1341 lines
50 KiB
Python
1341 lines
50 KiB
Python
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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_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_array_equal
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from sklearn.utils._testing import ignore_warnings
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from sklearn.metrics.pairwise import euclidean_distances
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from sklearn.metrics.pairwise import nan_euclidean_distances
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from sklearn.metrics.pairwise import manhattan_distances
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from sklearn.metrics.pairwise import haversine_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.metrics.pairwise import _euclidean_distances_upcast
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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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# Check to ensure NaNs work with pairwise_distances.
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X_masked = rng.random_sample((5, 4))
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Y_masked = rng.random_sample((2, 4))
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X_masked[0, 0] = np.nan
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Y_masked[0, 0] = np.nan
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S_masked = pairwise_distances(X_masked, Y_masked, metric="nan_euclidean")
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S2_masked = nan_euclidean_distances(X_masked, Y_masked)
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assert_array_almost_equal(S_masked, S2_masked)
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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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# Test haversine distance
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# The data should be valid latitude and longitude
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X = rng.random_sample((5, 2))
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X[:, 0] = (X[:, 0] - 0.5) * 2 * np.pi/2
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X[:, 1] = (X[:, 1] - 0.5) * 2 * np.pi
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S = pairwise_distances(X, metric="haversine")
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S2 = haversine_distances(X)
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assert_array_almost_equal(S, S2)
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# Test haversine distance, with Y != X
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Y = rng.random_sample((2, 2))
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Y[:, 0] = (Y[:, 0] - 0.5)*2*np.pi/2
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Y[:, 1] = (Y[:, 1] - 0.5)*2*np.pi
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S = pairwise_distances(X, Y, metric="haversine")
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S2 = haversine_distances(X, Y)
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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 S.shape[0] == S.shape[1]
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assert 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 S.shape[0] == X.shape[0]
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assert 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 S.shape[0] == X.shape[0]
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assert 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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with pytest.raises(TypeError):
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pairwise_distances(X_sparse, metric="minkowski")
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with pytest.raises(TypeError):
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pairwise_distances(X, Y_sparse, metric="minkowski")
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# Test that a value error is raised if the metric is unknown
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with pytest.raises(ValueError):
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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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# non-boolean arrays are converted to boolean for boolean
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# distance metrics with a data conversion warning
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msg = "Data was converted to boolean for metric %s" % metric
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with pytest.warns(DataConversionWarning, match=msg):
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pairwise_distances(X, metric=metric)
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# Check that the warning is raised if X is boolean by Y is not boolean:
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with pytest.warns(DataConversionWarning, match=msg):
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pairwise_distances(X.astype(bool), Y=Y, metric=metric)
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# Check that no warning is raised if X is already boolean and Y is None:
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with pytest.warns(None) as records:
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pairwise_distances(X.astype(bool), metric=metric)
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assert len(records) == 0
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def test_no_data_conversion_warning():
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# No warnings issued if metric is not a boolean distance function
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rng = np.random.RandomState(0)
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X = rng.randn(5, 4)
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with pytest.warns(None) as records:
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pairwise_distances(X, metric="minkowski")
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assert len(records) == 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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with pytest.raises(ValueError, match='.* shape .*'):
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func(np.zeros((5, 3)), metric='precomputed')
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# with two args
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with pytest.raises(ValueError, match='.* shape .*'):
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func(np.zeros((5, 3)), np.zeros((4, 4)), metric='precomputed')
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# even if shape[1] agrees (although thus second arg is spurious)
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with pytest.raises(ValueError, match='.* shape .*'):
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func(np.zeros((5, 3)), np.zeros((4, 3)), 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 '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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with pytest.raises(ValueError, match='.* non-negative values.*'):
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pairwise_distances(np.full((5, 5), -1), metric='precomputed')
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_wminkowski_kwds = {'w': np.arange(1, 5).astype('double', copy=False), '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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@pytest.mark.parametrize('array_constr', [np.array, csr_matrix])
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@pytest.mark.parametrize('dtype', [np.float64, int])
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def test_pairwise_parallel(func, metric, kwds, array_constr, dtype):
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rng = np.random.RandomState(0)
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X = array_constr(5 * rng.random_sample((5, 4)), dtype=dtype)
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Y = array_constr(5 * rng.random_sample((3, 4)), dtype=dtype)
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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 array_constr is csr_matrix:
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with pytest.raises(type(exc)):
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func(X, metric=metric, n_jobs=2, **kwds)
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return
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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_allclose(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_allclose(S, S2)
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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 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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with pytest.raises(ValueError):
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pairwise_kernels(X_sparse, Y=Y_sparse,
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metric=metric)
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||
|
|
return
|
||
|
|
K1 = pairwise_kernels(X_sparse, Y=Y_sparse, metric=metric)
|
||
|
|
assert_array_almost_equal(K1, K2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pairwise_kernels_callable():
|
||
|
|
# Test the pairwise_kernels helper function
|
||
|
|
# with a callable function, with given keywords.
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((5, 4))
|
||
|
|
Y = rng.random_sample((2, 4))
|
||
|
|
|
||
|
|
metric = callable_rbf_kernel
|
||
|
|
kwds = {'gamma': 0.1}
|
||
|
|
K1 = pairwise_kernels(X, Y=Y, metric=metric, **kwds)
|
||
|
|
K2 = rbf_kernel(X, Y=Y, **kwds)
|
||
|
|
assert_array_almost_equal(K1, K2)
|
||
|
|
|
||
|
|
# callable function, X=Y
|
||
|
|
K1 = pairwise_kernels(X, Y=X, metric=metric, **kwds)
|
||
|
|
K2 = rbf_kernel(X, Y=X, **kwds)
|
||
|
|
assert_array_almost_equal(K1, K2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pairwise_kernels_filter_param():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((5, 4))
|
||
|
|
Y = rng.random_sample((2, 4))
|
||
|
|
K = rbf_kernel(X, Y, gamma=0.1)
|
||
|
|
params = {"gamma": 0.1, "blabla": ":)"}
|
||
|
|
K2 = pairwise_kernels(X, Y, metric="rbf", filter_params=True, **params)
|
||
|
|
assert_array_almost_equal(K, K2)
|
||
|
|
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
pairwise_kernels(X, Y, metric="rbf", **params)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('metric, func', PAIRED_DISTANCES.items())
|
||
|
|
def test_paired_distances(metric, func):
|
||
|
|
# Test the pairwise_distance helper function.
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
# Euclidean distance should be equivalent to calling the function.
|
||
|
|
X = rng.random_sample((5, 4))
|
||
|
|
# Euclidean distance, with Y != X.
|
||
|
|
Y = rng.random_sample((5, 4))
|
||
|
|
|
||
|
|
S = paired_distances(X, Y, metric=metric)
|
||
|
|
S2 = func(X, Y)
|
||
|
|
assert_array_almost_equal(S, S2)
|
||
|
|
S3 = func(csr_matrix(X), csr_matrix(Y))
|
||
|
|
assert_array_almost_equal(S, S3)
|
||
|
|
if metric in PAIRWISE_DISTANCE_FUNCTIONS:
|
||
|
|
# Check the pairwise_distances implementation
|
||
|
|
# gives the same value
|
||
|
|
distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)
|
||
|
|
distances = np.diag(distances)
|
||
|
|
assert_array_almost_equal(distances, S)
|
||
|
|
|
||
|
|
|
||
|
|
def test_paired_distances_callable():
|
||
|
|
# Test the pairwise_distance helper function
|
||
|
|
# with the callable implementation
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
# Euclidean distance should be equivalent to calling the function.
|
||
|
|
X = rng.random_sample((5, 4))
|
||
|
|
# Euclidean distance, with Y != X.
|
||
|
|
Y = rng.random_sample((5, 4))
|
||
|
|
|
||
|
|
S = paired_distances(X, Y, metric='manhattan')
|
||
|
|
S2 = paired_distances(X, Y, metric=lambda x, y: np.abs(x - y).sum(axis=0))
|
||
|
|
assert_array_almost_equal(S, S2)
|
||
|
|
|
||
|
|
# Test that a value error is raised when the lengths of X and Y should not
|
||
|
|
# differ
|
||
|
|
Y = rng.random_sample((3, 4))
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
paired_distances(X, Y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pairwise_distances_argmin_min():
|
||
|
|
# Check pairwise minimum distances computation for any metric
|
||
|
|
X = [[0], [1]]
|
||
|
|
Y = [[-2], [3]]
|
||
|
|
|
||
|
|
Xsp = dok_matrix(X)
|
||
|
|
Ysp = csr_matrix(Y, dtype=np.float32)
|
||
|
|
|
||
|
|
expected_idx = [0, 1]
|
||
|
|
expected_vals = [2, 2]
|
||
|
|
expected_vals_sq = [4, 4]
|
||
|
|
|
||
|
|
# euclidean metric
|
||
|
|
idx, vals = pairwise_distances_argmin_min(X, Y, metric="euclidean")
|
||
|
|
idx2 = pairwise_distances_argmin(X, Y, metric="euclidean")
|
||
|
|
assert_array_almost_equal(idx, expected_idx)
|
||
|
|
assert_array_almost_equal(idx2, expected_idx)
|
||
|
|
assert_array_almost_equal(vals, expected_vals)
|
||
|
|
# sparse matrix case
|
||
|
|
idxsp, valssp = pairwise_distances_argmin_min(Xsp, Ysp, metric="euclidean")
|
||
|
|
assert_array_almost_equal(idxsp, expected_idx)
|
||
|
|
assert_array_almost_equal(valssp, expected_vals)
|
||
|
|
# We don't want np.matrix here
|
||
|
|
assert type(idxsp) == np.ndarray
|
||
|
|
assert type(valssp) == np.ndarray
|
||
|
|
|
||
|
|
# euclidean metric squared
|
||
|
|
idx, vals = pairwise_distances_argmin_min(X, Y, metric="euclidean",
|
||
|
|
metric_kwargs={"squared": True})
|
||
|
|
assert_array_almost_equal(idx, expected_idx)
|
||
|
|
assert_array_almost_equal(vals, expected_vals_sq)
|
||
|
|
|
||
|
|
# Non-euclidean scikit-learn metric
|
||
|
|
idx, vals = pairwise_distances_argmin_min(X, Y, metric="manhattan")
|
||
|
|
idx2 = pairwise_distances_argmin(X, Y, metric="manhattan")
|
||
|
|
assert_array_almost_equal(idx, expected_idx)
|
||
|
|
assert_array_almost_equal(idx2, expected_idx)
|
||
|
|
assert_array_almost_equal(vals, expected_vals)
|
||
|
|
# sparse matrix case
|
||
|
|
idxsp, valssp = pairwise_distances_argmin_min(Xsp, Ysp, metric="manhattan")
|
||
|
|
assert_array_almost_equal(idxsp, expected_idx)
|
||
|
|
assert_array_almost_equal(valssp, expected_vals)
|
||
|
|
|
||
|
|
# Non-euclidean Scipy distance (callable)
|
||
|
|
idx, vals = pairwise_distances_argmin_min(X, Y, metric=minkowski,
|
||
|
|
metric_kwargs={"p": 2})
|
||
|
|
assert_array_almost_equal(idx, expected_idx)
|
||
|
|
assert_array_almost_equal(vals, expected_vals)
|
||
|
|
|
||
|
|
# Non-euclidean Scipy distance (string)
|
||
|
|
idx, vals = pairwise_distances_argmin_min(X, Y, metric="minkowski",
|
||
|
|
metric_kwargs={"p": 2})
|
||
|
|
assert_array_almost_equal(idx, expected_idx)
|
||
|
|
assert_array_almost_equal(vals, expected_vals)
|
||
|
|
|
||
|
|
# Compare with naive implementation
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.randn(97, 149)
|
||
|
|
Y = rng.randn(111, 149)
|
||
|
|
|
||
|
|
dist = pairwise_distances(X, Y, metric="manhattan")
|
||
|
|
dist_orig_ind = dist.argmin(axis=0)
|
||
|
|
dist_orig_val = dist[dist_orig_ind, range(len(dist_orig_ind))]
|
||
|
|
|
||
|
|
dist_chunked_ind, dist_chunked_val = pairwise_distances_argmin_min(
|
||
|
|
X, Y, axis=0, metric="manhattan")
|
||
|
|
np.testing.assert_almost_equal(dist_orig_ind, dist_chunked_ind, decimal=7)
|
||
|
|
np.testing.assert_almost_equal(dist_orig_val, dist_chunked_val, decimal=7)
|
||
|
|
|
||
|
|
|
||
|
|
def _reduce_func(dist, start):
|
||
|
|
return dist[:, :100]
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_pairwise_distances_chunked_reduce_none():
|
||
|
|
# check that the reduce func is allowed to return None
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((10, 4))
|
||
|
|
S_chunks = pairwise_distances_chunked(X, None,
|
||
|
|
reduce_func=lambda dist, start: None,
|
||
|
|
working_memory=2 ** -16)
|
||
|
|
assert isinstance(S_chunks, GeneratorType)
|
||
|
|
S_chunks = list(S_chunks)
|
||
|
|
assert len(S_chunks) > 1
|
||
|
|
assert all(chunk is None for chunk in S_chunks)
|
||
|
|
|
||
|
|
|
||
|
|
@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)
|
||
|
|
with pytest.raises(err_type, match=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)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'metric',
|
||
|
|
('euclidean', 'l2', 'sqeuclidean'))
|
||
|
|
def test_parallel_pairwise_distances_diagonal(metric):
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.normal(size=(1000, 10), scale=1e10)
|
||
|
|
distances = pairwise_distances(X, metric=metric, n_jobs=2)
|
||
|
|
assert_allclose(np.diag(distances), 0, atol=1e-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((200, 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((100, 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
|
||
|
|
with pytest.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
|
||
|
|
with pytest.raises(StopIteration):
|
||
|
|
next(gen)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("x_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
@pytest.mark.parametrize("y_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
def test_euclidean_distances_known_result(x_array_constr, y_array_constr):
|
||
|
|
# Check the pairwise Euclidean distances computation on known result
|
||
|
|
X = x_array_constr([[0]])
|
||
|
|
Y = y_array_constr([[1], [2]])
|
||
|
|
D = euclidean_distances(X, Y)
|
||
|
|
assert_allclose(D, [[1., 2.]])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("y_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
def test_euclidean_distances_with_norms(dtype, y_array_constr):
|
||
|
|
# check that we still get the right answers with {X,Y}_norm_squared
|
||
|
|
# and that we get a wrong answer with wrong {X,Y}_norm_squared
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((10, 10)).astype(dtype, copy=False)
|
||
|
|
Y = rng.random_sample((20, 10)).astype(dtype, copy=False)
|
||
|
|
|
||
|
|
# norms will only be used if their dtype is float64
|
||
|
|
X_norm_sq = (X.astype(np.float64) ** 2).sum(axis=1).reshape(1, -1)
|
||
|
|
Y_norm_sq = (Y.astype(np.float64) ** 2).sum(axis=1).reshape(1, -1)
|
||
|
|
|
||
|
|
Y = y_array_constr(Y)
|
||
|
|
|
||
|
|
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_allclose(D2, D1)
|
||
|
|
assert_allclose(D3, D1)
|
||
|
|
assert_allclose(D4, D1)
|
||
|
|
|
||
|
|
# check we get the wrong answer with wrong {X,Y}_norm_squared
|
||
|
|
wrong_D = euclidean_distances(X, Y,
|
||
|
|
X_norm_squared=np.zeros_like(X_norm_sq),
|
||
|
|
Y_norm_squared=np.zeros_like(Y_norm_sq))
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
assert_allclose(wrong_D, D1)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("x_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
@pytest.mark.parametrize("y_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
def test_euclidean_distances(dtype, x_array_constr, y_array_constr):
|
||
|
|
# check that euclidean distances gives same result as scipy cdist
|
||
|
|
# when X and Y != X are provided
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((100, 10)).astype(dtype, copy=False)
|
||
|
|
X[X < 0.8] = 0
|
||
|
|
Y = rng.random_sample((10, 10)).astype(dtype, copy=False)
|
||
|
|
Y[Y < 0.8] = 0
|
||
|
|
|
||
|
|
expected = cdist(X, Y)
|
||
|
|
|
||
|
|
X = x_array_constr(X)
|
||
|
|
Y = y_array_constr(Y)
|
||
|
|
distances = euclidean_distances(X, Y)
|
||
|
|
|
||
|
|
# the default rtol=1e-7 is too close to the float32 precision
|
||
|
|
# and fails due to rounding errors.
|
||
|
|
assert_allclose(distances, expected, rtol=1e-6)
|
||
|
|
assert distances.dtype == dtype
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("x_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
def test_euclidean_distances_sym(dtype, x_array_constr):
|
||
|
|
# check that euclidean distances gives same result as scipy pdist
|
||
|
|
# when only X is provided
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((100, 10)).astype(dtype, copy=False)
|
||
|
|
X[X < 0.8] = 0
|
||
|
|
|
||
|
|
expected = squareform(pdist(X))
|
||
|
|
|
||
|
|
X = x_array_constr(X)
|
||
|
|
distances = euclidean_distances(X)
|
||
|
|
|
||
|
|
# the default rtol=1e-7 is too close to the float32 precision
|
||
|
|
# and fails due to rounding errors.
|
||
|
|
assert_allclose(distances, expected, rtol=1e-6)
|
||
|
|
assert distances.dtype == dtype
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("batch_size", [None, 5, 7, 101])
|
||
|
|
@pytest.mark.parametrize("x_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
@pytest.mark.parametrize("y_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
def test_euclidean_distances_upcast(batch_size, x_array_constr,
|
||
|
|
y_array_constr):
|
||
|
|
# check batches handling when Y != X (#13910)
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((100, 10)).astype(np.float32)
|
||
|
|
X[X < 0.8] = 0
|
||
|
|
Y = rng.random_sample((10, 10)).astype(np.float32)
|
||
|
|
Y[Y < 0.8] = 0
|
||
|
|
|
||
|
|
expected = cdist(X, Y)
|
||
|
|
|
||
|
|
X = x_array_constr(X)
|
||
|
|
Y = y_array_constr(Y)
|
||
|
|
distances = _euclidean_distances_upcast(X, Y=Y, batch_size=batch_size)
|
||
|
|
distances = np.sqrt(np.maximum(distances, 0))
|
||
|
|
|
||
|
|
# the default rtol=1e-7 is too close to the float32 precision
|
||
|
|
# and fails due to rounding errors.
|
||
|
|
assert_allclose(distances, expected, rtol=1e-6)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("batch_size", [None, 5, 7, 101])
|
||
|
|
@pytest.mark.parametrize("x_array_constr", [np.array, csr_matrix],
|
||
|
|
ids=["dense", "sparse"])
|
||
|
|
def test_euclidean_distances_upcast_sym(batch_size, x_array_constr):
|
||
|
|
# check batches handling when X is Y (#13910)
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((100, 10)).astype(np.float32)
|
||
|
|
X[X < 0.8] = 0
|
||
|
|
|
||
|
|
expected = squareform(pdist(X))
|
||
|
|
|
||
|
|
X = x_array_constr(X)
|
||
|
|
distances = _euclidean_distances_upcast(X, Y=X, batch_size=batch_size)
|
||
|
|
distances = np.sqrt(np.maximum(distances, 0))
|
||
|
|
|
||
|
|
# the default rtol=1e-7 is too close to the float32 precision
|
||
|
|
# and fails due to rounding errors.
|
||
|
|
assert_allclose(distances, expected, rtol=1e-6)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"dtype, eps, rtol",
|
||
|
|
[(np.float32, 1e-4, 1e-5),
|
||
|
|
pytest.param(
|
||
|
|
np.float64, 1e-8, 0.99,
|
||
|
|
marks=pytest.mark.xfail(reason='failing due to lack of precision'))])
|
||
|
|
@pytest.mark.parametrize("dim", [1, 1000000])
|
||
|
|
def test_euclidean_distances_extreme_values(dtype, eps, rtol, dim):
|
||
|
|
# check that euclidean distances is correct with float32 input thanks to
|
||
|
|
# upcasting. On float64 there are still precision issues.
|
||
|
|
X = np.array([[1.] * dim], dtype=dtype)
|
||
|
|
Y = np.array([[1. + eps] * dim], dtype=dtype)
|
||
|
|
|
||
|
|
distances = euclidean_distances(X, Y)
|
||
|
|
expected = cdist(X, Y)
|
||
|
|
|
||
|
|
assert_allclose(distances, expected, rtol=1e-5)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("squared", [True, False])
|
||
|
|
def test_nan_euclidean_distances_equal_to_euclidean_distance(squared):
|
||
|
|
# with no nan values
|
||
|
|
rng = np.random.RandomState(1337)
|
||
|
|
X = rng.randn(3, 4)
|
||
|
|
Y = rng.randn(4, 4)
|
||
|
|
|
||
|
|
normal_distance = euclidean_distances(X, Y=Y, squared=squared)
|
||
|
|
nan_distance = nan_euclidean_distances(X, Y=Y, squared=squared)
|
||
|
|
assert_allclose(normal_distance, nan_distance)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"X", [np.array([[np.inf, 0]]), np.array([[0, -np.inf]])])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"Y", [np.array([[np.inf, 0]]), np.array([[0, -np.inf]]), None])
|
||
|
|
def test_nan_euclidean_distances_infinite_values(X, Y):
|
||
|
|
|
||
|
|
with pytest.raises(ValueError) as excinfo:
|
||
|
|
nan_euclidean_distances(X, Y=Y)
|
||
|
|
|
||
|
|
exp_msg = ("Input contains infinity or a value too large for "
|
||
|
|
"dtype('float64').")
|
||
|
|
assert exp_msg == str(excinfo.value)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X, X_diag, missing_value", [
|
||
|
|
(np.array([[0, 1], [1, 0]]), np.sqrt(2), np.nan),
|
||
|
|
(np.array([[0, 1], [1, np.nan]]), np.sqrt(2), np.nan),
|
||
|
|
(np.array([[np.nan, 1], [1, np.nan]]), np.nan, np.nan),
|
||
|
|
(np.array([[np.nan, 1], [np.nan, 0]]), np.sqrt(2), np.nan),
|
||
|
|
(np.array([[0, np.nan], [1, np.nan]]), np.sqrt(2), np.nan),
|
||
|
|
(np.array([[0, 1], [1, 0]]), np.sqrt(2), -1),
|
||
|
|
(np.array([[0, 1], [1, -1]]), np.sqrt(2), -1),
|
||
|
|
(np.array([[-1, 1], [1, -1]]), np.nan, -1),
|
||
|
|
(np.array([[-1, 1], [-1, 0]]), np.sqrt(2), -1),
|
||
|
|
(np.array([[0, -1], [1, -1]]), np.sqrt(2), -1)
|
||
|
|
])
|
||
|
|
def test_nan_euclidean_distances_2x2(X, X_diag, missing_value):
|
||
|
|
|
||
|
|
exp_dist = np.array([[0., X_diag], [X_diag, 0]])
|
||
|
|
|
||
|
|
dist = nan_euclidean_distances(X, missing_values=missing_value)
|
||
|
|
assert_allclose(exp_dist, dist)
|
||
|
|
|
||
|
|
dist_sq = nan_euclidean_distances(
|
||
|
|
X, squared=True, missing_values=missing_value)
|
||
|
|
assert_allclose(exp_dist**2, dist_sq)
|
||
|
|
|
||
|
|
dist_two = nan_euclidean_distances(X, X, missing_values=missing_value)
|
||
|
|
assert_allclose(exp_dist, dist_two)
|
||
|
|
|
||
|
|
dist_two_copy = nan_euclidean_distances(
|
||
|
|
X, X.copy(), missing_values=missing_value)
|
||
|
|
assert_allclose(exp_dist, dist_two_copy)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("missing_value", [np.nan, -1])
|
||
|
|
def test_nan_euclidean_distances_complete_nan(missing_value):
|
||
|
|
X = np.array([[missing_value, missing_value], [0, 1]])
|
||
|
|
|
||
|
|
exp_dist = np.array([[np.nan, np.nan], [np.nan, 0]])
|
||
|
|
|
||
|
|
dist = nan_euclidean_distances(X, missing_values=missing_value)
|
||
|
|
assert_allclose(exp_dist, dist)
|
||
|
|
|
||
|
|
dist = nan_euclidean_distances(
|
||
|
|
X, X.copy(), missing_values=missing_value)
|
||
|
|
assert_allclose(exp_dist, dist)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("missing_value", [np.nan, -1])
|
||
|
|
def test_nan_euclidean_distances_not_trival(missing_value):
|
||
|
|
X = np.array([[1., missing_value, 3., 4., 2.],
|
||
|
|
[missing_value, 4., 6., 1., missing_value],
|
||
|
|
[3., missing_value, missing_value, missing_value, 1.]])
|
||
|
|
|
||
|
|
Y = np.array([[missing_value, 7., 7., missing_value, 2.],
|
||
|
|
[missing_value, missing_value, 5., 4., 7.],
|
||
|
|
[missing_value, missing_value, missing_value, 4., 5.]])
|
||
|
|
|
||
|
|
# Check for symmetry
|
||
|
|
D1 = nan_euclidean_distances(X, Y, missing_values=missing_value)
|
||
|
|
D2 = nan_euclidean_distances(Y, X, missing_values=missing_value)
|
||
|
|
|
||
|
|
assert_almost_equal(D1, D2.T)
|
||
|
|
|
||
|
|
# Check with explicit formula and squared=True
|
||
|
|
assert_allclose(
|
||
|
|
nan_euclidean_distances(
|
||
|
|
X[:1], Y[:1], squared=True, missing_values=missing_value),
|
||
|
|
[[5.0 / 2.0 * ((7 - 3)**2 + (2 - 2)**2)]])
|
||
|
|
|
||
|
|
# Check with explicit formula and squared=False
|
||
|
|
assert_allclose(
|
||
|
|
nan_euclidean_distances(
|
||
|
|
X[1:2], Y[1:2], squared=False, missing_values=missing_value),
|
||
|
|
[[np.sqrt(5.0 / 2.0 * ((6 - 5)**2 + (1 - 4)**2))]])
|
||
|
|
|
||
|
|
# Check when Y = X is explicitly passed
|
||
|
|
D3 = nan_euclidean_distances(X, missing_values=missing_value)
|
||
|
|
D4 = nan_euclidean_distances(X, X, missing_values=missing_value)
|
||
|
|
D5 = nan_euclidean_distances(X, X.copy(), missing_values=missing_value)
|
||
|
|
assert_allclose(D3, D4)
|
||
|
|
assert_allclose(D4, D5)
|
||
|
|
|
||
|
|
# Check copy = True against copy = False
|
||
|
|
D6 = nan_euclidean_distances(X, Y, copy=True)
|
||
|
|
D7 = nan_euclidean_distances(X, Y, copy=False)
|
||
|
|
assert_allclose(D6, D7)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("missing_value", [np.nan, -1])
|
||
|
|
def test_nan_euclidean_distances_one_feature_match_positive(missing_value):
|
||
|
|
# First feature is the only feature that is non-nan and in both
|
||
|
|
# samples. The result of `nan_euclidean_distances` with squared=True
|
||
|
|
# should be non-negative. The non-squared version should all be close to 0.
|
||
|
|
X = np.array([[-122.27, 648., missing_value, 37.85],
|
||
|
|
[-122.27, missing_value, 2.34701493, missing_value]])
|
||
|
|
|
||
|
|
dist_squared = nan_euclidean_distances(X, missing_values=missing_value,
|
||
|
|
squared=True)
|
||
|
|
assert np.all(dist_squared >= 0)
|
||
|
|
|
||
|
|
dist = nan_euclidean_distances(X, missing_values=missing_value,
|
||
|
|
squared=False)
|
||
|
|
assert_allclose(dist, 0.0)
|
||
|
|
|
||
|
|
|
||
|
|
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.)
|
||
|
|
|
||
|
|
|
||
|
|
def test_haversine_distances():
|
||
|
|
# Check haversine distance with distances computation
|
||
|
|
def slow_haversine_distances(x, y):
|
||
|
|
diff_lat = y[0] - x[0]
|
||
|
|
diff_lon = y[1] - x[1]
|
||
|
|
a = np.sin(diff_lat / 2) ** 2 + (
|
||
|
|
np.cos(x[0]) * np.cos(y[0]) * np.sin(diff_lon/2) ** 2
|
||
|
|
)
|
||
|
|
c = 2 * np.arcsin(np.sqrt(a))
|
||
|
|
return c
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.random_sample((5, 2))
|
||
|
|
Y = rng.random_sample((10, 2))
|
||
|
|
D1 = np.array([[slow_haversine_distances(x, y) for y in Y] for x in X])
|
||
|
|
D2 = haversine_distances(X, Y)
|
||
|
|
assert_array_almost_equal(D1, D2)
|
||
|
|
# Test haversine distance does not accept X where n_feature != 2
|
||
|
|
X = rng.random_sample((10, 3))
|
||
|
|
err_msg = "Haversine distance only valid in 2 dimensions"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
haversine_distances(X)
|
||
|
|
|
||
|
|
|
||
|
|
# 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 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 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 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 K[0, 0] > K[0, 1]
|
||
|
|
assert K[1, 1] > K[1, 0]
|
||
|
|
|
||
|
|
# test negative input
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
chi2_kernel([[0, -1]])
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
chi2_kernel([[0, -1]], [[-1, -1]])
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
chi2_kernel([[0, 1]], [[-1, -1]])
|
||
|
|
|
||
|
|
# different n_features in X and Y
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
chi2_kernel([[0, 1]], [[.2, .2, .6]])
|
||
|
|
|
||
|
|
# sparse matrices
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
chi2_kernel(csr_matrix(X), csr_matrix(Y))
|
||
|
|
with pytest.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))
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
check_pairwise_arrays(XA, XB)
|
||
|
|
|
||
|
|
XB = np.resize(np.arange(4 * 9), (4, 9))
|
||
|
|
with pytest.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)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
check_pairwise_arrays(XA, XB)
|
||
|
|
XA = np.arange(45).reshape(9, 5)
|
||
|
|
XB = np.arange(32).reshape(4, 8)
|
||
|
|
with pytest.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 abs(XA_sparse - XA_checked).sum() == 0
|
||
|
|
assert issparse(XB_checked)
|
||
|
|
assert abs(XB_sparse - XB_checked).sum() == 0
|
||
|
|
|
||
|
|
XA_checked, XA_2_checked = check_pairwise_arrays(XA_sparse, XA_sparse)
|
||
|
|
assert issparse(XA_checked)
|
||
|
|
assert abs(XA_sparse - XA_checked).sum() == 0
|
||
|
|
assert issparse(XA_2_checked)
|
||
|
|
assert 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 XA_checked.dtype == np.float32
|
||
|
|
|
||
|
|
# both float32
|
||
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
|
||
|
|
assert XA_checked.dtype == np.float32
|
||
|
|
assert XB_checked.dtype == np.float32
|
||
|
|
|
||
|
|
# mismatched A
|
||
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA.astype(np.float),
|
||
|
|
XB)
|
||
|
|
assert XA_checked.dtype == np.float
|
||
|
|
assert XB_checked.dtype == np.float
|
||
|
|
|
||
|
|
# mismatched B
|
||
|
|
XA_checked, XB_checked = check_pairwise_arrays(XA,
|
||
|
|
XB.astype(np.float))
|
||
|
|
assert XA_checked.dtype == np.float
|
||
|
|
assert 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((100, 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((100, 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)
|
||
|
|
# TODO: Remove warn_checker in 0.25
|
||
|
|
if y_is_x:
|
||
|
|
warn_checker = pytest.warns(None)
|
||
|
|
else:
|
||
|
|
warn_checker = pytest.warns(FutureWarning,
|
||
|
|
match="to be specified if Y is passed")
|
||
|
|
with warn_checker:
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'metric', [
|
||
|
|
'braycurtis', 'canberra', 'chebyshev',
|
||
|
|
'correlation', 'hamming', 'mahalanobis', 'minkowski', 'seuclidean',
|
||
|
|
'sqeuclidean', 'cityblock', 'cosine', 'euclidean'])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"dtype",
|
||
|
|
[np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("y_is_x", [True, False], ids=["Y is X", "Y is not X"])
|
||
|
|
def test_numeric_pairwise_distances_datatypes(metric, dtype, y_is_x):
|
||
|
|
# Check that pairwise distances gives the same result as pdist and cdist
|
||
|
|
# regardless of input datatype when using any scipy metric for comparing
|
||
|
|
# numeric vectors
|
||
|
|
#
|
||
|
|
# This test is necessary because pairwise_distances used to throw an
|
||
|
|
# error when using metric='seuclidean' and the input data was not
|
||
|
|
# of type np.float64 (#15730)
|
||
|
|
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
|
||
|
|
X = rng.random_sample((5, 4)).astype(dtype)
|
||
|
|
|
||
|
|
params = {}
|
||
|
|
if y_is_x:
|
||
|
|
Y = X
|
||
|
|
expected_dist = squareform(pdist(X, metric=metric))
|
||
|
|
else:
|
||
|
|
Y = rng.random_sample((5, 4)).astype(dtype)
|
||
|
|
expected_dist = cdist(X, Y, metric=metric)
|
||
|
|
# precompute parameters for seuclidean & mahalanobis when x is not y
|
||
|
|
if metric == 'seuclidean':
|
||
|
|
params = {'V': np.var(np.vstack([X, Y]),
|
||
|
|
axis=0, ddof=1, dtype=np.float64)}
|
||
|
|
elif metric == 'mahalanobis':
|
||
|
|
params = {'VI': np.linalg.inv(np.cov(np.vstack([X, Y]).T)).T}
|
||
|
|
|
||
|
|
dist = pairwise_distances(X, Y, metric=metric, **params)
|
||
|
|
|
||
|
|
# the default rtol=1e-7 is too close to the float32 precision
|
||
|
|
# and fails due to rounding errors
|
||
|
|
rtol = 1e-5 if dtype is np.float32 else 1e-7
|
||
|
|
assert_allclose(dist, expected_dist, rtol=rtol)
|