def test_get_config_space(self):
dataset_properties = dict(categorical_columns=[0, 1],
numerical_columns=[1, 2])
config = SimpleImputer.get_hyperparameter_search_space(
dataset_properties).sample_configuration()
estimator = SimpleImputer(**config)
estimator_clone = clone(estimator)
estimator_clone_params = estimator_clone.get_params()
# Make sure all keys are copied properly
for k, v in estimator.get_params().items():
self.assertIn(k, estimator_clone_params)
# Make sure the params getter of estimator are honored
klass = estimator.__class__
new_object_params = estimator.get_params(deep=False)
for name, param in new_object_params.items():
new_object_params[name] = clone(param, safe=False)
new_object = klass(**new_object_params)
params_set = new_object.get_params(deep=False)
for name in new_object_params:
param1 = new_object_params[name]
param2 = params_set[name]
self.assertEqual(param1, param2)
def _get_pipeline_steps(self, dataset_properties: Optional[Dict[str, Any]],
) -> List[Tuple[str, autoPyTorchChoice]]:
"""
Defines what steps a pipeline should follow.
The step itself has choices given via autoPyTorchChoice.
Returns:
List[Tuple[str, autoPyTorchChoice]]: list of steps sequentially exercised
by the pipeline.
"""
steps = [] # type: List[Tuple[str, autoPyTorchChoice]]
default_dataset_properties = {'target_type': 'tabular_classification'}
if dataset_properties is not None:
default_dataset_properties.update(dataset_properties)
steps.extend([
("imputer", SimpleImputer()),
("encoder", EncoderChoice(default_dataset_properties)),
("scaler", ScalerChoice(default_dataset_properties)),
("tabular_transformer", TabularColumnTransformer()),
("preprocessing", EarlyPreprocessing()),
("network", NetworkChoice(default_dataset_properties)),
("network_init", NetworkInitializerChoice(default_dataset_properties)),
("optimizer", OptimizerChoice(default_dataset_properties)),
("lr_scheduler", SchedulerChoice(default_dataset_properties)),
("data_loader", FeatureDataLoader()),
("trainer", TrainerChoice(default_dataset_properties)),
])
return steps
def _get_pipeline_steps(
self, dataset_properties: Optional[Dict[str,
BaseDatasetPropertiesType]]
) -> List[Tuple[str, PipelineStepType]]:
"""
Defines what steps a pipeline should follow.
The step itself has choices given via autoPyTorchChoice.
Returns:
List[Tuple[str, PipelineStepType]]:
list of steps sequentially exercised by the pipeline.
"""
steps: List[Tuple[str, PipelineStepType]] = []
default_dataset_properties: Dict[str, BaseDatasetPropertiesType] = {
'target_type': 'tabular_regression'
}
if dataset_properties is not None:
default_dataset_properties.update(dataset_properties)
steps.extend([
("imputer", SimpleImputer(random_state=self.random_state)),
("encoder",
EncoderChoice(default_dataset_properties,
random_state=self.random_state)),
("scaler",
ScalerChoice(default_dataset_properties,
random_state=self.random_state)),
("feature_preprocessor",
FeatureProprocessorChoice(default_dataset_properties,
random_state=self.random_state)),
("tabular_transformer",
TabularColumnTransformer(random_state=self.random_state)),
("preprocessing",
EarlyPreprocessing(random_state=self.random_state)),
("network_embedding",
NetworkEmbeddingChoice(default_dataset_properties,
random_state=self.random_state)),
("network_backbone",
NetworkBackboneChoice(default_dataset_properties,
random_state=self.random_state)),
("network_head",
NetworkHeadChoice(default_dataset_properties,
random_state=self.random_state)),
("network", NetworkComponent(random_state=self.random_state)),
("network_init",
NetworkInitializerChoice(default_dataset_properties,
random_state=self.random_state)),
("optimizer",
OptimizerChoice(default_dataset_properties,
random_state=self.random_state)),
("lr_scheduler",
SchedulerChoice(default_dataset_properties,
random_state=self.random_state)),
("data_loader", FeatureDataLoader(random_state=self.random_state)),
("trainer",
TrainerChoice(default_dataset_properties,
random_state=self.random_state)),
])
return steps
def test_constant_imputation(self):
data = np.array(
[['1.0', np.nan, 3], [np.nan, 8, 9], ['4.0', 5, np.nan],
[np.nan, 2, 3], ['7.0', np.nan, 9], ['4.0', np.nan, np.nan]],
dtype=object)
numerical_columns = [1, 2]
categorical_columns = [0]
train_indices = np.array([0, 2, 3])
test_indices = np.array([1, 4, 5])
dataset_properties = {
'categorical_columns': categorical_columns,
'numerical_columns': numerical_columns,
}
X = {
'X_train': data[train_indices],
'dataset_properties': dataset_properties
}
imputer_component = SimpleImputer(
numerical_strategy='constant_zero',
categorical_strategy='constant_!missing!')
imputer_component = imputer_component.fit(X)
X = imputer_component.transform(X)
categorical_imputer = X['imputer']['categorical']
numerical_imputer = X['imputer']['numerical']
# check if the fit dictionary X is modified as expected
self.assertIsInstance(X['imputer'], dict)
self.assertIsInstance(categorical_imputer, BaseEstimator)
self.assertIsInstance(numerical_imputer, BaseEstimator)
# make column transformer with returned encoder to fit on data
column_transformer = make_column_transformer(
(categorical_imputer,
X['dataset_properties']['categorical_columns']),
(numerical_imputer, X['dataset_properties']['numerical_columns']),
remainder='passthrough')
column_transformer = column_transformer.fit(X['X_train'])
transformed = column_transformer.transform(data[test_indices])
assert_array_equal(
transformed.astype(str),
np.array([['-1', 8, 9], [7.0, '0', 9], [4.0, '0', '0']],
dtype=str))
def _get_pipeline_steps(self, dataset_properties: Optional[Dict[str, Any]],
) -> List[Tuple[str, autoPyTorchChoice]]:
"""
Defines what steps a pipeline should follow.
The step itself has choices given via autoPyTorchChoice.
Returns:
List[Tuple[str, autoPyTorchChoice]]: list of steps sequentially exercised
by the pipeline.
"""
steps: List[Tuple[str, autoPyTorchChoice]] = []
default_dataset_properties = {'target_type': 'tabular_classification'}
if dataset_properties is not None:
default_dataset_properties.update(dataset_properties)
steps.extend([
("imputer", SimpleImputer()),
("encoder", EncoderChoice(default_dataset_properties)),
("scaler", ScalerChoice(default_dataset_properties)),
("tabular_transformer", TabularColumnTransformer()),
])
return steps