MAINT validate parameter in KernelPCA (scikit-learn#24020)

Co-authored-by: Julien Jerphanion <[email protected]>
Co-authored-by: jeremiedbb <[email protected]>

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)

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
 

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",