933 lines
35 KiB
Python
933 lines
35 KiB
Python
from types import GeneratorType
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import numpy as np
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from numpy import linalg
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from scipy.sparse import dok_matrix, csr_matrix, issparse
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from scipy.spatial.distance import cosine, cityblock, minkowski, wminkowski
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from scipy.spatial.distance import cdist, pdist, squareform
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import pytest
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from sklearn import config_context
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_allclose
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raises_regexp
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.testing import assert_warns_message
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from sklearn.metrics.pairwise import euclidean_distances
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from sklearn.metrics.pairwise import manhattan_distances
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from sklearn.metrics.pairwise import linear_kernel
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from sklearn.metrics.pairwise import chi2_kernel, additive_chi2_kernel
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from sklearn.metrics.pairwise import polynomial_kernel
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from sklearn.metrics.pairwise import rbf_kernel
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from sklearn.metrics.pairwise import laplacian_kernel
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from sklearn.metrics.pairwise import sigmoid_kernel
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from sklearn.metrics.pairwise import cosine_similarity
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from sklearn.metrics.pairwise import cosine_distances
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from sklearn.metrics.pairwise import pairwise_distances
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from sklearn.metrics.pairwise import pairwise_distances_chunked
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from sklearn.metrics.pairwise import pairwise_distances_argmin_min
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from sklearn.metrics.pairwise import pairwise_distances_argmin
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from sklearn.metrics.pairwise import pairwise_kernels
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from sklearn.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS
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from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
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from sklearn.metrics.pairwise import PAIRWISE_BOOLEAN_FUNCTIONS
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from sklearn.metrics.pairwise import PAIRED_DISTANCES
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from sklearn.metrics.pairwise import check_pairwise_arrays
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from sklearn.metrics.pairwise import check_paired_arrays
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from sklearn.metrics.pairwise import paired_distances
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from sklearn.metrics.pairwise import paired_euclidean_distances
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from sklearn.metrics.pairwise import paired_manhattan_distances
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from sklearn.preprocessing import normalize
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from sklearn.exceptions import DataConversionWarning
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def test_pairwise_distances():
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# Test the pairwise_distance helper function.
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rng = np.random.RandomState(0)
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# Euclidean distance should be equivalent to calling the function.
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X = rng.random_sample((5, 4))
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S = pairwise_distances(X, metric="euclidean")
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S2 = euclidean_distances(X)
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assert_array_almost_equal(S, S2)
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# Euclidean distance, with Y != X.
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Y = rng.random_sample((2, 4))
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S = pairwise_distances(X, Y, metric="euclidean")
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S2 = euclidean_distances(X, Y)
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assert_array_almost_equal(S, S2)
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# Test with tuples as X and Y
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X_tuples = tuple([tuple([v for v in row]) for row in X])
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Y_tuples = tuple([tuple([v for v in row]) for row in Y])
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S2 = pairwise_distances(X_tuples, Y_tuples, metric="euclidean")
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assert_array_almost_equal(S, S2)
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# "cityblock" uses scikit-learn metric, cityblock (function) is
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# scipy.spatial.
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S = pairwise_distances(X, metric="cityblock")
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S2 = pairwise_distances(X, metric=cityblock)
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assert_equal(S.shape[0], S.shape[1])
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assert_equal(S.shape[0], X.shape[0])
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assert_array_almost_equal(S, S2)
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# The manhattan metric should be equivalent to cityblock.
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S = pairwise_distances(X, Y, metric="manhattan")
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S2 = pairwise_distances(X, Y, metric=cityblock)
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assert_equal(S.shape[0], X.shape[0])
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assert_equal(S.shape[1], Y.shape[0])
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assert_array_almost_equal(S, S2)
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# Test cosine as a string metric versus cosine callable
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# The string "cosine" uses sklearn.metric,
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# while the function cosine is scipy.spatial
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S = pairwise_distances(X, Y, metric="cosine")
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S2 = pairwise_distances(X, Y, metric=cosine)
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assert_equal(S.shape[0], X.shape[0])
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assert_equal(S.shape[1], Y.shape[0])
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assert_array_almost_equal(S, S2)
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# Test with sparse X and Y,
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# currently only supported for Euclidean, L1 and cosine.
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X_sparse = csr_matrix(X)
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Y_sparse = csr_matrix(Y)
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S = pairwise_distances(X_sparse, Y_sparse, metric="euclidean")
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S2 = euclidean_distances(X_sparse, Y_sparse)
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assert_array_almost_equal(S, S2)
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S = pairwise_distances(X_sparse, Y_sparse, metric="cosine")
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S2 = cosine_distances(X_sparse, Y_sparse)
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assert_array_almost_equal(S, S2)
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S = pairwise_distances(X_sparse, Y_sparse.tocsc(), metric="manhattan")
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S2 = manhattan_distances(X_sparse.tobsr(), Y_sparse.tocoo())
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assert_array_almost_equal(S, S2)
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S2 = manhattan_distances(X, Y)
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assert_array_almost_equal(S, S2)
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# Test with scipy.spatial.distance metric, with a kwd
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kwds = {"p": 2.0}
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S = pairwise_distances(X, Y, metric="minkowski", **kwds)
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S2 = pairwise_distances(X, Y, metric=minkowski, **kwds)
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assert_array_almost_equal(S, S2)
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# same with Y = None
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kwds = {"p": 2.0}
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S = pairwise_distances(X, metric="minkowski", **kwds)
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S2 = pairwise_distances(X, metric=minkowski, **kwds)
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assert_array_almost_equal(S, S2)
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# Test that scipy distance metrics throw an error if sparse matrix given
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assert_raises(TypeError, pairwise_distances, X_sparse, metric="minkowski")
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assert_raises(TypeError, pairwise_distances, X, Y_sparse,
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metric="minkowski")
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# Test that a value error is raised if the metric is unknown
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assert_raises(ValueError, pairwise_distances, X, Y, metric="blah")
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@pytest.mark.parametrize('metric', PAIRWISE_BOOLEAN_FUNCTIONS)
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def test_pairwise_boolean_distance(metric):
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# test that we convert to boolean arrays for boolean distances
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rng = np.random.RandomState(0)
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X = rng.randn(5, 4)
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Y = X.copy()
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Y[0, 0] = 1 - Y[0, 0]
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# ignore conversion to boolean in pairwise_distances
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with ignore_warnings(category=DataConversionWarning):
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for Z in [Y, None]:
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res = pairwise_distances(X, Z, metric=metric)
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res[np.isnan(res)] = 0
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assert np.sum(res != 0) == 0
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@pytest.mark.parametrize('func', [pairwise_distances, pairwise_kernels])
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def test_pairwise_precomputed(func):
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# Test correct shape
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assert_raises_regexp(ValueError, '.* shape .*',
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func, np.zeros((5, 3)), metric='precomputed')
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# with two args
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assert_raises_regexp(ValueError, '.* shape .*',
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func, np.zeros((5, 3)), np.zeros((4, 4)),
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metric='precomputed')
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# even if shape[1] agrees (although thus second arg is spurious)
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assert_raises_regexp(ValueError, '.* shape .*',
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func, np.zeros((5, 3)), np.zeros((4, 3)),
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metric='precomputed')
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# Test not copied (if appropriate dtype)
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S = np.zeros((5, 5))
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S2 = func(S, metric="precomputed")
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assert S is S2
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# with two args
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S = np.zeros((5, 3))
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S2 = func(S, np.zeros((3, 3)), metric="precomputed")
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assert S is S2
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# Test always returns float dtype
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S = func(np.array([[1]], dtype='int'), metric='precomputed')
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assert_equal('f', S.dtype.kind)
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# Test converts list to array-like
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S = func([[1.]], metric='precomputed')
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assert isinstance(S, np.ndarray)
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def test_pairwise_precomputed_non_negative():
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# Test non-negative values
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assert_raises_regexp(ValueError, '.* non-negative values.*',
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pairwise_distances, np.full((5, 5), -1),
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metric='precomputed')
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def check_pairwise_parallel(func, metric, kwds):
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rng = np.random.RandomState(0)
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for make_data in (np.array, csr_matrix):
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X = make_data(rng.random_sample((5, 4)))
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Y = make_data(rng.random_sample((3, 4)))
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try:
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S = func(X, metric=metric, n_jobs=1, **kwds)
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except (TypeError, ValueError) as exc:
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# Not all metrics support sparse input
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# ValueError may be triggered by bad callable
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if make_data is csr_matrix:
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assert_raises(type(exc), func, X, metric=metric,
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n_jobs=2, **kwds)
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continue
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else:
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raise
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S2 = func(X, metric=metric, n_jobs=2, **kwds)
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assert_array_almost_equal(S, S2)
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S = func(X, Y, metric=metric, n_jobs=1, **kwds)
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S2 = func(X, Y, metric=metric, n_jobs=2, **kwds)
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assert_array_almost_equal(S, S2)
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_wminkowski_kwds = {'w': np.arange(1, 5).astype('double'), 'p': 1}
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def callable_rbf_kernel(x, y, **kwds):
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# Callable version of pairwise.rbf_kernel.
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K = rbf_kernel(np.atleast_2d(x), np.atleast_2d(y), **kwds)
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return K
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@pytest.mark.parametrize(
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'func, metric, kwds',
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[(pairwise_distances, 'euclidean', {}),
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(pairwise_distances, wminkowski, _wminkowski_kwds),
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(pairwise_distances, 'wminkowski', _wminkowski_kwds),
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(pairwise_kernels, 'polynomial', {'degree': 1}),
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(pairwise_kernels, callable_rbf_kernel, {'gamma': .1})])
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def test_pairwise_parallel(func, metric, kwds):
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check_pairwise_parallel(func, metric, kwds)
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def test_pairwise_callable_nonstrict_metric():
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# paired_distances should allow callable metric where metric(x, x) != 0
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# Knowing that the callable is a strict metric would allow the diagonal to
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# be left uncalculated and set to 0.
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assert_equal(pairwise_distances([[1.]], metric=lambda x, y: 5)[0, 0], 5)
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# Test with all metrics that should be in PAIRWISE_KERNEL_FUNCTIONS.
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@pytest.mark.parametrize(
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'metric',
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["rbf", "laplacian", "sigmoid", "polynomial", "linear",
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"chi2", "additive_chi2"])
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def test_pairwise_kernels(metric):
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# Test the pairwise_kernels helper function.
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rng = np.random.RandomState(0)
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X = rng.random_sample((5, 4))
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Y = rng.random_sample((2, 4))
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function = PAIRWISE_KERNEL_FUNCTIONS[metric]
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# Test with Y=None
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K1 = pairwise_kernels(X, metric=metric)
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K2 = function(X)
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assert_array_almost_equal(K1, K2)
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# Test with Y=Y
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K1 = pairwise_kernels(X, Y=Y, metric=metric)
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K2 = function(X, Y=Y)
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assert_array_almost_equal(K1, K2)
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# Test with tuples as X and Y
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X_tuples = tuple([tuple([v for v in row]) for row in X])
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Y_tuples = tuple([tuple([v for v in row]) for row in Y])
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K2 = pairwise_kernels(X_tuples, Y_tuples, metric=metric)
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assert_array_almost_equal(K1, K2)
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# Test with sparse X and Y
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X_sparse = csr_matrix(X)
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Y_sparse = csr_matrix(Y)
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if metric in ["chi2", "additive_chi2"]:
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# these don't support sparse matrices yet
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assert_raises(ValueError, pairwise_kernels,
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X_sparse, Y=Y_sparse, metric=metric)
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return
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K1 = pairwise_kernels(X_sparse, Y=Y_sparse, metric=metric)
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assert_array_almost_equal(K1, K2)
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def test_pairwise_kernels_callable():
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# Test the pairwise_kernels helper function
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# with a callable function, with given keywords.
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rng = np.random.RandomState(0)
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X = rng.random_sample((5, 4))
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Y = rng.random_sample((2, 4))
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metric = callable_rbf_kernel
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kwds = {'gamma': 0.1}
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K1 = pairwise_kernels(X, Y=Y, metric=metric, **kwds)
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K2 = rbf_kernel(X, Y=Y, **kwds)
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assert_array_almost_equal(K1, K2)
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# callable function, X=Y
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K1 = pairwise_kernels(X, Y=X, metric=metric, **kwds)
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K2 = rbf_kernel(X, Y=X, **kwds)
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assert_array_almost_equal(K1, K2)
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def test_pairwise_kernels_filter_param():
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rng = np.random.RandomState(0)
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X = rng.random_sample((5, 4))
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Y = rng.random_sample((2, 4))
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K = rbf_kernel(X, Y, gamma=0.1)
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params = {"gamma": 0.1, "blabla": ":)"}
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K2 = pairwise_kernels(X, Y, metric="rbf", filter_params=True, **params)
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assert_array_almost_equal(K, K2)
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assert_raises(TypeError, pairwise_kernels, X, Y, "rbf", **params)
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@pytest.mark.parametrize('metric, func', PAIRED_DISTANCES.items())
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def test_paired_distances(metric, func):
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# Test the pairwise_distance helper function.
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rng = np.random.RandomState(0)
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# Euclidean distance should be equivalent to calling the function.
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X = rng.random_sample((5, 4))
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# Euclidean distance, with Y != X.
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Y = rng.random_sample((5, 4))
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S = paired_distances(X, Y, metric=metric)
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S2 = func(X, Y)
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assert_array_almost_equal(S, S2)
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S3 = func(csr_matrix(X), csr_matrix(Y))
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assert_array_almost_equal(S, S3)
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if metric in PAIRWISE_DISTANCE_FUNCTIONS:
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# Check the pairwise_distances implementation
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# gives the same value
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distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)
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distances = np.diag(distances)
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assert_array_almost_equal(distances, S)
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def test_paired_distances_callable():
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# Test the pairwise_distance helper function
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# with the callable implementation
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rng = np.random.RandomState(0)
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# Euclidean distance should be equivalent to calling the function.
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X = rng.random_sample((5, 4))
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# Euclidean distance, with Y != X.
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Y = rng.random_sample((5, 4))
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S = paired_distances(X, Y, metric='manhattan')
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S2 = paired_distances(X, Y, metric=lambda x, y: np.abs(x - y).sum(axis=0))
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assert_array_almost_equal(S, S2)
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# Test that a value error is raised when the lengths of X and Y should not
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# differ
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Y = rng.random_sample((3, 4))
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assert_raises(ValueError, paired_distances, X, Y)
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def test_pairwise_distances_argmin_min():
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# Check pairwise minimum distances computation for any metric
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X = [[0], [1]]
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Y = [[-2], [3]]
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Xsp = dok_matrix(X)
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Ysp = csr_matrix(Y, dtype=np.float32)
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expected_idx = [0, 1]
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expected_vals = [2, 2]
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expected_vals_sq = [4, 4]
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# euclidean metric
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idx, vals = pairwise_distances_argmin_min(X, Y, metric="euclidean")
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idx2 = pairwise_distances_argmin(X, Y, metric="euclidean")
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assert_array_almost_equal(idx, expected_idx)
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assert_array_almost_equal(idx2, expected_idx)
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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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assert_array_almost_equal(idxsp, 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_equal(type(idxsp), np.ndarray)
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assert_equal(type(valssp), np.ndarray)
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# euclidean metric squared
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idx, vals = pairwise_distances_argmin_min(X, Y, metric="euclidean",
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metric_kwargs={"squared": True})
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assert_array_almost_equal(idx, expected_idx)
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assert_array_almost_equal(vals, expected_vals_sq)
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# Non-euclidean scikit-learn metric
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idx, vals = pairwise_distances_argmin_min(X, Y, metric="manhattan")
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idx2 = pairwise_distances_argmin(X, Y, metric="manhattan")
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assert_array_almost_equal(idx, expected_idx)
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assert_array_almost_equal(idx2, expected_idx)
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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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assert_array_almost_equal(idxsp, 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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idx, vals = pairwise_distances_argmin_min(X, Y, metric=minkowski,
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metric_kwargs={"p": 2})
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assert_array_almost_equal(idx, expected_idx)
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assert_array_almost_equal(vals, expected_vals)
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# Non-euclidean Scipy distance (string)
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idx, vals = pairwise_distances_argmin_min(X, Y, metric="minkowski",
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metric_kwargs={"p": 2})
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assert_array_almost_equal(idx, expected_idx)
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assert_array_almost_equal(vals, expected_vals)
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# Compare with naive implementation
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rng = np.random.RandomState(0)
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X = rng.randn(97, 149)
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Y = rng.randn(111, 149)
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dist = pairwise_distances(X, Y, metric="manhattan")
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dist_orig_ind = dist.argmin(axis=0)
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dist_orig_val = dist[dist_orig_ind, range(len(dist_orig_ind))]
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dist_chunked_ind, dist_chunked_val = pairwise_distances_argmin_min(
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X, Y, axis=0, metric="manhattan")
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np.testing.assert_almost_equal(dist_orig_ind, dist_chunked_ind, decimal=7)
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np.testing.assert_almost_equal(dist_orig_val, dist_chunked_val, decimal=7)
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# Test batch_size deprecation warning
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assert_warns_message(DeprecationWarning, "version 0.22",
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pairwise_distances_argmin_min, X, Y, batch_size=500,
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metric='euclidean')
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def _reduce_func(dist, start):
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return dist[:, :100]
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def test_pairwise_distances_chunked_reduce():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((400, 4))
|
|
# Reduced Euclidean distance
|
|
S = pairwise_distances(X)[:, :100]
|
|
S_chunks = pairwise_distances_chunked(X, None, reduce_func=_reduce_func,
|
|
working_memory=2 ** -16)
|
|
assert isinstance(S_chunks, GeneratorType)
|
|
S_chunks = list(S_chunks)
|
|
assert len(S_chunks) > 1
|
|
# atol is for diagonal where S is explicitly zeroed on the diagonal
|
|
assert_allclose(np.vstack(S_chunks), S, atol=1e-7)
|
|
|
|
|
|
@pytest.mark.parametrize('good_reduce', [
|
|
lambda D, start: list(D),
|
|
lambda D, start: np.array(D),
|
|
lambda D, start: csr_matrix(D),
|
|
lambda D, start: (list(D), list(D)),
|
|
lambda D, start: (dok_matrix(D), np.array(D), list(D)),
|
|
])
|
|
def test_pairwise_distances_chunked_reduce_valid(good_reduce):
|
|
X = np.arange(10).reshape(-1, 1)
|
|
S_chunks = pairwise_distances_chunked(X, None, reduce_func=good_reduce,
|
|
working_memory=64)
|
|
next(S_chunks)
|
|
|
|
|
|
@pytest.mark.parametrize(('bad_reduce', 'err_type', 'message'), [
|
|
(lambda D, s: np.concatenate([D, D[-1:]]), ValueError,
|
|
r'length 11\..* input: 10\.'),
|
|
(lambda D, s: (D, np.concatenate([D, D[-1:]])), ValueError,
|
|
r'length \(10, 11\)\..* input: 10\.'),
|
|
(lambda D, s: (D[:9], D), ValueError,
|
|
r'length \(9, 10\)\..* input: 10\.'),
|
|
(lambda D, s: 7, TypeError,
|
|
r'returned 7\. Expected sequence\(s\) of length 10\.'),
|
|
(lambda D, s: (7, 8), TypeError,
|
|
r'returned \(7, 8\)\. Expected sequence\(s\) of length 10\.'),
|
|
(lambda D, s: (np.arange(10), 9), TypeError,
|
|
r', 9\)\. Expected sequence\(s\) of length 10\.'),
|
|
])
|
|
def test_pairwise_distances_chunked_reduce_invalid(bad_reduce, err_type,
|
|
message):
|
|
X = np.arange(10).reshape(-1, 1)
|
|
S_chunks = pairwise_distances_chunked(X, None, reduce_func=bad_reduce,
|
|
working_memory=64)
|
|
assert_raises_regexp(err_type, message, next, S_chunks)
|
|
|
|
|
|
def check_pairwise_distances_chunked(X, Y, working_memory, metric='euclidean'):
|
|
gen = pairwise_distances_chunked(X, Y, working_memory=working_memory,
|
|
metric=metric)
|
|
assert isinstance(gen, GeneratorType)
|
|
blockwise_distances = list(gen)
|
|
Y = X if Y is None else Y
|
|
min_block_mib = len(Y) * 8 * 2 ** -20
|
|
|
|
for block in blockwise_distances:
|
|
memory_used = block.nbytes
|
|
assert memory_used <= max(working_memory, min_block_mib) * 2 ** 20
|
|
|
|
blockwise_distances = np.vstack(blockwise_distances)
|
|
S = pairwise_distances(X, Y, metric=metric)
|
|
assert_array_almost_equal(blockwise_distances, S)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'metric',
|
|
('euclidean', 'l2', 'sqeuclidean'))
|
|
def test_pairwise_distances_chunked_diagonal(metric):
|
|
rng = np.random.RandomState(0)
|
|
X = rng.normal(size=(1000, 10), scale=1e10)
|
|
chunks = list(pairwise_distances_chunked(X, working_memory=1,
|
|
metric=metric))
|
|
assert len(chunks) > 1
|
|
assert_array_almost_equal(np.diag(np.vstack(chunks)), 0, decimal=10)
|
|
|
|
|
|
@ignore_warnings
|
|
def test_pairwise_distances_chunked():
|
|
# Test the pairwise_distance helper function.
|
|
rng = np.random.RandomState(0)
|
|
# Euclidean distance should be equivalent to calling the function.
|
|
X = rng.random_sample((400, 4))
|
|
check_pairwise_distances_chunked(X, None, working_memory=1,
|
|
metric='euclidean')
|
|
# Test small amounts of memory
|
|
for power in range(-16, 0):
|
|
check_pairwise_distances_chunked(X, None, working_memory=2 ** power,
|
|
metric='euclidean')
|
|
# X as list
|
|
check_pairwise_distances_chunked(X.tolist(), None, working_memory=1,
|
|
metric='euclidean')
|
|
# Euclidean distance, with Y != X.
|
|
Y = rng.random_sample((200, 4))
|
|
check_pairwise_distances_chunked(X, Y, working_memory=1,
|
|
metric='euclidean')
|
|
check_pairwise_distances_chunked(X.tolist(), Y.tolist(), working_memory=1,
|
|
metric='euclidean')
|
|
# absurdly large working_memory
|
|
check_pairwise_distances_chunked(X, Y, working_memory=10000,
|
|
metric='euclidean')
|
|
# "cityblock" uses scikit-learn metric, cityblock (function) is
|
|
# scipy.spatial.
|
|
check_pairwise_distances_chunked(X, Y, working_memory=1,
|
|
metric='cityblock')
|
|
# Test that a value error is raised if the metric is unknown
|
|
assert_raises(ValueError, next,
|
|
pairwise_distances_chunked(X, Y, metric="blah"))
|
|
|
|
# Test precomputed returns all at once
|
|
D = pairwise_distances(X)
|
|
gen = pairwise_distances_chunked(D,
|
|
working_memory=2 ** -16,
|
|
metric='precomputed')
|
|
assert isinstance(gen, GeneratorType)
|
|
assert next(gen) is D
|
|
assert_raises(StopIteration, next, gen)
|
|
|
|
|
|
def test_euclidean_distances():
|
|
# Check the pairwise Euclidean distances computation
|
|
X = [[0]]
|
|
Y = [[1], [2]]
|
|
D = euclidean_distances(X, Y)
|
|
assert_array_almost_equal(D, [[1., 2.]])
|
|
|
|
X = csr_matrix(X)
|
|
Y = csr_matrix(Y)
|
|
D = euclidean_distances(X, Y)
|
|
assert_array_almost_equal(D, [[1., 2.]])
|
|
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((10, 4))
|
|
Y = rng.random_sample((20, 4))
|
|
X_norm_sq = (X ** 2).sum(axis=1).reshape(1, -1)
|
|
Y_norm_sq = (Y ** 2).sum(axis=1).reshape(1, -1)
|
|
|
|
# check that we still get the right answers with {X,Y}_norm_squared
|
|
D1 = euclidean_distances(X, Y)
|
|
D2 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq)
|
|
D3 = euclidean_distances(X, Y, Y_norm_squared=Y_norm_sq)
|
|
D4 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq,
|
|
Y_norm_squared=Y_norm_sq)
|
|
assert_array_almost_equal(D2, D1)
|
|
assert_array_almost_equal(D3, D1)
|
|
assert_array_almost_equal(D4, D1)
|
|
|
|
# check we get the wrong answer with wrong {X,Y}_norm_squared
|
|
X_norm_sq *= 0.5
|
|
Y_norm_sq *= 0.5
|
|
wrong_D = euclidean_distances(X, Y,
|
|
X_norm_squared=np.zeros_like(X_norm_sq),
|
|
Y_norm_squared=np.zeros_like(Y_norm_sq))
|
|
assert_greater(np.max(np.abs(wrong_D - D1)), .01)
|
|
|
|
|
|
def test_cosine_distances():
|
|
# Check the pairwise Cosine distances computation
|
|
rng = np.random.RandomState(1337)
|
|
x = np.abs(rng.rand(910))
|
|
XA = np.vstack([x, x])
|
|
D = cosine_distances(XA)
|
|
assert_array_almost_equal(D, [[0., 0.], [0., 0.]])
|
|
# check that all elements are in [0, 2]
|
|
assert np.all(D >= 0.)
|
|
assert np.all(D <= 2.)
|
|
# check that diagonal elements are equal to 0
|
|
assert_array_almost_equal(D[np.diag_indices_from(D)], [0., 0.])
|
|
|
|
XB = np.vstack([x, -x])
|
|
D2 = cosine_distances(XB)
|
|
# check that all elements are in [0, 2]
|
|
assert np.all(D2 >= 0.)
|
|
assert np.all(D2 <= 2.)
|
|
# check that diagonal elements are equal to 0 and non diagonal to 2
|
|
assert_array_almost_equal(D2, [[0., 2.], [2., 0.]])
|
|
|
|
# check large random matrix
|
|
X = np.abs(rng.rand(1000, 5000))
|
|
D = cosine_distances(X)
|
|
# check that diagonal elements are equal to 0
|
|
assert_array_almost_equal(D[np.diag_indices_from(D)], [0.] * D.shape[0])
|
|
assert np.all(D >= 0.)
|
|
assert np.all(D <= 2.)
|
|
|
|
|
|
# Paired distances
|
|
|
|
def test_paired_euclidean_distances():
|
|
# Check the paired Euclidean distances computation
|
|
X = [[0], [0]]
|
|
Y = [[1], [2]]
|
|
D = paired_euclidean_distances(X, Y)
|
|
assert_array_almost_equal(D, [1., 2.])
|
|
|
|
|
|
def test_paired_manhattan_distances():
|
|
# Check the paired manhattan distances computation
|
|
X = [[0], [0]]
|
|
Y = [[1], [2]]
|
|
D = paired_manhattan_distances(X, Y)
|
|
assert_array_almost_equal(D, [1., 2.])
|
|
|
|
|
|
def test_chi_square_kernel():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
Y = rng.random_sample((10, 4))
|
|
K_add = additive_chi2_kernel(X, Y)
|
|
gamma = 0.1
|
|
K = chi2_kernel(X, Y, gamma=gamma)
|
|
assert_equal(K.dtype, np.float)
|
|
for i, x in enumerate(X):
|
|
for j, y in enumerate(Y):
|
|
chi2 = -np.sum((x - y) ** 2 / (x + y))
|
|
chi2_exp = np.exp(gamma * chi2)
|
|
assert_almost_equal(K_add[i, j], chi2)
|
|
assert_almost_equal(K[i, j], chi2_exp)
|
|
|
|
# check diagonal is ones for data with itself
|
|
K = chi2_kernel(Y)
|
|
assert_array_equal(np.diag(K), 1)
|
|
# check off-diagonal is < 1 but > 0:
|
|
assert np.all(K > 0)
|
|
assert np.all(K - np.diag(np.diag(K)) < 1)
|
|
# check that float32 is preserved
|
|
X = rng.random_sample((5, 4)).astype(np.float32)
|
|
Y = rng.random_sample((10, 4)).astype(np.float32)
|
|
K = chi2_kernel(X, Y)
|
|
assert_equal(K.dtype, np.float32)
|
|
|
|
# check integer type gets converted,
|
|
# check that zeros are handled
|
|
X = rng.random_sample((10, 4)).astype(np.int32)
|
|
K = chi2_kernel(X, X)
|
|
assert np.isfinite(K).all()
|
|
assert_equal(K.dtype, np.float)
|
|
|
|
# check that kernel of similar things is greater than dissimilar ones
|
|
X = [[.3, .7], [1., 0]]
|
|
Y = [[0, 1], [.9, .1]]
|
|
K = chi2_kernel(X, Y)
|
|
assert_greater(K[0, 0], K[0, 1])
|
|
assert_greater(K[1, 1], K[1, 0])
|
|
|
|
# test negative input
|
|
assert_raises(ValueError, chi2_kernel, [[0, -1]])
|
|
assert_raises(ValueError, chi2_kernel, [[0, -1]], [[-1, -1]])
|
|
assert_raises(ValueError, chi2_kernel, [[0, 1]], [[-1, -1]])
|
|
|
|
# different n_features in X and Y
|
|
assert_raises(ValueError, chi2_kernel, [[0, 1]], [[.2, .2, .6]])
|
|
|
|
# sparse matrices
|
|
assert_raises(ValueError, chi2_kernel, csr_matrix(X), csr_matrix(Y))
|
|
assert_raises(ValueError, additive_chi2_kernel,
|
|
csr_matrix(X), csr_matrix(Y))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'kernel',
|
|
(linear_kernel, polynomial_kernel, rbf_kernel,
|
|
laplacian_kernel, sigmoid_kernel, cosine_similarity))
|
|
def test_kernel_symmetry(kernel):
|
|
# Valid kernels should be symmetric
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
K = kernel(X, X)
|
|
assert_array_almost_equal(K, K.T, 15)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'kernel',
|
|
(linear_kernel, polynomial_kernel, rbf_kernel,
|
|
laplacian_kernel, sigmoid_kernel, cosine_similarity))
|
|
def test_kernel_sparse(kernel):
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
X_sparse = csr_matrix(X)
|
|
K = kernel(X, X)
|
|
K2 = kernel(X_sparse, X_sparse)
|
|
assert_array_almost_equal(K, K2)
|
|
|
|
|
|
def test_linear_kernel():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
K = linear_kernel(X, X)
|
|
# the diagonal elements of a linear kernel are their squared norm
|
|
assert_array_almost_equal(K.flat[::6], [linalg.norm(x) ** 2 for x in X])
|
|
|
|
|
|
def test_rbf_kernel():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
K = rbf_kernel(X, X)
|
|
# the diagonal elements of a rbf kernel are 1
|
|
assert_array_almost_equal(K.flat[::6], np.ones(5))
|
|
|
|
|
|
def test_laplacian_kernel():
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
K = laplacian_kernel(X, X)
|
|
# the diagonal elements of a laplacian kernel are 1
|
|
assert_array_almost_equal(np.diag(K), np.ones(5))
|
|
|
|
# off-diagonal elements are < 1 but > 0:
|
|
assert np.all(K > 0)
|
|
assert np.all(K - np.diag(np.diag(K)) < 1)
|
|
|
|
|
|
@pytest.mark.parametrize('metric, pairwise_func',
|
|
[('linear', linear_kernel),
|
|
('cosine', cosine_similarity)])
|
|
def test_pairwise_similarity_sparse_output(metric, pairwise_func):
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
Y = rng.random_sample((3, 4))
|
|
Xcsr = csr_matrix(X)
|
|
Ycsr = csr_matrix(Y)
|
|
|
|
# should be sparse
|
|
K1 = pairwise_func(Xcsr, Ycsr, dense_output=False)
|
|
assert issparse(K1)
|
|
|
|
# should be dense, and equal to K1
|
|
K2 = pairwise_func(X, Y, dense_output=True)
|
|
assert not issparse(K2)
|
|
assert_array_almost_equal(K1.todense(), K2)
|
|
|
|
# show the kernel output equal to the sparse.todense()
|
|
K3 = pairwise_kernels(X, Y=Y, metric=metric)
|
|
assert_array_almost_equal(K1.todense(), K3)
|
|
|
|
|
|
def test_cosine_similarity():
|
|
# Test the cosine_similarity.
|
|
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((5, 4))
|
|
Y = rng.random_sample((3, 4))
|
|
Xcsr = csr_matrix(X)
|
|
Ycsr = csr_matrix(Y)
|
|
|
|
for X_, Y_ in ((X, None), (X, Y),
|
|
(Xcsr, None), (Xcsr, Ycsr)):
|
|
# Test that the cosine is kernel is equal to a linear kernel when data
|
|
# has been previously normalized by L2-norm.
|
|
K1 = pairwise_kernels(X_, Y=Y_, metric="cosine")
|
|
X_ = normalize(X_)
|
|
if Y_ is not None:
|
|
Y_ = normalize(Y_)
|
|
K2 = pairwise_kernels(X_, Y=Y_, metric="linear")
|
|
assert_array_almost_equal(K1, K2)
|
|
|
|
|
|
def test_check_dense_matrices():
|
|
# Ensure that pairwise array check works for dense matrices.
|
|
# Check that if XB is None, XB is returned as reference to XA
|
|
XA = np.resize(np.arange(40), (5, 8))
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA, None)
|
|
assert XA_checked is XB_checked
|
|
assert_array_equal(XA, XA_checked)
|
|
|
|
|
|
def test_check_XB_returned():
|
|
# Ensure that if XA and XB are given correctly, they return as equal.
|
|
# Check that if XB is not None, it is returned equal.
|
|
# Note that the second dimension of XB is the same as XA.
|
|
XA = np.resize(np.arange(40), (5, 8))
|
|
XB = np.resize(np.arange(32), (4, 8))
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
|
|
assert_array_equal(XA, XA_checked)
|
|
assert_array_equal(XB, XB_checked)
|
|
|
|
XB = np.resize(np.arange(40), (5, 8))
|
|
XA_checked, XB_checked = check_paired_arrays(XA, XB)
|
|
assert_array_equal(XA, XA_checked)
|
|
assert_array_equal(XB, XB_checked)
|
|
|
|
|
|
def test_check_different_dimensions():
|
|
# Ensure an error is raised if the dimensions are different.
|
|
XA = np.resize(np.arange(45), (5, 9))
|
|
XB = np.resize(np.arange(32), (4, 8))
|
|
assert_raises(ValueError, check_pairwise_arrays, XA, XB)
|
|
|
|
XB = np.resize(np.arange(4 * 9), (4, 9))
|
|
assert_raises(ValueError, check_paired_arrays, XA, XB)
|
|
|
|
|
|
def test_check_invalid_dimensions():
|
|
# Ensure an error is raised on 1D input arrays.
|
|
# The modified tests are not 1D. In the old test, the array was internally
|
|
# converted to 2D anyways
|
|
XA = np.arange(45).reshape(9, 5)
|
|
XB = np.arange(32).reshape(4, 8)
|
|
assert_raises(ValueError, check_pairwise_arrays, XA, XB)
|
|
XA = np.arange(45).reshape(9, 5)
|
|
XB = np.arange(32).reshape(4, 8)
|
|
assert_raises(ValueError, check_pairwise_arrays, XA, XB)
|
|
|
|
|
|
def test_check_sparse_arrays():
|
|
# Ensures that checks return valid sparse matrices.
|
|
rng = np.random.RandomState(0)
|
|
XA = rng.random_sample((5, 4))
|
|
XA_sparse = csr_matrix(XA)
|
|
XB = rng.random_sample((5, 4))
|
|
XB_sparse = csr_matrix(XB)
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA_sparse, XB_sparse)
|
|
# compare their difference because testing csr matrices for
|
|
# equality with '==' does not work as expected.
|
|
assert issparse(XA_checked)
|
|
assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
|
|
assert issparse(XB_checked)
|
|
assert_equal(abs(XB_sparse - XB_checked).sum(), 0)
|
|
|
|
XA_checked, XA_2_checked = check_pairwise_arrays(XA_sparse, XA_sparse)
|
|
assert issparse(XA_checked)
|
|
assert_equal(abs(XA_sparse - XA_checked).sum(), 0)
|
|
assert issparse(XA_2_checked)
|
|
assert_equal(abs(XA_2_checked - XA_checked).sum(), 0)
|
|
|
|
|
|
def tuplify(X):
|
|
# Turns a numpy matrix (any n-dimensional array) into tuples.
|
|
s = X.shape
|
|
if len(s) > 1:
|
|
# Tuplify each sub-array in the input.
|
|
return tuple(tuplify(row) for row in X)
|
|
else:
|
|
# Single dimension input, just return tuple of contents.
|
|
return tuple(r for r in X)
|
|
|
|
|
|
def test_check_tuple_input():
|
|
# Ensures that checks return valid tuples.
|
|
rng = np.random.RandomState(0)
|
|
XA = rng.random_sample((5, 4))
|
|
XA_tuples = tuplify(XA)
|
|
XB = rng.random_sample((5, 4))
|
|
XB_tuples = tuplify(XB)
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA_tuples, XB_tuples)
|
|
assert_array_equal(XA_tuples, XA_checked)
|
|
assert_array_equal(XB_tuples, XB_checked)
|
|
|
|
|
|
def test_check_preserve_type():
|
|
# Ensures that type float32 is preserved.
|
|
XA = np.resize(np.arange(40), (5, 8)).astype(np.float32)
|
|
XB = np.resize(np.arange(40), (5, 8)).astype(np.float32)
|
|
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA, None)
|
|
assert_equal(XA_checked.dtype, np.float32)
|
|
|
|
# both float32
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
|
|
assert_equal(XA_checked.dtype, np.float32)
|
|
assert_equal(XB_checked.dtype, np.float32)
|
|
|
|
# mismatched A
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA.astype(np.float),
|
|
XB)
|
|
assert_equal(XA_checked.dtype, np.float)
|
|
assert_equal(XB_checked.dtype, np.float)
|
|
|
|
# mismatched B
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA,
|
|
XB.astype(np.float))
|
|
assert_equal(XA_checked.dtype, np.float)
|
|
assert_equal(XB_checked.dtype, np.float)
|
|
|
|
|
|
@pytest.mark.parametrize("n_jobs", [1, 2])
|
|
@pytest.mark.parametrize("metric", ["seuclidean", "mahalanobis"])
|
|
@pytest.mark.parametrize("dist_function",
|
|
[pairwise_distances, pairwise_distances_chunked])
|
|
@pytest.mark.parametrize("y_is_x", [True, False], ids=["Y is X", "Y is not X"])
|
|
def test_pairwise_distances_data_derived_params(n_jobs, metric, dist_function,
|
|
y_is_x):
|
|
# check that pairwise_distances give the same result in sequential and
|
|
# parallel, when metric has data-derived parameters.
|
|
with config_context(working_memory=0.1): # to have more than 1 chunk
|
|
rng = np.random.RandomState(0)
|
|
X = rng.random_sample((1000, 10))
|
|
|
|
if y_is_x:
|
|
Y = X
|
|
expected_dist_default_params = squareform(pdist(X, metric=metric))
|
|
if metric == "seuclidean":
|
|
params = {'V': np.var(X, axis=0, ddof=1)}
|
|
else:
|
|
params = {'VI': np.linalg.inv(np.cov(X.T)).T}
|
|
else:
|
|
Y = rng.random_sample((1000, 10))
|
|
expected_dist_default_params = cdist(X, Y, metric=metric)
|
|
if metric == "seuclidean":
|
|
params = {'V': np.var(np.vstack([X, Y]), axis=0, ddof=1)}
|
|
else:
|
|
params = {'VI': np.linalg.inv(np.cov(np.vstack([X, Y]).T)).T}
|
|
|
|
expected_dist_explicit_params = cdist(X, Y, metric=metric, **params)
|
|
dist = np.vstack(tuple(dist_function(X, Y,
|
|
metric=metric, n_jobs=n_jobs)))
|
|
|
|
assert_allclose(dist, expected_dist_explicit_params)
|
|
assert_allclose(dist, expected_dist_default_params)
|