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utils.py
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import os
import random
import torch
import datetime
import argparse
import numpy as np
import logging
def set_logger(log_path, log_name='seqrec', mode='a'):
"""set up log file
mode : 'a'/'w' mean append/overwrite,
"""
logger = logging.getLogger(log_name)
logger.setLevel(logging.INFO)
fh = logging.FileHandler(log_path, mode=mode)
fh.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.propagate = False
return logger
def set_seed(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
def check_path(path):
if not os.path.exists(path):
os.makedirs(path)
print(f'{path} created')
def get_local_time():
cur = datetime.datetime.now()
cur = cur.strftime('%b-%d-%Y_%H-%M-%S')
return cur
def parse_args():
parser = argparse.ArgumentParser()
# basic args
parser.add_argument("--data_dir", default="./data/", type=str)
parser.add_argument("--output_dir", default="output/", type=str)
parser.add_argument("--data_name", default="Beauty", type=str)
parser.add_argument("--do_eval", action="store_true")
parser.add_argument("--load_model", default=None, type=str)
parser.add_argument("--train_name", default=get_local_time(), type=str)
parser.add_argument("--num_items", default=10, type=int)
parser.add_argument("--num_users", default=10, type=int)
# train args
parser.add_argument("--lr", default=0.001, type=float, help="learning rate of adam")
parser.add_argument("--batch_size", default=256, type=int, help="number of batch_size")
parser.add_argument("--epochs", default=200, type=int, help="number of epochs")
parser.add_argument("--no_cuda", action="store_true")
parser.add_argument("--log_freq", default=1, type=int, help="per epoch print res")
parser.add_argument("--patience", default=10, type=int, help="how long to wait after last time validation loss improved")
parser.add_argument("--num_workers", default=4, type=int)
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--weight_decay", default=0.0, type=float, help="weight_decay of adam")
parser.add_argument("--adam_beta1", default=0.9, type=float, help="adam first beta value")
parser.add_argument("--adam_beta2", default=0.999, type=float, help="adam second beta value")
parser.add_argument("--gpu_id", default="0", type=str, help="gpu_id")
parser.add_argument("--variance", default=5, type=float)
# model args
parser.add_argument("--model_type", default='BSARec', type=str)
parser.add_argument("--max_seq_length", default=50, type=int)
parser.add_argument("--hidden_size", default=64, type=int, help="embedding dimension")
parser.add_argument("--num_hidden_layers", default=2, type=int, help="number of blocks")
parser.add_argument("--hidden_act", default="gelu", type=str) # gelu relu
parser.add_argument("--num_attention_heads", default=2, type=int)
parser.add_argument("--attention_probs_dropout_prob", default=0.5, type=float)
parser.add_argument("--hidden_dropout_prob", default=0.5, type=float)
parser.add_argument("--initializer_range", default=0.02, type=float)
args, _ = parser.parse_known_args()
if args.model_type.lower() == 'bsarec':
parser.add_argument("--c", default=3, type=int)
parser.add_argument("--alpha", default=0.9, type=float)
elif args.model_type.lower() == 'bert4rec':
parser.add_argument("--mask_ratio", default=0.2, type=float)
elif args.model_type.lower() == 'caser':
parser.add_argument("--nh", default=8, type=int)
parser.add_argument("--nv", default=4, type=int)
parser.add_argument("--reg_weight", default=1e-4, type=float)
elif args.model_type.lower() == 'duorec':
parser.add_argument("--tau", default=1.0, type=float)
parser.add_argument("--lmd", default=0.1, type=float)
parser.add_argument("--lmd_sem", default=0.1, type=float)
parser.add_argument("--ssl", default='us_x', type=str)
parser.add_argument("--sim", default='dot', type=str)
elif args.model_type.lower() == 'fearec':
parser.add_argument("--tau", default=1.0, type=float)
parser.add_argument("--lmd", default=0.1, type=float)
parser.add_argument("--lmd_sem", default=0.1, type=float)
parser.add_argument("--ssl", default='us_x', type=str)
parser.add_argument("--sim", default='dot', type=str)
parser.add_argument("--spatial_ratio", default=0.1, type=float)
parser.add_argument("--global_ratio", default=0.6, type=float)
parser.add_argument("--fredom_type", default='us_x', type=str)
parser.add_argument("--fredom", default='True', type=str) # use eval function to use as boolean
elif args.model_type.lower() == 'gru4rec':
parser.add_argument("--gru_hidden_size", default=64, type=int, help="hidden size of GRU")
return parser.parse_args()
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, checkpoint_path, logger, patience=10, verbose=False, delta=0):
"""
Args:
patience (int): How long to wait after last time validation loss improved.
Default: 10
verbose (bool): If True, prints a message for each validation loss improvement.
Default: False
delta (float): Minimum change in the monitored quantity to qualify as an improvement.
Default: 0
"""
self.checkpoint_path = checkpoint_path
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.delta = delta
self.logger = logger
def compare(self, score):
for i in range(len(score)):
if score[i] > self.best_score[i]+self.delta:
return False
return True
def __call__(self, score, model):
if self.best_score is None:
self.best_score = score
self.score_min = np.array([0]*len(score))
self.save_checkpoint(score, model)
elif self.compare(score):
self.counter += 1
self.logger.info(f'EarlyStopping counter: {self.counter} out of {self.patience}')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(score, model)
self.counter = 0
def save_checkpoint(self, score, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
self.logger.info(f'Validation score increased. Saving model ...')
torch.save(model.state_dict(), self.checkpoint_path)
self.score_min = score