def _feature_by_type_splits(config, train_mode):
if train_mode:
feature_by_type_split = Step(name='feature_by_type_split',
transformer=fe.DataFrameByTypeSplitter(
**config.dataframe_by_type_splitter),
input_data=['input'],
adapter={
'X': ([('input', 'X')]),
},
cache_dirpath=config.env.cache_dirpath)
feature_by_type_split_valid = Step(
name='feature_by_type_split_valid',
transformer=feature_by_type_split,
input_data=['input'],
adapter={
'X': ([('input', 'X_valid')]),
},
cache_dirpath=config.env.cache_dirpath)
return feature_by_type_split, feature_by_type_split_valid
else:
feature_by_type_split = Step(name='feature_by_type_split',
transformer=fe.DataFrameByTypeSplitter(
**config.dataframe_by_type_splitter),
input_data=['input'],
adapter={
'X': ([('input', 'X')]),
},
cache_dirpath=config.env.cache_dirpath)
return feature_by_type_split
def _feature_by_type_splits(config, train_mode):
if train_mode:
feature_by_type_split = Step(name='feature_by_type_split',
transformer=fe.DataFrameByTypeSplitter(**config.dataframe_by_type_splitter),
input_data=['input'],
adapter=Adapter({'X': E('input', 'X')}),
experiment_directory=config.pipeline.experiment_directory)
feature_by_type_split_valid = Step(name='feature_by_type_split_valid',
transformer=feature_by_type_split,
input_data=['input'],
adapter=Adapter({'X': E('input', 'X_valid')}),
experiment_directory=config.pipeline.experiment_directory)
return feature_by_type_split, feature_by_type_split_valid
else:
feature_by_type_split = Step(name='feature_by_type_split',
transformer=fe.DataFrameByTypeSplitter(**config.dataframe_by_type_splitter),
input_data=['input'],
adapter=Adapter({'X': E('input', 'X')}),
experiment_directory=config.pipeline.experiment_directory)
return feature_by_type_split