def _get_pipeline_steps(self, dataset_properties):
steps = []
default_dataset_properties = {'target_type': 'classification'}
if dataset_properties is not None and isinstance(
dataset_properties, dict):
default_dataset_properties.update(dataset_properties)
steps.extend(
[[
"data_preprocessing",
DataPreprocessor(dataset_properties=default_dataset_properties)
], ["balancing", Balancing()],
[
"feature_preprocessor",
feature_preprocessing_components.FeaturePreprocessorChoice(
default_dataset_properties)
],
[
'classifier',
classification_components.ClassifierChoice(
default_dataset_properties)
]])
return steps
def _get_pipeline(self):
steps = []
default_dataset_properties = {'target_type': 'classification'}
# Add the always active preprocessing components
steps.extend([["one_hot_encoding", OneHotEncoder()],
["imputation", Imputation()],
[
"rescaling",
rescaling_components.RescalingChoice(
default_dataset_properties)
], ["balancing", Balancing()]])
# Add the preprocessing component
steps.append([
'preprocessor',
feature_preprocessing_components.FeaturePreprocessorChoice(
default_dataset_properties)
])
# Add the classification component
steps.append([
'classifier',
classification_components.ClassifierChoice(
default_dataset_properties)
])
return steps
def _get_pipeline_steps(self):
steps = []
print(" going execute pipeline autosklearn")
default_dataset_properties = {'target_type': 'classification'}
steps.extend([
["feature_preprocessor",
feature_preprocessing_components.FeaturePreprocessorChoice(
default_dataset_properties)],
["data_preprocessing",
DataPreprocessor(dataset_properties=default_dataset_properties)],
["balancing",
Balancing()],
['classifier',
classification_components.ClassifierChoice(
default_dataset_properties)]
])
return steps