def _read_one(path, is_train=False, max_size=None):
lang_id = guess_language_id(path)
logger.info("Reading: guess that the language of file %s is %s." % (path, lang_id))
one_data = conllx_data.read_data_to_variable(path, word_alphabet, char_alphabet, pos_alphabet,
type_alphabet, use_gpu=use_gpu, volatile=(not is_train),
use_bert=args.use_bert, symbolic_root=True, lang_id=lang_id,
max_size=max_size)
return one_data
def _read_one(path, is_train):
lang_id = guess_language_id(path)
logger.info("Reading: guess that the language of file %s is %s." %
(path, lang_id))
one_data = conllx_data.read_data_to_variable(
path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=(not is_train),
symbolic_root=True,
lang_id=lang_id,
len_thresh=(args.train_len_thresh if is_train else 100000))
return one_data
def main():
args_parser = argparse.ArgumentParser(description='Tuning with graph-based parsing')
args_parser.add_argument('--mode', choices=['RNN', 'LSTM', 'GRU', 'FastLSTM'],
help='architecture of rnn', required=True)
args_parser.add_argument('--num_epochs', type=int, default=200, help='Number of training epochs')
args_parser.add_argument('--batch_size', type=int, default=64, help='Number of sentences in each batch')
args_parser.add_argument('--hidden_size', type=int, default=256, help='Number of hidden units in RNN')
args_parser.add_argument('--arc_space', type=int, default=128, help='Dimension of tag space')
args_parser.add_argument('--type_space', type=int, default=128, help='Dimension of tag space')
args_parser.add_argument('--num_layers', type=int, default=1, help='Number of layers of RNN')
args_parser.add_argument('--num_filters', type=int, default=50, help='Number of filters in CNN')
args_parser.add_argument('--pos', action='store_true', help='use part-of-speech embedding.')
args_parser.add_argument('--pos_dim', type=int, default=50, help='Dimension of POS embeddings')
args_parser.add_argument('--char_dim', type=int, default=50, help='Dimension of Character embeddings')
args_parser.add_argument('--objective', choices=['cross_entropy', 'crf'], default='cross_entropy',
help='objective function of training procedure.')
args_parser.add_argument('--decode', choices=['mst', 'greedy'], help='decoding algorithm', required=True)
args_parser.add_argument('--learning_rate', type=float, default=0.01, help='Learning rate')
args_parser.add_argument('--decay_rate', type=float, default=0.05, help='Decay rate of learning rate')
args_parser.add_argument('--gamma', type=float, default=0.0, help='weight for regularization')
args_parser.add_argument('--p_rnn', nargs=2, type=float, required=True, help='dropout rate for RNN')
args_parser.add_argument('--p_in', type=float, default=0.33, help='dropout rate for input embeddings')
args_parser.add_argument('--p_out', type=float, default=0.33, help='dropout rate for output layer')
args_parser.add_argument('--schedule', type=int, help='schedule for learning rate decay')
args_parser.add_argument('--unk_replace', type=float, default=0.,
help='The rate to replace a singleton word with UNK')
args_parser.add_argument('--punctuation', nargs='+', type=str, help='List of punctuations')
args_parser.add_argument('--word_embedding', choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words', required=True)
args_parser.add_argument('--word_path', help='path for word embedding dict')
args_parser.add_argument('--char_embedding', choices=['random', 'polyglot'], help='Embedding for characters',
required=True)
args_parser.add_argument('--char_path', help='path for character embedding dict')
args_parser.add_argument('--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
args_parser.add_argument('--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
args_parser.add_argument('--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args_parser.add_argument('--model_path', help='path for saving model file.', required=True)
args = args_parser.parse_args()
print("*** Model UID: %s ***" % uid)
logger = get_logger("GraphParser")
mode = args.mode
obj = args.objective
decoding = args.decode
train_path = args.train
dev_path = args.dev
test_path = args.test
model_path = args.model_path
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
num_layers = args.num_layers
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
betas = (0.9, 0.9)
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
punctuation = args.punctuation
word_embedding = args.word_embedding
word_path = args.word_path
char_embedding = args.char_embedding
char_path = args.char_path
use_pos = args.pos
pos_dim = args.pos_dim
word_dict, word_dim = utils.load_embedding_dict(word_embedding, word_path)
char_dict = None
char_dim = args.char_dim
if char_embedding != 'random':
char_dict, char_dim = utils.load_embedding_dict(char_embedding, char_path)
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets/')
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_data.create_alphabets(alphabet_path, train_path, data_paths=[dev_path, test_path],
max_vocabulary_size=50000, embedd_dict=word_dict)
num_words = word_alphabet.size()
num_chars = char_alphabet.size()
num_pos = pos_alphabet.size()
num_types = type_alphabet.size()
logger.info("Word Alphabet Size: %d" % num_words)
logger.info("Character Alphabet Size: %d" % num_chars)
logger.info("POS Alphabet Size: %d" % num_pos)
logger.info("Type Alphabet Size: %d" % num_types)
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conllx_data.read_data_to_variable(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, symbolic_root=True)
# data_train = conllx_data.read_data(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
# num_data = sum([len(bucket) for bucket in data_train])
num_data = sum(data_train[1])
data_dev = conllx_data.read_data_to_variable(dev_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, volatile=True, symbolic_root=True)
data_test = conllx_data.read_data_to_variable(test_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, volatile=True, symbolic_root=True)
punct_set = None
if punctuation is not None:
punct_set = set(punctuation)
logger.info("punctuations(%d): %s" % (len(punct_set), ' '.join(punct_set)))
def construct_word_embedding_table():
scale = np.sqrt(3.0 / word_dim)
table = np.empty([word_alphabet.size(), word_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.random.uniform(-scale, scale, [1, word_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in word_dict:
embedding = word_dict[word]
elif word.lower() in word_dict:
embedding = word_dict[word.lower()]
else:
embedding = np.random.uniform(-scale, scale, [1, word_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('word OOV: %d' % oov)
return torch.from_numpy(table)
def construct_char_embedding_table():
if char_dict is None:
return None
scale = np.sqrt(3.0 / char_dim)
table = np.empty([num_chars, char_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.random.uniform(-scale, scale, [1, char_dim]).astype(np.float32)
oov = 0
for char, index, in char_alphabet.items():
if char in char_dict:
embedding = char_dict[char]
else:
embedding = np.random.uniform(-scale, scale, [1, char_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('character OOV: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
char_table = construct_char_embedding_table()
window = 3
if obj == 'cross_entropy':
network = BiRecurrentConvBiAffine(word_dim, num_words,
char_dim, num_chars,
pos_dim, num_pos,
num_filters, window,
mode, hidden_size, num_layers,
num_types, arc_space, type_space,
embedd_word=word_table, embedd_char=char_table,
p_in=p_in, p_out=p_out, p_rnn=p_rnn, biaffine=True, pos=use_pos)
elif obj == 'crf':
raise NotImplementedError
else:
raise RuntimeError('Unknown objective: %s' % obj)
if use_gpu:
network.cuda()
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
adam_epochs = 50
adam_rate = 0.001
if adam_epochs > 0:
lr = adam_rate
opt = 'adam'
optim = Adam(network.parameters(), lr=adam_rate, betas=betas, weight_decay=gamma)
else:
opt = 'sgd'
lr = learning_rate
optim = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=gamma, nesterov=True)
logger.info("Embedding dim: word=%d, char=%d, pos=%d (%s)" % (word_dim, char_dim, pos_dim, use_pos))
logger.info("Network: %s, num_layer=%d, hidden=%d, filter=%d, arc_space=%d, type_space=%d" % (
mode, num_layers, hidden_size, num_filters, arc_space, type_space))
logger.info("train: obj: %s, l2: %f, (#data: %d, batch: %d, dropout(in, out, rnn): (%.2f, %.2f, %s), unk replace: %.2f)" % (
obj, gamma, num_data, batch_size, p_in, p_out, p_rnn, unk_replace))
logger.info("decoding algorithm: %s" % decoding)
num_batches = num_data / batch_size + 1
dev_ucorrect = 0.0
dev_lcorrect = 0.0
dev_ucomlpete_match = 0.0
dev_lcomplete_match = 0.0
dev_ucorrect_nopunc = 0.0
dev_lcorrect_nopunc = 0.0
dev_ucomlpete_match_nopunc = 0.0
dev_lcomplete_match_nopunc = 0.0
dev_root_correct = 0.0
best_epoch = 0
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_root_correct = 0.0
test_total = 0
test_total_nopunc = 0
test_total_inst = 0
test_total_root = 0
if decoding == 'greedy':
decode = network.decode
elif decoding == 'mst':
decode = network.decode_mst
else:
raise ValueError('Unknown decoding algorithm: %s' % decoding)
for epoch in range(1, num_epochs + 1):
print('Epoch %d (%s, optim: %s, learning rate=%.4f, decay rate=%.4f (schedule=%d)): ' % (
epoch, mode, opt, lr, decay_rate, schedule))
train_err = 0.
train_err_arc = 0.
train_err_type = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, pos, heads, types, masks, lengths = conllx_data.get_batch_variable(data_train, batch_size,
unk_replace=unk_replace)
optim.zero_grad()
loss_arc, loss_type = network.loss(word, char, pos, heads, types, mask=masks, length=lengths)
loss = loss_arc + loss_type
loss.backward()
optim.step()
num_inst = word.size(0) if obj == 'crf' else masks.data.sum() - word.size(0)
train_err += loss.data[0] * num_inst
train_err_arc += loss_arc.data[0] * num_inst
train_err_type += loss_type.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 10 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, arc: %.4f, type: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total,
train_err_arc / train_total, train_err_type / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, arc: %.4f, type: %.4f, time: %.2fs' % (
num_batches, train_err / train_total, train_err_arc / train_total, train_err_type / train_total,
time.time() - start_time))
# evaluate performance on dev data
network.eval()
pred_filename = 'tmp/%spred_dev%d' % (str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = 'tmp/%sgold_dev%d' % (str(uid), epoch)
gold_writer.start(gold_filename)
print('[%s] Epoch %d complete' % (time.strftime("%Y-%m-%d %H:%M:%S"), epoch))
dev_ucorr = 0.0
dev_lcorr = 0.0
dev_total = 0
dev_ucomlpete = 0.0
dev_lcomplete = 0.0
dev_ucorr_nopunc = 0.0
dev_lcorr_nopunc = 0.0
dev_total_nopunc = 0
dev_ucomlpete_nopunc = 0.0
dev_lcomplete_nopunc = 0.0
dev_root_corr = 0.0
dev_total_root = 0.0
dev_total_inst = 0.0
for batch in conllx_data.iterate_batch_variable(data_dev, batch_size):
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(word, char, pos, mask=masks, length=lengths, leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word, pos, heads_pred, types_pred, lengths, symbolic_root=True)
gold_writer.write(word, pos, heads, types, lengths, symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser.eval(word, pos, heads_pred, types_pred, heads, types, word_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
dev_ucorr += ucorr
dev_lcorr += lcorr
dev_total += total
dev_ucomlpete += ucm
dev_lcomplete += lcm
dev_ucorr_nopunc += ucorr_nopunc
dev_lcorr_nopunc += lcorr_nopunc
dev_total_nopunc += total_nopunc
dev_ucomlpete_nopunc += ucm_nopunc
dev_lcomplete_nopunc += lcm_nopunc
dev_root_corr += corr_root
dev_total_root += total_root
dev_total_inst += num_inst
pred_writer.close()
gold_writer.close()
print('W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%' % (
dev_ucorr, dev_lcorr, dev_total, dev_ucorr * 100 / dev_total, dev_lcorr * 100 / dev_total,
dev_ucomlpete * 100 / dev_total_inst, dev_lcomplete * 100 / dev_total_inst))
print('Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%' % (
dev_ucorr_nopunc, dev_lcorr_nopunc, dev_total_nopunc, dev_ucorr_nopunc * 100 / dev_total_nopunc,
dev_lcorr_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_nopunc * 100 / dev_total_inst, dev_lcomplete_nopunc * 100 / dev_total_inst))
print('Root: corr: %d, total: %d, acc: %.2f%%' %(
dev_root_corr, dev_total_root, dev_root_corr * 100 / dev_total_root))
if dev_ucorrect_nopunc <= dev_ucorr_nopunc:
dev_ucorrect_nopunc = dev_ucorr_nopunc
dev_lcorrect_nopunc = dev_lcorr_nopunc
dev_ucomlpete_match_nopunc = dev_ucomlpete_nopunc
dev_lcomplete_match_nopunc = dev_lcomplete_nopunc
dev_ucorrect = dev_ucorr
dev_lcorrect = dev_lcorr
dev_ucomlpete_match = dev_ucomlpete
dev_lcomplete_match = dev_lcomplete
dev_root_correct = dev_root_corr
best_epoch = epoch
pred_filename = 'tmp/%spred_test%d' % (str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = 'tmp/%sgold_test%d' % (str(uid), epoch)
gold_writer.start(gold_filename)
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_total = 0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_total_nopunc = 0
test_total_inst = 0
test_root_correct = 0.0
test_total_root = 0
for batch in conllx_data.iterate_batch_variable(data_test, batch_size):
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(word, char, pos, mask=masks, length=lengths, leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word, pos, heads_pred, types_pred, lengths, symbolic_root=True)
gold_writer.write(word, pos, heads, types, lengths, symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser.eval(word, pos, heads_pred, types_pred, heads, types, word_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total += total
test_ucomlpete_match += ucm
test_lcomplete_match += lcm
test_ucorrect_nopunc += ucorr_nopunc
test_lcorrect_nopunc += lcorr_nopunc
test_total_nopunc += total_nopunc
test_ucomlpete_match_nopunc += ucm_nopunc
test_lcomplete_match_nopunc += lcm_nopunc
test_root_correct += corr_root
test_total_root += total_root
test_total_inst += num_inst
pred_writer.close()
gold_writer.close()
print('----------------------------------------------------------------------------------------------------------------------------')
print('best dev W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
dev_ucorrect, dev_lcorrect, dev_total, dev_ucorrect * 100 / dev_total, dev_lcorrect * 100 / dev_total,
dev_ucomlpete_match * 100 / dev_total_inst, dev_lcomplete_match * 100 / dev_total_inst,
best_epoch))
print('best dev Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
dev_ucorrect_nopunc, dev_lcorrect_nopunc, dev_total_nopunc,
dev_ucorrect_nopunc * 100 / dev_total_nopunc, dev_lcorrect_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_match_nopunc * 100 / dev_total_inst, dev_lcomplete_match_nopunc * 100 / dev_total_inst,
best_epoch))
print('best dev Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' % (
dev_root_correct, dev_total_root, dev_root_correct * 100 / dev_total_root, best_epoch))
print('----------------------------------------------------------------------------------------------------------------------------')
print('best test W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
test_ucorrect, test_lcorrect, test_total, test_ucorrect * 100 / test_total, test_lcorrect * 100 / test_total,
test_ucomlpete_match * 100 / test_total_inst, test_lcomplete_match * 100 / test_total_inst,
best_epoch))
print('best test Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
test_ucorrect_nopunc, test_lcorrect_nopunc, test_total_nopunc,
test_ucorrect_nopunc * 100 / test_total_nopunc, test_lcorrect_nopunc * 100 / test_total_nopunc,
test_ucomlpete_match_nopunc * 100 / test_total_inst, test_lcomplete_match_nopunc * 100 / test_total_inst,
best_epoch))
print('best test Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' % (
test_root_correct, test_total_root, test_root_correct * 100 / test_total_root, best_epoch))
print('============================================================================================================================')
if epoch % schedule == 0:
# lr = lr * decay_rate
if epoch < adam_epochs:
opt = 'adam'
lr = adam_rate / (1.0 + epoch * decay_rate)
optim = Adam(network.parameters(), lr=lr, betas=betas, weight_decay=gamma)
else:
opt = 'sgd'
lr = learning_rate / (1.0 + (epoch - adam_epochs) * decay_rate)
optim = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=gamma, nesterov=True)
def biaffine(model_path, model_name, test_path, punct_set, use_gpu, logger,
args):
alphabet_path = os.path.join(model_path, 'alphabets/')
model_name = os.path.join(model_path, model_name)
word_alphabet, char_alphabet, pos_alphabet, \
type_alphabet = conllx_data.create_alphabets(alphabet_path, None, data_paths=[None, None], max_vocabulary_size=50000, embedd_dict=None)
num_words = word_alphabet.size()
num_chars = char_alphabet.size()
num_pos = pos_alphabet.size()
num_types = type_alphabet.size()
logger.info("Word Alphabet Size: %d" % num_words)
logger.info("Character Alphabet Size: %d" % num_chars)
logger.info("POS Alphabet Size: %d" % num_pos)
logger.info("Type Alphabet Size: %d" % num_types)
decoding = args.decode
logger.info('use gpu: %s, decoding: %s' % (use_gpu, decoding))
data_test = conllx_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
symbolic_root=True)
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
logger.info('model: %s' % model_name)
def load_model_arguments_from_json():
arguments = json.load(open(arg_path, 'r'))
return arguments['args'], arguments['kwargs']
arg_path = model_name + '.arg.json'
args, kwargs = load_model_arguments_from_json()
network = BiRecurrentConvBiAffine(*args, **kwargs)
network.load_state_dict(torch.load(model_name))
if use_gpu:
network.cuda()
else:
network.cpu()
network.eval()
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_total = 0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_total_nopunc = 0
test_total_inst = 0
test_root_correct = 0.0
test_total_root = 0
if decoding == 'greedy':
decode = network.decode
elif decoding == 'mst':
decode = network.decode_mst
else:
raise ValueError('Unknown decoding algorithm: %s' % decoding)
pred_writer.start('tmp/analyze_pred_%s' % str(uid))
gold_writer.start('tmp/analyze_gold_%s' % str(uid))
sent = 0
start_time = time.time()
for batch in conllx_data.iterate_batch_variable(data_test, 1):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(
word,
char,
pos,
mask=masks,
length=lengths,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(word, pos, heads, types, lengths, symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser_bpe.eval(
word,
pos,
heads_pred,
types_pred,
heads,
types,
word_alphabet,
pos_alphabet,
lengths,
punct_set=punct_set,
symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total += total
test_ucomlpete_match += ucm
test_lcomplete_match += lcm
test_ucorrect_nopunc += ucorr_nopunc
test_lcorrect_nopunc += lcorr_nopunc
test_total_nopunc += total_nopunc
test_ucomlpete_match_nopunc += ucm_nopunc
test_lcomplete_match_nopunc += lcm_nopunc
test_root_correct += corr_root
test_total_root += total_root
test_total_inst += num_inst
pred_writer.close()
gold_writer.close()
print('\ntime: %.2fs' % (time.time() - start_time))
print(
'test W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%'
%
(test_ucorrect, test_lcorrect, test_total, test_ucorrect * 100 /
test_total, test_lcorrect * 100 / test_total, test_ucomlpete_match *
100 / test_total_inst, test_lcomplete_match * 100 / test_total_inst))
print(
'test Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%'
%
(test_ucorrect_nopunc, test_lcorrect_nopunc, test_total_nopunc,
test_ucorrect_nopunc * 100 / test_total_nopunc, test_lcorrect_nopunc *
100 / test_total_nopunc, test_ucomlpete_match_nopunc * 100 /
test_total_inst, test_lcomplete_match_nopunc * 100 / test_total_inst))
print('test Root: corr: %d, total: %d, acc: %.2f%%' %
(test_root_correct, test_total_root,
test_root_correct * 100 / test_total_root))
def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN-CRF')
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
required=True)
parser.add_argument('--num_epochs',
type=int,
default=1000,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN')
parser.add_argument('--num_filters',
type=int,
default=30,
help='Number of filters in CNN')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--learning_rate',
type=float,
default=0.01,
help='Learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.1,
help='Decay rate of learning rate')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
parser.add_argument('--dropout',
choices=['std', 'variational'],
help='type of dropout',
required=True)
parser.add_argument('--p', type=float, default=0.5, help='dropout rate')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("POSCRFTagger")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p = args.p
unk_replace = args.unk_replace
bigram = args.bigram
embedd_dict, embedd_dim = utils.load_embedding_dict(
'glove', "data/glove/glove.6B/glove.6B.100d.gz")
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
type_alphabet = conllx_data.create_alphabets("data/alphabets/pos_crf/", train_path,
data_paths=[dev_path, test_path],
max_vocabulary_size=50000, embedd_dict=embedd_dict)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conllx_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu)
# data_train = conllx_data.read_data(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
# num_data = sum([len(bucket) for bucket in data_train])
num_data = sum(data_train[1])
num_labels = pos_alphabet.size()
data_dev = conllx_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True)
data_test = conllx_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = 1
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
embedd_word=word_table,
p_rnn=p,
bigram=bigram)
else:
raise NotImplementedError
if use_gpu:
network.cuda()
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma)
logger.info("Network: %s, num_layer=%d, hidden=%d, filter=%d, crf=%s" %
(mode, num_layers, hidden_size, num_filters,
'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, dropout: %.2f, unk replace: %.2f)"
% (gamma, num_data, batch_size, p, unk_replace))
num_batches = num_data / batch_size + 1
dev_correct = 0.0
best_epoch = 0
test_correct = 0.0
test_total = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, labels, _, _, masks, lengths = conllx_data.get_batch_variable(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
dev_corr = 0.0
dev_total = 0
for batch in conllx_data.iterate_batch_variable(data_dev, batch_size):
word, char, labels, _, _, masks, lengths = batch
preds, corr = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
num_tokens = masks.data.sum()
dev_corr += corr
dev_total += num_tokens
print('dev corr: %d, total: %d, acc: %.2f%%' %
(dev_corr, dev_total, dev_corr * 100 / dev_total))
if dev_correct < dev_corr:
dev_correct = dev_corr
best_epoch = epoch
# evaluate on test data when better performance detected
test_corr = 0.0
test_total = 0
for batch in conllx_data.iterate_batch_variable(
data_test, batch_size):
word, char, labels, _, _, masks, lengths = batch
preds, corr = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
num_tokens = masks.data.sum()
test_corr += corr
test_total += num_tokens
test_correct = test_corr
print("best dev corr: %d, total: %d, acc: %.2f%% (epoch: %d)" %
(dev_correct, dev_total, dev_correct * 100 / dev_total,
best_epoch))
print("best test corr: %d, total: %d, acc: %.2f%% (epoch: %d)" %
(test_correct, test_total, test_correct * 100 / test_total,
best_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with graph-based parsing')
args_parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU', 'FastLSTM'],
help='architecture of rnn',
required=True)
args_parser.add_argument('--num_epochs',
type=int,
default=200,
help='Number of training epochs')
args_parser.add_argument('--batch_size',
type=int,
default=64,
help='Number of sentences in each batch')
args_parser.add_argument('--hidden_size',
type=int,
default=256,
help='Number of hidden units in RNN')
args_parser.add_argument('--arc_space',
type=int,
default=128,
help='Dimension of tag space')
args_parser.add_argument('--type_space',
type=int,
default=128,
help='Dimension of tag space')
args_parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
args_parser.add_argument('--num_filters',
type=int,
default=50,
help='Number of filters in CNN')
args_parser.add_argument('--pos',
action='store_true',
help='use part-of-speech embedding.')
args_parser.add_argument('--char',
action='store_true',
help='use character embedding and CNN.')
args_parser.add_argument('--pos_dim',
type=int,
default=50,
help='Dimension of POS embeddings')
args_parser.add_argument('--char_dim',
type=int,
default=50,
help='Dimension of Character embeddings')
args_parser.add_argument('--opt',
choices=['adam', 'sgd', 'adamax'],
help='optimization algorithm')
args_parser.add_argument('--objective',
choices=['cross_entropy', 'crf'],
default='cross_entropy',
help='objective function of training procedure.')
args_parser.add_argument('--decode',
choices=['mst', 'greedy'],
help='decoding algorithm',
required=True)
args_parser.add_argument('--learning_rate',
type=float,
default=0.01,
help='Learning rate')
args_parser.add_argument('--decay_rate',
type=float,
default=0.05,
help='Decay rate of learning rate')
args_parser.add_argument('--clip',
type=float,
default=5.0,
help='gradient clipping')
args_parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
args_parser.add_argument('--epsilon',
type=float,
default=1e-8,
help='epsilon for adam or adamax')
args_parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
args_parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input embeddings')
args_parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
args_parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
args_parser.add_argument(
'--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
args_parser.add_argument('--punctuation',
nargs='+',
type=str,
help='List of punctuations')
args_parser.add_argument(
'--word_embedding',
choices=['glove', 'senna', 'sskip', 'polyglot', 'NNLM'],
help='Embedding for words',
required=True)
args_parser.add_argument('--word_path',
help='path for word embedding dict')
args_parser.add_argument(
'--freeze',
action='store_true',
help='frozen the word embedding (disable fine-tuning).')
args_parser.add_argument('--char_embedding',
choices=['random', 'polyglot'],
help='Embedding for characters',
required=True)
args_parser.add_argument('--char_path',
help='path for character embedding dict')
args_parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
args_parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
args_parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args_parser.add_argument('--model_path',
help='path for saving model file.',
required=True)
args_parser.add_argument('--model_name',
help='name for saving model file.',
required=True)
args_parser.add_argument('--pos_embedding',
choices=[1, 2, 4],
type=int,
help='Embedding method for korean POS tag',
default=2)
args = args_parser.parse_args()
logger = get_logger("GraphParser")
mode = args.mode
obj = args.objective
decoding = args.decode
train_path = args.train
dev_path = args.dev
test_path = args.test
model_path = args.model_path
model_name = args.model_name
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
num_layers = args.num_layers
num_filters = args.num_filters
learning_rate = args.learning_rate
opt = args.opt
momentum = 0.9
betas = (0.9, 0.9)
eps = args.epsilon
decay_rate = args.decay_rate
clip = args.clip
gamma = args.gamma
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
punctuation = args.punctuation
freeze = args.freeze
word_embedding = args.word_embedding
word_path = args.word_path
use_char = args.char
char_embedding = args.char_embedding
char_path = args.char_path
use_pos = args.pos
pos_embedding = args.pos_embedding
pos_dim = args.pos_dim
word_dict, word_dim = utils.load_embedding_dict(word_embedding, word_path)
char_dict = None
char_dim = args.char_dim
if char_embedding != 'random':
char_dict, char_dim = utils.load_embedding_dict(
char_embedding, char_path)
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets/')
model_name = os.path.join(model_path, model_name)
data_paths = [dev_path, test_path] if test_path else [dev_path]
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_data.create_alphabets(
alphabet_path,
train_path,
data_paths=data_paths,
max_vocabulary_size=50000,
pos_embedding=pos_embedding,
embedd_dict=word_dict)
num_words = word_alphabet.size()
num_chars = char_alphabet.size()
num_pos = pos_alphabet.size()
num_types = type_alphabet.size()
logger.info("Word Alphabet Size: %d" % num_words)
logger.info("Character Alphabet Size: %d" % num_chars)
logger.info("POS Alphabet Size: %d" % num_pos)
logger.info("Type Alphabet Size: %d" % num_types)
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conllx_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
symbolic_root=True)
# data_train = conllx_data.read_data(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
# num_data = sum([len(bucket) for bucket in data_train])
num_data = sum(data_train[1])
data_dev = conllx_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
volatile=True,
symbolic_root=True)
if test_path:
data_test = conllx_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
volatile=True,
symbolic_root=True)
punct_set = None
if punctuation is not None:
punct_set = set(punctuation)
logger.info("punctuations(%d): %s" %
(len(punct_set), ' '.join(punct_set)))
def construct_word_embedding_table():
scale = np.sqrt(3.0 / word_dim)
table = np.empty([word_alphabet.size(), word_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.zeros([1, word_dim]).astype(
np.float32) if freeze else np.random.uniform(
-scale, scale, [1, word_dim]).astype(np.float32)
oov = 0
for word, index in list(word_alphabet.items()):
if word in word_dict:
embedding = word_dict[word]
elif word.lower() in word_dict:
embedding = word_dict[word.lower()]
else:
embedding = np.zeros([1, word_dim]).astype(
np.float32) if freeze else np.random.uniform(
-scale, scale, [1, word_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('word OOV: %d' % oov)
return torch.from_numpy(table)
def construct_char_embedding_table():
if char_dict is None:
return None
scale = np.sqrt(3.0 / char_dim)
table = np.empty([num_chars, char_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, char_dim]).astype(np.float32)
oov = 0
for char, index, in list(char_alphabet.items()):
if char in char_dict:
embedding = char_dict[char]
else:
embedding = np.random.uniform(-scale, scale,
[1, char_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('character OOV: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
char_table = construct_char_embedding_table()
window = 3
if obj == 'cross_entropy':
network = BiRecurrentConvBiAffine(word_dim,
num_words,
char_dim,
num_chars,
pos_dim,
num_pos,
num_filters,
window,
mode,
hidden_size,
num_layers,
num_types,
arc_space,
type_space,
embedd_word=word_table,
embedd_char=char_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
biaffine=True,
pos=use_pos,
char=use_char)
elif obj == 'crf':
raise NotImplementedError
else:
raise RuntimeError('Unknown objective: %s' % obj)
def save_args():
arg_path = model_name + '.arg.json'
arguments = [
word_dim, num_words, char_dim, num_chars, pos_dim, num_pos,
num_filters, window, mode, hidden_size, num_layers, num_types,
arc_space, type_space
]
kwargs = {
'p_in': p_in,
'p_out': p_out,
'p_rnn': p_rnn,
'biaffine': True,
'pos': use_pos,
'char': use_char
}
json.dump({
'args': arguments,
'kwargs': kwargs
},
open(arg_path, 'w'),
indent=4)
if freeze:
network.word_embedd.freeze()
if use_gpu:
network.cuda()
save_args()
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet, pos_embedding)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet, pos_embedding)
def generate_optimizer(opt, lr, params):
params = [param for param in params if param.requires_grad]
if opt == 'adam':
return Adam(params,
lr=lr,
betas=betas,
weight_decay=gamma,
eps=eps)
elif opt == 'sgd':
return SGD(params,
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
elif opt == 'adamax':
return Adamax(params,
lr=lr,
betas=betas,
weight_decay=gamma,
eps=eps)
else:
raise ValueError('Unknown optimization algorithm: %s' % opt)
lr = learning_rate
optim = generate_optimizer(opt, lr, network.parameters())
opt_info = 'opt: %s, ' % opt
if opt == 'adam':
opt_info += 'betas=%s, eps=%.1e' % (betas, eps)
elif opt == 'sgd':
opt_info += 'momentum=%.2f' % momentum
elif opt == 'adamax':
opt_info += 'betas=%s, eps=%.1e' % (betas, eps)
word_status = 'frozen' if freeze else 'fine tune'
char_status = 'enabled' if use_char else 'disabled'
pos_status = 'enabled' if use_pos else 'disabled'
logger.info(
"Embedding dim: word=%d (%s), char=%d (%s), pos=%d (%s)" %
(word_dim, word_status, char_dim, char_status, pos_dim, pos_status))
logger.info("CNN: filter=%d, kernel=%d" % (num_filters, window))
logger.info(
"RNN: %s, num_layer=%d, hidden=%d, arc_space=%d, type_space=%d" %
(mode, num_layers, hidden_size, arc_space, type_space))
logger.info(
"train: obj: %s, l2: %f, (#data: %d, batch: %d, clip: %.2f, unk replace: %.2f)"
% (obj, gamma, num_data, batch_size, clip, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
logger.info("decoding algorithm: %s" % decoding)
logger.info(opt_info)
num_batches = num_data / batch_size + 1
dev_ucorrect = 0.0
dev_lcorrect = 0.0
dev_ucomlpete_match = 0.0
dev_lcomplete_match = 0.0
dev_ucorrect_nopunc = 0.0
dev_lcorrect_nopunc = 0.0
dev_ucomlpete_match_nopunc = 0.0
dev_lcomplete_match_nopunc = 0.0
dev_root_correct = 0.0
best_epoch = 0
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_root_correct = 0.0
test_total = 0
test_total_nopunc = 0
test_total_inst = 0
test_total_root = 0
if decoding == 'greedy':
decode = network.decode
elif decoding == 'mst':
decode = network.decode_mst
else:
raise ValueError('Unknown decoding algorithm: %s' % decoding)
patient = 0
decay = 0
max_decay = 9
double_schedule_decay = 5
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s, optim: %s, learning rate=%.6f, eps=%.1e, decay rate=%.2f (schedule=%d, patient=%d, decay=%d)): '
%
(epoch, mode, opt, lr, eps, decay_rate, schedule, patient, decay))
train_err = 0.
train_err_arc = 0.
train_err_type = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, pos, heads, types, masks, lengths = conllx_data.get_batch_variable(
data_train, batch_size, pos_embedding, unk_replace=unk_replace)
optim.zero_grad()
loss_arc, loss_type = network.loss(word,
char,
pos,
heads,
types,
mask=masks,
length=lengths)
loss = loss_arc + loss_type
loss.backward()
clip_grad_norm_(network.parameters(), clip)
optim.step()
num_inst = word.size(
0) if obj == 'crf' else masks.data.sum() - word.size(0)
train_err += loss.data[0] * num_inst
train_err_arc += loss_arc.data[0] * num_inst
train_err_type += loss_type.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 10 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, arc: %.4f, type: %.4f, time left: %.2fs' % (
batch, num_batches, train_err / train_total, train_err_arc
/ train_total, train_err_type / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print(
'train: %d loss: %.4f, arc: %.4f, type: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, train_err_arc / train_total,
train_err_type / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
pred_filename = 'tmp/%spred_dev%d' % (str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = 'tmp/%sgold_dev%d' % (str(uid), epoch)
gold_writer.start(gold_filename)
dev_ucorr = 0.0
dev_lcorr = 0.0
dev_total = 0
dev_ucomlpete = 0.0
dev_lcomplete = 0.0
dev_ucorr_nopunc = 0.0
dev_lcorr_nopunc = 0.0
dev_total_nopunc = 0
dev_ucomlpete_nopunc = 0.0
dev_lcomplete_nopunc = 0.0
dev_root_corr = 0.0
dev_total_root = 0.0
dev_total_inst = 0.0
for batch in conllx_data.iterate_batch_variable(
data_dev, batch_size, pos_embedding):
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(
word,
char,
pos,
mask=masks,
length=lengths,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(word,
pos,
heads,
types,
lengths,
symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser_bpe.eval(
word,
pos,
heads_pred,
types_pred,
heads,
types,
word_alphabet,
pos_alphabet,
lengths,
punct_set=punct_set,
symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
dev_ucorr += ucorr
dev_lcorr += lcorr
dev_total += total
dev_ucomlpete += ucm
dev_lcomplete += lcm
dev_ucorr_nopunc += ucorr_nopunc
dev_lcorr_nopunc += lcorr_nopunc
dev_total_nopunc += total_nopunc
dev_ucomlpete_nopunc += ucm_nopunc
dev_lcomplete_nopunc += lcm_nopunc
dev_root_corr += corr_root
dev_total_root += total_root
dev_total_inst += num_inst
pred_writer.close()
gold_writer.close()
print(
'W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%'
% (dev_ucorr, dev_lcorr, dev_total, dev_ucorr * 100 / dev_total,
dev_lcorr * 100 / dev_total, dev_ucomlpete * 100 /
dev_total_inst, dev_lcomplete * 100 / dev_total_inst))
print(
'Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%'
% (dev_ucorr_nopunc, dev_lcorr_nopunc, dev_total_nopunc,
dev_ucorr_nopunc * 100 / dev_total_nopunc, dev_lcorr_nopunc *
100 / dev_total_nopunc, dev_ucomlpete_nopunc * 100 /
dev_total_inst, dev_lcomplete_nopunc * 100 / dev_total_inst))
print('Root: corr: %d, total: %d, acc: %.2f%%' %
(dev_root_corr, dev_total_root,
dev_root_corr * 100 / dev_total_root))
if dev_lcorrect_nopunc < dev_lcorr_nopunc or (
dev_lcorrect_nopunc == dev_lcorr_nopunc
and dev_ucorrect_nopunc < dev_ucorr_nopunc):
dev_ucorrect_nopunc = dev_ucorr_nopunc
dev_lcorrect_nopunc = dev_lcorr_nopunc
dev_ucomlpete_match_nopunc = dev_ucomlpete_nopunc
dev_lcomplete_match_nopunc = dev_lcomplete_nopunc
dev_ucorrect = dev_ucorr
dev_lcorrect = dev_lcorr
dev_ucomlpete_match = dev_ucomlpete
dev_lcomplete_match = dev_lcomplete
dev_root_correct = dev_root_corr
best_epoch = epoch
patient = 0
# torch.save(network, model_name)
torch.save(network.state_dict(), model_name)
if test_path:
pred_filename = 'tmp/%spred_test%d' % (str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = 'tmp/%sgold_test%d' % (str(uid), epoch)
gold_writer.start(gold_filename)
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_total = 0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_total_nopunc = 0
test_total_inst = 0
test_root_correct = 0.0
test_total_root = 0
for batch in conllx_data.iterate_batch_variable(
data_test, batch_size, pos_embedding):
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(
word,
char,
pos,
mask=masks,
length=lengths,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(word,
pos,
heads,
types,
lengths,
symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser_bpe.eval(
word,
pos,
heads_pred,
types_pred,
heads,
types,
word_alphabet,
pos_alphabet,
lengths,
punct_set=punct_set,
symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total += total
test_ucomlpete_match += ucm
test_lcomplete_match += lcm
test_ucorrect_nopunc += ucorr_nopunc
test_lcorrect_nopunc += lcorr_nopunc
test_total_nopunc += total_nopunc
test_ucomlpete_match_nopunc += ucm_nopunc
test_lcomplete_match_nopunc += lcm_nopunc
test_root_correct += corr_root
test_total_root += total_root
test_total_inst += num_inst
pred_writer.close()
gold_writer.close()
else:
if dev_ucorr_nopunc * 100 / dev_total_nopunc < dev_ucorrect_nopunc * 100 / dev_total_nopunc - 5 or patient >= schedule:
# network = torch.load(model_name)
network.load_state_dict(torch.load(model_name))
lr = lr * decay_rate
optim = generate_optimizer(opt, lr, network.parameters())
if decoding == 'greedy':
decode = network.decode
elif decoding == 'mst':
decode = network.decode_mst
else:
raise ValueError('Unknown decoding algorithm: %s' %
decoding)
patient = 0
decay += 1
if decay % double_schedule_decay == 0:
schedule *= 2
else:
patient += 1
print(
'----------------------------------------------------------------------------------------------------------------------------'
)
print(
'best dev W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
% (dev_ucorrect, dev_lcorrect, dev_total,
dev_ucorrect * 100 / dev_total, dev_lcorrect * 100 / dev_total,
dev_ucomlpete_match * 100 / dev_total_inst,
dev_lcomplete_match * 100 / dev_total_inst, best_epoch))
print(
'best dev Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
% (dev_ucorrect_nopunc, dev_lcorrect_nopunc, dev_total_nopunc,
dev_ucorrect_nopunc * 100 / dev_total_nopunc,
dev_lcorrect_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_match_nopunc * 100 / dev_total_inst,
dev_lcomplete_match_nopunc * 100 / dev_total_inst, best_epoch))
print('best dev Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' %
(dev_root_correct, dev_total_root,
dev_root_correct * 100 / dev_total_root, best_epoch))
print(
'----------------------------------------------------------------------------------------------------------------------------'
)
if test_path:
print(
'best test W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
% (test_ucorrect, test_lcorrect, test_total, test_ucorrect *
100 / test_total, test_lcorrect * 100 / test_total,
test_ucomlpete_match * 100 / test_total_inst,
test_lcomplete_match * 100 / test_total_inst, best_epoch))
print(
'best test Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
%
(test_ucorrect_nopunc, test_lcorrect_nopunc, test_total_nopunc,
test_ucorrect_nopunc * 100 / test_total_nopunc,
test_lcorrect_nopunc * 100 / test_total_nopunc,
test_ucomlpete_match_nopunc * 100 / test_total_inst,
test_lcomplete_match_nopunc * 100 / test_total_inst,
best_epoch))
print(
'best test Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)'
% (test_root_correct, test_total_root,
test_root_correct * 100 / test_total_root, best_epoch))
print(
'============================================================================================================================'
)
if decay == max_decay:
break
def save_result():
result_path = model_name + '.result.txt'
best_dev_Punc = 'best dev W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
dev_ucorrect, dev_lcorrect, dev_total,
dev_ucorrect * 100 / dev_total, dev_lcorrect * 100 / dev_total,
dev_ucomlpete_match * 100 / dev_total_inst,
dev_lcomplete_match * 100 / dev_total_inst, best_epoch)
best_dev_noPunc = 'best dev Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
dev_ucorrect_nopunc, dev_lcorrect_nopunc, dev_total_nopunc,
dev_ucorrect_nopunc * 100 / dev_total_nopunc,
dev_lcorrect_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_match_nopunc * 100 / dev_total_inst,
dev_lcomplete_match_nopunc * 100 / dev_total_inst, best_epoch)
best_dev_Root = 'best dev Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' % (
dev_root_correct, dev_total_root,
dev_root_correct * 100 / dev_total_root, best_epoch)
f = open(result_path, 'w')
f.write(best_dev_Punc.encode('utf-8') + '\n')
f.write(best_dev_noPunc.encode('utf-8') + '\n')
f.write(best_dev_Root.encode('utf-8'))
f.close()
save_result()
def main():
args_parser = argparse.ArgumentParser(description='Tuning with graph-based parsing')
args_parser.add_argument('--schedule', type=int, help='schedule for learning rate decay')
args_parser.add_argument('--unk_replace', type=float, default=0., help='The rate to replace a singleton word with UNK')
args_parser.add_argument('--freeze', action='store_true', help='frozen the word embedding (disable fine-tuning).')
args = args_parser.parse_args()
logger = get_logger("GraphParser")
mode = "FastLSTM" #fast lstm here
obj = "cross_entropy"
decoding = "mst" #mst decode here
train_path = "data/train.stanford.conll"
dev_path = "data/dev.stanford.conll"
test_path = "data/test.stanford.conll"
model_path = "models/parsing/biaffine/"
model_name = 'network.pt'
num_epochs = 80
batch_size = 32
hidden_size = 512
arc_space = 512
type_space = 128
num_layers = 10
num_filters = 1
learning_rate = 0.001
opt = "adam" #default adam
momentum = 0.9
betas = (0.9, 0.9)
eps = 1e-4
decay_rate = 0.75
clip = 5 #what is clip
gamma = 0
schedule = 10 #?What is this?
p_rnn = (0.05,0.05)
p_in = 0.33
p_out = 0.33
unk_replace = args.unk_replace# ?what is this?
punctuation = ['.','``', "''", ':', ',']
freeze = args.freeze
word_embedding = 'glove'
word_path = "data/glove.6B.100d.txt"
use_char = False
char_embedding = None
#char_path = args.char_path
use_pos = True
pos_dim = 100
word_dict, word_dim = utils.load_embedding_dict(word_embedding, word_path)
char_dict = None
char_dim = 0
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets/')
model_name = os.path.join(model_path, model_name)
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_data.create_alphabets(alphabet_path, train_path, data_paths=[dev_path, test_path],
max_vocabulary_size=50000, embedd_dict=word_dict)
num_words = word_alphabet.size()
num_chars = char_alphabet.size()
num_pos = pos_alphabet.size()
num_types = type_alphabet.size()
#print(word_alphabet.instance2index)
logger.info("Word Alphabet Size: %d" % num_words)
logger.info("Character Alphabet Size: %d" % num_chars)
logger.info("POS Alphabet Size: %d" % num_pos)
logger.info("Type Alphabet Size: %d" % num_types)
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
print(use_gpu)
data_train = conllx_data.read_data_to_variable(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, symbolic_root=True)
# data_train = conllx_data.read_data(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
# num_data = sum([len(bucket) for bucket in data_train])
num_data = sum(data_train[1])
"""
print("bucket_size")
print(data_train[1])
print("___________________________________data_train")
print(data_train[0])
"""
data_dev = conllx_data.read_data_to_variable(dev_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, volatile=True, symbolic_root=True)
data_test = conllx_data.read_data_to_variable(test_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, volatile=True, symbolic_root=True)
punct_set = None
if punctuation is not None:
punct_set = set(punctuation)
logger.info("punctuations(%d): %s" % (len(punct_set), ' '.join(punct_set)))
def construct_word_embedding_table():
scale = np.sqrt(3.0 / word_dim)
table = np.empty([word_alphabet.size(), word_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.zeros([1, word_dim]).astype(np.float32) if freeze else np.random.uniform(-scale, scale, [1, word_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in word_dict:
embedding = word_dict[word]
elif word.lower() in word_dict:
embedding = word_dict[word.lower()]
else:
embedding = np.zeros([1, word_dim]).astype(np.float32) if freeze else np.random.uniform(-scale, scale, [1, word_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('word OOV: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
window = 3
if obj == 'cross_entropy':
network = BiRecurrentConvBiAffine(word_dim, num_words, char_dim, num_chars, pos_dim, num_pos, num_filters, window,
mode, hidden_size, num_layers, num_types, arc_space, type_space,
embedd_word=word_table, embedd_char=None,
p_in=p_in, p_out=p_out, p_rnn=p_rnn, biaffine=True, pos=use_pos, char=use_char)
def save_args():
arg_path = model_name + '.arg.json'
arguments = [word_dim, num_words, char_dim, num_chars, pos_dim, num_pos, num_filters, window,
mode, hidden_size, num_layers, num_types, arc_space, type_space]
kwargs = {'p_in': p_in, 'p_out': p_out, 'p_rnn': p_rnn, 'biaffine': True, 'pos': use_pos, 'char': use_char}
json.dump({'args': arguments, 'kwargs': kwargs}, open(arg_path, 'w'), indent=4)
if freeze:
network.word_embedd.freeze()
if use_gpu:
network.cuda()
save_args()
#pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
#gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
##print parameters:
print("number of parameters")
num_param = sum([param.nelement() for param in network.parameters()])
print(num_param)
def generate_optimizer(opt, lr, params):
params = filter(lambda param: param.requires_grad, params)
if opt == 'adam':
return Adam(params, lr=lr, betas=betas, weight_decay=gamma, eps=eps)
lr = learning_rate
optim = generate_optimizer(opt, lr, network.parameters())
opt_info = 'opt: %s, ' % opt
if opt == 'adam':
opt_info += 'betas=%s, eps=%.1e' % (betas, eps)
word_status = 'frozen' if freeze else 'fine tune'
char_status = 'enabled' if use_char else 'disabled'
pos_status = 'enabled' if use_pos else 'disabled'
logger.info("Embedding dim: word=%d (%s), char=%d (%s), pos=%d (%s)" % (word_dim, word_status, char_dim, char_status, pos_dim, pos_status))
logger.info("CNN: filter=%d, kernel=%d" % (num_filters, window))
logger.info("RNN: %s, num_layer=%d, hidden=%d, arc_space=%d, type_space=%d" % (mode, num_layers, hidden_size, arc_space, type_space))
logger.info("train: obj: %s, l2: %f, (#data: %d, batch: %d, clip: %.2f, unk replace: %.2f)" % (obj, gamma, num_data, batch_size, clip, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" % (p_in, p_out, p_rnn))
logger.info("decoding algorithm: %s" % decoding)
logger.info(opt_info)
#logger.info("Attention")
num_batches = num_data / batch_size + 1
dev_ucorrect = 0.0
dev_lcorrect = 0.0
dev_ucomlpete_match = 0.0
dev_lcomplete_match = 0.0
dev_ucorrect_nopunc = 0.0
dev_lcorrect_nopunc = 0.0
dev_ucomlpete_match_nopunc = 0.0
dev_lcomplete_match_nopunc = 0.0
dev_root_correct = 0.0
best_epoch = 0
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_root_correct = 0.0
test_total = 0
test_total_nopunc = 0
test_total_inst = 0
test_total_root = 0
if decoding == 'greedy':
decode = network.decode
elif decoding == 'mst':
decode = network.decode_mst
else:
raise ValueError('Unknown decoding algorithm: %s' % decoding)
patient = 0
decay = 0
max_decay = 9
double_schedule_decay = 5
f = open("testout.csv", "wt")
writer = csv.writer(f)
writer.writerow(('train', 'dev'))
for epoch in range(1, num_epochs + 1):
print(epoch, mode, opt, lr, eps, decay_rate, schedule, patient, decay)
print('Epoch %d (%s, optim: %s, learning rate=%.6f, eps=%.1e, decay rate=%.2f (schedule=%d, patient=%d, decay=%d)): ' % (epoch, mode, opt, lr, eps, decay_rate, schedule, patient, decay))
train_err = 0.
train_err_arc = 0.
train_err_type = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, pos, heads, types, masks, lengths = conllx_data.get_batch_variable(data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss_arc, loss_type = network.loss(word, char, pos, heads, types, mask=masks, length=lengths)
loss = loss_arc + loss_type
loss.backward()
clip_grad_norm(network.parameters(), clip)
optim.step()
num_inst = word.size(0) if obj == 'crf' else masks.data.sum() - word.size(0)
train_err += loss.data[0] * num_inst
train_err_arc += loss_arc.data[0] * num_inst
train_err_type += loss_type.data[0] * num_inst
train_total += num_inst
#bp()
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 10 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, arc: %.4f, type: %.4f, time left: %.2fs' % (batch, num_batches, train_err / train_total,
train_err_arc / train_total, train_err_type / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, arc: %.4f, type: %.4f, time: %.2fs' % (num_batches, train_err / train_total, train_err_arc / train_total, train_err_type / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
dev_ucorr = 0.0
dev_lcorr = 0.0
dev_total = 0
dev_ucomlpete = 0.0
dev_lcomplete = 0.0
dev_ucorr_nopunc = 0.0
dev_lcorr_nopunc = 0.0
dev_total_nopunc = 0
dev_ucomlpete_nopunc = 0.0
dev_lcomplete_nopunc = 0.0
dev_root_corr = 0.0
dev_total_root = 0.0
dev_total_inst = 0.0
t_ucorr = 0.0
t_lcorr = 0.0
t_total = 0
t_ucomlpete = 0.0
t_lcomplete = 0.0
t_ucorr_nopunc = 0.0
t_lcorr_nopunc = 0.0
t_total_nopunc = 0
t_ucomlpete_nopunc = 0.0
t_lcomplete_nopunc = 0.0
t_root_corr = 0.0
t_total_root = 0.0
t_total_inst = 0.0
list_iter = iter(conllx_data.iterate_batch_variable(data_train, batch_size))
for batch in list_iter:
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(word, char, pos, mask=masks, length=lengths, leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
stats, stats_nopunc, stats_root, num_inst = parser.eval(word, pos, heads_pred, types_pred, heads, types, word_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
#print(t_ucorr)
t_ucorr += ucorr
t_lcorr += lcorr
t_total += total
t_ucomlpete += ucm
t_lcomplete += lcm
t_ucorr_nopunc += ucorr_nopunc
t_lcorr_nopunc += lcorr_nopunc
t_total_nopunc += total_nopunc
t_ucomlpete_nopunc += ucm_nopunc
t_lcomplete_nopunc += lcm_nopunc
t_root_corr += corr_root
t_total_root += total_root
t_total_inst += num_inst
for _ in range(10):
next(list_iter, None)
for batch in conllx_data.iterate_batch_variable(data_dev, batch_size):
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(word, char, pos, mask=masks, length=lengths, leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
stats, stats_nopunc, stats_root, num_inst = parser.eval(word, pos, heads_pred, types_pred, heads, types, word_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
dev_ucorr += ucorr
dev_lcorr += lcorr
dev_total += total
dev_ucomlpete += ucm
dev_lcomplete += lcm
dev_ucorr_nopunc += ucorr_nopunc
dev_lcorr_nopunc += lcorr_nopunc
dev_total_nopunc += total_nopunc
dev_ucomlpete_nopunc += ucm_nopunc
dev_lcomplete_nopunc += lcm_nopunc
dev_root_corr += corr_root
dev_total_root += total_root
dev_total_inst += num_inst
writer.writerow((t_ucorr_nopunc*100/t_total_nopunc,dev_ucorr_nopunc*100/dev_total_nopunc))
f.flush()
#pred_writer.close()
#gold_writer.close()
print('Train Wo Punct:%.2f%%'% (t_ucorr_nopunc*100/t_total_nopunc))
print('W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%' % (
dev_ucorr, dev_lcorr, dev_total, dev_ucorr * 100 / dev_total, dev_lcorr * 100 / dev_total, dev_ucomlpete * 100 / dev_total_inst, dev_lcomplete * 100 / dev_total_inst))
print('Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%' % (
dev_ucorr_nopunc, dev_lcorr_nopunc, dev_total_nopunc, dev_ucorr_nopunc * 100 / dev_total_nopunc,
dev_lcorr_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_nopunc * 100 / dev_total_inst, dev_lcomplete_nopunc * 100 / dev_total_inst))
print('Root: corr: %d, total: %d, acc: %.2f%%' %(dev_root_corr, dev_total_root, dev_root_corr * 100 / dev_total_root))
if dev_lcorrect_nopunc< dev_lcorr_nopunc or (dev_lcorrect_nopunc == dev_lcorr_nopunc and dev_ucorrect_nopunc < dev_ucorr_nopunc):
dev_ucorrect_nopunc = dev_ucorr_nopunc
dev_lcorrect_nopunc = dev_lcorr_nopunc
dev_ucomlpete_match_nopunc = dev_ucomlpete_nopunc
dev_lcomplete_match_nopunc = dev_lcomplete_nopunc
dev_ucorrect = dev_ucorr
dev_lcorrect = dev_lcorr
dev_ucomlpete_match = dev_ucomlpete
dev_lcomplete_match = dev_lcomplete
dev_root_correct = dev_root_corr
best_epoch = epoch
patient = 0
# torch.save(network, model_name)
torch.save(network.state_dict(), model_name)
#pred_filename = 'tmp/%spred_test%d' % (str(uid), epoch)
#pred_writer.start(pred_filename)
#gold_filename = 'tmp/%sgold_test%d' % (str(uid), epoch)
#gold_writer.start(gold_filename)
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_total = 0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_total_nopunc = 0
test_total_inst = 0
test_root_correct = 0.0
test_total_root = 0
for batch in conllx_data.iterate_batch_variable(data_test, batch_size):
word, char, pos, heads, types, masks, lengths = batch
heads_pred, types_pred = decode(word, char, pos, mask=masks, length=lengths, leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
#pred_writer.write(word, pos, heads_pred, types_pred, lengths, symbolic_root=True)
#gold_writer.write(word, pos, heads, types, lengths, symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser.eval(word, pos, heads_pred, types_pred, heads, types, word_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total += total
test_ucomlpete_match += ucm
test_lcomplete_match += lcm
test_ucorrect_nopunc += ucorr_nopunc
test_lcorrect_nopunc += lcorr_nopunc
test_total_nopunc += total_nopunc
test_ucomlpete_match_nopunc += ucm_nopunc
test_lcomplete_match_nopunc += lcm_nopunc
test_root_correct += corr_root
test_total_root += total_root
test_total_inst += num_inst
#pred_writer.close()
#gold_writer.close()
else:
if dev_ucorr_nopunc * 100 / dev_total_nopunc < dev_ucorrect_nopunc * 100 / dev_total_nopunc - 5 or patient >= schedule:
# network = torch.load(model_name)
network.load_state_dict(torch.load(model_name))
lr = lr * decay_rate
optim = generate_optimizer(opt, lr, network.parameters())
if decoding == 'greedy':
decode = network.decode
elif decoding == 'mst':
decode = network.decode_mst
else:
raise ValueError('Unknown decoding algorithm: %s' % decoding)
patient = 0
decay += 1
if decay % double_schedule_decay == 0:
schedule *= 2
else:
patient += 1
print('----------------------------------------------------------------------------------------------------------------------------')
print('best dev W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
dev_ucorrect, dev_lcorrect, dev_total, dev_ucorrect * 100 / dev_total, dev_lcorrect * 100 / dev_total,
dev_ucomlpete_match * 100 / dev_total_inst, dev_lcomplete_match * 100 / dev_total_inst,
best_epoch))
print('best dev Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
dev_ucorrect_nopunc, dev_lcorrect_nopunc, dev_total_nopunc,
dev_ucorrect_nopunc * 100 / dev_total_nopunc, dev_lcorrect_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_match_nopunc * 100 / dev_total_inst, dev_lcomplete_match_nopunc * 100 / dev_total_inst,
best_epoch))
print('best dev Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' % (
dev_root_correct, dev_total_root, dev_root_correct * 100 / dev_total_root, best_epoch))
print('----------------------------------------------------------------------------------------------------------------------------')
print('best test W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
test_ucorrect, test_lcorrect, test_total, test_ucorrect * 100 / test_total, test_lcorrect * 100 / test_total,
test_ucomlpete_match * 100 / test_total_inst, test_lcomplete_match * 100 / test_total_inst,
best_epoch))
print('best test Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)' % (
test_ucorrect_nopunc, test_lcorrect_nopunc, test_total_nopunc,
test_ucorrect_nopunc * 100 / test_total_nopunc, test_lcorrect_nopunc * 100 / test_total_nopunc,
test_ucomlpete_match_nopunc * 100 / test_total_inst, test_lcomplete_match_nopunc * 100 / test_total_inst,
best_epoch))
print('best test Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' % (
test_root_correct, test_total_root, test_root_correct * 100 / test_total_root, best_epoch))
print('============================================================================================================================')
if decay == max_decay:
break
