Source code for recbole.quick_start.quick_start

# @Time   : 2020/10/6
# @Author : Shanlei Mu
# @Email  : slmu@ruc.edu.cn

"""
recbole.quick_start
########################
"""
import logging
from logging import getLogger

from recbole.config import Config
from recbole.data import create_dataset, data_preparation
from recbole.utils import init_logger, get_model, get_trainer, init_seed


[docs]def run_recbole(model=None, dataset=None, config_file_list=None, config_dict=None, saved=True): r""" A fast running api, which includes the complete process of training and testing a model on a specified dataset Args: model (str): model name dataset (str): dataset name config_file_list (list): config files used to modify experiment parameters config_dict (dict): parameters dictionary used to modify experiment parameters saved (bool): whether to save the model """ # configurations initialization config = Config(model=model, dataset=dataset, config_file_list=config_file_list, config_dict=config_dict) init_seed(config['seed'], config['reproducibility']) # logger initialization init_logger(config) logger = getLogger() logger.info(config) # dataset filtering dataset = create_dataset(config) logger.info(dataset) # dataset splitting train_data, valid_data, test_data = data_preparation(config, dataset) # model loading and initialization model = get_model(config['model'])(config, train_data).to(config['device']) logger.info(model) # trainer loading and initialization trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) # model training best_valid_score, best_valid_result = trainer.fit( train_data, valid_data, saved=saved, show_progress=config['show_progress'] ) # model evaluation test_result = trainer.evaluate(test_data, load_best_model=saved, show_progress=config['show_progress']) logger.info('best valid result: {}'.format(best_valid_result)) logger.info('test result: {}'.format(test_result)) return { 'best_valid_score': best_valid_score, 'valid_score_bigger': config['valid_metric_bigger'], 'best_valid_result': best_valid_result, 'test_result': test_result }
[docs]def objective_function(config_dict=None, config_file_list=None, saved=True): r""" The default objective_function used in HyperTuning Args: config_dict (dict): parameters dictionary used to modify experiment parameters config_file_list (list): config files used to modify experiment parameters saved (bool): whether to save the model """ config = Config(config_dict=config_dict, config_file_list=config_file_list) init_seed(config['seed'], config['reproducibility']) logging.basicConfig(level=logging.ERROR) dataset = create_dataset(config) train_data, valid_data, test_data = data_preparation(config, dataset) model = get_model(config['model'])(config, train_data).to(config['device']) trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) best_valid_score, best_valid_result = trainer.fit(train_data, valid_data, verbose=False, saved=saved) test_result = trainer.evaluate(test_data, load_best_model=saved) return { 'best_valid_score': best_valid_score, 'valid_score_bigger': config['valid_metric_bigger'], 'best_valid_result': best_valid_result, 'test_result': test_result }