scikit-learn · glemaitre · Jan 31, 2022 · Jan 12, 2022 · Jan 13, 2022 · Jan 14, 2022
diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py
@@ -15,6 +15,7 @@
 
 from abc import abstractmethod
 
+import numbers
 import numpy as np
 
 from joblib import Parallel
@@ -27,6 +28,7 @@
 from ._base import _BaseHeterogeneousEnsemble
 from ..preprocessing import LabelEncoder
 from ..utils import Bunch
+from ..utils import check_scalar
 from ..utils.metaestimators import available_if
 from ..utils.validation import check_is_fitted
 from ..utils.multiclass import check_classification_targets
@@ -46,7 +48,7 @@ class _BaseVoting(TransformerMixin, _BaseHeterogeneousEnsemble):
     def _log_message(self, name, idx, total):
         if not self.verbose:
             return None
-        return "(%d of %d) Processing %s" % (idx, total, name)
+        return f"({idx} of {total}) Processing {name}"
 
     @property
     def _weights_not_none(self):
@@ -64,11 +66,17 @@ def fit(self, X, y, sample_weight=None):
         """Get common fit operations."""
         names, clfs = self._validate_estimators()
 
+        check_scalar(
+            self.verbose,
+            name="verbose",
+            target_type=(numbers.Integral, np.bool_),
+            min_val=0,
+        )
+
         if self.weights is not None and len(self.weights) != len(self.estimators):
             raise ValueError(
-                "Number of `estimators` and weights must be equal"
-                "; got %d weights, %d estimators"
-                % (len(self.weights), len(self.estimators))
+                "Number of `estimators` and weights must be equal; got"
+                f" {len(self.weights)} weights, {len(self.estimators)} estimators"
             )
 
         self.estimators_ = Parallel(n_jobs=self.n_jobs)(
@@ -312,9 +320,15 @@ def fit(self, X, y, sample_weight=None):
                 "Multilabel and multi-output classification is not supported."
             )
 
+        check_scalar(
+            self.flatten_transform,
+            name="flatten_transform",
+            target_type=(numbers.Integral, np.bool_),
+        )
+
         if self.voting not in ("soft", "hard"):
             raise ValueError(
-                "Voting must be 'soft' or 'hard'; got (voting=%r)" % self.voting
+                f"Voting must be 'soft' or 'hard'; got (voting={self.voting!r})"
             )
 
         self.le_ = LabelEncoder().fit(y)

diff --git a/sklearn/ensemble/tests/test_voting.py b/sklearn/ensemble/tests/test_voting.py
@@ -34,6 +34,40 @@
 X_r, y_r = datasets.load_diabetes(return_X_y=True)
 
 
+def test_invalid_type_for_flatten_transform():
+    # Test that invalid input raises the proper exception
+    ensemble = VotingClassifier(
+        estimators=[("lr", LogisticRegression())], flatten_transform="foo"
+    )
+    err_msg = "flatten_transform must be an instance of"
+    with pytest.raises(TypeError, match=err_msg):
+        ensemble.fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "X, y, voter, learner",
+    [
+        (X, y, VotingClassifier, {"estimators": [("lr", LogisticRegression())]}),
+        (X_r, y_r, VotingRegressor, {"estimators": [("lr", LinearRegression())]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        ({"verbose": -1}, ValueError, "verbose == -1, must be >= 0"),
+        ({"verbose": "foo"}, TypeError, "verbose must be an instance of"),
+    ],
+)
+def test_voting_estimators_param_validation(
+    X, y, voter, learner, params, err_type, err_msg
+):
+    # Test that invalid input raises the proper exception
+    params.update(learner)
+    ensemble = voter(**params)
+    with pytest.raises(err_type, match=err_msg):
+        ensemble.fit(X, y)
+
+
 @pytest.mark.parametrize(
     "params, err_msg",
     [