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resume_run_recbole.py
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resume_run_recbole.py
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# @Time : 2020/10/6
# @Author : Shanlei Mu
# @Email : [email protected]
"""
recbole.quick_start
########################
"""
import logging
from logging import getLogger
import torch
import pickle
import sys
import os
import argparse
import glob
from recbole.config import Config
from recbole.data import create_dataset, data_preparation, save_split_dataloaders, load_split_dataloaders
from recbole.utils import init_logger, get_model, get_trainer, init_seed, logger, set_color
def get_latest_model_file(model, dataset):
r"""
A function to get the latest model file.
"""
saved_dir = f'saved/saved_{dataset}/'
pattern = saved_dir + f'{model}-*.pth'
files = glob.glob(pattern)
if files:
latest_file = max(files, key=os.path.getctime)
return latest_file
else:
raise FileNotFoundError(f"No saved model file found for {model} and {dataset}")
def run_recbole(model=None, dataset=None, config_file_list=None, config_dict=None, saved=True, model_file=None):
r""" A fast running api, which includes the complete process of
training and testing a model on a specified dataset
Args:
model (str, optional): Model name. Defaults to ``None``.
dataset (str, optional): Dataset name. Defaults to ``None``.
config_file_list (list, optional): Config files used to modify experiment parameters. Defaults to ``None``.
config_dict (dict, optional): Parameters dictionary used to modify experiment parameters. Defaults to ``None``.
saved (bool, optional): Whether to save the model. Defaults to ``True``.
"""
# configurations initialization
# model_file = f'saved/saved_{model}/{dataset}_{model}.pth'
config, model, dataset, train_data, valid_data, test_data = load_data_and_model(
model=model,
dataset=dataset,
model_file=model_file,
dataset_file=None,
dataloader_file=None,
config_file_list=config_file_list,
config_dict=config_dict
)
# config = Config(model='UGF_MF', dataset='yahoo', config_file_list=['UGF_MF.yaml'])
# train_data, valid_data, test_data = data_preparation(config, dataset)
init_seed(config['seed'], config['reproducibility'])
# logger initialization
init_logger(config)
logger = getLogger()
logger.info(config)
logger.info(dataset)
logger.info(model)
# trainer loading and initialization
trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model)
trainer.resume_checkpoint(resume_file=model_file)
# trainer._save_sst_embed(train_data)
# model training
# best_valid_score, best_valid_result = trainer.fit(
# train_data, valid_data, saved=saved, show_progress=config['show_progress']
# )
# When calculate ItemCoverage metrics, we need to run this code for set item_nums in eval_collector.
trainer.eval_collector.data_collect(train_data)
# model evaluation
test_result = trainer.evaluate(test_data, load_best_model=saved, model_file=model_file,show_progress=config['show_progress'])
valid_result = trainer.evaluate(valid_data, load_best_model=saved, model_file=model_file, show_progress=config['show_progress'])
logger.info(set_color('test result', 'yellow') + f': {test_result}')
# best_valid_result = trainer.evaluate(test_data, load_best_model=saved, model_file=model_file, show_progress=config['show_progress'])
logger.info(set_color('valid result', 'yellow') + f': {valid_result}')
# logger.info(set_color('best valid ', 'yellow') + f': {best_valid_result}')
return valid_result[config['valid_metric'].lower()]
# return best_valid_result[config['valid_metric'].lower()]
# return {
# 'best_valid_score': best_valid_score,
# 'valid_score_bigger': config['valid_metric_bigger'],
# 'best_valid_result': best_valid_result,
# 'test_result': test_result
# }
def load_data_and_model(model, dataset, model_file, dataset_file=None, dataloader_file=None, config_file_list=None, config_dict=None):
r"""Load filtered dataset, split dataloaders and saved model.
Args:
model_file (str): The path of saved model file.
dataset_file (str, optional): The path of filtered dataset. Defaults to ``None``.
dataloader_file (str, optional): The path of split dataloaders. Defaults to ``None``.
Note:
The :attr:`dataset` will be loaded or created according to the following strategy:
If :attr:`dataset_file` is not ``None``, the :attr:`dataset` will be loaded from :attr:`dataset_file`.
If :attr:`dataset_file` is ``None`` and :attr:`dataloader_file` is ``None``,
the :attr:`dataset` will be created according to :attr:`config`.
If :attr:`dataset_file` is ``None`` and :attr:`dataloader_file` is not ``None``,
the :attr:`dataset` will neither be loaded or created.
The :attr:`dataloader` will be loaded or created according to the following strategy:
If :attr:`dataloader_file` is not ``None``, the :attr:`dataloader` will be loaded from :attr:`dataloader_file`.
If :attr:`dataloader_file` is ``None``, the :attr:`dataloader` will be created according to :attr:`config`.
Returns:
tuple:
- config (Config): An instance object of Config, which record parameter information in :attr:`model_file`.
- model (AbstractRecommender): The model load from :attr:`model_file`.
- dataset (Dataset): The filtered dataset.
- train_data (AbstractDataLoader): The dataloader for training.
- valid_data (AbstractDataLoader): The dataloader for validation.
- test_data (AbstractDataLoader): The dataloader for testing.
"""
checkpoint = torch.load(model_file)
if config_file_list is not None:
config = Config(model=model, dataset=dataset, config_file_list=config_file_list, config_dict=config_dict)
else:
config = checkpoint['config']
init_logger(config)
dataset = None
if dataset_file:
with open(dataset_file, 'rb') as f:
dataset = pickle.load(f)
if dataloader_file:
train_data, valid_data, test_data = load_split_dataloaders(config)
else:
if dataset is None:
dataset = create_dataset(config)
train_data, valid_data, test_data = data_preparation(config, dataset)
model = get_model(config['model'])(config, train_data.dataset).to(config['device'])
model.load_state_dict(checkpoint['state_dict'])
model.load_other_parameter(checkpoint.get('other_parameter'))
return config, model, dataset, train_data, valid_data, test_data
if __name__ == '__main__':
os.chdir(sys.path[0])
parser = argparse.ArgumentParser()
#parser.add_argument('--model', '-m', type=str, default='PFCN_PMF', help='name of models')
parser.add_argument('--model', '-m', type=str, default='ItemKNN', help='name of models')
parser.add_argument('--dataset', '-d', type=str, default='ml-1M', help='name of datasets')
parser.add_argument('--config_files', type=str, default='test.yaml', help='config files')
args, _ = parser.parse_known_args()
config_file_list = args.config_files.strip().split(' ') if args.config_files else None
model_file = get_latest_model_file(args.model, args.dataset)
results = run_recbole(model=args.model, dataset=args.dataset, config_file_list=config_file_list, model_file=model_file)
print('---'*10)
#print(results)