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
|
"""Test for the testing module"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
import pytest
from sklearn.neighbors._base import KNeighborsMixin
from imblearn.base import SamplerMixin
from imblearn.utils.testing import _CustomNearestNeighbors, all_estimators
def test_all_estimators():
# check if the filtering is working with a list or a single string
type_filter = "sampler"
all_estimators(type_filter=type_filter)
type_filter = ["sampler"]
estimators = all_estimators(type_filter=type_filter)
for estimator in estimators:
# check that all estimators are sampler
assert issubclass(estimator[1], SamplerMixin)
# check that an error is raised when the type is unknown
type_filter = "rnd"
with pytest.raises(ValueError, match="Parameter type_filter must be 'sampler'"):
all_estimators(type_filter=type_filter)
def test_custom_nearest_neighbors():
"""Check that our custom nearest neighbors can be used for our internal
duck-typing."""
neareat_neighbors = _CustomNearestNeighbors(n_neighbors=3)
assert not isinstance(neareat_neighbors, KNeighborsMixin)
assert hasattr(neareat_neighbors, "kneighbors")
assert hasattr(neareat_neighbors, "kneighbors_graph")
rng = np.random.RandomState(42)
X = rng.randn(150, 3)
y = rng.randint(0, 2, 150)
neareat_neighbors.fit(X, y)
distances, indices = neareat_neighbors.kneighbors(X)
assert distances.shape == (150, 3)
assert indices.shape == (150, 3)
np.testing.assert_allclose(distances[:, 0], 0.0)
np.testing.assert_allclose(indices[:, 0], np.arange(150))
|
