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FEA: example for session-based rec benchmarks
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# @Time : 2021/7/14 | ||
# @Author : Yupeng Hou | ||
# @Email : houyupeng@ruc.edu.cn | ||
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""" | ||
session-based recommendation example | ||
======================== | ||
Here is the sample code for running session-based recommendation benchmarks using RecBole. | ||
args.dataset can be one of diginetica-session/tmall-session/nowplaying-session | ||
""" | ||
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import argparse | ||
from logging import getLogger | ||
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from recbole.config import Config | ||
from recbole.data import create_dataset | ||
from recbole.data.utils import get_dataloader | ||
from recbole.utils import init_logger, init_seed, get_model, get_trainer, set_color | ||
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def get_args(): | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument('--model', '-m', type=str, default='GRU4Rec', help='Model for session-based rec.') | ||
parser.add_argument('--dataset', '-d', type=str, default='diginetica-session', help='Benchmarks for session-based rec.') | ||
parser.add_argument('--validation', action='store_true', help='Whether evaluating on validation set (split from train set), otherwise on test set.') | ||
parser.add_argument('--valid_portion', type=float, default=0.1, help='ratio of validation set.') | ||
return parser.parse_args() | ||
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if __name__ == '__main__': | ||
args = get_args() | ||
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# configurations initialization | ||
config_dict = { | ||
'USER_ID_FIELD': 'session_id', | ||
'load_col': None, | ||
'training_neg_sample_num': 0, | ||
'benchmark_filename': ['train', 'test'], | ||
'alias_of_item_id': ['item_id_list'], | ||
'topk': [20], | ||
'metrics': ['Recall', 'MRR'], | ||
'valid_metric': 'MRR@20' | ||
} | ||
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config = Config(model=args.model, dataset=f'{args.dataset}', config_dict=config_dict) | ||
init_seed(config['seed'], config['reproducibility']) | ||
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# logger initialization | ||
init_logger(config) | ||
logger = getLogger() | ||
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logger.info(args) | ||
logger.info(config) | ||
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# dataset filtering | ||
dataset = create_dataset(config) | ||
logger.info(dataset) | ||
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# dataset splitting | ||
train_dataset, test_dataset = dataset.build() | ||
if args.validation: | ||
train_dataset.shuffle() | ||
new_train_dataset, new_test_dataset = train_dataset.split_by_ratio([1 - args.valid_portion, args.valid_portion]) | ||
train_data = get_dataloader(config, 'train')(config, new_train_dataset, None, shuffle=True) | ||
test_data = get_dataloader(config, 'test')(config, new_test_dataset, None, shuffle=False) | ||
else: | ||
train_data = get_dataloader(config, 'train')(config, train_dataset, None, shuffle=True) | ||
test_data = get_dataloader(config, 'test')(config, test_dataset, None, shuffle=False) | ||
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# model loading and initialization | ||
model = get_model(config['model'])(config, train_data.dataset).to(config['device']) | ||
logger.info(model) | ||
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# trainer loading and initialization | ||
trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) | ||
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# model training and evaluation | ||
test_score, test_result = trainer.fit( | ||
train_data, test_data, saved=True, show_progress=config['show_progress'] | ||
) | ||
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logger.info(set_color('test result', 'yellow') + f': {test_result}') |