503 lines
18 KiB
Python
503 lines
18 KiB
Python
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"""Test the stacking classifier and regressor."""
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# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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# License: BSD 3 clause
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import pytest
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import numpy as np
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import scipy.sparse as sparse
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from sklearn.base import BaseEstimator
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from sklearn.base import ClassifierMixin
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from sklearn.base import RegressorMixin
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from sklearn.base import clone
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_diabetes
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from sklearn.datasets import load_breast_cancer
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from sklearn.datasets import make_regression
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from sklearn.datasets import make_classification
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from sklearn.dummy import DummyClassifier
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from sklearn.dummy import DummyRegressor
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from sklearn.linear_model import LogisticRegression
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from sklearn.linear_model import LinearRegression
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from sklearn.svm import LinearSVC
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from sklearn.svm import LinearSVR
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from sklearn.svm import SVC
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.preprocessing import scale
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from sklearn.ensemble import StackingClassifier
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from sklearn.ensemble import StackingRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import KFold
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from sklearn.utils._mocking import CheckingClassifier
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import assert_allclose_dense_sparse
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from sklearn.utils._testing import ignore_warnings
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X_diabetes, y_diabetes = load_diabetes(return_X_y=True)
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X_iris, y_iris = load_iris(return_X_y=True)
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@pytest.mark.parametrize(
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"cv", [3, StratifiedKFold(n_splits=3, shuffle=True, random_state=42)]
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)
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@pytest.mark.parametrize(
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"final_estimator", [None, RandomForestClassifier(random_state=42)]
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)
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@pytest.mark.parametrize("passthrough", [False, True])
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def test_stacking_classifier_iris(cv, final_estimator, passthrough):
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# prescale the data to avoid convergence warning without using a pipeline
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# for later assert
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X_train, X_test, y_train, y_test = train_test_split(
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scale(X_iris), y_iris, stratify=y_iris, random_state=42
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)
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estimators = [('lr', LogisticRegression()), ('svc', LinearSVC())]
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clf = StackingClassifier(
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estimators=estimators, final_estimator=final_estimator, cv=cv,
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passthrough=passthrough
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)
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clf.fit(X_train, y_train)
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clf.predict(X_test)
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clf.predict_proba(X_test)
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assert clf.score(X_test, y_test) > 0.8
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X_trans = clf.transform(X_test)
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expected_column_count = 10 if passthrough else 6
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assert X_trans.shape[1] == expected_column_count
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if passthrough:
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assert_allclose(X_test, X_trans[:, -4:])
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clf.set_params(lr='drop')
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clf.fit(X_train, y_train)
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clf.predict(X_test)
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clf.predict_proba(X_test)
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if final_estimator is None:
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# LogisticRegression has decision_function method
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clf.decision_function(X_test)
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X_trans = clf.transform(X_test)
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expected_column_count_drop = 7 if passthrough else 3
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assert X_trans.shape[1] == expected_column_count_drop
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if passthrough:
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assert_allclose(X_test, X_trans[:, -4:])
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def test_stacking_classifier_drop_column_binary_classification():
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# check that a column is dropped in binary classification
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X, y = load_breast_cancer(return_X_y=True)
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X_train, X_test, y_train, _ = train_test_split(
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scale(X), y, stratify=y, random_state=42
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)
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# both classifiers implement 'predict_proba' and will both drop one column
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estimators = [('lr', LogisticRegression()),
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('rf', RandomForestClassifier(random_state=42))]
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clf = StackingClassifier(estimators=estimators, cv=3)
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clf.fit(X_train, y_train)
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X_trans = clf.transform(X_test)
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assert X_trans.shape[1] == 2
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# LinearSVC does not implement 'predict_proba' and will not drop one column
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estimators = [('lr', LogisticRegression()), ('svc', LinearSVC())]
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clf.set_params(estimators=estimators)
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clf.fit(X_train, y_train)
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X_trans = clf.transform(X_test)
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assert X_trans.shape[1] == 2
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def test_stacking_classifier_drop_estimator():
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# prescale the data to avoid convergence warning without using a pipeline
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# for later assert
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X_train, X_test, y_train, _ = train_test_split(
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scale(X_iris), y_iris, stratify=y_iris, random_state=42
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)
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estimators = [('lr', 'drop'), ('svc', LinearSVC(random_state=0))]
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rf = RandomForestClassifier(n_estimators=10, random_state=42)
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clf = StackingClassifier(
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estimators=[('svc', LinearSVC(random_state=0))],
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final_estimator=rf, cv=5
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)
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clf_drop = StackingClassifier(
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estimators=estimators, final_estimator=rf, cv=5
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)
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clf.fit(X_train, y_train)
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clf_drop.fit(X_train, y_train)
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assert_allclose(clf.predict(X_test), clf_drop.predict(X_test))
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assert_allclose(clf.predict_proba(X_test), clf_drop.predict_proba(X_test))
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assert_allclose(clf.transform(X_test), clf_drop.transform(X_test))
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def test_stacking_regressor_drop_estimator():
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# prescale the data to avoid convergence warning without using a pipeline
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# for later assert
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X_train, X_test, y_train, _ = train_test_split(
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scale(X_diabetes), y_diabetes, random_state=42
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)
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estimators = [('lr', 'drop'), ('svr', LinearSVR(random_state=0))]
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rf = RandomForestRegressor(n_estimators=10, random_state=42)
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reg = StackingRegressor(
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estimators=[('svr', LinearSVR(random_state=0))],
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final_estimator=rf, cv=5
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)
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reg_drop = StackingRegressor(
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estimators=estimators, final_estimator=rf, cv=5
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)
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reg.fit(X_train, y_train)
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reg_drop.fit(X_train, y_train)
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assert_allclose(reg.predict(X_test), reg_drop.predict(X_test))
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assert_allclose(reg.transform(X_test), reg_drop.transform(X_test))
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@pytest.mark.parametrize(
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"cv", [3, KFold(n_splits=3, shuffle=True, random_state=42)]
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)
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@pytest.mark.parametrize(
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"final_estimator, predict_params",
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[(None, {}),
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(RandomForestRegressor(random_state=42), {}),
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(DummyRegressor(), {'return_std': True})]
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)
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@pytest.mark.parametrize("passthrough", [False, True])
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def test_stacking_regressor_diabetes(cv, final_estimator, predict_params,
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passthrough):
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# prescale the data to avoid convergence warning without using a pipeline
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# for later assert
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X_train, X_test, y_train, _ = train_test_split(
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scale(X_diabetes), y_diabetes, random_state=42
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)
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estimators = [('lr', LinearRegression()), ('svr', LinearSVR())]
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reg = StackingRegressor(
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estimators=estimators, final_estimator=final_estimator, cv=cv,
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passthrough=passthrough
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)
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reg.fit(X_train, y_train)
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result = reg.predict(X_test, **predict_params)
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expected_result_length = 2 if predict_params else 1
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if predict_params:
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assert len(result) == expected_result_length
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X_trans = reg.transform(X_test)
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expected_column_count = 12 if passthrough else 2
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assert X_trans.shape[1] == expected_column_count
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if passthrough:
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assert_allclose(X_test, X_trans[:, -10:])
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reg.set_params(lr='drop')
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reg.fit(X_train, y_train)
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reg.predict(X_test)
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X_trans = reg.transform(X_test)
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expected_column_count_drop = 11 if passthrough else 1
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assert X_trans.shape[1] == expected_column_count_drop
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if passthrough:
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assert_allclose(X_test, X_trans[:, -10:])
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@pytest.mark.parametrize('fmt', ['csc', 'csr', 'coo'])
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def test_stacking_regressor_sparse_passthrough(fmt):
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# Check passthrough behavior on a sparse X matrix
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X_train, X_test, y_train, _ = train_test_split(
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sparse.coo_matrix(scale(X_diabetes)).asformat(fmt),
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y_diabetes, random_state=42
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)
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estimators = [('lr', LinearRegression()), ('svr', LinearSVR())]
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rf = RandomForestRegressor(n_estimators=10, random_state=42)
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clf = StackingRegressor(
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estimators=estimators, final_estimator=rf, cv=5, passthrough=True
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)
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clf.fit(X_train, y_train)
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X_trans = clf.transform(X_test)
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assert_allclose_dense_sparse(X_test, X_trans[:, -10:])
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assert sparse.issparse(X_trans)
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assert X_test.format == X_trans.format
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@pytest.mark.parametrize('fmt', ['csc', 'csr', 'coo'])
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def test_stacking_classifier_sparse_passthrough(fmt):
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# Check passthrough behavior on a sparse X matrix
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X_train, X_test, y_train, _ = train_test_split(
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sparse.coo_matrix(scale(X_iris)).asformat(fmt),
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y_iris, random_state=42
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)
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estimators = [('lr', LogisticRegression()), ('svc', LinearSVC())]
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rf = RandomForestClassifier(n_estimators=10, random_state=42)
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clf = StackingClassifier(
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estimators=estimators, final_estimator=rf, cv=5, passthrough=True
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)
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clf.fit(X_train, y_train)
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X_trans = clf.transform(X_test)
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assert_allclose_dense_sparse(X_test, X_trans[:, -4:])
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assert sparse.issparse(X_trans)
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assert X_test.format == X_trans.format
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def test_stacking_classifier_drop_binary_prob():
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# check that classifier will drop one of the probability column for
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# binary classification problem
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# Select only the 2 first classes
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X_, y_ = scale(X_iris[:100]), y_iris[:100]
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estimators = [
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('lr', LogisticRegression()), ('rf', RandomForestClassifier())
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]
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clf = StackingClassifier(estimators=estimators)
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clf.fit(X_, y_)
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X_meta = clf.transform(X_)
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assert X_meta.shape[1] == 2
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class NoWeightRegressor(RegressorMixin, BaseEstimator):
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def fit(self, X, y):
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self.reg = DummyRegressor()
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return self.reg.fit(X, y)
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def predict(self, X):
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return np.ones(X.shape[0])
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class NoWeightClassifier(ClassifierMixin, BaseEstimator):
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def fit(self, X, y):
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self.clf = DummyClassifier(strategy='stratified')
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return self.clf.fit(X, y)
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@pytest.mark.parametrize(
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"y, params, type_err, msg_err",
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[(y_iris,
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{'estimators': None},
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ValueError, "Invalid 'estimators' attribute,"),
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(y_iris,
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{'estimators': []},
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ValueError, "Invalid 'estimators' attribute,"),
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(y_iris,
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{'estimators': [('lr', LogisticRegression()),
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('svm', SVC(max_iter=5e4))],
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'stack_method': 'predict_proba'},
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ValueError, 'does not implement the method predict_proba'),
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(y_iris,
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{'estimators': [('lr', LogisticRegression()),
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('cor', NoWeightClassifier())]},
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TypeError, 'does not support sample weight'),
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(y_iris,
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{'estimators': [('lr', LogisticRegression()),
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('cor', LinearSVC(max_iter=5e4))],
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'final_estimator': NoWeightClassifier()},
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TypeError, 'does not support sample weight')]
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)
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def test_stacking_classifier_error(y, params, type_err, msg_err):
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with pytest.raises(type_err, match=msg_err):
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clf = StackingClassifier(**params, cv=3)
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clf.fit(
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scale(X_iris), y, sample_weight=np.ones(X_iris.shape[0])
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)
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@pytest.mark.parametrize(
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"y, params, type_err, msg_err",
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[(y_diabetes,
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{'estimators': None},
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ValueError, "Invalid 'estimators' attribute,"),
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(y_diabetes,
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{'estimators': []},
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ValueError, "Invalid 'estimators' attribute,"),
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(y_diabetes,
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{'estimators': [('lr', LinearRegression()),
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('cor', NoWeightRegressor())]},
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TypeError, 'does not support sample weight'),
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(y_diabetes,
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{'estimators': [('lr', LinearRegression()),
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('cor', LinearSVR())],
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'final_estimator': NoWeightRegressor()},
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TypeError, 'does not support sample weight')]
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)
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def test_stacking_regressor_error(y, params, type_err, msg_err):
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with pytest.raises(type_err, match=msg_err):
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reg = StackingRegressor(**params, cv=3)
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reg.fit(
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scale(X_diabetes), y, sample_weight=np.ones(X_diabetes.shape[0])
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)
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|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"estimator, X, y",
|
||
|
|
[(StackingClassifier(
|
||
|
|
estimators=[('lr', LogisticRegression(random_state=0)),
|
||
|
|
('svm', LinearSVC(random_state=0))]),
|
||
|
|
X_iris[:100], y_iris[:100]), # keep only classes 0 and 1
|
||
|
|
(StackingRegressor(
|
||
|
|
estimators=[('lr', LinearRegression()),
|
||
|
|
('svm', LinearSVR(random_state=0))]),
|
||
|
|
X_diabetes, y_diabetes)],
|
||
|
|
ids=['StackingClassifier', 'StackingRegressor']
|
||
|
|
)
|
||
|
|
def test_stacking_randomness(estimator, X, y):
|
||
|
|
# checking that fixing the random state of the CV will lead to the same
|
||
|
|
# results
|
||
|
|
estimator_full = clone(estimator)
|
||
|
|
estimator_full.set_params(
|
||
|
|
cv=KFold(shuffle=True, random_state=np.random.RandomState(0))
|
||
|
|
)
|
||
|
|
|
||
|
|
estimator_drop = clone(estimator)
|
||
|
|
estimator_drop.set_params(lr='drop')
|
||
|
|
estimator_drop.set_params(
|
||
|
|
cv=KFold(shuffle=True, random_state=np.random.RandomState(0))
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_allclose(
|
||
|
|
estimator_full.fit(X, y).transform(X)[:, 1:],
|
||
|
|
estimator_drop.fit(X, y).transform(X)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_stacking_classifier_stratify_default():
|
||
|
|
# check that we stratify the classes for the default CV
|
||
|
|
clf = StackingClassifier(
|
||
|
|
estimators=[('lr', LogisticRegression(max_iter=1e4)),
|
||
|
|
('svm', LinearSVC(max_iter=1e4))]
|
||
|
|
)
|
||
|
|
# since iris is not shuffled, a simple k-fold would not contain the
|
||
|
|
# 3 classes during training
|
||
|
|
clf.fit(X_iris, y_iris)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"stacker, X, y",
|
||
|
|
[(StackingClassifier(
|
||
|
|
estimators=[('lr', LogisticRegression()),
|
||
|
|
('svm', LinearSVC(random_state=42))],
|
||
|
|
final_estimator=LogisticRegression(),
|
||
|
|
cv=KFold(shuffle=True, random_state=42)),
|
||
|
|
*load_breast_cancer(return_X_y=True)),
|
||
|
|
(StackingRegressor(
|
||
|
|
estimators=[('lr', LinearRegression()),
|
||
|
|
('svm', LinearSVR(random_state=42))],
|
||
|
|
final_estimator=LinearRegression(),
|
||
|
|
cv=KFold(shuffle=True, random_state=42)),
|
||
|
|
X_diabetes, y_diabetes)],
|
||
|
|
ids=['StackingClassifier', 'StackingRegressor']
|
||
|
|
)
|
||
|
|
def test_stacking_with_sample_weight(stacker, X, y):
|
||
|
|
# check that sample weights has an influence on the fitting
|
||
|
|
# note: ConvergenceWarning are catch since we are not worrying about the
|
||
|
|
# convergence here
|
||
|
|
n_half_samples = len(y) // 2
|
||
|
|
total_sample_weight = np.array(
|
||
|
|
[0.1] * n_half_samples + [0.9] * (len(y) - n_half_samples)
|
||
|
|
)
|
||
|
|
X_train, X_test, y_train, _, sample_weight_train, _ = train_test_split(
|
||
|
|
X, y, total_sample_weight, random_state=42
|
||
|
|
)
|
||
|
|
|
||
|
|
with ignore_warnings(category=ConvergenceWarning):
|
||
|
|
stacker.fit(X_train, y_train)
|
||
|
|
y_pred_no_weight = stacker.predict(X_test)
|
||
|
|
|
||
|
|
with ignore_warnings(category=ConvergenceWarning):
|
||
|
|
stacker.fit(X_train, y_train, sample_weight=np.ones(y_train.shape))
|
||
|
|
y_pred_unit_weight = stacker.predict(X_test)
|
||
|
|
|
||
|
|
assert_allclose(y_pred_no_weight, y_pred_unit_weight)
|
||
|
|
|
||
|
|
with ignore_warnings(category=ConvergenceWarning):
|
||
|
|
stacker.fit(X_train, y_train, sample_weight=sample_weight_train)
|
||
|
|
y_pred_biased = stacker.predict(X_test)
|
||
|
|
|
||
|
|
assert np.abs(y_pred_no_weight - y_pred_biased).sum() > 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_stacking_classifier_sample_weight_fit_param():
|
||
|
|
# check sample_weight is passed to all invocations of fit
|
||
|
|
stacker = StackingClassifier(
|
||
|
|
estimators=[
|
||
|
|
('lr', CheckingClassifier(expected_fit_params=['sample_weight']))
|
||
|
|
],
|
||
|
|
final_estimator=CheckingClassifier(
|
||
|
|
expected_fit_params=['sample_weight']
|
||
|
|
)
|
||
|
|
)
|
||
|
|
stacker.fit(X_iris, y_iris, sample_weight=np.ones(X_iris.shape[0]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"stacker, X, y",
|
||
|
|
[(StackingClassifier(
|
||
|
|
estimators=[('lr', LogisticRegression()),
|
||
|
|
('svm', LinearSVC(random_state=42))],
|
||
|
|
final_estimator=LogisticRegression()),
|
||
|
|
*load_breast_cancer(return_X_y=True)),
|
||
|
|
(StackingRegressor(
|
||
|
|
estimators=[('lr', LinearRegression()),
|
||
|
|
('svm', LinearSVR(random_state=42))],
|
||
|
|
final_estimator=LinearRegression()),
|
||
|
|
X_diabetes, y_diabetes)],
|
||
|
|
ids=['StackingClassifier', 'StackingRegressor']
|
||
|
|
)
|
||
|
|
def test_stacking_cv_influence(stacker, X, y):
|
||
|
|
# check that the stacking affects the fit of the final estimator but not
|
||
|
|
# the fit of the base estimators
|
||
|
|
# note: ConvergenceWarning are catch since we are not worrying about the
|
||
|
|
# convergence here
|
||
|
|
stacker_cv_3 = clone(stacker)
|
||
|
|
stacker_cv_5 = clone(stacker)
|
||
|
|
|
||
|
|
stacker_cv_3.set_params(cv=3)
|
||
|
|
stacker_cv_5.set_params(cv=5)
|
||
|
|
|
||
|
|
stacker_cv_3.fit(X, y)
|
||
|
|
stacker_cv_5.fit(X, y)
|
||
|
|
|
||
|
|
# the base estimators should be identical
|
||
|
|
for est_cv_3, est_cv_5 in zip(stacker_cv_3.estimators_,
|
||
|
|
stacker_cv_5.estimators_):
|
||
|
|
assert_allclose(est_cv_3.coef_, est_cv_5.coef_)
|
||
|
|
|
||
|
|
# the final estimator should be different
|
||
|
|
with pytest.raises(AssertionError, match='Not equal'):
|
||
|
|
assert_allclose(stacker_cv_3.final_estimator_.coef_,
|
||
|
|
stacker_cv_5.final_estimator_.coef_)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("make_dataset, Stacking, Estimator", [
|
||
|
|
(make_classification, StackingClassifier, LogisticRegression),
|
||
|
|
(make_regression, StackingRegressor, LinearRegression)
|
||
|
|
])
|
||
|
|
def test_stacking_without_n_features_in(make_dataset, Stacking, Estimator):
|
||
|
|
# Stacking supports estimators without `n_features_in_`. Regression test
|
||
|
|
# for #17353
|
||
|
|
|
||
|
|
class MyEstimator(Estimator):
|
||
|
|
"""Estimator without n_features_in_"""
|
||
|
|
def fit(self, X, y):
|
||
|
|
super().fit(X, y)
|
||
|
|
del self.n_features_in_
|
||
|
|
|
||
|
|
X, y = make_dataset(random_state=0, n_samples=100)
|
||
|
|
stacker = Stacking(estimators=[('lr', MyEstimator())])
|
||
|
|
|
||
|
|
msg = f"{Stacking.__name__} object has no attribute n_features_in_"
|
||
|
|
with pytest.raises(AttributeError, match=msg):
|
||
|
|
stacker.n_features_in_
|
||
|
|
|
||
|
|
# Does not raise
|
||
|
|
stacker.fit(X, y)
|
||
|
|
|
||
|
|
msg = "'MyEstimator' object has no attribute 'n_features_in_'"
|
||
|
|
with pytest.raises(AttributeError, match=msg):
|
||
|
|
stacker.n_features_in_
|