forked from ukosuagwu/scikit-learn
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_mocking.py
More file actions
183 lines (143 loc) · 5 KB
/
Copy pathtest_mocking.py
File metadata and controls
183 lines (143 loc) · 5 KB
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
import numpy as np
import pytest
from scipy import sparse
from numpy.testing import assert_array_equal
from numpy.testing import assert_allclose
from sklearn.datasets import load_iris
from sklearn.utils import check_array
from sklearn.utils import _safe_indexing
from sklearn.utils._testing import _convert_container
from sklearn.utils._mocking import CheckingClassifier
@pytest.fixture
def iris():
return load_iris(return_X_y=True)
def _success(x):
return True
def _fail(x):
return False
@pytest.mark.parametrize(
"kwargs",
[
{},
{"check_X": _success},
{"check_y": _success},
{"check_X": _success, "check_y": _success},
],
)
def test_check_on_fit_success(iris, kwargs):
X, y = iris
CheckingClassifier(**kwargs).fit(X, y)
@pytest.mark.parametrize(
"kwargs",
[
{"check_X": _fail},
{"check_y": _fail},
{"check_X": _success, "check_y": _fail},
{"check_X": _fail, "check_y": _success},
{"check_X": _fail, "check_y": _fail},
],
)
def test_check_on_fit_fail(iris, kwargs):
X, y = iris
clf = CheckingClassifier(**kwargs)
with pytest.raises(AssertionError):
clf.fit(X, y)
@pytest.mark.parametrize(
"pred_func", ["predict", "predict_proba", "decision_function", "score"]
)
def test_check_X_on_predict_success(iris, pred_func):
X, y = iris
clf = CheckingClassifier(check_X=_success).fit(X, y)
getattr(clf, pred_func)(X)
@pytest.mark.parametrize(
"pred_func", ["predict", "predict_proba", "decision_function", "score"]
)
def test_check_X_on_predict_fail(iris, pred_func):
X, y = iris
clf = CheckingClassifier(check_X=_success).fit(X, y)
clf.set_params(check_X=_fail)
with pytest.raises(AssertionError):
getattr(clf, pred_func)(X)
@pytest.mark.parametrize("input_type", ["list", "array", "sparse", "dataframe"])
def test_checking_classifier(iris, input_type):
# Check that the CheckingClassifier outputs what we expect
X, y = iris
X = _convert_container(X, input_type)
clf = CheckingClassifier()
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
assert len(clf.classes_) == 3
assert clf.n_features_in_ == 4
y_pred = clf.predict(X)
assert_array_equal(y_pred, np.zeros(y_pred.size, dtype=int))
assert clf.score(X) == pytest.approx(0)
clf.set_params(foo_param=10)
assert clf.fit(X, y).score(X) == pytest.approx(1)
y_proba = clf.predict_proba(X)
assert y_proba.shape == (150, 3)
assert_allclose(y_proba[:, 0], 1)
assert_allclose(y_proba[:, 1:], 0)
y_decision = clf.decision_function(X)
assert y_decision.shape == (150, 3)
assert_allclose(y_decision[:, 0], 1)
assert_allclose(y_decision[:, 1:], 0)
# check the shape in case of binary classification
first_2_classes = np.logical_or(y == 0, y == 1)
X = _safe_indexing(X, first_2_classes)
y = _safe_indexing(y, first_2_classes)
clf.fit(X, y)
y_proba = clf.predict_proba(X)
assert y_proba.shape == (100, 2)
assert_allclose(y_proba[:, 0], 1)
assert_allclose(y_proba[:, 1], 0)
y_decision = clf.decision_function(X)
assert y_decision.shape == (100,)
assert_allclose(y_decision, 0)
def test_checking_classifier_with_params(iris):
X, y = iris
X_sparse = sparse.csr_matrix(X)
clf = CheckingClassifier(check_X=sparse.issparse)
with pytest.raises(AssertionError):
clf.fit(X, y)
clf.fit(X_sparse, y)
clf = CheckingClassifier(
check_X=check_array, check_X_params={"accept_sparse": False}
)
clf.fit(X, y)
with pytest.raises(TypeError, match="A sparse matrix was passed"):
clf.fit(X_sparse, y)
def test_checking_classifier_fit_params(iris):
# check the error raised when the number of samples is not the one expected
X, y = iris
clf = CheckingClassifier(expected_sample_weight=True)
sample_weight = np.ones(len(X) // 2)
msg = f"sample_weight.shape == ({len(X) // 2},), expected ({len(X)},)!"
with pytest.raises(ValueError) as exc:
clf.fit(X, y, sample_weight=sample_weight)
assert exc.value.args[0] == msg
def test_checking_classifier_missing_fit_params(iris):
X, y = iris
clf = CheckingClassifier(expected_sample_weight=True)
err_msg = "Expected sample_weight to be passed"
with pytest.raises(AssertionError, match=err_msg):
clf.fit(X, y)
@pytest.mark.parametrize(
"methods_to_check",
[["predict"], ["predict", "predict_proba"]],
)
@pytest.mark.parametrize(
"predict_method", ["predict", "predict_proba", "decision_function", "score"]
)
def test_checking_classifier_methods_to_check(iris, methods_to_check, predict_method):
# check that methods_to_check allows to bypass checks
X, y = iris
clf = CheckingClassifier(
check_X=sparse.issparse,
methods_to_check=methods_to_check,
)
clf.fit(X, y)
if predict_method in methods_to_check:
with pytest.raises(AssertionError):
getattr(clf, predict_method)(X)
else:
getattr(clf, predict_method)(X)