def test_ranking(model, test_batches):
num_batches = len(test_batches)
map, ndcg_1, ndcg_3, ndcg_10 = 0, 0, 0, 0
for batch_no in range(1, num_batches + 1):
test_sessions, length, test_clicks, click_labels = helper.session_to_tensor(test_batches[batch_no - 1],
model.dictionary)
if model.config.cuda:
test_sessions = test_sessions.cuda()
test_clicks = test_clicks.cuda()
click_labels = click_labels.cuda()
ret_val = compute_ranking_performance(model, test_sessions, test_clicks, click_labels)
map += ret_val[0]
ndcg_1 += ret_val[1]
ndcg_3 += ret_val[2]
ndcg_10 += ret_val[3]
map = map / num_batches
ndcg_1 = ndcg_1 / num_batches
ndcg_3 = ndcg_3 / num_batches
ndcg_10 = ndcg_10 / num_batches
print('MAP - ', map)
print('NDCG@1 - ', ndcg_1)
print('NDCG@3 - ', ndcg_3)
print('NDCG@10 - ', ndcg_10)
def evaluate(model, dictionary, session_queries):
sess = Session()
sess.queries = session_queries
session_tensor, query_lengths = helper.session_to_tensor([sess],
dictionary, True)
if args.cuda:
session_tensor = session_tensor.cuda()
query_lengths = query_lengths.cuda()
return suggest_next_query(model, dictionary, session_tensor, query_lengths)
def evaluate(model, dictionary, session_queries):
session = Session()
session.queries = session_queries
session_queries, session_query_length, rel_docs, rel_docs_length, doc_labels = helper.session_to_tensor(
[session], dictionary, iseval=True)
if model.config.cuda:
session_queries = session_queries.cuda()
session_query_length = session_query_length.cuda()
return suggest_next_query(model, session_queries, session_query_length, dictionary)
def evaluate(model, dictionary, session_queries):
session_queries, session_query_length, rel_docs, rel_docs_length, doc_labels = helper.session_to_tensor(
[session_queries], dictionary, True)
if model.config.cuda:
session_queries = session_queries.cuda()
session_query_length = session_query_length.cuda()
output_words = suggest_next_query(model, session_queries,
session_query_length)
return " ".join(output_words[:-1])
def train(self, train_corpus):
# Turn on training mode which enables dropout.
self.model.train()
# splitting the data in batches
train_batches = helper.batchify(train_corpus.data,
self.config.batch_size)
print('number of train batches = ', len(train_batches))
start = time.time()
print_loss_total = 0
plot_loss_total = 0
num_batches = len(train_batches)
for batch_no in range(1, num_batches + 1):
# Clearing out all previous gradient computations.
self.optimizer.zero_grad()
train_sessions, length, train_clicks, click_labels = helper.session_to_tensor(
train_batches[batch_no - 1], self.dictionary)
if self.config.cuda:
# batch_size x session_length x max_query_length
train_sessions = train_sessions.cuda()
# batch_size x session_length x num_clicks_per_query x max_document_length
train_clicks = train_clicks.cuda()
# batch_size x session_length
length = length.cuda()
# batch_size x session_length x num_clicks_per_query
click_labels = click_labels.cuda()
loss = self.model(train_sessions, length, train_clicks,
click_labels)
# Important if we are using nn.DataParallel()
if loss.size(0) > 1:
loss = loss.mean()
loss.backward()
self.optimizer.step()
print_loss_total += loss.data[0]
plot_loss_total += loss.data[0]
if batch_no % self.config.print_every == 0:
print_loss_avg = print_loss_total / self.config.print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' %
(helper.show_progress(start, batch_no / num_batches),
batch_no, batch_no / num_batches * 100, print_loss_avg))
if batch_no % self.config.plot_every == 0:
plot_loss_avg = plot_loss_total / self.config.plot_every
self.train_losses.append(plot_loss_avg)
plot_loss_total = 0
def train(self, train_corpus):
# Turn on training mode which enables dropout.
self.model.train()
# splitting the data in batches
train_batches = helper.batchify(train_corpus.data,
self.config.batch_size)
print('number of train batches = ', len(train_batches))
start = time.time()
print_loss_total = 0
plot_loss_total = 0
num_batches = len(train_batches)
for batch_no in range(1, num_batches + 1):
# Clearing out all previous gradient computations.
self.optimizer.zero_grad()
session_queries, session_query_length = helper.session_to_tensor(
train_batches[batch_no - 1], self.dictionary)
if self.config.cuda:
# batch_size x session_length x max_query_length
session_queries = session_queries.cuda()
# batch_size x session_length
session_query_length = session_query_length.cuda()
loss = self.model(session_queries, session_query_length)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs.
clip_grad_norm(
filter(lambda p: p.requires_grad, self.model.parameters()),
self.config.max_norm)
self.optimizer.step()
print_loss_total += loss.data[0]
plot_loss_total += loss.data[0]
if batch_no % self.config.print_every == 0:
print_loss_avg = print_loss_total / self.config.print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' %
(helper.show_progress(start, batch_no / num_batches),
batch_no, batch_no / num_batches * 100, print_loss_avg))
if batch_no % self.config.plot_every == 0:
plot_loss_avg = plot_loss_total / self.config.plot_every
self.train_losses.append(plot_loss_avg)
plot_loss_total = 0
def test_loss(model, test_batches):
num_batches = len(test_batches)
test_loss = 0
for batch_no in range(1, num_batches + 1):
test_sessions, length, test_clicks, click_labels = helper.session_to_tensor(test_batches[batch_no - 1],
model.dictionary)
if model.config.cuda:
test_sessions = test_sessions.cuda()
test_clicks = test_clicks.cuda()
length = length.cuda()
click_labels = click_labels.cuda()
loss = model(test_sessions, length, test_clicks, click_labels)
if loss.size(0) > 1:
loss = torch.mean(loss)
test_loss += loss.data[0]
print('test loss - ', (test_loss / num_batches))
def validate(self, dev_corpus):
# Turn on evaluation mode which disables dropout.
self.model.eval()
dev_batches = helper.batchify(dev_corpus.data, self.config.batch_size)
print('number of dev batches = ', len(dev_batches))
dev_loss = 0
num_batches = len(dev_batches)
for batch_no in range(1, num_batches + 1):
session_queries, session_query_length = helper.session_to_tensor(
dev_batches[batch_no - 1], self.dictionary, True)
if self.config.cuda:
session_queries = session_queries.cuda()
session_query_length = session_query_length.cuda()
loss = self.model(session_queries, session_query_length)
dev_loss += loss.data[0]
return dev_loss / num_batches
def validate(self, dev_batches):
# Turn on evaluation mode which disables dropout.
self.model.eval()
dev_loss = 0
num_batches = len(dev_batches)
for batch_no in range(1, num_batches + 1):
dev_sessions, length = helper.session_to_tensor(
dev_batches[batch_no - 1], self.dictionary)
if self.config.cuda:
dev_sessions = dev_sessions.cuda()
length = length.cuda()
loss = self.model(dev_sessions, length)
if loss.size(0) > 1:
loss = torch.mean(loss)
dev_loss += loss.data[0]
# Turn on training mode at the end of validation.
self.model.train()
return dev_loss / num_batches
def validate(self, dev_corpus):
# Turn on evaluation mode which disables dropout.
self.model.eval()
dev_batches = helper.batchify(dev_corpus.data, self.config.batch_size)
print('number of dev batches = ', len(dev_batches))
dev_loss = 0
num_batches = len(dev_batches)
for batch_no in range(1, num_batches + 1):
dev_sessions, length, dev_clicks, click_labels = helper.session_to_tensor(
dev_batches[batch_no - 1], self.dictionary)
if self.config.cuda:
dev_sessions = dev_sessions.cuda()
dev_clicks = dev_clicks.cuda()
length = length.cuda()
click_labels = click_labels.cuda()
loss = self.model(dev_sessions, length, dev_clicks, click_labels)
if loss.size(0) > 1:
loss = loss.mean()
dev_loss += loss.data[0]
return dev_loss / num_batches
def train(self, train_corpus):
# Turn on training mode which enables dropout.
self.model.train()
# splitting the data in batches
train_batches = helper.batchify(train_corpus.data,
self.config.batch_size)
print('number of train batches = ', len(train_batches))
start = time.time()
plot_loss_total = 0
print_click_loss, print_decoding_loss = 0, 0
num_batches = len(train_batches)
for batch_no in range(1, num_batches + 1):
# Clearing out all previous gradient computations.
self.optimizer.zero_grad()
session_queries, session_query_length, rel_docs, rel_docs_length, doc_labels = helper.session_to_tensor(
train_batches[batch_no - 1], self.dictionary)
if self.config.cuda:
# batch_size x session_length x max_query_length
session_queries = session_queries.cuda()
# batch_size x session_length
session_query_length = session_query_length.cuda()
# batch_size x session_length x num_rel_docs_per_query x max_doc_length
rel_docs = rel_docs.cuda()
# batch_size x session_length x num_rel_docs_per_query
rel_docs_length = rel_docs_length.cuda()
# batch_size x session_length x num_rel_docs_per_query
doc_labels = doc_labels.cuda()
click_loss, decoding_loss = self.model(session_queries,
session_query_length,
rel_docs, rel_docs_length,
doc_labels)
loss = click_loss + decoding_loss
if click_loss.size(0) > 1:
click_loss = click_loss.mean()
if decoding_loss.size(0) > 1:
decoding_loss = decoding_loss.mean()
print_click_loss += click_loss.data[0]
print_decoding_loss += decoding_loss.data[0]
# Important if we are using nn.DataParallel()
if loss.size(0) > 1:
loss = loss.mean()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs.
clip_grad_norm(
filter(lambda p: p.requires_grad, self.model.parameters()),
self.config.max_norm)
self.optimizer.step()
plot_loss_total += loss.data[0]
if batch_no % self.config.print_every == 0:
click_loss_avg = print_click_loss / self.config.print_every
decoding_loss_avg = print_decoding_loss / self.config.print_every
print_click_loss, print_decoding_loss = 0, 0
print('%s (%d %d%%) %.4f %.4f' %
(helper.show_progress(start, batch_no / num_batches),
batch_no, batch_no / num_batches * 100, click_loss_avg,
decoding_loss_avg))
if batch_no % self.config.plot_every == 0:
plot_loss_avg = plot_loss_total / self.config.plot_every
self.train_losses.append(plot_loss_avg)
plot_loss_total = 0
def validate(self, dev_corpus):
# Turn on evaluation mode which disables dropout.
self.model.eval()
dev_batches = helper.batchify(dev_corpus.data, self.config.batch_size)
print('number of dev batches = ', len(dev_batches))
dev_loss, dev_click_loss, dev_decoding_loss = 0, 0, 0
num_batches = len(dev_batches)
for batch_no in range(1, num_batches + 1):
session_queries, session_query_length, rel_docs, rel_docs_length, doc_labels = helper.session_to_tensor(
dev_batches[batch_no - 1], self.dictionary, True)
if self.config.cuda:
session_queries = session_queries.cuda()
session_query_length = session_query_length.cuda()
rel_docs = rel_docs.cuda()
rel_docs_length = rel_docs_length.cuda()
doc_labels = doc_labels.cuda()
click_loss, decoding_loss = self.model(session_queries,
session_query_length,
rel_docs, rel_docs_length,
doc_labels)
if click_loss.size(0) > 1:
click_loss = click_loss.mean()
if decoding_loss.size(0) > 1:
decoding_loss = decoding_loss.mean()
dev_click_loss += click_loss.data[0]
dev_decoding_loss += decoding_loss.data[0]
dev_loss += click_loss.data[0] + decoding_loss.data[0]
print('validation loss = %.4f %.4f' %
((dev_click_loss / num_batches),
(dev_decoding_loss / num_batches)))
return dev_loss / num_batches
def test_ranking(model, test_batches, dictionary):
num_batches = len(test_batches)
map, mrr, ndcg_1, ndcg_3, ndcg_5, ndcg_10 = 0, 0, 0, 0, 0, 0
for batch_no in range(1, num_batches + 1):
session_queries, session_query_length, rel_docs, rel_docs_length, doc_labels = helper.session_to_tensor(
test_batches[batch_no - 1], dictionary, True)
if model.config.cuda:
session_queries = session_queries.cuda()
session_query_length = session_query_length.cuda()
rel_docs = rel_docs.cuda()
rel_docs_length = rel_docs_length.cuda()
doc_labels = doc_labels.cuda()
ret_val = compute_ranking_performance(model, session_queries,
session_query_length, rel_docs,
rel_docs_length, doc_labels)
map += ret_val[0]
mrr += ret_val[1]
ndcg_1 += ret_val[2]
ndcg_3 += ret_val[3]
ndcg_5 += ret_val[4]
ndcg_10 += ret_val[5]
_map = map / num_batches
mrr = mrr / num_batches
ndcg_1 = ndcg_1 / num_batches
ndcg_3 = ndcg_3 / num_batches
ndcg_5 = ndcg_5 / num_batches
ndcg_10 = ndcg_10 / num_batches
print('MAP - ', _map)
print('MRR - ', mrr)
print('NDCG@1 - ', ndcg_1)
print('NDCG@3 - ', ndcg_3)
print('NDCG@5 - ', ndcg_5)
print('NDCG@10 - ', ndcg_10)
for sess_len, sessions in test_corpus.data.items():
for session in sessions:
anchor_query_text = ' '.join(session.queries[sess_len -
2].query_terms[1:-1])
if anchor_query_text not in candidate_map:
continue
cands = []
for query_text in candidate_map[anchor_query_text]:
query = data.Query()
query.add_text(query_text, args.tokenize,
args.max_query_length)
cands.append(query)
cands.append(session.queries[sess_len - 1])
scores = []
session_queries, session_query_length = helper.session_to_tensor(
[session], dictionary)
for cand in cands:
next_query_tensor = helper.sequence_to_tensors(
cand.query_terms, len(cand.query_terms), dictionary)
next_query_tensor = Variable(next_query_tensor).unsqueeze(0)
if model.config.cuda:
session_queries = session_queries.cuda(
) # 1 x session_length x max_query_length
session_query_length = session_query_length.cuda(
) # 1 x session_length
next_query_tensor = next_query_tensor.cuda(
) # 1 x max_query_length
score = suggest_next_query(model, session_queries,
session_query_length,
next_query_tensor)
def train(self, train_batches, dev_batches, epoch_no):
# Turn on training mode which enables dropout.
self.model.train()
start = time.time()
print_loss_total = 0
plot_loss_total = 0
num_batches = len(train_batches)
print('epoch %d started' % epoch_no)
for batch_no in range(1, num_batches + 1):
# Clearing out all previous gradient computations.
self.optimizer.zero_grad()
train_sessions, length = helper.session_to_tensor(
train_batches[batch_no - 1], self.dictionary)
if self.config.cuda:
train_sessions = train_sessions.cuda()
length = length.cuda()
loss = self.model(train_sessions, length)
# Important if we are using nn.DataParallel()
if loss.size(0) > 1:
loss = torch.mean(loss)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs.
clip_grad_norm(self.model.parameters(), self.config.clip)
self.optimizer.step()
print_loss_total += loss.data[0]
plot_loss_total += loss.data[0]
if batch_no % self.config.print_every == 0:
print_loss_avg = print_loss_total / self.config.print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' %
(helper.show_progress(start, batch_no / num_batches),
batch_no, batch_no / num_batches * 100, print_loss_avg))
if batch_no % self.config.plot_every == 0:
plot_loss_avg = plot_loss_total / self.config.plot_every
self.train_losses.append(plot_loss_avg)
plot_loss_total = 0
if batch_no % self.config.dev_every == 0:
dev_loss = self.validate(dev_batches)
self.dev_losses.append(dev_loss)
print('validation loss = %.4f' % dev_loss)
if self.best_dev_loss == -1 or self.best_dev_loss > dev_loss:
self.best_dev_loss = dev_loss
helper.save_checkpoint(
{
'epoch': epoch_no,
'state_dict': self.model.state_dict(),
'best_loss': self.best_dev_loss,
'optimizer': self.optimizer.state_dict(),
}, self.config.save_path + 'model_best.pth.tar')
else:
self.times_no_improvement += 1
# no improvement in validation loss for last n times, so stop training
if self.times_no_improvement == 20:
self.stop = True
break
