scikit-learn · jjerphan · Dec 1, 2021 · Oct 15, 2021 · Oct 19, 2021 · Oct 19, 2021
diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
@@ -187,6 +187,11 @@ Changelog
   multilabel classification.
   :pr:`19689` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- |Enhancement| :class:`linear_model.Ridge` and :class:`linear_model.RidgeClassifier`
+  now raise consistent error message when passed invalid values for `alpha`,
+  `max_iter` and `tol`.
+  :pr:`21341` by :user:`Arturo Amor <ArturoAmorQ>`.
+
 :mod:`sklearn.linear_model`
 ...........................
 

diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py
@@ -13,6 +13,7 @@
 import warnings
 
 import numpy as np
+import numbers
 from scipy import linalg
 from scipy import sparse
 from scipy import optimize
@@ -26,6 +27,7 @@
 from ..utils.extmath import row_norms
 from ..utils import check_array
 from ..utils import check_consistent_length
+from ..utils import check_scalar
 from ..utils import compute_sample_weight
 from ..utils import column_or_1d
 from ..utils.validation import check_is_fitted
@@ -557,6 +559,17 @@ def _ridge_regression(
             # we implement sample_weight via a simple rescaling.
             X, y = _rescale_data(X, y, sample_weight)
 
+    # Some callers of this method might pass alpha as single
+    # element array which already has been validated.
+    if alpha is not None and not isinstance(alpha, (np.ndarray, tuple)):
+        alpha = check_scalar(
+            alpha,
+            "alpha",
+            target_type=numbers.Real,
+            min_val=0.0,
+            include_boundaries="left",
+        )
+
     # There should be either 1 or n_targets penalties
     alpha = np.asarray(alpha, dtype=X.dtype).ravel()
     if alpha.size not in [1, n_targets]:
@@ -742,6 +755,13 @@ def fit(self, X, y, sample_weight=None):
         if sample_weight is not None:
             sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
 
+        if self.max_iter is not None:
+            self.max_iter = check_scalar(
+                self.max_iter, "max_iter", target_type=numbers.Integral, min_val=1
+            )
+
+        self.tol = check_scalar(self.tol, "tol", target_type=numbers.Real, min_val=0.0)
+
         # when X is sparse we only remove offset from y
         X, y, X_offset, y_offset, X_scale = self._preprocess_data(
             X,

diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
@@ -335,6 +335,42 @@ def test_ridge_individual_penalties():
         ridge.fit(X, y)
 
 
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        ({"alpha": -1}, ValueError, "alpha == -1, must be >= 0.0"),
+        (
+            {"alpha": "1"},
+            TypeError,
+            "alpha must be an instance of <class 'numbers.Real'>, not <class 'str'>",
+        ),
+        ({"max_iter": 0}, ValueError, "max_iter == 0, must be >= 1."),
+        (
+            {"max_iter": "1"},
+            TypeError,
+            "max_iter must be an instance of <class 'numbers.Integral'>, not <class"
+            " 'str'>",
+        ),
+        ({"tol": -1.0}, ValueError, "tol == -1.0, must be >= 0."),
+        (
+            {"tol": "1"},
+            TypeError,
+            "tol must be an instance of <class 'numbers.Real'>, not <class 'str'>",
+        ),
+    ],
+)
+def test_ridge_params_validation(params, err_type, err_msg):
+    """Check the parameters validation in Ridge."""
+
+    rng = np.random.RandomState(42)
+    n_samples, n_features, n_targets = 20, 10, 5
+    X = rng.randn(n_samples, n_features)
+    y = rng.randn(n_samples, n_targets)
+
+    with pytest.raises(err_type, match=err_msg):
+        Ridge(**params).fit(X, y)
+
+
 @pytest.mark.parametrize("n_col", [(), (1,), (3,)])
 def test_X_CenterStackOp(n_col):
     rng = np.random.RandomState(0)