def predict(args):
# GPU / CPU
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
# load data
data = DataLoaderKGAT(args, logging)
user_ids = list(data.test_user_dict.keys())
user_ids_batches = [user_ids[i: i + args.test_batch_size] for i in range(0, len(user_ids), args.test_batch_size)]
user_ids_batches = [torch.LongTensor(d) for d in user_ids_batches]
if use_cuda:
user_ids_batches = [d.to(device) for d in user_ids_batches]
item_ids = torch.arange(data.n_items, dtype=torch.long)
if use_cuda:
item_ids = item_ids.to(device)
# load model
model = KGAT(args, data.n_users, data.n_entities, data.n_relations)
model = load_model(model, args.pretrain_model_path)
model.to(device)
# if n_gpu > 1:
# model = nn.parallel.DistributedDataParallel(model)
# move graph data to GPU
if use_cuda:
data.train_graph = data.train_graph.to(device)
# data.test_graph = data.test_graph.to(device)
train_graph = data.train_graph
# test_graph = data.test_graph
# predict
cf_scores, precision, recall, ndcg = evaluate(model, train_graph, data.train_user_dict, data.test_user_dict, user_ids_batches, item_ids, args.K)
np.save(args.save_dir + 'cf_scores.npy', cf_scores)
print('CF Evaluation: Precision {:.4f} Recall {:.4f} NDCG {:.4f}'.format(precision, recall, ndcg))
def train(args):
# seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# 创建日志文件
log_save_id = create_log_id(args.save_dir)
logging_config(folder=args.save_dir, name='log{:d}'.format(log_save_id), no_console=False)
logging.info(args)
# GPU / CPU
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
print('device:', device, 'n_gpu:', n_gpu)
# load data
print('load data ...')
data = DataLoaderKGAT(args, logging)
print('load data finish.')
# embedding
if args.use_pretrain == 1:
user_pre_embed = torch.tensor(data.user_pre_embed)
item_pre_embed = torch.tensor(data.item_pre_embed)
else:
user_pre_embed, item_pre_embed = None, None
user_ids = list(data.test_user_dict.keys())
user_ids_batches = [user_ids[i: i + args.test_batch_size] for i in range(0, len(user_ids), args.test_batch_size)]
user_ids_batches = [torch.LongTensor(d) for d in user_ids_batches]
if use_cuda:
user_ids_batches = [d.to(device) for d in user_ids_batches]
item_ids = torch.arange(data.n_items, dtype=torch.long)
if use_cuda:
item_ids = item_ids.to(device)
# construct model & optimizer
model = KGAT(args, data.n_users, data.n_entities, data.n_relations, user_pre_embed, item_pre_embed)
if args.use_pretrain == 2:
model = load_model(model, args.pretrain_model_path)
model.to(device)
# if n_gpu > 1:
# model = nn.parallel.DistributedDataParallel(model)
logging.info(model)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# move graph data to GPU
if use_cuda:
data.train_graph = data.train_graph.to(device)
# data.test_graph = data.test_graph.to(device)
train_graph = data.train_graph
# test_graph = data.test_graph
# initialize metrics
best_epoch = -1
epoch_list = []
precision_list = []
recall_list = []
ndcg_list = []
epoch = 0
# train model
for epoch in range(1, args.n_epoch + 1):
time0 = time()
model.train()
# update attention scores
with torch.no_grad():
att = model('calc_att', train_graph)
train_graph.edata['att'] = att
logging.info('Update attention scores: Epoch {:04d} | Total Time {:.1f}s'.format(epoch, time() - time0))
# train cf
time1 = time()
cf_total_loss = 0
n_cf_batch = data.n_cf_train // data.cf_batch_size + 1
for iter in range(1, n_cf_batch + 1):
time2 = time()
cf_batch_user, cf_batch_pos_item, cf_batch_neg_item = data.generate_cf_batch(data.train_user_dict)
if use_cuda:
cf_batch_user = cf_batch_user.to(device)
cf_batch_pos_item = cf_batch_pos_item.to(device)
cf_batch_neg_item = cf_batch_neg_item.to(device)
cf_batch_loss = model('calc_cf_loss', train_graph, cf_batch_user, cf_batch_pos_item, cf_batch_neg_item)
cf_batch_loss.backward()
optimizer.step()
optimizer.zero_grad()
cf_total_loss += cf_batch_loss.item()
if (iter % args.cf_print_every) == 0:
logging.info('CF Training: Epoch {:04d} Iter {:04d} / {:04d} | Time {:.1f}s | Iter Loss {:.4f} | Iter Mean Loss {:.4f}'.format(epoch, iter, n_cf_batch, time() - time2, cf_batch_loss.item(), cf_total_loss / iter))
logging.info('CF Training: Epoch {:04d} Total Iter {:04d} | Total Time {:.1f}s | Iter Mean Loss {:.4f}'.format(epoch, n_cf_batch, time() - time1, cf_total_loss / n_cf_batch))
# train kg
time1 = time()
kg_total_loss = 0
n_kg_batch = data.n_kg_train // data.kg_batch_size + 1
for iter in range(1, n_kg_batch + 1):
time2 = time()
kg_batch_head, kg_batch_relation, kg_batch_pos_tail, kg_batch_neg_tail = data.generate_kg_batch(data.train_kg_dict)
if use_cuda:
kg_batch_head = kg_batch_head.to(device)
kg_batch_relation = kg_batch_relation.to(device)
kg_batch_pos_tail = kg_batch_pos_tail.to(device)
kg_batch_neg_tail = kg_batch_neg_tail.to(device)
kg_batch_loss = model('calc_kg_loss', kg_batch_head, kg_batch_relation, kg_batch_pos_tail, kg_batch_neg_tail)
kg_batch_loss.backward()
optimizer.step()
optimizer.zero_grad()
kg_total_loss += kg_batch_loss.item()
if (iter % args.kg_print_every) == 0:
logging.info('KG Training: Epoch {:04d} Iter {:04d} / {:04d} | Time {:.1f}s | Iter Loss {:.4f} | Iter Mean Loss {:.4f}'.format(epoch, iter, n_kg_batch, time() - time2, kg_batch_loss.item(), kg_total_loss / iter))
logging.info('KG Training: Epoch {:04d} Total Iter {:04d} | Total Time {:.1f}s | Iter Mean Loss {:.4f}'.format(epoch, n_kg_batch, time() - time1, kg_total_loss / n_kg_batch))
logging.info('CF + KG Training: Epoch {:04d} | Total Time {:.1f}s'.format(epoch, time() - time0))
# evaluate cf
if (epoch % args.evaluate_every) == 0:
time1 = time()
_, precision, recall, ndcg = evaluate(model, train_graph, data.train_user_dict, data.test_user_dict, user_ids_batches, item_ids, args.K)
logging.info('CF Evaluation: Epoch {:04d} | Total Time {:.1f}s | Precision {:.4f} Recall {:.4f} NDCG {:.4f}'.format(epoch, time() - time1, precision, recall, ndcg))
epoch_list.append(epoch)
precision_list.append(precision)
recall_list.append(recall)
ndcg_list.append(ndcg)
best_recall, should_stop = early_stopping(recall_list, args.stopping_steps)
if should_stop:
break
if recall_list.index(best_recall) == len(recall_list) - 1:
save_model(model, args.save_dir, epoch, best_epoch)
logging.info('Save model on epoch {:04d}!'.format(epoch))
best_epoch = epoch
# save model
# save_model(model, args.save_dir, epoch)
# logging.info('Save model on epoch {:04d}!'.format(epoch))
#
# # save metrics
# _, precision, recall, ndcg = evaluate(model, train_graph, data.train_user_dict, data.test_user_dict, user_ids_batches, item_ids, args.K)
# logging.info('Final CF Evaluation: Precision {:.4f} Recall {:.4f} NDCG {:.4f}'.format(precision, recall, ndcg))
#
# epoch_list.append(epoch)
# precision_list.append(precision)
# recall_list.append(recall)
# ndcg_list.append(ndcg)
metrics = pd.DataFrame([epoch_list, precision_list, recall_list, ndcg_list]).transpose()
metrics.columns = ['epoch_idx', 'precision@{}'.format(args.K), 'recall@{}'.format(args.K), 'ndcg@{}'.format(args.K)]
metrics.to_csv(args.save_dir + '/metrics.tsv', sep='\t', index=False)