685 lines
24 KiB
Python
685 lines
24 KiB
Python
"""Testing for the VotingClassifier and VotingRegressor"""
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import re
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import numpy as np
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import pytest
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from sklearn import datasets
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from sklearn.base import BaseEstimator, ClassifierMixin, clone
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from sklearn.datasets import make_multilabel_classification
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from sklearn.dummy import DummyRegressor
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from sklearn.ensemble import (
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RandomForestClassifier,
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RandomForestRegressor,
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VotingClassifier,
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VotingRegressor,
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)
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from sklearn.exceptions import NotFittedError
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from sklearn.linear_model import LinearRegression, LogisticRegression
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from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from sklearn.utils._testing import (
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_convert_container,
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assert_almost_equal,
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assert_array_almost_equal,
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assert_array_equal,
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)
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# Load datasets
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iris = datasets.load_iris()
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X, y = iris.data[:, 1:3], iris.target
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# Scaled to solve ConvergenceWarning throw by Logistic Regression
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X_scaled = StandardScaler().fit_transform(X)
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X_r, y_r = datasets.load_diabetes(return_X_y=True)
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@pytest.mark.parametrize(
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"params, err_msg",
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[
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(
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{"estimators": []},
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"Invalid 'estimators' attribute, 'estimators' should be a non-empty list",
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),
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(
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{"estimators": [("lr", LogisticRegression())], "weights": [1, 2]},
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"Number of `estimators` and weights must be equal",
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),
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],
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)
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def test_voting_classifier_estimator_init(params, err_msg):
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ensemble = VotingClassifier(**params)
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with pytest.raises(ValueError, match=err_msg):
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ensemble.fit(X, y)
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def test_predictproba_hardvoting():
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eclf = VotingClassifier(
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estimators=[("lr1", LogisticRegression()), ("lr2", LogisticRegression())],
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voting="hard",
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)
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inner_msg = "predict_proba is not available when voting='hard'"
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outer_msg = "'VotingClassifier' has no attribute 'predict_proba'"
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with pytest.raises(AttributeError, match=outer_msg) as exec_info:
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eclf.predict_proba
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assert isinstance(exec_info.value.__cause__, AttributeError)
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assert inner_msg in str(exec_info.value.__cause__)
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assert not hasattr(eclf, "predict_proba")
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eclf.fit(X_scaled, y)
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assert not hasattr(eclf, "predict_proba")
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def test_notfitted():
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eclf = VotingClassifier(
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estimators=[("lr1", LogisticRegression()), ("lr2", LogisticRegression())],
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voting="soft",
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)
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ereg = VotingRegressor([("dr", DummyRegressor())])
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msg = (
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"This %s instance is not fitted yet. Call 'fit'"
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" with appropriate arguments before using this estimator."
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)
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with pytest.raises(NotFittedError, match=msg % "VotingClassifier"):
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eclf.predict(X)
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with pytest.raises(NotFittedError, match=msg % "VotingClassifier"):
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eclf.predict_proba(X)
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with pytest.raises(NotFittedError, match=msg % "VotingClassifier"):
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eclf.transform(X)
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with pytest.raises(NotFittedError, match=msg % "VotingRegressor"):
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ereg.predict(X_r)
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with pytest.raises(NotFittedError, match=msg % "VotingRegressor"):
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ereg.transform(X_r)
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def test_majority_label_iris(global_random_seed):
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"""Check classification by majority label on dataset iris."""
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clf1 = LogisticRegression(solver="liblinear", random_state=global_random_seed)
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clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
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clf3 = GaussianNB()
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="hard"
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)
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scores = cross_val_score(eclf, X, y, scoring="accuracy")
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assert scores.mean() >= 0.9
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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, solver="liblinear")
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clf2 = RandomForestClassifier(random_state=123)
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eclf = VotingClassifier(estimators=[("lr", clf1), ("rf", clf2)], voting="hard")
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assert clf1.fit(X, y).predict(X)[73] == 2
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assert clf2.fit(X, y).predict(X)[73] == 1
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assert eclf.fit(X, y).predict(X)[73] == 1
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def test_weights_iris(global_random_seed):
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"""Check classification by average probabilities on dataset iris."""
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clf1 = LogisticRegression(random_state=global_random_seed)
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clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
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clf3 = GaussianNB()
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
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voting="soft",
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weights=[1, 2, 10],
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)
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scores = cross_val_score(eclf, X_scaled, y, scoring="accuracy")
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assert scores.mean() >= 0.9
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def test_weights_regressor():
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"""Check weighted average regression prediction on diabetes dataset."""
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reg1 = DummyRegressor(strategy="mean")
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reg2 = DummyRegressor(strategy="median")
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reg3 = DummyRegressor(strategy="quantile", quantile=0.2)
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ereg = VotingRegressor(
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[("mean", reg1), ("median", reg2), ("quantile", reg3)], weights=[1, 2, 10]
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)
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X_r_train, X_r_test, y_r_train, y_r_test = train_test_split(
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X_r, y_r, test_size=0.25
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)
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reg1_pred = reg1.fit(X_r_train, y_r_train).predict(X_r_test)
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reg2_pred = reg2.fit(X_r_train, y_r_train).predict(X_r_test)
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reg3_pred = reg3.fit(X_r_train, y_r_train).predict(X_r_test)
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ereg_pred = ereg.fit(X_r_train, y_r_train).predict(X_r_test)
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avg = np.average(
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np.asarray([reg1_pred, reg2_pred, reg3_pred]), axis=0, weights=[1, 2, 10]
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)
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assert_almost_equal(ereg_pred, avg, decimal=2)
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ereg_weights_none = VotingRegressor(
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[("mean", reg1), ("median", reg2), ("quantile", reg3)], weights=None
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)
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ereg_weights_equal = VotingRegressor(
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[("mean", reg1), ("median", reg2), ("quantile", reg3)], weights=[1, 1, 1]
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)
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ereg_weights_none.fit(X_r_train, y_r_train)
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ereg_weights_equal.fit(X_r_train, y_r_train)
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ereg_none_pred = ereg_weights_none.predict(X_r_test)
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ereg_equal_pred = ereg_weights_equal.predict(X_r_test)
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assert_almost_equal(ereg_none_pred, ereg_equal_pred, decimal=2)
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def test_predict_on_toy_problem(global_random_seed):
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"""Manually check predicted class labels for toy dataset."""
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clf1 = LogisticRegression(random_state=global_random_seed)
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clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
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clf3 = GaussianNB()
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X = np.array(
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[[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]]
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)
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y = np.array([1, 1, 1, 2, 2, 2])
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assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
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assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
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assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
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voting="hard",
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weights=[1, 1, 1],
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)
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assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
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voting="soft",
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weights=[1, 1, 1],
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)
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assert_array_equal(eclf.fit(X, y).predict(X), [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(
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[
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[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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]
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)
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clf2_res = np.array([[0.8, 0.2], [0.8, 0.2], [0.2, 0.8], [0.3, 0.7]])
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clf3_res = np.array(
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[[0.9985082, 0.0014918], [0.99845843, 0.00154157], [0.0, 1.0], [0.0, 1.0]]
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)
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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(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
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voting="soft",
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weights=[2, 1, 1],
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)
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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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inner_msg = "predict_proba is not available when voting='hard'"
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outer_msg = "'VotingClassifier' has no attribute 'predict_proba'"
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with pytest.raises(AttributeError, match=outer_msg) as exec_info:
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="hard"
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)
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eclf.fit(X, y).predict_proba(X)
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assert isinstance(exec_info.value.__cause__, AttributeError)
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assert inner_msg in str(exec_info.value.__cause__)
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@pytest.mark.parametrize("container_type", ["list", "array", "dataframe"])
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def test_multilabel(container_type):
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"""Check if error is raised for multilabel classification."""
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X, y = make_multilabel_classification(
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n_classes=2, n_labels=1, allow_unlabeled=False, random_state=123
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)
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y = _convert_container(y, container_type)
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clf = OneVsRestClassifier(SVC(kernel="linear"))
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eclf = VotingClassifier(estimators=[("ovr", clf)], voting="hard")
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err_msg = "only supports binary or multiclass classification"
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with pytest.raises(NotImplementedError, match=err_msg):
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eclf.fit(X, y)
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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, n_estimators=3)
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clf3 = GaussianNB()
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="soft"
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)
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params = {
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"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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}
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grid = GridSearchCV(estimator=eclf, param_grid=params, cv=2)
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grid.fit(X_scaled, y)
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def test_parallel_fit(global_random_seed):
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"""Check parallel backend of VotingClassifier on toy dataset."""
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clf1 = LogisticRegression(random_state=global_random_seed)
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clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
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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(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="soft", n_jobs=1
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).fit(X, y)
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eclf2 = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="soft", n_jobs=2
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).fit(X, y)
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assert_array_equal(eclf1.predict(X), eclf2.predict(X))
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assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
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def test_sample_weight(global_random_seed):
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"""Tests sample_weight parameter of VotingClassifier"""
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clf1 = LogisticRegression(random_state=global_random_seed)
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clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
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clf3 = SVC(probability=True, random_state=global_random_seed)
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eclf1 = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("svc", clf3)], voting="soft"
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).fit(X_scaled, y, sample_weight=np.ones((len(y),)))
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eclf2 = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("svc", clf3)], voting="soft"
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).fit(X_scaled, y)
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assert_array_equal(eclf1.predict(X_scaled), eclf2.predict(X_scaled))
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assert_array_almost_equal(
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eclf1.predict_proba(X_scaled), eclf2.predict_proba(X_scaled)
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)
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sample_weight = np.random.RandomState(global_random_seed).uniform(size=(len(y),))
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eclf3 = VotingClassifier(estimators=[("lr", clf1)], voting="soft")
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eclf3.fit(X_scaled, y, sample_weight)
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clf1.fit(X_scaled, y, sample_weight)
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assert_array_equal(eclf3.predict(X_scaled), clf1.predict(X_scaled))
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assert_array_almost_equal(
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eclf3.predict_proba(X_scaled), clf1.predict_proba(X_scaled)
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)
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# check that an error is raised and indicative if sample_weight is not
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# supported.
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clf4 = KNeighborsClassifier()
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eclf3 = VotingClassifier(
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estimators=[("lr", clf1), ("svc", clf3), ("knn", clf4)], voting="soft"
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)
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msg = "Underlying estimator KNeighborsClassifier does not support sample weights."
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with pytest.raises(TypeError, match=msg):
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eclf3.fit(X_scaled, y, sample_weight)
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# check that _fit_single_estimator will raise the right error
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# it should raise the original error if this is not linked to sample_weight
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class ClassifierErrorFit(ClassifierMixin, BaseEstimator):
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def fit(self, X_scaled, y, sample_weight):
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raise TypeError("Error unrelated to sample_weight.")
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clf = ClassifierErrorFit()
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with pytest.raises(TypeError, match="Error unrelated to sample_weight"):
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clf.fit(X_scaled, y, sample_weight=sample_weight)
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def test_sample_weight_kwargs():
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"""Check that VotingClassifier passes sample_weight as kwargs"""
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class MockClassifier(ClassifierMixin, BaseEstimator):
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"""Mock Classifier to check that sample_weight is received as kwargs"""
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def fit(self, X, y, *args, **sample_weight):
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assert "sample_weight" in sample_weight
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clf = MockClassifier()
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eclf = VotingClassifier(estimators=[("mock", clf)], voting="soft")
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# Should not raise an error.
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eclf.fit(X, y, sample_weight=np.ones((len(y),)))
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def test_voting_classifier_set_params(global_random_seed):
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# check equivalence in the output when setting underlying estimators
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clf1 = LogisticRegression(random_state=global_random_seed)
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clf2 = RandomForestClassifier(
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n_estimators=10, random_state=global_random_seed, max_depth=None
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)
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clf3 = GaussianNB()
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eclf1 = VotingClassifier(
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[("lr", clf1), ("rf", clf2)], voting="soft", weights=[1, 2]
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).fit(X_scaled, y)
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eclf2 = VotingClassifier(
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[("lr", clf1), ("nb", clf3)], voting="soft", weights=[1, 2]
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)
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eclf2.set_params(nb=clf2).fit(X_scaled, y)
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assert_array_equal(eclf1.predict(X_scaled), eclf2.predict(X_scaled))
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assert_array_almost_equal(
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eclf1.predict_proba(X_scaled), eclf2.predict_proba(X_scaled)
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)
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assert eclf2.estimators[0][1].get_params() == clf1.get_params()
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assert eclf2.estimators[1][1].get_params() == clf2.get_params()
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def test_set_estimator_drop():
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# VotingClassifier set_params should be able to set estimators as drop
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# Test predict
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clf1 = LogisticRegression(random_state=123)
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clf2 = RandomForestClassifier(n_estimators=10, random_state=123)
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clf3 = GaussianNB()
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eclf1 = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("nb", clf3)],
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voting="hard",
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weights=[1, 0, 0.5],
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).fit(X, y)
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eclf2 = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("nb", clf3)],
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voting="hard",
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weights=[1, 1, 0.5],
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)
|
|
eclf2.set_params(rf="drop").fit(X, y)
|
|
|
|
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
|
|
|
|
assert dict(eclf2.estimators)["rf"] == "drop"
|
|
assert len(eclf2.estimators_) == 2
|
|
assert all(
|
|
isinstance(est, (LogisticRegression, GaussianNB)) for est in eclf2.estimators_
|
|
)
|
|
assert eclf2.get_params()["rf"] == "drop"
|
|
|
|
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 dropped. At least one is required"
|
|
with pytest.raises(ValueError, match=msg):
|
|
eclf2.set_params(lr="drop", rf="drop", nb="drop").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="drop").fit(X1, y1)
|
|
assert_array_almost_equal(
|
|
eclf1.transform(X1),
|
|
np.array([[[0.7, 0.3], [0.3, 0.7]], [[1.0, 0.0], [0.0, 1.0]]]),
|
|
)
|
|
assert_array_almost_equal(eclf2.transform(X1), np.array([[[1.0, 0.0], [0.0, 1.0]]]))
|
|
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]]))
|
|
|
|
|
|
def test_estimator_weights_format(global_random_seed):
|
|
# Test estimator weights inputs as list and array
|
|
clf1 = LogisticRegression(random_state=global_random_seed)
|
|
clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
|
|
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_scaled, y)
|
|
eclf2.fit(X_scaled, y)
|
|
assert_array_almost_equal(
|
|
eclf1.predict_proba(X_scaled), eclf2.predict_proba(X_scaled)
|
|
)
|
|
|
|
|
|
def test_transform(global_random_seed):
|
|
"""Check transform method of VotingClassifier on toy dataset."""
|
|
clf1 = LogisticRegression(random_state=global_random_seed)
|
|
clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed)
|
|
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)
|
|
|
|
assert_array_equal(eclf1.transform(X).shape, (4, 6))
|
|
assert_array_equal(eclf2.transform(X).shape, (4, 6))
|
|
assert_array_equal(eclf3.transform(X).shape, (3, 4, 2))
|
|
assert_array_almost_equal(eclf1.transform(X), eclf2.transform(X))
|
|
assert_array_almost_equal(
|
|
eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)), eclf2.transform(X)
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"X, y, voter",
|
|
[
|
|
(
|
|
X,
|
|
y,
|
|
VotingClassifier(
|
|
[
|
|
("lr", LogisticRegression()),
|
|
("rf", RandomForestClassifier(n_estimators=5)),
|
|
]
|
|
),
|
|
),
|
|
(
|
|
X_r,
|
|
y_r,
|
|
VotingRegressor(
|
|
[
|
|
("lr", LinearRegression()),
|
|
("rf", RandomForestRegressor(n_estimators=5)),
|
|
]
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_none_estimator_with_weights(X, y, voter):
|
|
# check that an estimator can be set to 'drop' and passing some weight
|
|
# regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/issues/13777
|
|
voter = clone(voter)
|
|
# Scaled to solve ConvergenceWarning throw by Logistic Regression
|
|
X_scaled = StandardScaler().fit_transform(X)
|
|
voter.fit(X_scaled, y, sample_weight=np.ones(y.shape))
|
|
voter.set_params(lr="drop")
|
|
voter.fit(X_scaled, y, sample_weight=np.ones(y.shape))
|
|
y_pred = voter.predict(X_scaled)
|
|
assert y_pred.shape == y.shape
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"est",
|
|
[
|
|
VotingRegressor(
|
|
estimators=[
|
|
("lr", LinearRegression()),
|
|
("tree", DecisionTreeRegressor(random_state=0)),
|
|
]
|
|
),
|
|
VotingClassifier(
|
|
estimators=[
|
|
("lr", LogisticRegression(random_state=0)),
|
|
("tree", DecisionTreeClassifier(random_state=0)),
|
|
]
|
|
),
|
|
],
|
|
ids=["VotingRegressor", "VotingClassifier"],
|
|
)
|
|
def test_n_features_in(est):
|
|
X = [[1, 2], [3, 4], [5, 6]]
|
|
y = [0, 1, 2]
|
|
|
|
assert not hasattr(est, "n_features_in_")
|
|
est.fit(X, y)
|
|
assert est.n_features_in_ == 2
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"estimator",
|
|
[
|
|
VotingRegressor(
|
|
estimators=[
|
|
("lr", LinearRegression()),
|
|
("rf", RandomForestRegressor(random_state=123)),
|
|
],
|
|
verbose=True,
|
|
),
|
|
VotingClassifier(
|
|
estimators=[
|
|
("lr", LogisticRegression(random_state=123)),
|
|
("rf", RandomForestClassifier(random_state=123)),
|
|
],
|
|
verbose=True,
|
|
),
|
|
],
|
|
)
|
|
def test_voting_verbose(estimator, capsys):
|
|
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])
|
|
|
|
pattern = (
|
|
r"\[Voting\].*\(1 of 2\) Processing lr, total=.*\n"
|
|
r"\[Voting\].*\(2 of 2\) Processing rf, total=.*\n$"
|
|
)
|
|
|
|
estimator.fit(X, y)
|
|
assert re.match(pattern, capsys.readouterr()[0])
|
|
|
|
|
|
def test_get_features_names_out_regressor():
|
|
"""Check get_feature_names_out output for regressor."""
|
|
|
|
X = [[1, 2], [3, 4], [5, 6]]
|
|
y = [0, 1, 2]
|
|
|
|
voting = VotingRegressor(
|
|
estimators=[
|
|
("lr", LinearRegression()),
|
|
("tree", DecisionTreeRegressor(random_state=0)),
|
|
("ignore", "drop"),
|
|
]
|
|
)
|
|
voting.fit(X, y)
|
|
|
|
names_out = voting.get_feature_names_out()
|
|
expected_names = ["votingregressor_lr", "votingregressor_tree"]
|
|
assert_array_equal(names_out, expected_names)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"kwargs, expected_names",
|
|
[
|
|
(
|
|
{"voting": "soft", "flatten_transform": True},
|
|
[
|
|
"votingclassifier_lr0",
|
|
"votingclassifier_lr1",
|
|
"votingclassifier_lr2",
|
|
"votingclassifier_tree0",
|
|
"votingclassifier_tree1",
|
|
"votingclassifier_tree2",
|
|
],
|
|
),
|
|
({"voting": "hard"}, ["votingclassifier_lr", "votingclassifier_tree"]),
|
|
],
|
|
)
|
|
def test_get_features_names_out_classifier(kwargs, expected_names):
|
|
"""Check get_feature_names_out for classifier for different settings."""
|
|
X = [[1, 2], [3, 4], [5, 6], [1, 1.2]]
|
|
y = [0, 1, 2, 0]
|
|
|
|
voting = VotingClassifier(
|
|
estimators=[
|
|
("lr", LogisticRegression(random_state=0)),
|
|
("tree", DecisionTreeClassifier(random_state=0)),
|
|
],
|
|
**kwargs,
|
|
)
|
|
voting.fit(X, y)
|
|
X_trans = voting.transform(X)
|
|
names_out = voting.get_feature_names_out()
|
|
|
|
assert X_trans.shape[1] == len(expected_names)
|
|
assert_array_equal(names_out, expected_names)
|
|
|
|
|
|
def test_get_features_names_out_classifier_error():
|
|
"""Check that error is raised when voting="soft" and flatten_transform=False."""
|
|
X = [[1, 2], [3, 4], [5, 6]]
|
|
y = [0, 1, 2]
|
|
|
|
voting = VotingClassifier(
|
|
estimators=[
|
|
("lr", LogisticRegression(random_state=0)),
|
|
("tree", DecisionTreeClassifier(random_state=0)),
|
|
],
|
|
voting="soft",
|
|
flatten_transform=False,
|
|
)
|
|
voting.fit(X, y)
|
|
|
|
msg = (
|
|
"get_feature_names_out is not supported when `voting='soft'` and "
|
|
"`flatten_transform=False`"
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
voting.get_feature_names_out()
|