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main.py
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main.py
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'''
Created on July 1, 2020
@author: Tinglin Huang ([email protected])
'''
__author__ = "huangtinglin"
import random
import torch
import numpy as np
from time import time
from prettytable import PrettyTable
from utils.parser import parse_args
from utils.data_loader import load_data
from modules.KGIN import Recommender
from utils.evaluate import test
from utils.helper import early_stopping
n_users = 0
n_items = 0
n_entities = 0
n_nodes = 0
n_relations = 0
def get_feed_dict(train_entity_pairs, start, end, train_user_set):
def negative_sampling(user_item, train_user_set):
neg_items = []
for user, _ in user_item.cpu().numpy():
user = int(user)
while True:
neg_item = np.random.randint(low=0, high=n_items, size=1)[0]
if neg_item not in train_user_set[user]:
break
neg_items.append(neg_item)
return neg_items
feed_dict = {}
entity_pairs = train_entity_pairs[start:end].to(device)
feed_dict['users'] = entity_pairs[:, 0]
feed_dict['pos_items'] = entity_pairs[:, 1]
feed_dict['neg_items'] = torch.LongTensor(negative_sampling(entity_pairs,
train_user_set)).to(device)
return feed_dict
if __name__ == '__main__':
"""fix the random seed"""
seed = 2020
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
"""read args"""
global args, device
args = parse_args()
device = torch.device("cuda:"+str(args.gpu_id)) if args.cuda else torch.device("cpu")
"""build dataset"""
train_cf, test_cf, user_dict, n_params, graph, mat_list = load_data(args)
adj_mat_list, norm_mat_list, mean_mat_list = mat_list
n_users = n_params['n_users']
n_items = n_params['n_items']
n_entities = n_params['n_entities']
n_relations = n_params['n_relations']
n_nodes = n_params['n_nodes']
"""cf data"""
train_cf_pairs = torch.LongTensor(np.array([[cf[0], cf[1]] for cf in train_cf], np.int32))
test_cf_pairs = torch.LongTensor(np.array([[cf[0], cf[1]] for cf in test_cf], np.int32))
"""define model"""
model = Recommender(n_params, args, graph, mean_mat_list[0]).to(device)
"""define optimizer"""
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
cur_best_pre_0 = 0
stopping_step = 0
should_stop = False
print("start training ...")
for epoch in range(args.epoch):
"""training CF"""
# shuffle training data
index = np.arange(len(train_cf))
np.random.shuffle(index)
train_cf_pairs = train_cf_pairs[index]
"""training"""
loss, s, cor_loss = 0, 0, 0
train_s_t = time()
while s + args.batch_size <= len(train_cf):
batch = get_feed_dict(train_cf_pairs,
s, s + args.batch_size,
user_dict['train_user_set'])
batch_loss, _, _, batch_cor = model(batch)
batch_loss = batch_loss
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
loss += batch_loss
cor_loss += batch_cor
s += args.batch_size
train_e_t = time()
if epoch % 10 == 9 or epoch == 1:
"""testing"""
test_s_t = time()
ret = test(model, user_dict, n_params)
test_e_t = time()
train_res = PrettyTable()
train_res.field_names = ["Epoch", "training time", "tesing time", "Loss", "recall", "ndcg", "precision", "hit_ratio"]
train_res.add_row(
[epoch, train_e_t - train_s_t, test_e_t - test_s_t, loss.item(), ret['recall'], ret['ndcg'], ret['precision'], ret['hit_ratio']]
)
print(train_res)
# *********************************************************
# early stopping when cur_best_pre_0 is decreasing for ten successive steps.
cur_best_pre_0, stopping_step, should_stop = early_stopping(ret['recall'][0], cur_best_pre_0,
stopping_step, expected_order='acc',
flag_step=10)
if should_stop:
break
"""save weight"""
if ret['recall'][0] == cur_best_pre_0 and args.save:
torch.save(model.state_dict(), args.out_dir + 'model_' + args.dataset + '.ckpt')
else:
# logging.info('training loss at epoch %d: %f' % (epoch, loss.item()))
print('using time %.4f, training loss at epoch %d: %.4f, cor: %.6f' % (train_e_t - train_s_t, epoch, loss.item(), cor_loss.item()))
print('early stopping at %d, recall@20:%.4f' % (epoch, cur_best_pre_0))