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