1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
|
import itertools
import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
from distutils.version import LooseVersion
from scipy import __version__ as scipy_version
from scipy.spatial.distance import cdist
from sklearn.neighbors.dist_metrics import DistanceMetric
from sklearn.neighbors import BallTree
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_raises_regex
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')
X2 = rng.random_sample((n2, d)).astype('float64')
# 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))
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))
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 LooseVersion(scipy_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
# Previoulsly, 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=dist_func, p=2)
eucl = DistanceMetric.get_metric("euclidean")
assert_array_almost_equal(pyfunc.pairwise(X), eucl.pairwise(X))
|
