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
|
"""
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
import numpy as np
import pytest
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble._base import _set_random_states
from sklearn.linear_model import Perceptron
from collections import OrderedDict
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectFromModel
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(
estimator=Perceptron(random_state=None), n_estimators=3
)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
random_state = np.random.RandomState(3)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(append=False)
assert 3 == len(ensemble)
assert 3 == len(ensemble.estimators_)
assert isinstance(ensemble[0], Perceptron)
assert ensemble[0].random_state is None
assert isinstance(ensemble[1].random_state, int)
assert isinstance(ensemble[2].random_state, int)
assert ensemble[1].random_state != ensemble[2].random_state
np_int_ensemble = BaggingClassifier(
estimator=Perceptron(), n_estimators=np.int32(3)
)
np_int_ensemble.fit(iris.data, iris.target)
def test_set_random_states():
# Linear Discriminant Analysis doesn't have random state: smoke test
_set_random_states(LinearDiscriminantAnalysis(), random_state=17)
clf1 = Perceptron(random_state=None)
assert clf1.random_state is None
# check random_state is None still sets
_set_random_states(clf1, None)
assert isinstance(clf1.random_state, int)
# check random_state fixes results in consistent initialisation
_set_random_states(clf1, 3)
assert isinstance(clf1.random_state, int)
clf2 = Perceptron(random_state=None)
_set_random_states(clf2, 3)
assert clf1.random_state == clf2.random_state
# nested random_state
def make_steps():
return [
("sel", SelectFromModel(Perceptron(random_state=None))),
("clf", Perceptron(random_state=None)),
]
est1 = Pipeline(make_steps())
_set_random_states(est1, 3)
assert isinstance(est1.steps[0][1].estimator.random_state, int)
assert isinstance(est1.steps[1][1].random_state, int)
assert (
est1.get_params()["sel__estimator__random_state"]
!= est1.get_params()["clf__random_state"]
)
# ensure multiple random_state parameters are invariant to get_params()
# iteration order
class AlphaParamPipeline(Pipeline):
def get_params(self, *args, **kwargs):
params = Pipeline.get_params(self, *args, **kwargs).items()
return OrderedDict(sorted(params))
class RevParamPipeline(Pipeline):
def get_params(self, *args, **kwargs):
params = Pipeline.get_params(self, *args, **kwargs).items()
return OrderedDict(sorted(params, reverse=True))
for cls in [AlphaParamPipeline, RevParamPipeline]:
est2 = cls(make_steps())
_set_random_states(est2, 3)
assert (
est1.get_params()["sel__estimator__random_state"]
== est2.get_params()["sel__estimator__random_state"]
)
assert (
est1.get_params()["clf__random_state"]
== est2.get_params()["clf__random_state"]
)
# TODO(1.4): remove
def test_validate_estimator_value_error():
X = np.array([[1, 2], [3, 4]])
y = np.array([1, 0])
model = BaggingClassifier(estimator=Perceptron(), base_estimator=Perceptron())
err_msg = "Both `estimator` and `base_estimator` were set. Only set `estimator`."
with pytest.raises(ValueError, match=err_msg):
model.fit(X, y)
|
