Model

mne/decoding/tests/test_base.py

@@ -99,7 +99,7 @@ def test_get_coef():
     """Test getting linear coefficients (filters/patterns) from estimators."""
     lm_classification = LinearModel(LogisticRegression(solver="liblinear"))
     assert hasattr(lm_classification, "__sklearn_tags__")
-    if check_version("sklearn", "1.4"):
+    if check_version("sklearn", "1.6"):
         print(lm_classification.__sklearn_tags__())
     assert is_classifier(lm_classification.model)
     assert is_classifier(lm_classification)