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
|
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
Author(s): Marc Glisse
Copyright (C) 2020 Inria
Modification(s):
- YYYY/MM Author: Description of the modification
"""
from gudhi.point_cloud.knn import KNearestNeighbors
import numpy as np
import pytest
def test_knn_explicit():
base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
query = np.array([[1.0, 1], [2, 2], [4, 4]])
knn = KNearestNeighbors(2, metric="manhattan", return_distance=True, return_index=True)
knn.fit(base)
r = knn.transform(query)
assert r[0] == pytest.approx(np.array([[0, 1], [1, 0], [3, 2]]))
assert r[1] == pytest.approx(np.array([[0.0, 1], [1, 2], [1, 2]]))
knn = KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False)
knn.fit(base)
r = knn.transform(query)
assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
r = (
KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops")
.fit(base)
.transform(query)
)
assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
r = (
KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops", enable_autodiff=True)
.fit(base)
.transform(query)
)
assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
knn = KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True)
knn.fit(base)
r = knn.transform(query)
assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
r = (
KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops")
.fit(base)
.transform(query)
)
assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
dist = np.array([[0.0, 3, 8], [1, 0, 5], [1, 2, 0]])
knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False)
r = knn.fit_transform(dist)
assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]])
knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, sort_results=True)
r = knn.fit_transform(dist)
assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]])
assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]])
# Second time in parallel
knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False, n_jobs=2, sort_results=True)
r = knn.fit_transform(dist)
assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]])
knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, n_jobs=2)
r = knn.fit_transform(dist)
assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]])
assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]])
def test_knn_compare():
base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
query = np.array([[1.0, 1], [2, 2], [4, 4]])
r0 = (
KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=False)
.fit(base)
.transform(query)
)
r1 = (
KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=False)
.fit(base)
.transform(query)
)
r2 = (
KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query)
)
r3 = (
KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=False)
.fit(base)
.transform(query)
)
assert np.array_equal(r0, r1) and np.array_equal(r0, r2) and np.array_equal(r0, r3)
r0 = (
KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=True)
.fit(base)
.transform(query)
)
r1 = (
KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=True)
.fit(base)
.transform(query)
)
r2 = KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query)
r3 = (
KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query)
)
assert np.array_equal(r0[0], r1[0]) and np.array_equal(r0[0], r2[0]) and np.array_equal(r0[0], r3[0])
d0 = pytest.approx(r0[1])
assert r1[1] == d0 and r2[1] == d0 and r3[1] == d0
def test_knn_nop():
# This doesn't look super useful...
p = np.array([[0.0]])
assert None is KNearestNeighbors(
k=1, return_index=False, return_distance=False, implementation="sklearn"
).fit_transform(p)
assert None is KNearestNeighbors(
k=1, return_index=False, return_distance=False, implementation="ckdtree"
).fit_transform(p)
assert None is KNearestNeighbors(
k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5
).fit_transform(p)
assert None is KNearestNeighbors(
k=1, return_index=False, return_distance=False, implementation="keops"
).fit_transform(p)
assert None is KNearestNeighbors(
k=1, return_index=False, return_distance=False, metric="precomputed"
).fit_transform(p)
|
