261 lines
10 KiB
Python
261 lines
10 KiB
Python
"""Testing for the VotingClassifier"""
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import numpy as np
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from sklearn.utils.testing import assert_almost_equal, assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_raise_message
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from sklearn.exceptions import NotFittedError
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import GaussianNB
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import VotingClassifier
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from sklearn.model_selection import GridSearchCV
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from sklearn import datasets
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from sklearn.model_selection import cross_val_score
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from sklearn.datasets import make_multilabel_classification
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from sklearn.svm import SVC
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.neighbors import KNeighborsClassifier
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# Load the iris dataset and randomly permute it
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iris = datasets.load_iris()
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X, y = iris.data[:, 1:3], iris.target
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def test_estimator_init():
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eclf = VotingClassifier(estimators=[])
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msg = ('Invalid `estimators` attribute, `estimators` should be'
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' a list of (string, estimator) tuples')
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assert_raise_message(AttributeError, msg, eclf.fit, X, y)
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clf = LogisticRegression(random_state=1)
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eclf = VotingClassifier(estimators=[('lr', clf)], voting='error')
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msg = ('Voting must be \'soft\' or \'hard\'; got (voting=\'error\')')
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assert_raise_message(ValueError, msg, eclf.fit, X, y)
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eclf = VotingClassifier(estimators=[('lr', clf)], weights=[1, 2])
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msg = ('Number of classifiers and weights must be equal'
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'; got 2 weights, 1 estimators')
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assert_raise_message(ValueError, msg, eclf.fit, X, y)
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def test_predictproba_hardvoting():
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eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
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('lr2', LogisticRegression())],
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voting='hard')
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msg = "predict_proba is not available when voting='hard'"
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assert_raise_message(AttributeError, msg, eclf.predict_proba, X)
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def test_notfitted():
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eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
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('lr2', LogisticRegression())],
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voting='soft')
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msg = ("This VotingClassifier instance is not fitted yet. Call \'fit\'"
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" with appropriate arguments before using this method.")
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assert_raise_message(NotFittedError, msg, eclf.predict_proba, X)
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def test_majority_label_iris():
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"""Check classification by majority label on dataset iris."""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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clf3 = GaussianNB()
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='hard')
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scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
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assert_almost_equal(scores.mean(), 0.95, decimal=2)
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def test_tie_situation():
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"""Check voting classifier selects smaller class label in tie situation."""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],
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voting='hard')
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assert_equal(clf1.fit(X, y).predict(X)[73], 2)
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assert_equal(clf2.fit(X, y).predict(X)[73], 1)
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assert_equal(eclf.fit(X, y).predict(X)[73], 1)
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def test_weights_iris():
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"""Check classification by average probabilities on dataset iris."""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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clf3 = GaussianNB()
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft',
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weights=[1, 2, 10])
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scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
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assert_almost_equal(scores.mean(), 0.93, decimal=2)
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def test_predict_on_toy_problem():
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"""Manually check predicted class labels for toy dataset."""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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clf3 = GaussianNB()
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X = np.array([[-1.1, -1.5],
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[-1.2, -1.4],
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[-3.4, -2.2],
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[1.1, 1.2],
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[2.1, 1.4],
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[3.1, 2.3]])
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y = np.array([1, 1, 1, 2, 2, 2])
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assert_equal(all(clf1.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))
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assert_equal(all(clf2.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))
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assert_equal(all(clf3.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='hard',
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weights=[1, 1, 1])
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assert_equal(all(eclf.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft',
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weights=[1, 1, 1])
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assert_equal(all(eclf.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))
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def test_predict_proba_on_toy_problem():
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"""Calculate predicted probabilities on toy dataset."""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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clf3 = GaussianNB()
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X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
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y = np.array([1, 1, 2, 2])
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clf1_res = np.array([[0.59790391, 0.40209609],
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[0.57622162, 0.42377838],
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[0.50728456, 0.49271544],
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[0.40241774, 0.59758226]])
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clf2_res = np.array([[0.8, 0.2],
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[0.8, 0.2],
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[0.2, 0.8],
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[0.3, 0.7]])
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clf3_res = np.array([[0.9985082, 0.0014918],
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[0.99845843, 0.00154157],
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[0., 1.],
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[0., 1.]])
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t00 = (2*clf1_res[0][0] + clf2_res[0][0] + clf3_res[0][0]) / 4
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t11 = (2*clf1_res[1][1] + clf2_res[1][1] + clf3_res[1][1]) / 4
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t21 = (2*clf1_res[2][1] + clf2_res[2][1] + clf3_res[2][1]) / 4
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t31 = (2*clf1_res[3][1] + clf2_res[3][1] + clf3_res[3][1]) / 4
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft',
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weights=[2, 1, 1])
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eclf_res = eclf.fit(X, y).predict_proba(X)
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assert_almost_equal(t00, eclf_res[0][0], decimal=1)
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assert_almost_equal(t11, eclf_res[1][1], decimal=1)
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assert_almost_equal(t21, eclf_res[2][1], decimal=1)
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assert_almost_equal(t31, eclf_res[3][1], decimal=1)
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try:
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='hard')
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eclf.fit(X, y).predict_proba(X)
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except AttributeError:
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pass
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else:
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raise AssertionError('AttributeError for voting == "hard"'
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' and with predict_proba not raised')
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def test_multilabel():
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"""Check if error is raised for multilabel classification."""
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X, y = make_multilabel_classification(n_classes=2, n_labels=1,
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allow_unlabeled=False,
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random_state=123)
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clf = OneVsRestClassifier(SVC(kernel='linear'))
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eclf = VotingClassifier(estimators=[('ovr', clf)], voting='hard')
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try:
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eclf.fit(X, y)
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except NotImplementedError:
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return
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def test_gridsearch():
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"""Check GridSearch support."""
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clf1 = LogisticRegression(random_state=1)
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clf2 = RandomForestClassifier(random_state=1)
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clf3 = GaussianNB()
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eclf = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft')
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params = {'lr__C': [1.0, 100.0],
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'voting': ['soft', 'hard'],
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'weights': [[0.5, 0.5, 0.5], [1.0, 0.5, 0.5]]}
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grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
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grid.fit(iris.data, iris.target)
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def test_parallel_predict():
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"""Check parallel backend of VotingClassifier on toy dataset."""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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clf3 = GaussianNB()
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X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
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y = np.array([1, 1, 2, 2])
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eclf1 = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft',
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n_jobs=1).fit(X, y)
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eclf2 = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('gnb', clf3)],
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voting='soft',
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n_jobs=2).fit(X, y)
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assert_array_equal(eclf1.predict(X), eclf2.predict(X))
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assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
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def test_sample_weight():
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"""Tests sample_weight parameter of VotingClassifier"""
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(random_state=123)
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clf3 = SVC(probability=True, random_state=123)
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eclf1 = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('svc', clf3)],
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voting='soft').fit(X, y, sample_weight=np.ones((len(y),)))
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eclf2 = VotingClassifier(estimators=[
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('lr', clf1), ('rf', clf2), ('svc', clf3)],
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voting='soft').fit(X, y)
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assert_array_equal(eclf1.predict(X), eclf2.predict(X))
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assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
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sample_weight = np.random.RandomState(123).uniform(size=(len(y),))
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eclf3 = VotingClassifier(estimators=[('lr', clf1)], voting='soft')
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eclf3.fit(X, y, sample_weight)
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clf1.fit(X, y, sample_weight)
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assert_array_equal(eclf3.predict(X), clf1.predict(X))
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assert_array_equal(eclf3.predict_proba(X), clf1.predict_proba(X))
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clf4 = KNeighborsClassifier()
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eclf3 = VotingClassifier(estimators=[
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('lr', clf1), ('svc', clf3), ('knn', clf4)],
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voting='soft')
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msg = ('Underlying estimator \'knn\' does not support sample weights.')
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assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight)
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