8000 Test and document pairwise_distances_argmin on sparse data · scikit-learn/scikit-learn@9723115 · GitHub
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Test and document pairwise_distances_argmin on sparse data
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‎sklearn/metrics/pairwise.py‎

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@@ -721,10 +721,10 @@ def pairwise_distances_argmin(X, Y, *, axis=1, metric="euclidean", metric_kwargs
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Parameters
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----------
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X : array-like of shape (n_samples_X, n_features)
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X : {array-like, sparse matrix} of shape (n_samples_X, n_features)
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Array containing points.
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Y : array-like of shape (n_samples_Y, n_features)
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Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features)
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Arrays containing points.
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axis : int, default=1

‎sklearn/metrics/tests/test_pairwise.py‎

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@@ -446,7 +446,9 @@ def test_pairwise_distances_argmin_min(dtype):
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assert_array_almost_equal(vals, expected_vals)
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# sparse matrix case
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idxsp, valssp = pairwise_distances_argmin_min(Xsp, Ysp, metric="euclidean")
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idxsp2 = pairwise_distances_argmin(Xsp, Ysp, metric="euclidean")
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assert_array_almost_equal(idxsp, expected_idx)
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assert_array_almost_equal(idxsp2, expected_idx)
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assert_array_almost_equal(valssp, expected_vals)
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# We don't want np.matrix here
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assert type(idxsp) == np.ndarray
@@ -478,7 +480,9 @@ def test_pairwise_distances_argmin_min(dtype):
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assert_array_almost_equal(vals, expected_vals)
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# sparse matrix case
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idxsp, valssp = pairwise_distances_argmin_min(Xsp, Ysp, metric="manhattan")
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idxsp2 = pairwise_distances_argmin(Xsp, Ysp, metric="manhattan")
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assert_array_almost_equal(idxsp, expected_idx)
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assert_array_almost_equal(idxsp2, expected_idx)
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assert_array_almost_equal(valssp, expected_vals)
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# Non-euclidean Scipy distance (callable)

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