8000 MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosine_distances by Charlie-XIAO · Pull Request #26075 · scikit-learn/scikit-learn · GitHub
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MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosine_distances #26075

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Merged
merged 2 commits into from
Apr 4, 2023
Merged

MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosine_distances #26075

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MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosin…
Charlie-XIAO Apr 3, 2023
7e3f89e
Merge branch 'main' into param-val-paired_cosine_distances
jeremiedbb Apr 4, 2023
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7 changes: 5 additions & 2 deletions sklearn/metrics/pairwise.py
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Expand Up @@ -1094,6 +1094,9 @@ def paired_manhattan_distances(X, Y):
return np.abs(diff).sum(axis=-1)


@validate_params(
{"X": ["array-like", "sparse matrix"], "Y": ["array-like", "sparse matrix"]}
)
def paired_cosine_distances(X, Y):
"""
Compute the paired cosine distances between X and Y.
Expand All @@ -1102,10 +1105,10 @@ def paired_cosine_distances(X, Y):

Parameters
----------
X : array-like of shape (n_samples, n_features)
X : {array-like, sparse matrix} of shape (n_samples, n_features)
An array where each row is a sample and each column is a feature.

Y : array-like of shape (n_samples, n_features)
Y : {array-like, sparse matrix} of shape (n_samples, n_features)
An array where each row is a sample and each column is a feature.

Returns
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9 changes: 9 additions & 0 deletions sklearn/metrics/tests/test_pairwise.py
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Expand Up @@ -53,6 +53,7 @@
from sklearn.metrics.pairwise import paired_distances
from sklearn.metrics.pairwise import paired_euclidean_distances
from sklearn.metrics.pairwise import paired_manhattan_distances
from sklearn.metrics.pairwise import paired_cosine_distances
from sklearn.metrics.pairwise import _euclidean_distances_upcast
from sklearn.preprocessing import normalize
from sklearn.exceptions import DataConversionWarning
Expand Down Expand Up @@ -1177,6 +1178,14 @@ def test_paired_manhattan_distances():
assert_allclose(D, [1.0, 2.0])


def test_paired_cosine_distances():
# Check the paired manhattan distances computation
X = [[0], [0]]
Y = [[1], [2]]
D = paired_cosine_distances(X, Y)
assert_allclose(D, [0.5, 0.5])


def test_chi_square_kernel():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -209,6 +209,7 @@ def _check_function_param_validation(
"sklearn.metrics.pairwise.haversine_distances",
"sklearn.metrics.pairwise.laplacian_kernel",
"sklearn.metrics.pairwise.linear_kernel",
"sklearn.metrics.pairwise.paired_cosine_distances",
"sklearn.metrics.pairwise.paired_euclidean_distances",
"sklearn.metrics.pairwise.paired_manhattan_distances",
"sklearn.metrics.precision_recall_curve",
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0