def test_stacked_ensemble_n_jobs_negative_one(
X_y_regression, linear_regression_pipeline_class):
X, y = X_y_regression
input_pipelines = [linear_regression_pipeline_class(parameters={})]
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines)
expected_parameters = {
"input_pipelines": input_pipelines,
"final_estimator": None,
'cv': None,
'n_jobs': -1
}
assert clf.parameters == expected_parameters
clf.fit(X, y)
y_pred = clf.predict(X)
assert len(y_pred) == len(y)
assert not np.isnan(y_pred.to_series()).all()
def test_stacked_ensemble_init_with_multiple_same_estimators(X_y_regression, linear_regression_pipeline_class):
# Checks that it is okay to pass multiple of the same type of estimator
X, y = X_y_regression
input_pipelines = [linear_regression_pipeline_class(parameters={}),
linear_regression_pipeline_class(parameters={})]
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines, n_jobs=1)
expected_parameters = {
"input_pipelines": input_pipelines,
"final_estimator": None,
'cv': None,
'n_jobs': 1
}
assert clf.parameters == expected_parameters
fitted = clf.fit(X, y)
assert isinstance(fitted, StackedEnsembleRegressor)
y_pred = clf.predict(X)
assert len(y_pred) == len(y)
assert not np.isnan(y_pred).all()