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run_finetune_sample.py
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run_finetune_sample.py
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# -*- coding: utf-8 -*-
# @Time : 2020/11/5 21:11
# @Author : Hui Wang
import os
import numpy as np
import random
import torch
import argparse
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from datasets import SASRecDataset
from trainers import FinetuneTrainer
from models import S3RecModel
from utils import EarlyStopping, get_user_seqs_and_sample, get_item2attribute_json, check_path, set_seed
def main():
parser = argparse.ArgumentParser()
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('--ckp', default=10, type=int, help="pretrain epochs 10, 20, 30...")
# model args
parser.add_argument("--model_name", default='Finetune_sample', type=str)
parser.add_argument("--hidden_size", type=int, default=64, help="hidden size of transformer model")
parser.add_argument("--num_hidden_layers", type=int, default=2, help="number of layers")
parser.add_argument('--num_attention_heads', default=2, type=int)
parser.add_argument('--hidden_act', default="gelu", type=str) # gelu relu
parser.add_argument("--attention_probs_dropout_prob", type=float, default=0.5, help="attention dropout p")
parser.add_argument("--hidden_dropout_prob", type=float, default=0.5, help="hidden dropout p")
parser.add_argument("--initializer_range", type=float, default=0.02)
parser.add_argument('--max_seq_length', default=50, type=int)
# train args
parser.add_argument("--lr", type=float, default=0.001, help="learning rate of adam")
parser.add_argument("--batch_size", type=int, default=256, help="number of batch_size")
parser.add_argument("--epochs", type=int, default=200, help="number of epochs")
parser.add_argument("--no_cuda", action="store_true")
parser.add_argument("--log_freq", type=int, default=1, help="per epoch print res")
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--weight_decay", type=float, default=0.0, help="weight_decay of adam")
parser.add_argument("--adam_beta1", type=float, default=0.9, help="adam first beta value")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="adam second beta value")
parser.add_argument("--gpu_id", type=str, default="0", help="gpu_id")
args = parser.parse_args()
set_seed(args.seed)
check_path(args.output_dir)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
args.cuda_condition = torch.cuda.is_available() and not args.no_cuda
args.data_file = args.data_dir + args.data_name + '.txt'
args.sample_file = args.data_dir + args.data_name + '_sample.txt'
item2attribute_file = args.data_dir + args.data_name + '_item2attributes.json'
user_seq, max_item, sample_seq = \
get_user_seqs_and_sample(args.data_file, args.sample_file)
item2attribute, attribute_size = get_item2attribute_json(item2attribute_file)
args.item_size = max_item + 2
args.mask_id = max_item + 1
args.attribute_size = attribute_size + 1
# save model args
args_str = f'{args.model_name}-{args.data_name}-{args.ckp}'
args.log_file = os.path.join(args.output_dir, args_str + '.txt')
print(str(args))
with open(args.log_file, 'a') as f:
f.write(str(args) + '\n')
args.item2attribute = item2attribute
# save model
checkpoint = args_str + '.pt'
args.checkpoint_path = os.path.join(args.output_dir, checkpoint)
train_dataset = SASRecDataset(args, user_seq, data_type='train')
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.batch_size)
eval_dataset = SASRecDataset(args, user_seq, test_neg_items=sample_seq, data_type='valid')
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.batch_size)
test_dataset = SASRecDataset(args, user_seq, test_neg_items=sample_seq, data_type='test')
test_sampler = SequentialSampler(test_dataset)
test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.batch_size)
model = S3RecModel(args=args)
trainer = FinetuneTrainer(model, train_dataloader, eval_dataloader,
test_dataloader, args)
if args.do_eval:
trainer.load(args.checkpoint_path)
print(f'Load model from {args.checkpoint_path} for test!')
scores, result_info = trainer.test(0, full_sort=False)
else:
pretrained_path = os.path.join(args.output_dir, f'{args.data_name}-epochs-{args.ckp}.pt')
try:
trainer.load(pretrained_path)
print(f'Load Checkpoint From {pretrained_path}!')
except FileNotFoundError:
print(f'{pretrained_path} Not Found! The Model is same as SASRec')
early_stopping = EarlyStopping(args.checkpoint_path, patience=10, verbose=True)
for epoch in range(args.epochs):
trainer.train(epoch)
scores, _ = trainer.valid(epoch, full_sort=False)
# evaluate on MRR
early_stopping(np.array(scores[-1:]), trainer.model)
if early_stopping.early_stop:
print("Early stopping")
break
print('---------------Sample 99 results-------------------')
# load the best model
trainer.model.load_state_dict(torch.load(args.checkpoint_path))
scores, result_info = trainer.test(0, full_sort=False)
print(args_str)
print(result_info)
with open(args.log_file, 'a') as f:
f.write(args_str + '\n')
f.write(result_info + '\n')
main()