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