def test(parser,
vocab,
num_buckets_test,
test_batch_size,
test_file,
output_file,
debug=False):
data_loader = DataLoader(test_file, num_buckets_test, vocab)
record = data_loader.idx_sequence
results = [None] * len(record)
idx = 0
for words, tags, arcs, rels in data_loader.get_batches(
batch_size=test_batch_size, shuffle=False):
outputs = parser.run(words, tags, is_train=False)
for output in outputs:
sent_idx = record[idx]
results[sent_idx] = output
idx += 1
arcs = reduce(lambda x, y: x + y, [list(result[0]) for result in results])
rels = reduce(lambda x, y: x + y, [list(result[1]) for result in results])
idx = 0
with open(test_file) as f:
if debug:
f = f.readlines()[:1000]
with open(output_file, 'w') as fo:
for line in f:
info = line.strip().split()
if info:
assert len(info) == 10, 'Illegal line: %s' % line
info[6] = str(arcs[idx])
info[7] = vocab.id2rel(rels[idx])
fo.write('\t'.join(info) + '\n')
idx += 1
else:
fo.write('\n')
os.system('perl run/eval.pl -q -b -g %s -s %s -o tmp' %
(test_file, output_file))
os.system('tail -n 3 tmp > score_tmp')
LAS, UAS = [
float(line.strip().split()[-2])
for line in open('score_tmp').readlines()[:2]
]
print('LAS %.2f, UAS %.2f' % (LAS, UAS))
os.system('rm tmp score_tmp')
return LAS, UAS
vocab)
# trainer = dy.AdamTrainer(pc, config.learning_rate, config.beta_1, config.beta_2, config.epsilon)
trainer = gluon.Trainer(parser.collect_params(), 'adam',
{'learning_rate': config.learning_rate})
global_step = 0
epoch = 0
best_UAS = 0.
history = lambda x, y: open(
os.path.join(config.save_dir, 'valid_history'), 'a').write(
'%.2f %.2f\n' % (x, y))
while global_step < config.train_iters:
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
' Start training epoch #%d' % (epoch, ))
epoch += 1
for words, tags, arcs, rels in data_loader.get_batches(
batch_size=config.train_batch_size, shuffle=True):
with autograd.record():
arc_accuracy, rel_accuracy, overall_accuracy, loss = parser.run(
words, tags, arcs, rels)
loss = loss * 0.5
loss_value = loss.asscalar()
print(
"Step #%d: Acc: arc %.2f, rel %.2f, overall %.2f, loss %.3f\r\r"
% (global_step, arc_accuracy, rel_accuracy,
overall_accuracy, loss_value))
# trainer.set_learning_rate(config.learning_rate * config.decay ** (global_step / config.decay_steps))
loss.backward()
trainer.step(config.train_batch_size)
global_step += 1
if global_step % config.validate_every == 0:
print('\nTest on development set')