def train(model, args):
logging.info('args: %s' % str(args))
logging.info('model: %s, setup: %s' %
(type(model).__name__, str(model.args)))
logging.info('loading dataset')
data = get_dataset(args.dataset)
data.random_level = args.random_level
if args.split_method == 'user':
data, _ = data.split_user(args.frac)
elif args.split_method == 'future':
data, _ = data.split_future(args.frac)
elif args.split_method == 'old':
data, _, _, _ = data.split()
data = data.get_seq()
if type(model).__name__.startswith('DK'):
topic_dic = {}
kcat = Categorical(one_hot=True)
kcat.load_dict(open('data/know_list.txt').read().split('\n'))
for line in open('data/id_know.txt'):
uuid, know = line.strip().split(' ')
know = know.split(',')
topic_dic[uuid] = \
torch.LongTensor(kcat.apply(None, know)) \
.max(0)[0] \
.type(torch.LongTensor)
zero = [0] * len(kcat.apply(None, '<NULL>'))
else:
topics = get_topics(args.dataset, model.words)
optimizer = torch.optim.Adam(model.parameters())
start_epoch = load_last_snapshot(model, args.workspace)
if use_cuda:
model.cuda()
for epoch in range(start_epoch, args.epochs):
logging.info(('epoch {}:'.format(epoch)))
then = time.time()
total_loss = 0
total_mae = 0
total_acc = 0
total_seq_cnt = 0
users = list(data)
random.shuffle(users)
seq_cnt = len(users)
MSE = torch.nn.MSELoss()
MAE = torch.nn.L1Loss()
for user in users:
total_seq_cnt += 1
seq = data[user]
length = len(seq)
optimizer.zero_grad()
loss = 0
mae = 0
acc = 0
h = None
for i, item in enumerate(seq):
if type(model).__name__.startswith('DK'):
if item.topic in topic_dic:
x = topic_dic[item.topic]
else:
x = zero
else:
x = topics.get(item.topic).content
x = Variable(torch.LongTensor(x))
# print(x.size())
score = Variable(torch.FloatTensor([round(item.score)]))
t = Variable(torch.FloatTensor([item.time]))
s, h = model(x, score, t, h)
if args.loss == 'cross_entropy':
loss += F.binary_cross_entropy_with_logits(
s, score.view_as(s))
m = MAE(F.sigmoid(s), score).data[0]
else:
loss += MSE(s, score)
m = MAE(s, score).data[0]
mae += m
acc += m < 0.5
loss /= length
mae /= length
acc /= length
total_loss += loss.data[0]
total_mae += mae
total_acc += acc
loss.backward()
optimizer.step()
if total_seq_cnt % args.save_every == 0:
save_snapshot(model, args.workspace,
'%d.%d' % (epoch, total_seq_cnt))
if total_seq_cnt % args.print_every != 0 and \
total_seq_cnt != seq_cnt:
continue
now = time.time()
duration = (now - then) / 60
logging.info(
'[%d:%d/%d] (%.2f seqs/min) '
'loss %.6f, mae %.6f, acc %.6f' %
(epoch, total_seq_cnt, seq_cnt,
((total_seq_cnt - 1) % args.print_every + 1) / duration,
total_loss / total_seq_cnt, total_mae / total_seq_cnt,
total_acc / total_seq_cnt))
then = now
save_snapshot(model, args.workspace, epoch + 1)
def trainn(model, args):
logging.info('model: %s, setup: %s' %
(type(model).__name__, str(model.args)))
logging.info('loading dataset')
data = get_dataset(args.dataset)
data.random_level = args.random_level
if args.split_method == 'user':
data, _ = data.split_user(args.frac)
elif args.split_method == 'future':
data, _ = data.split_future(args.frac)
elif args.split_method == 'old':
data, _, _, _ = data.split()
data = data.get_seq()
if args.input_knowledge:
logging.info('loading knowledge concepts')
topic_dic = {}
kcat = Categorical(one_hot=True)
kcat.load_dict(open(model.args['knows']).read().split('\n'))
know = 'data/id_firstknow.txt' if 'first' in model.args['knows'] \
else 'data/id_know.txt'
for line in open(know):
uuid, know = line.strip().split(' ')
know = know.split(',')
topic_dic[uuid] = torch.LongTensor(kcat.apply(None,
know)).max(0)[0]
zero = [0] * len(kcat.apply(None, '<NULL>'))
if args.input_text:
logging.info('loading exercise texts')
topics = get_topics(args.dataset, model.words)
optimizer = torch.optim.Adam(model.parameters())
start_epoch = load_last_snapshot(model, args.workspace)
if use_cuda:
model.cuda()
for epoch in range(start_epoch, args.epochs):
logging.info('epoch {}:'.format(epoch))
then = time.time()
total_loss = 0
total_mae = 0
total_acc = 0
total_seq_cnt = 0
users = list(data)
random.shuffle(users)
seq_cnt = len(users)
MSE = torch.nn.MSELoss()
MAE = torch.nn.L1Loss()
for user in users:
total_seq_cnt += 1
seq = data[user]
seq_length = len(seq)
optimizer.zero_grad()
loss = 0
mae = 0
acc = 0
h = None
for i, item in enumerate(seq):
# score = round(item.score)
if args.input_knowledge:
if item.topic in topic_dic:
knowledge = topic_dic[item.topic]
else:
knowledge = zero
# knowledge = torch.LongTensor(knowledge).view(-1).type(torch.FloatTensor)
# one_index = torch.nonzero(knowledge).view(-1)
# expand_vec = torch.zeros(knowledge.size()).view(-1)
# expand_vec[one_index] = score
# cks = torch.cat([knowledge, expand_vec]).view(1, -1)
knowledge = Variable(torch.LongTensor(knowledge))
# cks = Variable(cks)
if args.input_text:
text = topics.get(item.topic).content
text = Variable(torch.LongTensor(text))
score = Variable(torch.FloatTensor([item.score]))
item_time = Variable(torch.FloatTensor([item.time]))
if type(model).__name__.startswith('DK'):
s, h = model(knowledge, score, item_time, h)
elif type(model).__name__.startswith('RA'):
s, h = model(text, score, item_time, h)
elif type(model).__name__.startswith('EK'):
s, h = model(text, knowledge, score, item_time, h)
s = s[0]
if args.loss == 'cross_entropy':
loss += F.binary_cross_entropy_with_logits(
s, score.view_as(s))
m = MAE(F.sigmoid(s), score).data[0]
else:
loss += MSE(s, score)
m = MAE(s, score).data[0]
mae += m
acc += m < 0.5
loss /= seq_length
mae /= seq_length
acc = float(acc) / seq_length
total_loss += loss.data[0]
total_mae += mae
total_acc += acc
loss.backward()
optimizer.step()
if total_seq_cnt % args.save_every == 0:
save_snapshot(model, args.workspace,
'%d.%d' % (epoch, total_seq_cnt))
if total_seq_cnt % args.print_every != 0 and total_seq_cnt != seq_cnt:
continue
now = time.time()
duration = (now - then) / 60
logging.info(
'[%d:%d/%d] (%.2f seqs/min) loss %.6f, mae %.6f, acc %.6f' %
(epoch, total_seq_cnt, seq_cnt,
((total_seq_cnt - 1) % args.print_every + 1) / duration,
total_loss / total_seq_cnt, total_mae / total_seq_cnt,
total_acc / total_seq_cnt))
then = now
save_snapshot(model, args.workspace, epoch + 1)