228 lines
7.2 KiB
Python
228 lines
7.2 KiB
Python
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import itertools
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import pickle
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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import pytest
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from scipy.spatial.distance import cdist
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from sklearn.neighbors import DistanceMetric
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from sklearn.neighbors import BallTree
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import assert_raises_regex
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from sklearn.utils.fixes import sp_version, parse_version
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def dist_func(x1, x2, p):
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return np.sum((x1 - x2) ** p) ** (1. / p)
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rng = check_random_state(0)
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d = 4
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n1 = 20
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n2 = 25
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X1 = rng.random_sample((n1, d)).astype('float64', copy=False)
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X2 = rng.random_sample((n2, d)).astype('float64', copy=False)
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# make boolean arrays: ones and zeros
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X1_bool = X1.round(0)
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X2_bool = X2.round(0)
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V = rng.random_sample((d, d))
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VI = np.dot(V, V.T)
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BOOL_METRICS = ['matching', 'jaccard', 'dice',
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'kulsinski', 'rogerstanimoto', 'russellrao',
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'sokalmichener', 'sokalsneath']
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METRICS_DEFAULT_PARAMS = {'euclidean': {},
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'cityblock': {},
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'minkowski': dict(p=(1, 1.5, 2, 3)),
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'chebyshev': {},
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'seuclidean': dict(V=(rng.random_sample(d),)),
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'wminkowski': dict(p=(1, 1.5, 3),
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w=(rng.random_sample(d),)),
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'mahalanobis': dict(VI=(VI,)),
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'hamming': {},
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'canberra': {},
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'braycurtis': {}}
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@pytest.mark.parametrize('metric', METRICS_DEFAULT_PARAMS)
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def test_cdist(metric):
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argdict = METRICS_DEFAULT_PARAMS[metric]
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keys = argdict.keys()
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for vals in itertools.product(*argdict.values()):
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kwargs = dict(zip(keys, vals))
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if metric == "wminkowski":
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if sp_version >= parse_version("1.8.0"):
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pytest.skip("wminkowski will be removed in SciPy 1.8.0")
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# wminkoski is deprecated in SciPy 1.6.0 and removed in 1.8.0
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ExceptionToAssert = None
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if sp_version >= parse_version("1.6.0"):
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ExceptionToAssert = DeprecationWarning
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with pytest.warns(ExceptionToAssert):
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D_true = cdist(X1, X2, metric, **kwargs)
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else:
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D_true = cdist(X1, X2, metric, **kwargs)
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check_cdist(metric, kwargs, D_true)
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@pytest.mark.parametrize('metric', BOOL_METRICS)
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def test_cdist_bool_metric(metric):
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D_true = cdist(X1_bool, X2_bool, metric)
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check_cdist_bool(metric, D_true)
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def check_cdist(metric, kwargs, D_true):
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dm = DistanceMetric.get_metric(metric, **kwargs)
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D12 = dm.pairwise(X1, X2)
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assert_array_almost_equal(D12, D_true)
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def check_cdist_bool(metric, D_true):
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dm = DistanceMetric.get_metric(metric)
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D12 = dm.pairwise(X1_bool, X2_bool)
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assert_array_almost_equal(D12, D_true)
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@pytest.mark.parametrize('metric', METRICS_DEFAULT_PARAMS)
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def test_pdist(metric):
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argdict = METRICS_DEFAULT_PARAMS[metric]
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keys = argdict.keys()
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for vals in itertools.product(*argdict.values()):
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kwargs = dict(zip(keys, vals))
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if metric == "wminkowski":
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if sp_version >= parse_version("1.8.0"):
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pytest.skip("wminkowski will be removed in SciPy 1.8.0")
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# wminkoski is deprecated in SciPy 1.6.0 and removed in 1.8.0
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ExceptionToAssert = None
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if sp_version >= parse_version("1.6.0"):
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ExceptionToAssert = DeprecationWarning
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with pytest.warns(ExceptionToAssert):
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D_true = cdist(X1, X1, metric, **kwargs)
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else:
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D_true = cdist(X1, X1, metric, **kwargs)
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check_pdist(metric, kwargs, D_true)
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@pytest.mark.parametrize('metric', BOOL_METRICS)
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def test_pdist_bool_metrics(metric):
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D_true = cdist(X1_bool, X1_bool, metric)
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check_pdist_bool(metric, D_true)
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def check_pdist(metric, kwargs, D_true):
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dm = DistanceMetric.get_metric(metric, **kwargs)
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D12 = dm.pairwise(X1)
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assert_array_almost_equal(D12, D_true)
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def check_pdist_bool(metric, D_true):
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dm = DistanceMetric.get_metric(metric)
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D12 = dm.pairwise(X1_bool)
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# Based on https://github.com/scipy/scipy/pull/7373
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# When comparing two all-zero vectors, scipy>=1.2.0 jaccard metric
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# was changed to return 0, instead of nan.
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if metric == 'jaccard' and sp_version < parse_version('1.2.0'):
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D_true[np.isnan(D_true)] = 0
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assert_array_almost_equal(D12, D_true)
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@pytest.mark.parametrize('metric', METRICS_DEFAULT_PARAMS)
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def test_pickle(metric):
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argdict = METRICS_DEFAULT_PARAMS[metric]
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keys = argdict.keys()
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for vals in itertools.product(*argdict.values()):
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kwargs = dict(zip(keys, vals))
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check_pickle(metric, kwargs)
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@pytest.mark.parametrize('metric', BOOL_METRICS)
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def test_pickle_bool_metrics(metric):
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dm = DistanceMetric.get_metric(metric)
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D1 = dm.pairwise(X1_bool)
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dm2 = pickle.loads(pickle.dumps(dm))
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D2 = dm2.pairwise(X1_bool)
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assert_array_almost_equal(D1, D2)
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def check_pickle(metric, kwargs):
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dm = DistanceMetric.get_metric(metric, **kwargs)
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D1 = dm.pairwise(X1)
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dm2 = pickle.loads(pickle.dumps(dm))
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D2 = dm2.pairwise(X1)
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assert_array_almost_equal(D1, D2)
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def test_haversine_metric():
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def haversine_slow(x1, x2):
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return 2 * np.arcsin(np.sqrt(np.sin(0.5 * (x1[0] - x2[0])) ** 2
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+ np.cos(x1[0]) * np.cos(x2[0]) *
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np.sin(0.5 * (x1[1] - x2[1])) ** 2))
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X = np.random.random((10, 2))
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haversine = DistanceMetric.get_metric("haversine")
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D1 = haversine.pairwise(X)
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D2 = np.zeros_like(D1)
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for i, x1 in enumerate(X):
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for j, x2 in enumerate(X):
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D2[i, j] = haversine_slow(x1, x2)
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assert_array_almost_equal(D1, D2)
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assert_array_almost_equal(haversine.dist_to_rdist(D1),
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np.sin(0.5 * D2) ** 2)
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def test_pyfunc_metric():
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X = np.random.random((10, 3))
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euclidean = DistanceMetric.get_metric("euclidean")
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pyfunc = DistanceMetric.get_metric("pyfunc", func=dist_func, p=2)
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# Check if both callable metric and predefined metric initialized
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# DistanceMetric object is picklable
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euclidean_pkl = pickle.loads(pickle.dumps(euclidean))
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pyfunc_pkl = pickle.loads(pickle.dumps(pyfunc))
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D1 = euclidean.pairwise(X)
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D2 = pyfunc.pairwise(X)
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D1_pkl = euclidean_pkl.pairwise(X)
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D2_pkl = pyfunc_pkl.pairwise(X)
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assert_array_almost_equal(D1, D2)
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assert_array_almost_equal(D1_pkl, D2_pkl)
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def test_bad_pyfunc_metric():
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def wrong_distance(x, y):
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return "1"
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X = np.ones((5, 2))
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assert_raises_regex(TypeError,
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"Custom distance function must accept two vectors",
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BallTree, X, metric=wrong_distance)
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def test_input_data_size():
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# Regression test for #6288
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# Previously, a metric requiring a particular input dimension would fail
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def custom_metric(x, y):
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assert x.shape[0] == 3
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return np.sum((x - y) ** 2)
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rng = check_random_state(0)
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X = rng.rand(10, 3)
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pyfunc = DistanceMetric.get_metric("pyfunc", func=custom_metric)
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eucl = DistanceMetric.get_metric("euclidean")
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assert_array_almost_equal(pyfunc.pairwise(X), eucl.pairwise(X) ** 2)
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