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main.py
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import os
import argparse
import logging
import time
import torch
from tqdm import tqdm
from data.multi_sess_graph import MultiSessionsGraph
from torch_geometric.data import DataLoader
from model.model import GraphModel
from train import forward
from tensorboardX import SummaryWriter
# Logger configuration
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(filename)s[line:%(lineno)d] %(message)s')
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='yoochoose1_64', help='dataset name: diginetica/yoochoose1_64/sample')
parser.add_argument('--batch_size', type=int, default=128, help='input batch size')
parser.add_argument('--hidden_size', type=int, default=100, help='hidden state size')
parser.add_argument('--epoch', type=int, default=15, help='the number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate') # [0.001, 0.0005, 0.0001]
parser.add_argument('--lr_dc', type=float, default=0.5, help='learning rate decay rate')
parser.add_argument('--lr_dc_step', type=int, default=4, help='the number of steps after which the learning rate decay')
parser.add_argument('--l2', type=float, default=1e-5, help='l2 penalty') # [0.001, 0.0005, 0.0001, 0.00005, 0.00001]
parser.add_argument('--top_k', type=int, default=20, help='top K indicator for evaluation')
parser.add_argument('--negative_slope', type=float, default=0.2, help='negative_slope')
parser.add_argument('--gat_dropout', type=float, default=0.6, help='dropout rate in gat')
parser.add_argument('--heads', type=int, default=8, help='gat heads number')
parser.add_argument('--num_filters', type=int, default=2, help='gat heads number')
parser.add_argument('--using_represent', type=str, default='comb', help='comb, h_s, h_group')
parser.add_argument('--predict', type=bool, default=False, help='gat heads number')
parser.add_argument('--item_fusing', type=bool, default=True, help='gat heads number')
parser.add_argument('--random_seed', type=int, default=24, help='input batch size')
parser.add_argument('--id', type=int, default=120, help='id')
opt = parser.parse_args()
logging.warning(opt)
def main():
torch.manual_seed(opt.random_seed)
torch.cuda.manual_seed(opt.random_seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# device = torch.device('cpu')
cur_dir = os.getcwd()
train_dataset = MultiSessionsGraph(cur_dir + '/datasets/' + opt.dataset, phrase='train', knn_phrase='neigh_data_'+str(opt.id))
train_loader = DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True)
test_dataset = MultiSessionsGraph(cur_dir + '/datasets/' + opt.dataset, phrase='test', knn_phrase='neigh_data_'+str(opt.id))
test_loader = DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False)
log_dir = cur_dir + '/log/' + str(opt.dataset) + '/' + time.strftime(
"%Y-%m-%d %H:%M:%S", time.localtime())
if not os.path.exists(log_dir):
os.makedirs(log_dir)
logging.warning('logging to {}'.format(log_dir))
writer = SummaryWriter(log_dir)
if opt.dataset == 'cikm16':
n_node = 43097
elif opt.dataset == 'yoochoose1_64':
n_node = 17400
else:
n_node = 309
model = GraphModel(opt, n_node=n_node).to(device)
multigraph_parameters = list(map(id, model.group_graph.parameters()))
srgnn_parameters = (p for p in model.parameters() if id(p) not in multigraph_parameters)
parameters = [{"params": model.group_graph.parameters(), "lr": 0.001}, {"params": srgnn_parameters}]
# best 0.1
lambda1 = lambda epoch: 0.1 ** (epoch // 3)
lambda2 = lambda epoch: 0.1 ** (epoch // 3)
optimizer = torch.optim.Adam(parameters, lr=opt.lr, weight_decay=opt.l2)
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=opt.lr_dc_step, gamma=opt.lr_dc)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
logging.warning(model)
if not opt.predict:
best_result20 = [0, 0]
best_epoch20 = [0, 0]
best_result10 = [0, 0]
best_epoch10 = [0, 0]
best_result5 = [0, 0]
best_epoch5 = [0, 0]
for epoch in range(opt.epoch):
scheduler.step(epoch)
print("Epoch ", epoch)
forward(model, train_loader, device, writer, epoch, top_k=opt.top_k, optimizer=optimizer, train_flag=True)
with torch.no_grad():
mrr20, hit20, mrr10, hit10, mrr5, hit5 = forward(model, test_loader, device, writer, epoch, top_k=opt.top_k, train_flag=False)
if hit20 >= best_result20[0]:
best_result20[0] = hit20
best_epoch20[0] = epoch
# torch.save(model.state_dict(), log_dir+'/best_recall_params.pkl')
if mrr20 >= best_result20[1]:
best_result20[1] = mrr20
best_epoch20[1] = epoch
if hit10 >= best_result10[0]:
best_result10[0] = hit10
best_epoch10[0] = epoch
# torch.save(model.state_dict(), log_dir+'/best_recall_params.pkl')
if mrr10 >= best_result10[1]:
best_result10[1] = mrr10
best_epoch10[1] = epoch
# torch.save(model.state_dict(), log_dir+'/best_mrr_params.pkl')
if hit5 >= best_result5[0]:
best_result5[0] = hit5
best_epoch5[0] = epoch
# torch.save(model.state_dict(), log_dir+'/best_recall_params.pkl')
if mrr5 >= best_result5[1]:
best_result5[1] = mrr5
best_epoch5[1] = epoch
print('Best Result:')
print('\tMrr@%d:\t%.4f\tEpoch:\t%d' % (20, best_result20[1], best_epoch20[1]))
print('\tRecall@%d:\t%.4f\tEpoch:\t%d\n' % (20, best_result20[0], best_epoch20[0]))
print('\tMrr@%d:\t%.4f\tEpoch:\t%d' % (opt.top_k, best_result10[1], best_epoch10[1]))
print('\tRecall@%d:\t%.4f\tEpoch:\t%d\n' % (opt.top_k, best_result10[0], best_epoch10[0]))
print('\tMrr@%d:\t%.4f\tEpoch:\t%d' % (5, best_result5[1], best_epoch5[1]))
print('\tRecall@%d:\t%.4f\tEpoch:\t%d' % (5, best_result5[0], best_epoch5[0]))
print("-"*20)
# print_txt(log_dir, opt, best_result, best_epoch, opt.top_k, note, save_config=True)
else:
log_dir = 'log/cikm16/2019-08-19 14:27:33'
model.load_state_dict(torch.load(log_dir+'/best_mrr_params.pkl'))
mrr, hit = forward(model, test_loader, device, writer, 0, top_k=opt.top_k, train_flag=False)
best_result = [hit, mrr]
best_epoch = [0, 0]
# print_txt(log_dir, opt, best_result, best_epoch, opt.top_k, save_config=False)
if __name__ == '__main__':
main()