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
|
# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import math
import numpy as np
import pytest
import scipy.stats
from sklearn.utils._testing import assert_array_equal
from sklearn.utils.fixes import _joblib_parallel_args
from sklearn.utils.fixes import _object_dtype_isnan
from sklearn.utils.fixes import loguniform
from sklearn.utils.fixes import MaskedArray
@pytest.mark.parametrize('joblib_version', ('0.11', '0.12.0'))
def test_joblib_parallel_args(monkeypatch, joblib_version):
import joblib
monkeypatch.setattr(joblib, '__version__', joblib_version)
if joblib_version == '0.12.0':
# arguments are simply passed through
assert _joblib_parallel_args(prefer='threads') == {'prefer': 'threads'}
assert _joblib_parallel_args(prefer='processes', require=None) == {
'prefer': 'processes', 'require': None}
assert _joblib_parallel_args(non_existing=1) == {'non_existing': 1}
elif joblib_version == '0.11':
# arguments are mapped to the corresponding backend
assert _joblib_parallel_args(prefer='threads') == {
'backend': 'threading'}
assert _joblib_parallel_args(prefer='processes') == {
'backend': 'multiprocessing'}
with pytest.raises(ValueError):
_joblib_parallel_args(prefer='invalid')
assert _joblib_parallel_args(
prefer='processes', require='sharedmem') == {
'backend': 'threading'}
with pytest.raises(ValueError):
_joblib_parallel_args(require='invalid')
with pytest.raises(NotImplementedError):
_joblib_parallel_args(verbose=True)
else:
raise ValueError
@pytest.mark.parametrize("dtype, val", ([object, 1],
[object, "a"],
[float, 1]))
def test_object_dtype_isnan(dtype, val):
X = np.array([[val, np.nan],
[np.nan, val]], dtype=dtype)
expected_mask = np.array([[False, True],
[True, False]])
mask = _object_dtype_isnan(X)
assert_array_equal(mask, expected_mask)
@pytest.mark.parametrize("low,high,base",
[(-1, 0, 10), (0, 2, np.exp(1)), (-1, 1, 2)])
def test_loguniform(low, high, base):
rv = loguniform(base ** low, base ** high)
assert isinstance(rv, scipy.stats._distn_infrastructure.rv_frozen)
rvs = rv.rvs(size=2000, random_state=0)
# Test the basics; right bounds, right size
assert (base ** low <= rvs).all() and (rvs <= base ** high).all()
assert len(rvs) == 2000
# Test that it's actually (fairly) uniform
log_rvs = np.array([math.log(x, base) for x in rvs])
counts, _ = np.histogram(log_rvs)
assert counts.mean() == 200
assert np.abs(counts - counts.mean()).max() <= 40
# Test that random_state works
assert (
loguniform(base ** low, base ** high).rvs(random_state=0)
== loguniform(base ** low, base ** high).rvs(random_state=0)
)
def test_masked_array_deprecated(): # TODO: remove in 0.25
with pytest.warns(FutureWarning, match='is deprecated'):
MaskedArray()
|
