ai-content-maker/.venv/Lib/site-packages/sklearn/ensemble/tests/test_base.py

110 lines
3.6 KiB
Python

"""
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from collections import OrderedDict
import numpy as np
from sklearn.datasets import load_iris
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble._base import _set_random_states
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import Perceptron
from sklearn.pipeline import Pipeline
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"]
)