"""Testing for the VotingClassifier""" import pytest import numpy as np from sklearn.utils.testing import assert_almost_equal, assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raise_message from sklearn.utils.testing import assert_warns_message from sklearn.exceptions import NotFittedError from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import VotingClassifier from sklearn.model_selection import GridSearchCV from sklearn import datasets from sklearn.model_selection import cross_val_score from sklearn.datasets import make_multilabel_classification from sklearn.svm import SVC from sklearn.multiclass import OneVsRestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.base import BaseEstimator, ClassifierMixin # Load the iris dataset and randomly permute it iris = datasets.load_iris() X, y = iris.data[:, 1:3], iris.target @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 def test_estimator_init(): eclf = VotingClassifier(estimators=[]) msg = ('Invalid `estimators` attribute, `estimators` should be' ' a list of (string, estimator) tuples') assert_raise_message(AttributeError, msg, eclf.fit, X, y) clf = LogisticRegression(random_state=1) eclf = VotingClassifier(estimators=[('lr', clf)], voting='error') msg = ('Voting must be \'soft\' or \'hard\'; got (voting=\'error\')') assert_raise_message(ValueError, msg, eclf.fit, X, y) eclf = VotingClassifier(estimators=[('lr', clf)], weights=[1, 2]) msg = ('Number of classifiers and weights must be equal' '; got 2 weights, 1 estimators') assert_raise_message(ValueError, msg, eclf.fit, X, y) eclf = VotingClassifier(estimators=[('lr', clf), ('lr', clf)], weights=[1, 2]) msg = "Names provided are not unique: ['lr', 'lr']" assert_raise_message(ValueError, msg, eclf.fit, X, y) eclf = VotingClassifier(estimators=[('lr__', clf)]) msg = "Estimator names must not contain __: got ['lr__']" assert_raise_message(ValueError, msg, eclf.fit, X, y) eclf = VotingClassifier(estimators=[('estimators', clf)]) msg = "Estimator names conflict with constructor arguments: ['estimators']" assert_raise_message(ValueError, msg, eclf.fit, X, y) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 def test_predictproba_hardvoting(): eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()), ('lr2', LogisticRegression())], voting='hard') msg = "predict_proba is not available when voting='hard'" assert_raise_message(AttributeError, msg, eclf.predict_proba, X) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 def test_notfitted(): eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()), ('lr2', LogisticRegression())], voting='soft') msg = ("This VotingClassifier instance is not fitted yet. Call \'fit\'" " with appropriate arguments before using this method.") assert_raise_message(NotFittedError, msg, eclf.predict_proba, X) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_majority_label_iris(): """Check classification by majority label on dataset iris.""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard') scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy') assert_almost_equal(scores.mean(), 0.95, decimal=2) @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_tie_situation(): """Check voting classifier selects smaller class label in tie situation.""" clf1 = LogisticRegression(random_state=123, multi_class='ovr', solver='liblinear') clf2 = RandomForestClassifier(random_state=123) eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)], voting='hard') assert_equal(clf1.fit(X, y).predict(X)[73], 2) assert_equal(clf2.fit(X, y).predict(X)[73], 1) assert_equal(eclf.fit(X, y).predict(X)[73], 1) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_weights_iris(): """Check classification by average probabilities on dataset iris.""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', weights=[1, 2, 10]) scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy') assert_almost_equal(scores.mean(), 0.93, decimal=2) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_predict_on_toy_problem(): """Manually check predicted class labels for toy dataset.""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]]) y = np.array([1, 1, 1, 2, 2, 2]) assert_equal(all(clf1.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2])) assert_equal(all(clf2.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2])) assert_equal(all(clf3.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2])) eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard', weights=[1, 1, 1]) assert_equal(all(eclf.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2])) eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', weights=[1, 1, 1]) assert_equal(all(eclf.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2])) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_predict_proba_on_toy_problem(): """Calculate predicted probabilities on toy dataset.""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]]) y = np.array([1, 1, 2, 2]) clf1_res = np.array([[0.59790391, 0.40209609], [0.57622162, 0.42377838], [0.50728456, 0.49271544], [0.40241774, 0.59758226]]) clf2_res = np.array([[0.8, 0.2], [0.8, 0.2], [0.2, 0.8], [0.3, 0.7]]) clf3_res = np.array([[0.9985082, 0.0014918], [0.99845843, 0.00154157], [0., 1.], [0., 1.]]) t00 = (2*clf1_res[0][0] + clf2_res[0][0] + clf3_res[0][0]) / 4 t11 = (2*clf1_res[1][1] + clf2_res[1][1] + clf3_res[1][1]) / 4 t21 = (2*clf1_res[2][1] + clf2_res[2][1] + clf3_res[2][1]) / 4 t31 = (2*clf1_res[3][1] + clf2_res[3][1] + clf3_res[3][1]) / 4 eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', weights=[2, 1, 1]) eclf_res = eclf.fit(X, y).predict_proba(X) assert_almost_equal(t00, eclf_res[0][0], decimal=1) assert_almost_equal(t11, eclf_res[1][1], decimal=1) assert_almost_equal(t21, eclf_res[2][1], decimal=1) assert_almost_equal(t31, eclf_res[3][1], decimal=1) with pytest.raises( AttributeError, match="predict_proba is not available when voting='hard'"): eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard') eclf.fit(X, y).predict_proba(X) def test_multilabel(): """Check if error is raised for multilabel classification.""" X, y = make_multilabel_classification(n_classes=2, n_labels=1, allow_unlabeled=False, random_state=123) clf = OneVsRestClassifier(SVC(kernel='linear')) eclf = VotingClassifier(estimators=[('ovr', clf)], voting='hard') try: eclf.fit(X, y) except NotImplementedError: return @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_gridsearch(): """Check GridSearch support.""" clf1 = LogisticRegression(random_state=1) clf2 = RandomForestClassifier(random_state=1) clf3 = GaussianNB() eclf = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft') params = {'lr__C': [1.0, 100.0], 'voting': ['soft', 'hard'], 'weights': [[0.5, 0.5, 0.5], [1.0, 0.5, 0.5]]} grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5) grid.fit(iris.data, iris.target) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_parallel_fit(): """Check parallel backend of VotingClassifier on toy dataset.""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]]) y = np.array([1, 1, 2, 2]) eclf1 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', n_jobs=1).fit(X, y) eclf2 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', n_jobs=2).fit(X, y) assert_array_equal(eclf1.predict(X), eclf2.predict(X)) assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X)) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_sample_weight(): """Tests sample_weight parameter of VotingClassifier""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = SVC(gamma='scale', probability=True, random_state=123) eclf1 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('svc', clf3)], voting='soft').fit(X, y, sample_weight=np.ones((len(y),))) eclf2 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('svc', clf3)], voting='soft').fit(X, y) assert_array_equal(eclf1.predict(X), eclf2.predict(X)) assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X)) sample_weight = np.random.RandomState(123).uniform(size=(len(y),)) eclf3 = VotingClassifier(estimators=[('lr', clf1)], voting='soft') eclf3.fit(X, y, sample_weight) clf1.fit(X, y, sample_weight) assert_array_equal(eclf3.predict(X), clf1.predict(X)) assert_array_almost_equal(eclf3.predict_proba(X), clf1.predict_proba(X)) clf4 = KNeighborsClassifier() eclf3 = VotingClassifier(estimators=[ ('lr', clf1), ('svc', clf3), ('knn', clf4)], voting='soft') msg = ('Underlying estimator \'knn\' does not support sample weights.') assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight) def test_sample_weight_kwargs(): """Check that VotingClassifier passes sample_weight as kwargs""" class MockClassifier(BaseEstimator, ClassifierMixin): """Mock Classifier to check that sample_weight is received as kwargs""" def fit(self, X, y, *args, **sample_weight): assert 'sample_weight' in sample_weight clf = MockClassifier() eclf = VotingClassifier(estimators=[('mock', clf)], voting='soft') # Should not raise an error. eclf.fit(X, y, sample_weight=np.ones((len(y),))) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_set_params(): """set_params should be able to set estimators""" clf1 = LogisticRegression(random_state=123, C=1.0) clf2 = RandomForestClassifier(random_state=123, max_depth=None) clf3 = GaussianNB() eclf1 = VotingClassifier([('lr', clf1), ('rf', clf2)], voting='soft', weights=[1, 2]) assert 'lr' in eclf1.named_estimators assert eclf1.named_estimators.lr is eclf1.estimators[0][1] assert eclf1.named_estimators.lr is eclf1.named_estimators['lr'] eclf1.fit(X, y) assert 'lr' in eclf1.named_estimators_ assert eclf1.named_estimators_.lr is eclf1.estimators_[0] assert eclf1.named_estimators_.lr is eclf1.named_estimators_['lr'] eclf2 = VotingClassifier([('lr', clf1), ('nb', clf3)], voting='soft', weights=[1, 2]) eclf2.set_params(nb=clf2).fit(X, y) assert not hasattr(eclf2, 'nb') assert_array_equal(eclf1.predict(X), eclf2.predict(X)) assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X)) assert_equal(eclf2.estimators[0][1].get_params(), clf1.get_params()) assert_equal(eclf2.estimators[1][1].get_params(), clf2.get_params()) eclf1.set_params(lr__C=10.0) eclf2.set_params(nb__max_depth=5) assert eclf1.estimators[0][1].get_params()['C'] == 10.0 assert eclf2.estimators[1][1].get_params()['max_depth'] == 5 assert_equal(eclf1.get_params()["lr__C"], eclf1.get_params()["lr"].get_params()['C']) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_set_estimator_none(): """VotingClassifier set_params should be able to set estimators as None""" # Test predict clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('nb', clf3)], voting='hard', weights=[1, 0, 0.5]).fit(X, y) eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('nb', clf3)], voting='hard', weights=[1, 1, 0.5]) eclf2.set_params(rf=None).fit(X, y) assert_array_equal(eclf1.predict(X), eclf2.predict(X)) assert dict(eclf2.estimators)["rf"] is None assert len(eclf2.estimators_) == 2 assert all(isinstance(est, (LogisticRegression, GaussianNB)) for est in eclf2.estimators_) assert eclf2.get_params()["rf"] is None eclf1.set_params(voting='soft').fit(X, y) eclf2.set_params(voting='soft').fit(X, y) assert_array_equal(eclf1.predict(X), eclf2.predict(X)) assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X)) msg = ('All estimators are None. At least one is required' ' to be a classifier!') assert_raise_message( ValueError, msg, eclf2.set_params(lr=None, rf=None, nb=None).fit, X, y) # Test soft voting transform X1 = np.array([[1], [2]]) y1 = np.array([1, 2]) eclf1 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)], voting='soft', weights=[0, 0.5], flatten_transform=False).fit(X1, y1) eclf2 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)], voting='soft', weights=[1, 0.5], flatten_transform=False) eclf2.set_params(rf=None).fit(X1, y1) assert_array_almost_equal(eclf1.transform(X1), np.array([[[0.7, 0.3], [0.3, 0.7]], [[1., 0.], [0., 1.]]])) assert_array_almost_equal(eclf2.transform(X1), np.array([[[1., 0.], [0., 1.]]])) eclf1.set_params(voting='hard') eclf2.set_params(voting='hard') assert_array_equal(eclf1.transform(X1), np.array([[0, 0], [1, 1]])) assert_array_equal(eclf2.transform(X1), np.array([[0], [1]])) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_estimator_weights_format(): # Test estimator weights inputs as list and array clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) eclf1 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2)], weights=[1, 2], voting='soft') eclf2 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2)], weights=np.array((1, 2)), voting='soft') eclf1.fit(X, y) eclf2.fit(X, y) assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X)) @pytest.mark.filterwarnings('ignore: Default solver will be changed') # 0.22 @pytest.mark.filterwarnings('ignore: Default multi_class will') # 0.22 @pytest.mark.filterwarnings('ignore:The default value of n_estimators') def test_transform(): """Check transform method of VotingClassifier on toy dataset.""" clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]]) y = np.array([1, 1, 2, 2]) eclf1 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft').fit(X, y) eclf2 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', flatten_transform=True).fit(X, y) eclf3 = VotingClassifier(estimators=[ ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', flatten_transform=False).fit(X, y) warn_msg = ("'flatten_transform' default value will be " "changed to True in 0.21. " "To silence this warning you may" " explicitly set flatten_transform=False.") res = assert_warns_message(DeprecationWarning, warn_msg, eclf1.transform, X) assert_array_equal(res.shape, (3, 4, 2)) assert_array_equal(eclf2.transform(X).shape, (4, 6)) assert_array_equal(eclf3.transform(X).shape, (3, 4, 2)) assert_array_almost_equal(res.swapaxes(0, 1).reshape((4, 6)), eclf2.transform(X)) assert_array_almost_equal( eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)), eclf2.transform(X) )