scikit-learn · jeremiedbb · Jul 28, 2022 · Jul 26, 2022 · Jul 27, 2022 · Jul 28, 2022
diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py
@@ -5,7 +5,7 @@
 # License: BSD 3 clause
 
 import numpy as np
-import numbers
+from numbers import Integral, Real
 from scipy import linalg
 from scipy.sparse.linalg import eigsh
 
@@ -14,8 +14,8 @@
 from ..utils.validation import (
     check_is_fitted,
     _check_psd_eigenvalues,
-    check_scalar,
 )
+from ..utils._param_validation import Interval, StrOptions
 from ..utils.deprecation import deprecated
 from ..exceptions import NotFittedError
 from ..base import BaseEstimator, TransformerMixin, _ClassNamePrefixFeaturesOutMixin
@@ -42,8 +42,8 @@ class KernelPCA(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato
     n_components : int, default=None
         Number of components. If None, all non-zero components are kept.
 
-    kernel : {'linear', 'poly', \
-            'rbf', 'sigmoid', 'cosine', 'precomputed'}, default='linear'
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'} \
+            or callable, default='linear'
         Kernel used for PCA.
 
     gamma : float, default=None
@@ -239,6 +239,40 @@ class KernelPCA(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato
     (1797, 7)
     """
 
+    _parameter_constraints = {
+        "n_components": [
+            Interval(Integral, 1, None, closed="left"),
+            None,
+        ],
+        "kernel": [
+            StrOptions({"linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"}),
+            callable,
+        ],
+        "gamma": [
+            Interval(Real, 0, None, closed="left"),
+            None,
+        ],
+        "degree": [Interval(Integral, 0, None, closed="left")],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+        "kernel_params": [dict, None],
+        "alpha": [Interval(Real, 0, None, closed="left")],
+        "fit_inverse_transform": ["boolean"],
+        "eigen_solver": [StrOptions({"auto", "dense", "arpack", "randomized"})],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "max_iter": [
+            Interval(Integral, 1, None, closed="left"),
+            None,
+        ],
+        "iterated_power": [
+            Interval(Integral, 0, None, closed="left"),
+            StrOptions({"auto"}),
+        ],
+        "remove_zero_eig": ["boolean"],
+        "random_state": ["random_state"],
+        "copy_X": ["boolean"],
+        "n_jobs": [None, Integral],
+    }
+
     def __init__(
         self,
         n_components=None,
@@ -313,7 +347,6 @@ def _fit_transform(self, K):
         if self.n_components is None:
             n_components = K.shape[0]  # use all dimensions
         else:
-            check_scalar(self.n_components, "n_components", numbers.Integral, min_val=1)
             n_components = min(K.shape[0], self.n_components)
 
         # compute eigenvectors
@@ -343,8 +376,6 @@ def _fit_transform(self, K):
                 random_state=self.random_state,
                 selection="module",
             )
-        else:
-            raise ValueError("Unsupported value for `eigen_solver`: %r" % eigen_solver)
 
         # make sure that the eigenvalues are ok and fix numerical issues
         self.eigenvalues_ = _check_psd_eigenvalues(
@@ -416,6 +447,8 @@ def fit(self, X, y=None):
         self : object
             Returns the instance itself.
         """
+        self._validate_params()
+
         if self.fit_inverse_transform and self.kernel == "precomputed":
             raise ValueError("Cannot fit_inverse_transform with a precomputed kernel.")
         X = self._validate_data(X, accept_sparse="csr", copy=self.copy_X)

diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py
@@ -67,12 +67,6 @@ def histogram(x, y, **kwargs):
                 assert X_pred2.shape == X_pred.shape
 
 
-def test_kernel_pca_invalid_solver():
-    """Check that kPCA raises an error if the solver parameter is invalid"""
-    with pytest.raises(ValueError):
-        KernelPCA(eigen_solver="unknown").fit(np.random.randn(10, 10))
-
-
 def test_kernel_pca_invalid_parameters():
     """Check that kPCA raises an error if the parameters are invalid
 
@@ -204,16 +198,6 @@ def test_kernel_pca_n_components():
             assert shape == (2, c)
 
 
-@pytest.mark.parametrize("n_components", [-1, 0])
-def test_kernal_pca_too_few_components(n_components):
-    rng = np.random.RandomState(0)
-    X_fit = rng.random_sample((5, 4))
-    kpca = KernelPCA(n_components=n_components)
-    msg = "n_components.* must be >= 1"
-    with pytest.raises(ValueError, match=msg):
-        kpca.fit(X_fit)
-
-
 def test_remove_zero_eig():
     """Check that the ``remove_zero_eig`` parameter works correctly.
 
@@ -326,18 +310,6 @@ def test_kernel_pca_precomputed_non_symmetric(solver):
     assert_array_equal(kpca.eigenvalues_, kpca_c.eigenvalues_)
 
 
-def test_kernel_pca_invalid_kernel():
-    """Tests that using an invalid kernel name raises a ValueError
-
-    An invalid kernel name should raise a ValueError at fit time.
-    """
-    rng = np.random.RandomState(0)
-    X_fit = rng.random_sample((2, 4))
-    kpca = KernelPCA(kernel="tototiti")
-    with pytest.raises(ValueError):
-        kpca.fit(X_fit)
-
-
 def test_gridsearch_pipeline():
     """Check that kPCA works as expected in a grid search pipeline
 

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -459,7 +459,6 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
     "HashingVectorizer",
     "Isomap",
     "IterativeImputer",
-    "KernelPCA",
     "LabelPropagation",
     "LabelSpreading",
     "Lars",