def catboost_ensemble(config, is_train):
catboost_ensemble = Step(
name='catboost_ensemble',
transformer=CatboostClassifierMultilabel(**config.catboost_ensemble),
input_data=['input'],
cache_dirpath=config.env.cache_dirpath)
output = Step(name='output',
transformer=Dummy(),
input_steps=[catboost_ensemble],
adapter={
'y_pred':
([('catboost_ensemble', 'prediction_probability')])
},
cache_dirpath=config.env.cache_dirpath)
if is_train:
catboost_ensemble.overwrite_transformer = True
return output
def gru_stacker_ensemble(config, is_train):
if is_train:
gru_stacker_ensemble = Step(
name='gru_stacker_ensemble',
transformer=StackerGru(**config.gru_stacker),
input_data=['input'],
adapter={
'X': ([('input', 'X')]),
'y': ([('input', 'y')]),
'validation_data': ([('input', 'X_valid'),
('input', 'y_valid')], to_tuple_inputs),
},
cache_dirpath=config.env.cache_dirpath)
else:
gru_stacker_ensemble = Step(
name='gru_stacker_ensemble',
transformer=StackerGru(**config.gru_stacker),
input_data=['input'],
adapter={
'X': ([('input', 'X')]),
'y': ([('input', 'y')]),
},
cache_dirpath=config.env.cache_dirpath)
output = Step(name='output',
transformer=Dummy(),
input_steps=[gru_stacker_ensemble],
adapter={
'y_pred':
([('gru_stacker_ensemble', 'prediction_probability')])
},
cache_dirpath=config.env.cache_dirpath)
if is_train:
gru_stacker_ensemble.overwrite_transformer = True
return output