def test_StackingEstimator_4():
"""Assert that the StackingEstimator worked as expected in scikit-learn pipeline in regression."""
stack_reg = StackingEstimator(estimator=RandomForestRegressor(
random_state=42))
meta_reg = Lasso(random_state=42)
sklearn_pipeline = make_pipeline(stack_reg, meta_reg)
# fit in pipeline
sklearn_pipeline.fit(training_features_r, training_target_r)
# fit step by step
stack_reg.fit(training_features_r, training_target_r)
X_reg_transformed = stack_reg.transform(training_features_r)
meta_reg.fit(X_reg_transformed, training_target_r)
# scoring
score = meta_reg.score(X_reg_transformed, training_target_r)
pipeline_score = sklearn_pipeline.score(training_features_r,
training_target_r)
assert np.allclose(score, pipeline_score)
# test cv score
cv_score = np.mean(
cross_val_score(sklearn_pipeline,
training_features_r,
training_target_r,
cv=3,
scoring='r2'))
known_cv_score = 0.8216045257587923
assert np.allclose(known_cv_score, cv_score)
def test_StackingEstimator_3():
"""Assert that the StackingEstimator worked as expected in scikit-learn pipeline in classification."""
stack_clf = StackingEstimator(estimator=RandomForestClassifier(
random_state=42))
meta_clf = LogisticRegression()
sklearn_pipeline = make_pipeline(stack_clf, meta_clf)
# fit in pipeline
sklearn_pipeline.fit(training_features, training_target)
# fit step by step
stack_clf.fit(training_features, training_target)
X_clf_transformed = stack_clf.transform(training_features)
meta_clf.fit(X_clf_transformed, training_target)
# scoring
score = meta_clf.score(X_clf_transformed, training_target)
pipeline_score = sklearn_pipeline.score(training_features, training_target)
assert np.allclose(score, pipeline_score)
# test cv score
cv_score = np.mean(
cross_val_score(sklearn_pipeline,
training_features,
training_target,
cv=3,
scoring='accuracy'))
known_cv_score = 0.9643652561247217
assert np.allclose(known_cv_score, cv_score)
def test_StackingEstimator_4():
"""Assert that the StackingEstimator worked as expected in scikit-learn pipeline in regression."""
stack_reg = StackingEstimator(estimator=RandomForestRegressor(
random_state=42))
meta_reg = Lasso(random_state=42)
sklearn_pipeline = make_pipeline(stack_reg, meta_reg)
# fit in pipeline
sklearn_pipeline.fit(training_features_r, training_target_r)
# fit step by step
stack_reg.fit(training_features_r, training_target_r)
X_reg_transformed = stack_reg.transform(training_features_r)
meta_reg.fit(X_reg_transformed, training_target_r)
# scoring
score = meta_reg.score(X_reg_transformed, training_target_r)
pipeline_score = sklearn_pipeline.score(training_features_r,
training_target_r)
assert np.allclose(score, pipeline_score)
# test cv score
cv_score = np.mean(
cross_val_score(sklearn_pipeline,
training_features_r,
training_target_r,
cv=3,
scoring='r2'))
known_cv_score = 0.8216045257587923
# On some non-amd64 systems such as arm64, a resulting score of
# 0.8207525232725118 was observed, so we need to add a tolerance there
if platform.machine() != 'x86_64':
assert np.allclose(known_cv_score, cv_score, rtol=0.01)
else:
assert np.allclose(known_cv_score, cv_score)
def test_StackingEstimator_2():
"""Assert that the StackingEstimator returns transformed X with a synthetic feature in regression."""
reg = RandomForestRegressor(random_state=42)
stack_reg = StackingEstimator(estimator=RandomForestRegressor(random_state=42))
# fit
reg.fit(training_features_r, training_target_r)
stack_reg.fit(training_features_r, training_target_r)
# get transformd X
X_reg_transformed = stack_reg.transform(training_features_r)
assert np.allclose(reg.predict(training_features_r), X_reg_transformed[:, 0])
def test_StackingEstimator_1():
"""Assert that the StackingEstimator returns transformed X with synthetic features in classification."""
clf = RandomForestClassifier(random_state=42)
stack_clf = StackingEstimator(estimator=RandomForestClassifier(random_state=42))
# fit
clf.fit(training_features, training_target)
stack_clf.fit(training_features, training_target)
# get transformd X
X_clf_transformed = stack_clf.transform(training_features)
assert np.allclose(clf.predict(training_features), X_clf_transformed[:, 0])
assert np.allclose(clf.predict_proba(training_features), X_clf_transformed[:, 1:1 + len(np.unique(training_target))])
