8000 MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel by Charlie-XIAO · Pull Request #26072 · scikit-learn/scikit-learn · GitHub
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MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel #26072

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Merged
merged 7 commits into from
Apr 4, 2023
Merged

MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel #26072

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b2fd75f
MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel
Charlie-XIAO Apr 3, 2023
7f8193e
modified docstring
Charlie-XIAO Apr 3, 2023
6b6b6f7
resolved conversations
Charlie-XIAO Apr 4, 2023
c3e213b
Merge branch 'main' into param-val-sigmoid_kernel
Charlie-XIAO Apr 4, 2023
2272c74
Nystroem standards
Charlie-XIAO Apr 4, 2023
3f42f97
Update test_public_functions.py
jeremiedbb Apr 4, 2023
7f8ce84
Merge branch 'main' into param-val-sigmoid_kernel
jeremiedbb Apr 4, 2023
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16 changes: 14 additions & 2 deletions sklearn/metrics/pairwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1290,6 +1290,18 @@ def polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1):
return K


@validate_params(
{
"X": ["array-like", "sparse matrix"],
"Y": ["array-like", "sparse matrix", None],
"gamma": [
Interval(Real, 0, None, closed="left"),
None,
Hidden(np.ndarray),
],
"coef0": [Interval(Real, None, None, closed="neither")],
}
)
def sigmoid_kernel(X, Y=None, gamma=None, coef0=1):
"""Compute the sigmoid kernel between X and Y.

Expand All @@ -1299,10 +1311,10 @@ def sigmoid_kernel(X, Y=None, gamma=None, coef0=1):

Parameters
----------
X : ndarray of shape (n_samples_X, n_features)
X : {array-like, sparse matrix} of shape (n_samples_X, n_features)
A feature array.

Y : ndarray of shape (n_samples_Y, n_features), default=None
Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
An optional second feature array. If `None`, uses `Y=X`.

gamma : float, default=None
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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -217,6 +217,7 @@ def _check_function_param_validation(
"sklearn.metrics.pairwise.paired_manhattan_distances",
"sklearn.metrics.pairwise.polynomial_kernel",
"sklearn.metrics.pairwise.rbf_kernel",
"sklearn.metrics.pairwise.sigmoid_kernel",
"sklearn.metrics.precision_recall_curve",
"sklearn.metrics.precision_recall_fscore_support",
"sklearn.metrics.precision_score",
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0