8000 FIX check that parameters validation happen in fit for KernelPCA by MaggieChege · Pull Request #21567 · scikit-learn/scikit-learn · GitHub
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FIX check that parameters validation happen in fit for KernelPCA #21567

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Merged
merged 4 commits into from
Nov 15, 2021
Merged

FIX check that parameters validation happen in fit for KernelPCA #21567

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f3535cf
fix validate parameters in fit for # KernelPCA,
Nov 5, 2021
10d0282
fix tests. Add whats new on the current changes
Nov 11, 2021
68a2e76
rebase master
Nov 11, 2021
588ec25
changes requested
Nov 14, 2021
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7 changes: 7 additions & 0 deletions doc/whats_new/v1.1.rst
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Expand Up @@ -173,6 +173,13 @@ Changelog
instead of `__init__`.
:pr:`21434` by :user:`Krum Arnaudov <krumeto>`.


:mod:`sklearn.decomposition.KernelPCA`
......................................
- |Fix| :class:`decomposition.KernelPCA` now validates input parameters in
`fit` instead of `__init__`.
:pr:`21567` by :user:`Maggie Chege <MaggieChege>`.

:mod:`sklearn.svm`
..................

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4 changes: 2 additions & 2 deletions sklearn/decomposition/_kernel_pca.py
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Expand Up @@ -257,8 +257,6 @@ def __init__(
copy_X=True,
n_jobs=None,
):
if fit_inverse_transform and kernel == "precomputed":
raise ValueError("Cannot fit_inverse_transform with a precomputed kernel.")
self.n_components = n_components
self.kernel = kernel
self.kernel_params = kernel_params
Expand Down Expand Up @@ -429,6 +427,8 @@ def fit(self, X, y=None):
self : object
Returns the instance itself.
"""
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)
self._centerer = KernelCenterer()
K = self._get_kernel(X)
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8 changes: 6 additions & 2 deletions sklearn/decomposition/tests/test_kernel_pca.py
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Expand Up @@ -78,8 +78,12 @@ def test_kernel_pca_invalid_parameters():
Tests fitting inverse transform with a precomputed kernel raises a
ValueError.
"""
with pytest.raises(ValueError):
KernelPCA(10, fit_inverse_transform=True, kernel="precomputed")
estimator = KernelPCA(
n_components=10, fit_inverse_transform=True, kernel="precomputed"
)
err_ms = "Cannot fit_inverse_transform with a precomputed kernel"
with pytest.raises(ValueError, match=err_ms):
estimator.fit(np.random.randn(10, 10))


def test_kernel_pca_consistent_transform():
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1 change: 0 additions & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -410,7 +410,6 @@ def test_transformers_get_feature_names_out(transformer):
"FeatureUnion",
"GridSearchCV",
"HalvingGridSearchCV",
"KernelPCA",
"Pipeline",
"SGDOneClassSVM",
"TheilSenRegressor",
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0