def test_stacked_ensemble_init_with_invalid_estimators_parameter():
with pytest.raises(EnsembleMissingPipelinesError,
match='must not be None or an empty list.'):
StackedEnsembleRegressor()
with pytest.raises(EnsembleMissingPipelinesError,
match='must not be None or an empty list.'):
StackedEnsembleRegressor(input_pipelines=[])
def test_stacked_feature_importance(mock_fit, X_y_regression, stackable_regressors):
X, y = X_y_regression
input_pipelines = [make_pipeline_from_components([regressor], ProblemTypes.REGRESSION)
for regressor in stackable_regressors]
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines, n_jobs=1)
clf.fit(X, y)
mock_fit.assert_called()
clf._is_fitted = True
with pytest.raises(NotImplementedError, match="feature_importance is not implemented"):
clf.feature_importance
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).all()
def test_stacked_fit_predict_regression(X_y_regression, stackable_regressors):
X, y = X_y_regression
input_pipelines = [make_pipeline_from_components([regressor], ProblemTypes.REGRESSION)
for regressor in stackable_regressors]
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines, n_jobs=1)
clf.fit(X, y)
y_pred = clf.predict(X)
assert len(y_pred) == len(y)
assert isinstance(y_pred, pd.Series)
assert not np.isnan(y_pred).all()
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines, final_estimator=RandomForestRegressor(), n_jobs=1)
clf.fit(X, y)
y_pred = clf.predict(X)
assert len(y_pred) == len(y)
assert isinstance(y_pred, pd.Series)
assert not np.isnan(y_pred).all()
def test_stacked_ensemble_does_not_overwrite_pipeline_random_state(mock_stack,
linear_regression_pipeline_class):
input_pipelines = [linear_regression_pipeline_class(parameters={}, random_state=3),
linear_regression_pipeline_class(parameters={}, random_state=4)]
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines, random_state=5, n_jobs=1)
estimators_used_in_ensemble = mock_stack.call_args[1]['estimators']
assert check_random_state_equality(clf.random_state, np.random.RandomState(5))
assert check_random_state_equality(estimators_used_in_ensemble[0][1].pipeline.random_state, np.random.RandomState(3))
assert check_random_state_equality(estimators_used_in_ensemble[1][1].pipeline.random_state, np.random.RandomState(4))
def test_stacked_different_input_pipelines_regression():
input_pipelines = [
make_pipeline_from_components([RandomForestRegressor()],
ProblemTypes.REGRESSION),
make_pipeline_from_components([RandomForestClassifier()],
ProblemTypes.BINARY)
]
with pytest.raises(ValueError,
match="All pipelines must have the same problem type."):
StackedEnsembleRegressor(input_pipelines=input_pipelines)
def test_stacked_ensemble_nonstackable_model_families():
with pytest.raises(
ValueError,
match=
"Pipelines with any of the following model families cannot be used as base pipelines"
):
StackedEnsembleRegressor(input_pipelines=[
make_pipeline_from_components([BaselineRegressor()],
ProblemTypes.REGRESSION)
])
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()
def test_stacked_ensemble_does_not_overwrite_pipeline_random_seed(
mock_stack, linear_regression_pipeline_class):
input_pipelines = [
linear_regression_pipeline_class(parameters={}, random_seed=3),
linear_regression_pipeline_class(parameters={}, random_seed=4)
]
clf = StackedEnsembleRegressor(input_pipelines=input_pipelines,
random_seed=5,
n_jobs=1)
estimators_used_in_ensemble = mock_stack.call_args[1]['estimators']
assert clf.random_seed == 5
assert estimators_used_in_ensemble[0][1].pipeline.random_seed == 3
assert estimators_used_in_ensemble[1][1].pipeline.random_seed == 4
def test_stacked_ensemble_multilevel(linear_regression_pipeline_class):
# checks passing a stacked ensemble classifier as a final estimator
X = pd.DataFrame(np.random.rand(50, 5))
y = pd.Series(np.random.rand(50,))
base = StackedEnsembleRegressor(input_pipelines=[linear_regression_pipeline_class(parameters={})], n_jobs=1)
clf = StackedEnsembleRegressor(input_pipelines=[linear_regression_pipeline_class(parameters={})],
final_estimator=base,
n_jobs=1)
clf.fit(X, y)
y_pred = clf.predict(X)
assert len(y_pred) == len(y)
assert not np.isnan(y_pred).all()