def train(args):
print(args)
dataset = MovieLens(args.data_name,
args.device,
use_one_hot_fea=args.use_one_hot_fea,
symm=args.gcn_agg_norm_symm,
test_ratio=args.data_test_ratio,
valid_ratio=args.data_valid_ratio)
print("Loading data finished ...\n")
args.src_in_units = dataset.user_feature_shape[1]
args.dst_in_units = dataset.movie_feature_shape[1]
args.rating_vals = dataset.possible_rating_values
### build the net
net = Net(args=args)
net = net.to(args.device)
nd_possible_rating_values = th.FloatTensor(
dataset.possible_rating_values).to(args.device)
rating_loss_net = nn.CrossEntropyLoss()
learning_rate = args.train_lr
optimizer = get_optimizer(args.train_optimizer)(net.parameters(),
lr=learning_rate)
print("Loading network finished ...\n")
### perpare training data
train_gt_labels = dataset.train_labels
train_gt_ratings = dataset.train_truths
### prepare the logger
train_loss_logger = MetricLogger(
['iter', 'loss', 'rmse'], ['%d', '%.4f', '%.4f'],
os.path.join(args.save_dir, 'train_loss%d.csv' % args.save_id))
valid_loss_logger = MetricLogger(['iter', 'rmse'], ['%d', '%.4f'],
os.path.join(
args.save_dir,
'valid_loss%d.csv' % args.save_id))
test_loss_logger = MetricLogger(['iter', 'rmse'], ['%d', '%.4f'],
os.path.join(
args.save_dir,
'test_loss%d.csv' % args.save_id))
### declare the loss information
best_valid_rmse = np.inf
no_better_valid = 0
best_iter = -1
count_rmse = 0
count_num = 0
count_loss = 0
dataset.train_enc_graph = dataset.train_enc_graph.to(args.device)
dataset.train_dec_graph = dataset.train_dec_graph.to(args.device)
dataset.valid_enc_graph = dataset.train_enc_graph
dataset.valid_dec_graph = dataset.valid_dec_graph.to(args.device)
dataset.test_enc_graph = dataset.test_enc_graph.to(args.device)
dataset.test_dec_graph = dataset.test_dec_graph.to(args.device)
print("Start training ...")
dur = []
for iter_idx in range(1, args.train_max_iter):
if iter_idx > 3:
t0 = time.time()
net.train()
pred_ratings = net(dataset.train_enc_graph, dataset.train_dec_graph,
dataset.user_feature, dataset.movie_feature)
loss = rating_loss_net(pred_ratings, train_gt_labels).mean()
count_loss += loss.item()
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(net.parameters(), args.train_grad_clip)
optimizer.step()
if iter_idx > 3:
dur.append(time.time() - t0)
if iter_idx == 1:
print("Total #Param of net: %d" % (torch_total_param_num(net)))
print(
torch_net_info(net,
save_path=os.path.join(
args.save_dir, 'net%d.txt' % args.save_id)))
real_pred_ratings = (th.softmax(pred_ratings, dim=1) *
nd_possible_rating_values.view(1, -1)).sum(dim=1)
rmse = ((real_pred_ratings - train_gt_ratings)**2).sum()
count_rmse += rmse.item()
count_num += pred_ratings.shape[0]
if iter_idx % args.train_log_interval == 0:
train_loss_logger.log(iter=iter_idx,
loss=count_loss / (iter_idx + 1),
rmse=count_rmse / count_num)
logging_str = "Iter={}, loss={:.4f}, rmse={:.4f}, time={:.4f}".format(
iter_idx, count_loss / iter_idx, count_rmse / count_num,
np.average(dur))
count_rmse = 0
count_num = 0
if iter_idx % args.train_valid_interval == 0:
valid_rmse = evaluate(args=args,
net=net,
dataset=dataset,
segment='valid')
valid_loss_logger.log(iter=iter_idx, rmse=valid_rmse)
logging_str += ',\tVal RMSE={:.4f}'.format(valid_rmse)
if valid_rmse < best_valid_rmse:
best_valid_rmse = valid_rmse
no_better_valid = 0
best_iter = iter_idx
test_rmse = evaluate(args=args,
net=net,
dataset=dataset,
segment='test')
best_test_rmse = test_rmse
test_loss_logger.log(iter=iter_idx, rmse=test_rmse)
logging_str += ', Test RMSE={:.4f}'.format(test_rmse)
else:
no_better_valid += 1
if no_better_valid > args.train_early_stopping_patience\
and learning_rate <= args.train_min_lr:
logging.info(
"Early stopping threshold reached. Stop training.")
break
if no_better_valid > args.train_decay_patience:
new_lr = max(learning_rate * args.train_lr_decay_factor,
args.train_min_lr)
if new_lr < learning_rate:
learning_rate = new_lr
logging.info("\tChange the LR to %g" % new_lr)
for p in optimizer.param_groups:
p['lr'] = learning_rate
no_better_valid = 0
if iter_idx % args.train_log_interval == 0:
print(logging_str)
print('Best Iter Idx={}, Best Valid RMSE={:.4f}, Best Test RMSE={:.4f}'.
format(best_iter, best_valid_rmse, best_test_rmse))
train_loss_logger.close()
valid_loss_logger.close()
test_loss_logger.close()
def train(args):
print(args)
dataset = MovieLens(args.data_name,
args.ctx,
use_one_hot_fea=args.use_one_hot_fea,
symm=args.gcn_agg_norm_symm,
test_ratio=args.data_test_ratio,
valid_ratio=args.data_valid_ratio)
print("Loading data finished ...\n")
args.src_in_units = dataset.user_feature_shape[1]
args.dst_in_units = dataset.movie_feature_shape[1]
args.rating_vals = dataset.possible_rating_values
### build the net
net = Net(args=args)
net.initialize(init=mx.init.Xavier(factor_type='in'), ctx=args.ctx)
net.hybridize()
nd_possible_rating_values = mx.nd.array(dataset.possible_rating_values,
ctx=args.ctx,
dtype=np.float32)
rating_loss_net = gluon.loss.SoftmaxCELoss()
rating_loss_net.hybridize()
trainer = gluon.Trainer(net.collect_params(), args.train_optimizer,
{'learning_rate': args.train_lr})
print("Loading network finished ...\n")
### perpare training data
train_gt_labels = dataset.train_labels
train_gt_ratings = dataset.train_truths
### prepare the logger
train_loss_logger = MetricLogger(
['iter', 'loss', 'rmse'], ['%d', '%.4f', '%.4f'],
os.path.join(args.save_dir, 'train_loss%d.csv' % args.save_id))
valid_loss_logger = MetricLogger(['iter', 'rmse'], ['%d', '%.4f'],
os.path.join(
args.save_dir,
'valid_loss%d.csv' % args.save_id))
test_loss_logger = MetricLogger(['iter', 'rmse'], ['%d', '%.4f'],
os.path.join(
args.save_dir,
'test_loss%d.csv' % args.save_id))
### declare the loss information
best_valid_rmse = np.inf
no_better_valid = 0
best_iter = -1
avg_gnorm = 0
count_rmse = 0
count_num = 0
count_loss = 0
dataset.train_enc_graph = dataset.train_enc_graph.to(args.ctx)
dataset.train_dec_graph = dataset.train_dec_graph.to(args.ctx)
dataset.valid_enc_graph = dataset.train_enc_graph
dataset.valid_dec_graph = dataset.valid_dec_graph.to(args.ctx)
dataset.test_enc_graph = dataset.test_enc_graph.to(args.ctx)
dataset.test_dec_graph = dataset.test_dec_graph.to(args.ctx)
print("Start training ...")
dur = []
for iter_idx in range(1, args.train_max_iter):
if iter_idx > 3:
t0 = time.time()
with mx.autograd.record():
pred_ratings = net(dataset.train_enc_graph,
dataset.train_dec_graph, dataset.user_feature,
dataset.movie_feature)
loss = rating_loss_net(pred_ratings, train_gt_labels).mean()
loss.backward()
count_loss += loss.asscalar()
gnorm = params_clip_global_norm(net.collect_params(),
args.train_grad_clip, args.ctx)
avg_gnorm += gnorm
trainer.step(1.0)
if iter_idx > 3:
dur.append(time.time() - t0)
if iter_idx == 1:
print("Total #Param of net: %d" % (gluon_total_param_num(net)))
print(
gluon_net_info(net,
save_path=os.path.join(
args.save_dir, 'net%d.txt' % args.save_id)))
real_pred_ratings = (mx.nd.softmax(pred_ratings, axis=1) *
nd_possible_rating_values.reshape(
(1, -1))).sum(axis=1)
rmse = mx.nd.square(real_pred_ratings - train_gt_ratings).sum()
count_rmse += rmse.asscalar()
count_num += pred_ratings.shape[0]
if iter_idx % args.train_log_interval == 0:
train_loss_logger.log(iter=iter_idx,
loss=count_loss / (iter_idx + 1),
rmse=count_rmse / count_num)
logging_str = "Iter={}, gnorm={:.3f}, loss={:.4f}, rmse={:.4f}, time={:.4f}".format(
iter_idx, avg_gnorm / args.train_log_interval,
count_loss / iter_idx, count_rmse / count_num, np.average(dur))
avg_gnorm = 0
count_rmse = 0
count_num = 0
if iter_idx % args.train_valid_interval == 0:
valid_rmse = evaluate(args=args,
net=net,
dataset=dataset,
segment='valid')
valid_loss_logger.log(iter=iter_idx, rmse=valid_rmse)
logging_str += ',\tVal RMSE={:.4f}'.format(valid_rmse)
if valid_rmse < best_valid_rmse:
best_valid_rmse = valid_rmse
no_better_valid = 0
best_iter = iter_idx
#net.save_parameters(filename=os.path.join(args.save_dir, 'best_valid_net{}.params'.format(args.save_id)))
test_rmse = evaluate(args=args,
net=net,
dataset=dataset,
segment='test')
best_test_rmse = test_rmse
test_loss_logger.log(iter=iter_idx, rmse=test_rmse)
logging_str += ', Test RMSE={:.4f}'.format(test_rmse)
else:
no_better_valid += 1
if no_better_valid > args.train_early_stopping_patience\
and trainer.learning_rate <= args.train_min_lr:
logging.info(
"Early stopping threshold reached. Stop training.")
break
if no_better_valid > args.train_decay_patience:
new_lr = max(
trainer.learning_rate * args.train_lr_decay_factor,
args.train_min_lr)
if new_lr < trainer.learning_rate:
logging.info("\tChange the LR to %g" % new_lr)
trainer.set_learning_rate(new_lr)
no_better_valid = 0
if iter_idx % args.train_log_interval == 0:
print(logging_str)
print('Best Iter Idx={}, Best Valid RMSE={:.4f}, Best Test RMSE={:.4f}'.
format(best_iter, best_valid_rmse, best_test_rmse))
train_loss_logger.close()
valid_loss_logger.close()
test_loss_logger.close()