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_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.to_series()).all()
def _make_stacked_ensemble_pipeline(input_pipelines,
problem_type,
n_jobs=-1,
random_seed=0):
"""
Creates a pipeline with a stacked ensemble estimator.
Arguments:
input_pipelines (list(PipelineBase or subclass obj)): List of pipeline instances to use as the base estimators for the stacked ensemble.
This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
problem_type (ProblemType): problem type of pipeline
n_jobs (int or None): Integer describing level of parallelism used for pipelines.
None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
Defaults to -1.
Returns:
Pipeline with appropriate stacked ensemble estimator.
"""
if problem_type in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]:
return make_pipeline_from_components(
[StackedEnsembleClassifier(input_pipelines, n_jobs=n_jobs)],
problem_type,
custom_name="Stacked Ensemble Classification Pipeline",
random_seed=random_seed)
else:
return make_pipeline_from_components(
[StackedEnsembleRegressor(input_pipelines, n_jobs=n_jobs)],
problem_type,
custom_name="Stacked Ensemble Regression Pipeline",
random_seed=random_seed)
def test_stacked_different_input_pipelines_regression():
input_pipelines = [
RegressionPipeline([RandomForestRegressor]),
BinaryClassificationPipeline([RandomForestClassifier])
]
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=[RegressionPipeline([BaselineRegressor])])
def test_stacked_fit_predict_regression(X_y_regression, stackable_regressors):
X, y = X_y_regression
input_pipelines = [
RegressionPipeline([regressor]) 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, ww.DataColumn)
assert not np.isnan(y_pred.to_series()).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, ww.DataColumn)
assert not np.isnan(y_pred.to_series()).all()
def test_stacked_feature_importance(mock_fit, X_y_regression,
stackable_regressors):
X, y = X_y_regression
input_pipelines = [
RegressionPipeline([regressor]) 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_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_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 _make_stacked_ensemble_pipeline(input_pipelines, problem_type, random_state=0):
"""
Creates a pipeline with a stacked ensemble estimator.
Arguments:
input_pipelines (list(PipelineBase or subclass obj)): List of pipeline instances to use as the base estimators for the stacked ensemble.
This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
problem_type (ProblemType): problem type of pipeline
Returns:
Pipeline with appropriate stacked ensemble estimator.
"""
if problem_type in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]:
return make_pipeline_from_components([StackedEnsembleClassifier(input_pipelines)], problem_type,
custom_name="Stacked Ensemble Classification Pipeline",
random_state=random_state)
else:
return make_pipeline_from_components([StackedEnsembleRegressor(input_pipelines)], problem_type,
custom_name="Stacked Ensemble Regression Pipeline",
random_state=random_state)
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.to_series()).all()
def dummy_stacked_ensemble_regressor_estimator(
linear_regression_pipeline_class):
p1 = linear_regression_pipeline_class({})
ensemble_estimator = StackedEnsembleRegressor(input_pipelines=[p1],
random_seed=0)
return ensemble_estimator