132 lines
5.2 KiB
Python
132 lines
5.2 KiB
Python
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"""
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Testing for the base module (sklearn.ensemble.base).
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"""
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# Authors: Gilles Louppe
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# License: BSD 3 clause
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import numpy as np
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from numpy.testing import assert_equal
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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.testing import assert_not_equal
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from sklearn.utils.testing import assert_true
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from sklearn.datasets import load_iris
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from sklearn.ensemble import BaggingClassifier
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from sklearn.ensemble.base import _set_random_states
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from sklearn.linear_model import Perceptron
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from collections import OrderedDict
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearn.pipeline import Pipeline
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from sklearn.feature_selection import SelectFromModel
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def test_base():
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# Check BaseEnsemble methods.
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ensemble = BaggingClassifier(
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base_estimator=Perceptron(tol=1e-3, random_state=None), n_estimators=3)
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iris = load_iris()
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ensemble.fit(iris.data, iris.target)
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ensemble.estimators_ = [] # empty the list and create estimators manually
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ensemble._make_estimator()
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random_state = np.random.RandomState(3)
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ensemble._make_estimator(random_state=random_state)
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ensemble._make_estimator(random_state=random_state)
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ensemble._make_estimator(append=False)
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assert_equal(3, len(ensemble))
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assert_equal(3, len(ensemble.estimators_))
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assert_true(isinstance(ensemble[0], Perceptron))
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assert_equal(ensemble[0].random_state, None)
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assert_true(isinstance(ensemble[1].random_state, int))
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assert_true(isinstance(ensemble[2].random_state, int))
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assert_not_equal(ensemble[1].random_state, ensemble[2].random_state)
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np_int_ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
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n_estimators=np.int32(3))
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np_int_ensemble.fit(iris.data, iris.target)
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def test_base_zero_n_estimators():
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# Check that instantiating a BaseEnsemble with n_estimators<=0 raises
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# a ValueError.
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ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
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n_estimators=0)
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iris = load_iris()
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assert_raise_message(ValueError,
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"n_estimators must be greater than zero, got 0.",
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ensemble.fit, iris.data, iris.target)
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def test_base_not_int_n_estimators():
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# Check that instantiating a BaseEnsemble with a string as n_estimators
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# raises a ValueError demanding n_estimators to be supplied as an integer.
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string_ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
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n_estimators='3')
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iris = load_iris()
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assert_raise_message(ValueError,
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"n_estimators must be an integer",
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string_ensemble.fit, iris.data, iris.target)
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float_ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
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n_estimators=3.0)
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assert_raise_message(ValueError,
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"n_estimators must be an integer",
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float_ensemble.fit, iris.data, iris.target)
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def test_set_random_states():
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# Linear Discriminant Analysis doesn't have random state: smoke test
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_set_random_states(LinearDiscriminantAnalysis(), random_state=17)
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clf1 = Perceptron(tol=1e-3, random_state=None)
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assert_equal(clf1.random_state, None)
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# check random_state is None still sets
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_set_random_states(clf1, None)
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assert_true(isinstance(clf1.random_state, int))
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# check random_state fixes results in consistent initialisation
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_set_random_states(clf1, 3)
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assert_true(isinstance(clf1.random_state, int))
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clf2 = Perceptron(tol=1e-3, random_state=None)
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_set_random_states(clf2, 3)
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assert_equal(clf1.random_state, clf2.random_state)
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# nested random_state
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def make_steps():
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return [('sel', SelectFromModel(Perceptron(tol=1e-3,
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random_state=None))),
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('clf', Perceptron(tol=1e-3, random_state=None))]
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est1 = Pipeline(make_steps())
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_set_random_states(est1, 3)
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assert_true(isinstance(est1.steps[0][1].estimator.random_state, int))
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assert_true(isinstance(est1.steps[1][1].random_state, int))
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assert_not_equal(est1.get_params()['sel__estimator__random_state'],
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est1.get_params()['clf__random_state'])
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# ensure multiple random_state parameters are invariant to get_params()
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# iteration order
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class AlphaParamPipeline(Pipeline):
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def get_params(self, *args, **kwargs):
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params = Pipeline.get_params(self, *args, **kwargs).items()
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return OrderedDict(sorted(params))
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class RevParamPipeline(Pipeline):
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def get_params(self, *args, **kwargs):
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params = Pipeline.get_params(self, *args, **kwargs).items()
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return OrderedDict(sorted(params, reverse=True))
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for cls in [AlphaParamPipeline, RevParamPipeline]:
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est2 = cls(make_steps())
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_set_random_states(est2, 3)
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assert_equal(est1.get_params()['sel__estimator__random_state'],
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est2.get_params()['sel__estimator__random_state'])
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assert_equal(est1.get_params()['clf__random_state'],
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est2.get_params()['clf__random_state'])
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