def main():
args_parser = argparse.ArgumentParser(description='Testing with stack pointer parser')
args_parser.add_argument('--model_path', help='path for parser model directory', required=True)
args_parser.add_argument('--model_name', help='parser model file', required=True)
args_parser.add_argument('--output_path', help='path for result with parser model', required=True)
args_parser.add_argument('--test', required=True)
args_parser.add_argument('--beam', type=int, default=1, help='Beam size for decoding')
args_parser.add_argument('--use_gpu', action='store_true', help='use the gpu')
args_parser.add_argument('--batch_size', type=int, default=32)
args = args_parser.parse_args()
logger = get_logger("PtrParser Decoding")
model_path = args.model_path
model_name = os.path.join(model_path, args.model_name)
output_path = args.output_path
beam = args.beam
use_gpu = args.use_gpu
test_path = args.test
batch_size = args.batch_size
def load_args():
with open("{}.arg.json".format(model_name)) as f:
key_parameters = json.loads(f.read())
return key_parameters['args'], key_parameters['kwargs']
# arguments = [word_dim, num_words, char_dim, num_chars, pos_dim, num_pos, char_num_filters, char_window, eojul_num_filters, eojul_window,
# mode, input_size_decoder, hidden_size, encoder_layers, decoder_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, 'eojul': use_eojul, 'prior_order': prior_order,
# 'skipConnect': skipConnect, 'grandPar': grandPar, 'sibling': sibling}
arguments, kwarguments = load_args()
mode = arguments[10]
input_size_decoder = arguments[11]
hidden_size = arguments[12]
arc_space = arguments[16]
type_space = arguments[17]
encoder_layers = arguments[13]
decoder_layers = arguments[14]
char_num_filters = arguments[6]
eojul_num_filters = arguments[8]
p_rnn = kwarguments['p_rnn']
p_in = kwarguments['p_in']
p_out = kwarguments['p_out']
prior_order = kwarguments['prior_order']
skipConnect = kwarguments['skipConnect']
grandPar = kwarguments['grandPar']
sibling = kwarguments['sibling']
use_char = kwarguments['char']
use_pos = kwarguments['pos']
use_eojul = kwarguments['eojul']
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets/')
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_stacked_data.load_alphabets(alphabet_path)
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")
data_test = conllx_stacked_data.read_stacked_data_to_variable(test_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, use_gpu=use_gpu, prior_order=prior_order)
num_data = sum(data_test[1])
word_table = None
word_dim = arguments[0]
char_table = None
char_dim = arguments[2]
pos_table = None
pos_dim = arguments[4]
char_window = arguments[7]
eojul_window = arguments[9]
if arguments[1] != num_words:
print("Mismatching number of word vocabulary({} != {})".format(arguments[1], num_words))
exit()
if arguments[3] != num_chars:
print("Mismatching number of character vocabulary({} != {})".format(arguments[3], num_chars))
exit()
if arguments[5] != num_pos:
print("Mismatching number of part-of-speech vocabulary({} != {})".format(arguments[5], num_pos))
exit()
if arguments[15] != num_types:
print("Mismatching number types of vocabulary({} != {})".format(arguments[14], num_types))
exit()
network = StackPtrNet(word_dim, num_words, char_dim, num_chars, pos_dim, num_pos, char_num_filters, char_window, eojul_num_filters, eojul_window,
mode, input_size_decoder, hidden_size, encoder_layers, decoder_layers,
num_types, arc_space, type_space,
embedd_word=word_table, embedd_char=char_table, embedd_pos=pos_table, p_in=p_in, p_out=p_out, p_rnn=p_rnn,
biaffine=True, pos=use_pos, char=use_char, eojul=use_eojul, prior_order=prior_order,
skipConnect=skipConnect, grandPar=grandPar, sibling=sibling)
if use_gpu:
network.cuda()
print("loading model: {}".format(model_name))
if use_gpu:
network.load_state_dict(torch.load(model_name))
else:
network.load_state_dict(torch.load(model_name, map_location='cpu'))
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
logger.info("Embedding dim: word=%d, char=%d, pos=%d" % (word_dim, char_dim, pos_dim))
logger.info("Char CNN: filter=%d, kernel=%d" % (char_num_filters, char_window))
logger.info("Eojul CNN: filter=%d, kernel=%d" % (eojul_num_filters, eojul_window))
logger.info("RNN: %s, num_layer=(%d, %d), input_dec=%d, hidden=%d, arc_space=%d, type_space=%d" % (
mode, encoder_layers, decoder_layers, input_size_decoder, hidden_size, arc_space, type_space))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" % (p_in, p_out, p_rnn))
logger.info('prior order: %s, grand parent: %s, sibling: %s, ' % (prior_order, grandPar, sibling))
logger.info('skip connect: %s, beam: %d, use_gpu: %s' % (skipConnect, beam, use_gpu))
network.eval()
pred_filename = '%s/pred_test.txt' % (output_path, )
pred_writer.start(pred_filename)
gold_filename = '%s/gold_test.txt' % (output_path, )
gold_writer.start(gold_filename)
test_ucorrect = 0.0
test_lcorrect = 0.0
test_total = 0
test_total_inst = 0
test_root_correct = 0.0
test_total_root = 0
num_back = 0
for batch in conllx_stacked_data.iterate_batch_stacked_variable(data_test, batch_size, use_gpu=use_gpu):
input_encoder, _, sentences = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(word, char, pos, mask=masks, length=lengths, beam=beam, leading_symbolic=conllx_stacked_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(sentences, word, pos, heads_pred, types_pred, lengths, symbolic_root=True)
gold_writer.write(sentences, word, pos, heads, types, lengths, symbolic_root=True)
stats, _, stats_root, num_inst = parser.eval(word, pos, heads_pred, types_pred, heads, types, word_alphabet, pos_alphabet, lengths, punct_set=None,
symbolic_root=True)
ucorr, lcorr, total, _, _ = stats
corr_root, total_root = stats_root
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total += total
test_root_correct += corr_root
test_total_root += total_root
test_total_inst += num_inst
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = "({:.1f}%){}/{}".format(test_total_inst * 100 / num_data, test_total_inst, num_data)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
pred_writer.close()
gold_writer.close()
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('----------------------------------------------------------------------------------------------------------------------------')
print('best test W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%' % (
test_ucorrect, test_lcorrect, test_total, test_ucorrect * 100 / test_total, test_lcorrect * 100 / test_total))
print('best test Root: corr: %d, total: %d, acc: %.2f%%' % (test_root_correct, test_total_root, test_root_correct * 100 / test_total_root))
print('============================================================================================================================')
def analyze():
np.set_printoptions(linewidth=100000)
pred_path = 'tmp/analyze_pred_%s' % str(uid)
data_gold = conllx_stacked_data.read_stacked_data_to_tensor(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
data_pred = conllx_stacked_data.read_stacked_data_to_tensor(
pred_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
gold_iter = conllx_stacked_data.iterate_batch_stacked_variable(
data_gold, 1)
test_iter = conllx_stacked_data.iterate_batch_stacked_variable(
data_pred, 1)
model_err = 0
search_err = 0
type_err = 0
for gold, pred in zip(gold_iter, test_iter):
gold_encoder, gold_decoder = gold
word, char, pos, gold_heads, gold_types, masks, lengths = gold_encoder
gold_stacked_heads, gold_children, gold_siblings, gold_stacked_types, gold_skip_connect, gold_mask_d, gold_lengths_d = gold_decoder
pred_encoder, pred_decoder = pred
_, _, _, pred_heads, pred_types, _, _ = pred_encoder
pred_stacked_heads, pred_children, pred_siblings, pred_stacked_types, pred_skip_connect, pred_mask_d, pred_lengths_d = pred_decoder
assert gold_heads.size() == pred_heads.size(
), 'sentence dis-match.'
ucorr_stack = torch.eq(pred_children, gold_children).float()
lcorr_stack = ucorr_stack * torch.eq(pred_stacked_types,
gold_stacked_types).float()
ucorr_stack = (ucorr_stack * gold_mask_d).data.sum()
lcorr_stack = (lcorr_stack * gold_mask_d).data.sum()
num_stack = gold_mask_d.data.sum()
if lcorr_stack < num_stack:
loss_pred, loss_pred_arc, loss_pred_type = calc_loss(
network, word, char, pos, pred_heads, pred_stacked_heads,
pred_children, pred_siblings, pred_stacked_types,
pred_skip_connect, masks, lengths, pred_mask_d,
pred_lengths_d)
loss_gold, loss_gold_arc, loss_gold_type = calc_loss(
network, word, char, pos, gold_heads, gold_stacked_heads,
gold_children, gold_siblings, gold_stacked_types,
gold_skip_connect, masks, lengths, gold_mask_d,
gold_lengths_d)
if display_inst:
print('%d, %d, %d' % (ucorr_stack, lcorr_stack, num_stack))
print(
'pred(arc, type): %.4f (%.4f, %.4f), gold(arc, type): %.4f (%.4f, %.4f)'
% (loss_pred, loss_pred_arc, loss_pred_type, loss_gold,
loss_gold_arc, loss_gold_type))
word = word[0].data.cpu().numpy()
pos = pos[0].data.cpu().numpy()
head_gold = gold_heads[0].data.cpu().numpy()
type_gold = gold_types[0].data.cpu().numpy()
head_pred = pred_heads[0].data.cpu().numpy()
type_pred = pred_types[0].data.cpu().numpy()
display(word, pos, head_gold, type_gold, head_pred,
type_pred, lengths[0], word_alphabet, pos_alphabet,
type_alphabet)
length_dec = gold_lengths_d[0]
gold_display = np.empty([3, length_dec])
gold_display[0] = gold_stacked_types.data[0].cpu().numpy(
)[:length_dec]
gold_display[1] = gold_children.data[0].cpu().numpy(
)[:length_dec]
gold_display[2] = gold_stacked_heads.data[0].cpu().numpy(
)[:length_dec]
print(gold_display)
print(
'--------------------------------------------------------'
)
pred_display = np.empty([3,
pred_lengths_d[0]])[:length_dec]
pred_display[0] = pred_stacked_types.data[0].cpu().numpy(
)[:length_dec]
pred_display[1] = pred_children.data[0].cpu().numpy(
)[:length_dec]
pred_display[2] = pred_stacked_heads.data[0].cpu().numpy(
)[:length_dec]
print(pred_display)
print(
'========================================================'
)
raw_input()
if ucorr_stack == num_stack:
type_err += 1
elif loss_pred < loss_gold:
model_err += 1
else:
search_err += 1
print('type errors: %d' % type_err)
print('model errors: %d' % model_err)
print('search errors: %d' % search_err)
def main():
uid = uuid.uuid4().hex[:6]
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
args_parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU', 'FastLSTM'],
help='architecture of rnn',
default='FastLSTM')
args_parser.add_argument('--num_epochs',
type=int,
default=10,
help='Number of training epochs')
args_parser.add_argument('--batch_size',
type=int,
default=32,
help='Number of sentences in each batch')
args_parser.add_argument('--decoder_input_size',
type=int,
default=256,
help='Number of input units in decoder RNN.')
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('--encoder_layers',
type=int,
default=1,
help='Number of layers of encoder RNN')
args_parser.add_argument('--decoder_layers',
type=int,
default=1,
help='Number of layers of decoder RNN')
args_parser.add_argument('--char_num_filters',
type=int,
default=50,
help='Number of filters in CNN(Character Level)')
args_parser.add_argument('--eojul_num_filters',
type=int,
default=100,
help='Number of filters in CNN(Eojul Level)')
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('--eojul',
action='store_true',
help='use eojul embedding and CNN.')
args_parser.add_argument('--word_dim',
type=int,
default=100,
help='Dimension of Word embeddings')
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',
default='adam')
args_parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Learning rate')
args_parser.add_argument('--decay_rate',
type=float,
default=0.75,
help='Decay rate of learning rate')
args_parser.add_argument('--max_decay',
type=int,
default=9,
help='Number of decays before stop')
args_parser.add_argument('--double_schedule_decay',
type=int,
default=5,
help='Number of decays to double schedule')
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('--coverage',
type=float,
default=0.0,
help='weight for coverage loss')
args_parser.add_argument('--p_rnn',
nargs=2,
type=float,
default=[0.33, 0.33],
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('--label_smooth',
type=float,
default=1.0,
help='weight of label smoothing method')
args_parser.add_argument('--skipConnect',
action='store_true',
help='use skip connection for decoder RNN.')
args_parser.add_argument('--grandPar',
action='store_true',
help='use grand parent.')
args_parser.add_argument('--sibling',
action='store_true',
help='use sibling.')
args_parser.add_argument(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
args_parser.add_argument('--schedule',
type=int,
default=20,
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('--beam',
type=int,
default=1,
help='Beam size for decoding')
args_parser.add_argument(
'--word_embedding',
choices=['random', 'word2vec', '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(
'--freeze',
action='store_true',
help='frozen the word embedding (disable fine-tuning).')
args_parser.add_argument('--char_embedding',
choices=['random', 'word2vec'],
help='Embedding for characters',
required=True)
args_parser.add_argument('--char_path',
help='path for character embedding dict')
args_parser.add_argument('--pos_embedding',
choices=['random', 'word2vec'],
help='Embedding for part of speeches',
required=True)
args_parser.add_argument('--pos_path',
help='path for part of speech 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('--use_gpu',
action='store_true',
help='use the gpu')
args = args_parser.parse_args()
logger = get_logger("PtrParser")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
model_path = args.model_path
model_name = "{}_{}".format(str(uid), args.model_name)
num_epochs = args.num_epochs
batch_size = args.batch_size
input_size_decoder = args.decoder_input_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
encoder_layers = args.encoder_layers
decoder_layers = args.decoder_layers
char_num_filters = args.char_num_filters
eojul_num_filters = args.eojul_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
cov = args.coverage
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
label_smooth = args.label_smooth
unk_replace = args.unk_replace
prior_order = args.prior_order
skipConnect = args.skipConnect
grandPar = args.grandPar
sibling = args.sibling
use_gpu = args.use_gpu
beam = args.beam
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
pos_embedding = args.pos_embedding
pos_path = args.pos_path
use_pos = args.pos
if word_embedding != 'random':
word_dict, word_dim = utils.load_embedding_dict(
word_embedding, word_path)
else:
word_dict = {}
word_dim = args.word_dim
if char_embedding != 'random':
char_dict, char_dim = utils.load_embedding_dict(
char_embedding, char_path)
else:
if use_char:
char_dict = {}
char_dim = args.char_dim
else:
char_dict = None
if pos_embedding != 'random':
pos_dict, pos_dim = utils.load_embedding_dict(pos_embedding, pos_path)
else:
if use_pos:
pos_dict = {}
pos_dim = args.pos_dim
else:
pos_dict = None
use_eojul = args.eojul
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_stacked_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 = use_gpu
data_train = conllx_stacked_data.read_stacked_data_to_variable(
train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
prior_order=prior_order)
num_data = sum(data_train[1])
data_dev = conllx_stacked_data.read_stacked_data_to_variable(
dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
prior_order=prior_order)
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
prior_order=prior_order)
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_stacked_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)
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_stacked_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)
def construct_pos_embedding_table():
if pos_dict is None:
return None
scale = np.sqrt(3.0 / pos_dim)
table = np.empty([num_pos, pos_dim], dtype=np.float32)
table[conllx_stacked_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, pos_dim]).astype(np.float32)
oov = 0
for pos, index in pos_alphabet.items():
if pos in pos_dict:
embedding = pos_dict[pos]
else:
embedding = np.random.uniform(-scale, scale,
[1, pos_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('pos OOV: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
char_table = construct_char_embedding_table()
pos_table = construct_pos_embedding_table()
char_window = 3
eojul_window = 3
network = StackPtrNet(word_dim,
num_words,
char_dim,
num_chars,
pos_dim,
num_pos,
char_num_filters,
char_window,
eojul_num_filters,
eojul_window,
mode,
input_size_decoder,
hidden_size,
encoder_layers,
decoder_layers,
num_types,
arc_space,
type_space,
embedd_word=word_table,
embedd_char=char_table,
embedd_pos=pos_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
biaffine=True,
pos=use_pos,
char=use_char,
eojul=use_eojul,
prior_order=prior_order,
skipConnect=skipConnect,
grandPar=grandPar,
sibling=sibling)
def save_args():
arg_path = model_name + '.arg.json'
arguments = [
word_dim, num_words, char_dim, num_chars, pos_dim, num_pos,
char_num_filters, char_window, eojul_num_filters, eojul_window,
mode, input_size_decoder, hidden_size, encoder_layers,
decoder_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,
'eojul': use_eojul,
'prior_order': prior_order,
'skipConnect': skipConnect,
'grandPar': grandPar,
'sibling': sibling
}
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)
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)
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("Char CNN: filter=%d, kernel=%d" %
(char_num_filters, char_window))
logger.info("Eojul CNN: filter=%d, kernel=%d" %
(eojul_num_filters, eojul_window))
logger.info(
"RNN: %s, num_layer=(%d, %d), input_dec=%d, hidden=%d, arc_space=%d, type_space=%d"
% (mode, encoder_layers, decoder_layers, input_size_decoder,
hidden_size, arc_space, type_space))
logger.info(
"train: cov: %.1f, (#data: %d, batch: %d, clip: %.2f, label_smooth: %.2f, unk_repl: %.2f)"
% (cov, num_data, batch_size, clip, label_smooth, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
logger.info('prior order: %s, grand parent: %s, sibling: %s, ' %
(prior_order, grandPar, sibling))
logger.info('skip connect: %s, beam: %d, use_gpu: %s' %
(skipConnect, beam, use_gpu))
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
patient = 0
decay = 0.
max_decay = args.max_decay
double_schedule_decay = args.double_schedule_decay
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 (%d, %d))): '
% (epoch, mode, opt, lr, eps, decay_rate, schedule, patient, decay,
max_decay, double_schedule_decay))
train_err_arc_leaf = 0.
train_err_arc_non_leaf = 0.
train_err_type_leaf = 0.
train_err_type_non_leaf = 0.
train_err_cov = 0.
train_total_leaf = 0.
train_total_non_leaf = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
input_encoder, input_decoder = conllx_stacked_data.get_batch_stacked_variable(
data_train,
batch_size,
unk_replace=unk_replace,
use_gpu=use_gpu)
word, char, pos, heads, types, masks_e, lengths_e = input_encoder
stacked_heads, children, sibling, stacked_types, skip_connect, masks_d, lengths_d = input_decoder
optim.zero_grad()
loss_arc_leaf, loss_arc_non_leaf, \
loss_type_leaf, loss_type_non_leaf, \
loss_cov, num_leaf, num_non_leaf = network.loss(word, char, pos, heads, stacked_heads, children, sibling, stacked_types, label_smooth,
skip_connect=skip_connect, mask_e=masks_e, length_e=lengths_e, mask_d=masks_d, length_d=lengths_d)
loss_arc = loss_arc_leaf + loss_arc_non_leaf
loss_type = loss_type_leaf + loss_type_non_leaf
loss = loss_arc + loss_type + cov * loss_cov
loss.backward()
clip_grad_norm_(network.parameters(), clip)
optim.step()
num_leaf = num_leaf.item() ##180809 data[0] --> item()
num_non_leaf = num_non_leaf.item() ##180809 data[0] --> item()
train_err_arc_leaf += loss_arc_leaf.item(
) * num_leaf ##180809 data[0] --> item()
train_err_arc_non_leaf += loss_arc_non_leaf.item(
) * num_non_leaf ##180809 data[0] --> item()
train_err_type_leaf += loss_type_leaf.item(
) * num_leaf ##180809 data[0] --> item()
train_err_type_non_leaf += loss_type_non_leaf.item(
) * num_non_leaf ##180809 data[0] --> item()
train_err_cov += loss_cov.item() * (num_leaf + num_non_leaf
) ##180809 data[0] --> item()
train_total_leaf += num_leaf
train_total_non_leaf += num_non_leaf
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)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf +
train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
log_info = 'train: %d/%d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, err, err_arc, err_arc_leaf,
err_arc_non_leaf, err_type, err_type_leaf,
err_type_non_leaf, err_cov, 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)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf + train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
print(
'train: %d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time: %.2fs'
% (num_batches, err, err_arc, err_arc_leaf, err_arc_non_leaf,
err_type, err_type_leaf, err_type_non_leaf, err_cov,
time.time() - start_time))
#torch.save(network.state_dict(), model_name+"."+str(epoch))
#continue
# evaluate performance on dev data
network.eval()
tmp_root = 'tmp'
if not os.path.isdir(tmp_root):
logger.info('Creating temporary folder(%s)' % (tmp_root, ))
os.makedirs(tmp_root)
pred_filename = '%s/%spred_dev%d' % (tmp_root, str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = '%s/%sgold_dev%d' % (tmp_root, 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_stacked_data.iterate_batch_stacked_variable(
data_dev, batch_size, use_gpu=use_gpu):
input_encoder, _, sentences = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_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(sentences,
word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(sentences,
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_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_stacked_data.iterate_batch_stacked_variable(
data_test, batch_size, use_gpu=use_gpu):
input_encoder, _, sentences = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_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(sentences,
word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(sentences,
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())
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))
if test_total_inst != 0 or test_total != 0:
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
def stackptr(model_path, model_name, test_path, punct_set, use_gpu, logger,
args):
pos_embedding = args.pos_embedding
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_stacked_data.create_alphabets(alphabet_path, None, pos_embedding,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)
beam = args.beam
ordered = args.ordered
display_inst = args.display
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()
prior_order = kwargs['prior_order']
logger.info('use gpu: %s, beam: %d, order: %s (%s)' %
(use_gpu, beam, prior_order, ordered))
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order,
is_test=True)
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet, pos_embedding)
#gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
logger.info('model: %s' % model_name)
network = StackPtrNet(*args, **kwargs)
network.load_state_dict(torch.load(model_name))
if use_gpu:
network.cuda()
else:
network.cpu()
network.eval()
pred_writer.start(model_path + 'tmp/analyze_pred')
sent = 0
start_time = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(
data_test, 1):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks, lengths = input_encoder
stacked_heads, children, siblings, stacked_types, skip_connect, mask_d, lengths_d = input_decoder
heads_pred, types_pred, children_pred, stacked_types_pred = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
ordered=ordered,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
mask_d = mask_d.data
mask_leaf = torch.eq(children, stacked_heads).float()
mask_non_leaf = (1.0 - mask_leaf)
mask_leaf = mask_leaf * mask_d
mask_non_leaf = mask_non_leaf * mask_d
num_leaf = mask_leaf.sum()
num_non_leaf = mask_non_leaf.sum()
# ------------------------------------------------------------------------------------------------
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)
pred_writer.close()
def stackptr(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_stacked_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)
beam = args.beam
ordered = args.ordered
display_inst = args.display
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()
prior_order = kwargs['prior_order']
logger.info('use gpu: %s, beam: %d, order: %s (%s)' %
(use_gpu, beam, prior_order, ordered))
data_test = conllx_stacked_data.read_stacked_data_to_tensor(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
logger.info('model: %s' % model_name)
network = StackPtrNet(*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
test_ucorrect_stack_leaf = 0.0
test_ucorrect_stack_non_leaf = 0.0
test_lcorrect_stack_leaf = 0.0
test_lcorrect_stack_non_leaf = 0.0
test_leaf = 0
test_non_leaf = 0
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_stacked_data.iterate_batch_stacked_variable(
data_test, 1):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks, lengths = input_encoder
stacked_heads, children, siblings, stacked_types, skip_connect, mask_d, lengths_d = input_decoder
heads_pred, types_pred, children_pred, stacked_types_pred = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
ordered=ordered,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
mask_d = mask_d.data
mask_leaf = torch.eq(children, stacked_heads).float()
mask_non_leaf = (1.0 - mask_leaf)
mask_leaf = mask_leaf * mask_d
mask_non_leaf = mask_non_leaf * mask_d
num_leaf = mask_leaf.sum()
num_non_leaf = mask_non_leaf.sum()
ucorr_stack = torch.eq(children_pred, children).float()
lcorr_stack = ucorr_stack * torch.eq(stacked_types_pred,
stacked_types).float()
ucorr_stack_leaf = (ucorr_stack * mask_leaf).sum()
ucorr_stack_non_leaf = (ucorr_stack * mask_non_leaf).sum()
lcorr_stack_leaf = (lcorr_stack * mask_leaf).sum()
lcorr_stack_non_leaf = (lcorr_stack * mask_non_leaf).sum()
test_ucorrect_stack_leaf += ucorr_stack_leaf
test_ucorrect_stack_non_leaf += ucorr_stack_non_leaf
test_lcorrect_stack_leaf += lcorr_stack_leaf
test_lcorrect_stack_non_leaf += lcorr_stack_non_leaf
test_leaf += num_leaf
test_non_leaf += num_non_leaf
# ------------------------------------------------------------------------------------------------
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('\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))
print(
'============================================================================================================================'
)
print(
'Stack leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%'
% (test_ucorrect_stack_leaf, test_lcorrect_stack_leaf, test_leaf,
test_ucorrect_stack_leaf * 100 / test_leaf,
test_lcorrect_stack_leaf * 100 / test_leaf))
print(
'Stack non_leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%'
% (test_ucorrect_stack_non_leaf, test_lcorrect_stack_non_leaf,
test_non_leaf, test_ucorrect_stack_non_leaf * 100 / test_non_leaf,
test_lcorrect_stack_non_leaf * 100 / test_non_leaf))
print(
'============================================================================================================================'
)
def analyze():
np.set_printoptions(linewidth=100000)
pred_path = 'tmp/analyze_pred_%s' % str(uid)
data_gold = conllx_stacked_data.read_stacked_data_to_tensor(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
data_pred = conllx_stacked_data.read_stacked_data_to_tensor(
pred_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
gold_iter = conllx_stacked_data.iterate_batch_stacked_variable(
data_gold, 1)
test_iter = conllx_stacked_data.iterate_batch_stacked_variable(
data_pred, 1)
model_err = 0
search_err = 0
type_err = 0
for gold, pred in zip(gold_iter, test_iter):
gold_encoder, gold_decoder = gold
word, char, pos, gold_heads, gold_types, masks, lengths = gold_encoder
gold_stacked_heads, gold_children, gold_siblings, gold_stacked_types, gold_skip_connect, gold_mask_d, gold_lengths_d = gold_decoder
pred_encoder, pred_decoder = pred
_, _, _, pred_heads, pred_types, _, _ = pred_encoder
pred_stacked_heads, pred_children, pred_siblings, pred_stacked_types, pred_skip_connect, pred_mask_d, pred_lengths_d = pred_decoder
assert gold_heads.size() == pred_heads.size(
), 'sentence dis-match.'
ucorr_stack = torch.eq(pred_children, gold_children).float()
lcorr_stack = ucorr_stack * torch.eq(pred_stacked_types,
gold_stacked_types).float()
ucorr_stack = (ucorr_stack * gold_mask_d).data.sum()
lcorr_stack = (lcorr_stack * gold_mask_d).data.sum()
num_stack = gold_mask_d.data.sum()
if lcorr_stack < num_stack:
loss_pred, loss_pred_arc, loss_pred_type = calc_loss(
network, word, char, pos, pred_heads, pred_stacked_heads,
pred_children, pred_siblings, pred_stacked_types,
pred_skip_connect, masks, lengths, pred_mask_d,
pred_lengths_d)
loss_gold, loss_gold_arc, loss_gold_type = calc_loss(
network, word, char, pos, gold_heads, gold_stacked_heads,
gold_children, gold_siblings, gold_stacked_types,
gold_skip_connect, masks, lengths, gold_mask_d,
gold_lengths_d)
if display_inst:
print('%d, %d, %d' % (ucorr_stack, lcorr_stack, num_stack))
print(
'pred(arc, type): %.4f (%.4f, %.4f), gold(arc, type): %.4f (%.4f, %.4f)'
% (loss_pred, loss_pred_arc, loss_pred_type, loss_gold,
loss_gold_arc, loss_gold_type))
word = word[0].data.cpu().numpy()
pos = pos[0].data.cpu().numpy()
head_gold = gold_heads[0].data.cpu().numpy()
type_gold = gold_types[0].data.cpu().numpy()
head_pred = pred_heads[0].data.cpu().numpy()
type_pred = pred_types[0].data.cpu().numpy()
display(word, pos, head_gold, type_gold, head_pred,
type_pred, lengths[0], word_alphabet, pos_alphabet,
type_alphabet)
length_dec = gold_lengths_d[0]
gold_display = np.empty([3, length_dec])
gold_display[0] = gold_stacked_types.data[0].cpu().numpy(
)[:length_dec]
gold_display[1] = gold_children.data[0].cpu().numpy(
)[:length_dec]
gold_display[2] = gold_stacked_heads.data[0].cpu().numpy(
)[:length_dec]
print(gold_display)
print(
'--------------------------------------------------------'
)
pred_display = np.empty([3,
pred_lengths_d[0]])[:length_dec]
pred_display[0] = pred_stacked_types.data[0].cpu().numpy(
)[:length_dec]
pred_display[1] = pred_children.data[0].cpu().numpy(
)[:length_dec]
pred_display[2] = pred_stacked_heads.data[0].cpu().numpy(
)[:length_dec]
print(pred_display)
print(
'========================================================'
)
raw_input()
if ucorr_stack == num_stack:
type_err += 1
elif loss_pred < loss_gold:
model_err += 1
else:
search_err += 1
print('type errors: %d' % type_err)
print('model errors: %d' % model_err)
print('search errors: %d' % search_err)
analyze()
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
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('--punctuation',
nargs='+',
type=str,
help='List of punctuations')
args_parser.add_argument('--beam',
type=int,
default=1,
help='Beam size for decoding')
args_parser.add_argument('--ordered',
action='store_true',
help='Using order constraints in decoding')
args_parser.add_argument('--display',
action='store_true',
help='Display wrong examples')
args_parser.add_argument('--gpu', action='store_true', help='Using GPU')
args_parser.add_argument(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
args = args_parser.parse_args()
logger = get_logger("Analyzer")
test_path = args.test
model_path = args.model_path
model_name = args.model_name
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_stacked_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)
use_gpu = args.gpu
prior_order = args.prior_order
beam = args.beam
ordered = args.ordered
display_inst = args.display
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
logger.info('use gpu: %s, beam: %d, ordered: %s' %
(use_gpu, beam, ordered))
punct_set = None
punctuation = args.punctuation
if punctuation is not None:
punct_set = set(punctuation)
logger.info("punctuations(%d): %s" %
(len(punct_set), ' '.join(punct_set)))
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
logger.info('model: %s' % model_name)
network = 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
test_ucorrect_stack_leaf = 0.0
test_ucorrect_stack_non_leaf = 0.0
test_lcorrect_stack_leaf = 0.0
test_lcorrect_stack_non_leaf = 0.0
test_leaf = 0
test_non_leaf = 0
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_stacked_data.iterate_batch_stacked_variable(
data_test, 1):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks, lengths = input_encoder
stacked_heads, children, siblings, stacked_types, skip_connect, mask_d, lengths_d = input_decoder
heads_pred, types_pred, children_pred, stacked_types_pred = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
ordered=ordered,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
mask_d = mask_d.data
mask_leaf = torch.eq(children, stacked_heads).float()
mask_non_leaf = (1.0 - mask_leaf)
mask_leaf = mask_leaf * mask_d
mask_non_leaf = mask_non_leaf * mask_d
num_leaf = mask_leaf.sum()
num_non_leaf = mask_non_leaf.sum()
ucorr_stack = torch.eq(children_pred, children).float()
lcorr_stack = ucorr_stack * torch.eq(stacked_types_pred,
stacked_types).float()
ucorr_stack_leaf = (ucorr_stack * mask_leaf).sum()
ucorr_stack_non_leaf = (ucorr_stack * mask_non_leaf).sum()
lcorr_stack_leaf = (lcorr_stack * mask_leaf).sum()
lcorr_stack_non_leaf = (lcorr_stack * mask_non_leaf).sum()
test_ucorrect_stack_leaf += ucorr_stack_leaf
test_ucorrect_stack_non_leaf += ucorr_stack_non_leaf
test_lcorrect_stack_leaf += lcorr_stack_leaf
test_lcorrect_stack_non_leaf += lcorr_stack_non_leaf
test_leaf += num_leaf
test_non_leaf += num_non_leaf
# ------------------------------------------------------------------------------------------------
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('\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))
print(
'============================================================================================================================'
)
print(
'Stack leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%'
% (test_ucorrect_stack_leaf, test_lcorrect_stack_leaf, test_leaf,
test_ucorrect_stack_leaf * 100 / test_leaf,
test_lcorrect_stack_leaf * 100 / test_leaf))
print(
'Stack non_leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%'
% (test_ucorrect_stack_non_leaf, test_lcorrect_stack_non_leaf,
test_non_leaf, test_ucorrect_stack_non_leaf * 100 / test_non_leaf,
test_lcorrect_stack_non_leaf * 100 / test_non_leaf))
print(
'============================================================================================================================'
)
def analyze():
np.set_printoptions(linewidth=100000)
pred_path = 'tmp/analyze_pred_%s' % str(uid)
data_gold = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
data_pred = conllx_stacked_data.read_stacked_data_to_variable(
pred_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
gold_iter = conllx_stacked_data.iterate_batch_stacked_variable(
data_gold, 1)
test_iter = conllx_stacked_data.iterate_batch_stacked_variable(
data_pred, 1)
model_err = 0
search_err = 0
type_err = 0
for gold, pred in zip(gold_iter, test_iter):
gold_encoder, gold_decoder = gold
word, char, pos, gold_heads, gold_types, masks, lengths = gold_encoder
gold_stacked_heads, gold_children, gold_siblings, gold_stacked_types, gold_skip_connect, gold_mask_d, gold_lengths_d = gold_decoder
pred_encoder, pred_decoder = pred
_, _, _, pred_heads, pred_types, _, _ = pred_encoder
pred_stacked_heads, pred_children, pred_siblings, pred_stacked_types, pred_skip_connect, pred_mask_d, pred_lengths_d = pred_decoder
assert gold_heads.size() == pred_heads.size(
), 'sentence dis-match.'
ucorr_stack = torch.eq(pred_children, gold_children).float()
lcorr_stack = ucorr_stack * torch.eq(pred_stacked_types,
gold_stacked_types).float()
ucorr_stack = (ucorr_stack * gold_mask_d).data.sum()
lcorr_stack = (lcorr_stack * gold_mask_d).data.sum()
num_stack = gold_mask_d.data.sum()
if lcorr_stack < num_stack:
loss_pred, loss_pred_arc, loss_pred_type = calc_loss(
network, word, char, pos, pred_heads, pred_stacked_heads,
pred_children, pred_siblings, pred_stacked_types,
pred_skip_connect, masks, lengths, pred_mask_d,
pred_lengths_d)
loss_gold, loss_gold_arc, loss_gold_type = calc_loss(
network, word, char, pos, gold_heads, gold_stacked_heads,
gold_children, gold_siblings, gold_stacked_types,
gold_skip_connect, masks, lengths, gold_mask_d,
gold_lengths_d)
if display_inst:
print('%d, %d, %d' % (ucorr_stack, lcorr_stack, num_stack))
print(
'pred(arc, type): %.4f (%.4f, %.4f), gold(arc, type): %.4f (%.4f, %.4f)'
% (loss_pred, loss_pred_arc, loss_pred_type, loss_gold,
loss_gold_arc, loss_gold_type))
word = word[0].data.cpu().numpy()
pos = pos[0].data.cpu().numpy()
head_gold = gold_heads[0].data.cpu().numpy()
type_gold = gold_types[0].data.cpu().numpy()
head_pred = pred_heads[0].data.cpu().numpy()
type_pred = pred_types[0].data.cpu().numpy()
display(word, pos, head_gold, type_gold, head_pred,
type_pred, lengths[0], word_alphabet, pos_alphabet,
type_alphabet)
length_dec = gold_lengths_d[0]
gold_display = np.empty([3, length_dec])
gold_display[0] = gold_stacked_types.data[0].cpu().numpy(
)[:length_dec]
gold_display[1] = gold_children.data[0].cpu().numpy(
)[:length_dec]
gold_display[2] = gold_stacked_heads.data[0].cpu().numpy(
)[:length_dec]
print(gold_display)
print(
'--------------------------------------------------------'
)
pred_display = np.empty([3,
pred_lengths_d[0]])[:length_dec]
pred_display[0] = pred_stacked_types.data[0].cpu().numpy(
)[:length_dec]
pred_display[1] = pred_children.data[0].cpu().numpy(
)[:length_dec]
pred_display[2] = pred_stacked_heads.data[0].cpu().numpy(
)[:length_dec]
print(pred_display)
print(
'========================================================'
)
raw_input()
if ucorr_stack == num_stack:
type_err += 1
elif loss_pred < loss_gold:
model_err += 1
else:
search_err += 1
print('type errors: %d' % type_err)
print('model errors: %d' % model_err)
print('search errors: %d' % search_err)
analyze()
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
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_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', 'adadelta'],
help='optimization algorithm')
args_parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Learning rate')
args_parser.add_argument('--decay_rate',
type=float,
default=0.5,
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('--coverage',
type=float,
default=0.0,
help='weight for coverage loss')
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(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
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('--beam',
type=int,
default=1,
help='Beam size for decoding')
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_parser.add_argument('--model_name',
help='name for saving model file.',
required=True)
args = args_parser.parse_args()
logger = get_logger("PtrParser")
mode = args.mode
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)
rho = 0.9
eps = 1e-6
decay_rate = args.decay_rate
clip = args.clip
gamma = args.gamma
cov = args.coverage
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
prior_order = args.prior_order
beam = args.beam
punctuation = args.punctuation
word_embedding = args.word_embedding
word_path = args.word_path
char_embedding = args.char_embedding
char_path = args.char_path
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)
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_stacked_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_stacked_data.read_stacked_data_to_variable(
train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
prior_order=prior_order)
num_data = sum(data_train[1])
data_dev = conllx_stacked_data.read_stacked_data_to_variable(
dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
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_stacked_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_stacked_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
network = StackPtrNet(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,
prior_order=prior_order)
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)
def generate_optimizer(opt, lr, params):
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 == 'adadelta':
return Adadelta(params,
lr=lr,
rho=rho,
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 == 'adadelta':
opt_info += 'rho=%.2f, eps=%.1e' % (rho, eps)
logger.info("Embedding dim: word=%d, char=%d, pos=%d" %
(word_dim, char_dim, pos_dim))
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: cov: %.1f, (#data: %d, batch: %d, clip: %.2f, dropout(in, out, rnn): (%.2f, %.2f, %s), unk_repl: %.2f)"
% (cov, num_data, batch_size, clip, p_in, p_out, p_rnn, unk_replace))
logger.info('prior order: %s, beam: %d' % (prior_order, beam))
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
patient = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s, optim: %s, learning rate=%.6f, decay rate=%.2f (schedule=%d, patient=%d)): '
% (epoch, mode, opt, lr, decay_rate, schedule, patient))
train_err_arc_leaf = 0.
train_err_arc_non_leaf = 0.
train_err_type_leaf = 0.
train_err_type_non_leaf = 0.
train_err_cov = 0.
train_total_leaf = 0.
train_total_non_leaf = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
input_encoder, input_decoder = conllx_stacked_data.get_batch_stacked_variable(
data_train, batch_size, unk_replace=unk_replace)
word, char, pos, heads, types, masks_e, lengths_e = input_encoder
stacked_heads, children, stacked_types, masks_d, lengths_d = input_decoder
optim.zero_grad()
loss_arc_leaf, loss_arc_non_leaf, \
loss_type_leaf, loss_type_non_leaf, \
loss_cov, num_leaf, num_non_leaf = network.loss(word, char, pos, stacked_heads, children, stacked_types,
mask_e=masks_e, length_e=lengths_e, mask_d=masks_d, length_d=lengths_d)
loss_arc = loss_arc_leaf + loss_arc_non_leaf
loss_type = loss_type_leaf + loss_type_non_leaf
loss = loss_arc + loss_type + cov * loss_cov
loss.backward()
clip_grad_norm(network.parameters(), clip)
optim.step()
num_leaf = num_leaf.data[0]
num_non_leaf = num_non_leaf.data[0]
train_err_arc_leaf += loss_arc_leaf.data[0] * num_leaf
train_err_arc_non_leaf += loss_arc_non_leaf.data[0] * num_non_leaf
train_err_type_leaf += loss_type_leaf.data[0] * num_leaf
train_err_type_non_leaf += loss_type_non_leaf.data[0] * num_non_leaf
train_err_cov += loss_cov.data[0] * (num_leaf + num_non_leaf)
train_total_leaf += num_leaf
train_total_non_leaf += num_non_leaf
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)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf +
train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
log_info = 'train: %d/%d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, err, err_arc, err_arc_leaf,
err_arc_non_leaf, err_type, err_type_leaf,
err_type_non_leaf, err_cov, 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)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf + train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
print(
'train: %d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time: %.2fs'
% (num_batches, err, err_arc, err_arc_leaf, err_arc_non_leaf,
err_type, err_type_leaf, err_type_non_leaf, err_cov,
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_stacked_data.iterate_batch_stacked_variable(
data_dev, batch_size):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_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
patient = 0
torch.save(network, 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_stacked_data.iterate_batch_stacked_variable(
data_test, batch_size):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_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 patient < schedule:
patient += 1
else:
network = torch.load(model_name)
lr = lr * decay_rate
optim = generate_optimizer(opt, lr, network.parameters())
patient = 0
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(
'============================================================================================================================'
)
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
args_parser.add_argument('--seed',
type=int,
default=1234,
help='random seed for reproducibility')
args_parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU', 'FastLSTM'],
help='architecture of rnn',
required=True)
args_parser.add_argument('--batch_size',
type=int,
default=64,
help='Number of sentences in each batch')
args_parser.add_argument('--decoder_input_size',
type=int,
default=256,
help='Number of input units in decoder RNN.')
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('--encoder_layers',
type=int,
default=1,
help='Number of layers of encoder RNN')
args_parser.add_argument('--decoder_layers',
type=int,
default=1,
help='Number of layers of decoder RNN')
args_parser.add_argument('--num_filters',
type=int,
default=50,
help='Number of filters in CNN')
args_parser.add_argument(
'--trans_hid_size',
type=int,
default=1024,
help='#hidden units in point-wise feed-forward in transformer')
args_parser.add_argument(
'--d_k',
type=int,
default=64,
help='d_k for multi-head-attention in transformer encoder')
args_parser.add_argument(
'--d_v',
type=int,
default=64,
help='d_v for multi-head-attention in transformer encoder')
args_parser.add_argument('--multi_head_attn',
action='store_true',
help='use multi-head-attention.')
args_parser.add_argument('--num_head',
type=int,
default=8,
help='Value of h in multi-head attention')
args_parser.add_argument(
'--pool_type',
default='mean',
choices=['max', 'mean', 'weight'],
help='pool type to form fixed length vector from word embeddings')
args_parser.add_argument('--train_position',
action='store_true',
help='train positional encoding for transformer.')
args_parser.add_argument('--no_word',
action='store_true',
help='do not use word embedding.')
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('--no_CoRNN',
action='store_true',
help='do not use context RNN.')
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('--learning_rate',
type=float,
default=0.001,
help='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('--coverage',
type=float,
default=0.0,
help='weight for coverage loss')
args_parser.add_argument('--p_rnn',
nargs='+',
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('--label_smooth',
type=float,
default=1.0,
help='weight of label smoothing method')
args_parser.add_argument('--skipConnect',
action='store_true',
help='use skip connection for decoder RNN.')
args_parser.add_argument('--grandPar',
action='store_true',
help='use grand parent.')
args_parser.add_argument('--sibling',
action='store_true',
help='use sibling.')
args_parser.add_argument(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
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('--beam',
type=int,
default=1,
help='Beam size for decoding')
args_parser.add_argument(
'--word_embedding',
choices=['word2vec', '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(
'--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('--vocab_path',
help='path for prebuilt alphabets.',
default=None)
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(
'--position_embed_num',
type=int,
default=200,
help=
'Minimum value of position embedding num, which usually is max-sent-length.'
)
args_parser.add_argument('--num_epochs',
type=int,
default=2000,
help='Number of training epochs')
# lrate schedule with warmup in the first iter.
args_parser.add_argument('--use_warmup_schedule',
action='store_true',
help="Use warmup lrate schedule.")
args_parser.add_argument('--decay_rate',
type=float,
default=0.75,
help='Decay rate of learning rate')
args_parser.add_argument('--max_decay',
type=int,
default=9,
help='Number of decays before stop')
args_parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
args_parser.add_argument('--double_schedule_decay',
type=int,
default=5,
help='Number of decays to double schedule')
args_parser.add_argument(
'--check_dev',
type=int,
default=5,
help='Check development performance in every n\'th iteration')
#
# about decoder's bi-attention scoring with features (default is not using any)
args_parser.add_argument(
'--dec_max_dist',
type=int,
default=0,
help=
"The clamp range of decoder's distance feature, 0 means turning off.")
args_parser.add_argument('--dec_dim_feature',
type=int,
default=10,
help="Dim for feature embed.")
args_parser.add_argument(
'--dec_use_neg_dist',
action='store_true',
help="Use negative distance for dec's distance feature.")
args_parser.add_argument(
'--dec_use_encoder_pos',
action='store_true',
help="Use pos feature combined with distance feature for child nodes.")
args_parser.add_argument(
'--dec_use_decoder_pos',
action='store_true',
help="Use pos feature combined with distance feature for head nodes.")
args_parser.add_argument('--dec_drop_f_embed',
type=float,
default=0.2,
help="Dropout for dec feature embeddings.")
#
# about relation-aware self attention for the transformer encoder (default is not using any)
# args_parser.add_argument('--rel_aware', action='store_true',
# help="Enable relation-aware self-attention (multi_head_attn flag needs to be set).")
args_parser.add_argument(
'--enc_use_neg_dist',
action='store_true',
help="Use negative distance for enc's relational-distance embedding.")
args_parser.add_argument(
'--enc_clip_dist',
type=int,
default=0,
help="The clipping distance for relative position features.")
#
# other options about how to combine multiple input features (have to make some dims fit if not concat)
args_parser.add_argument('--input_concat_embeds',
action='store_true',
help="Concat input embeddings, otherwise add.")
args_parser.add_argument('--input_concat_position',
action='store_true',
help="Concat position embeddings, otherwise add.")
args_parser.add_argument('--position_dim',
type=int,
default=300,
help='Dimension of Position embeddings.')
#
args_parser.add_argument(
'--train_len_thresh',
type=int,
default=100,
help='In training, discard sentences longer than this.')
args = args_parser.parse_args()
# =====
# fix data-prepare seed
random.seed(1234)
np.random.seed(1234)
# model's seed
torch.manual_seed(args.seed)
# =====
# if output directory doesn't exist, create it
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
logger = get_logger("PtrParser", args.model_path + 'log.txt')
logger.info('\ncommand-line params : {0}\n'.format(sys.argv[1:]))
logger.info('{0}\n'.format(args))
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
vocab_path = args.vocab_path if args.vocab_path is not None else args.model_path
model_path = args.model_path
model_name = args.model_name
num_epochs = args.num_epochs
batch_size = args.batch_size
input_size_decoder = args.decoder_input_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
encoder_layers = args.encoder_layers
decoder_layers = args.decoder_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
cov = args.coverage
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
label_smooth = args.label_smooth
unk_replace = args.unk_replace
prior_order = args.prior_order
skipConnect = args.skipConnect
grandPar = args.grandPar
sibling = args.sibling
beam = args.beam
punctuation = args.punctuation
freeze = args.freeze
use_word_emb = not args.no_word
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_con_rnn = not args.no_CoRNN
use_pos = args.pos
pos_dim = args.pos_dim
word_dict, word_dim = utils.load_embedding_dict(
word_embedding, word_path) if use_word_emb else (None, 0)
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(vocab_path, 'alphabets/')
model_name = os.path.join(model_path, model_name)
# todo(warn): should build vocabs previously
assert os.path.isdir(alphabet_path), "should have build vocabs previously"
word_alphabet, char_alphabet, pos_alphabet, type_alphabet, max_sent_length = conllx_stacked_data.create_alphabets(
alphabet_path,
train_path,
data_paths=[dev_path, test_path],
max_vocabulary_size=50000,
embedd_dict=word_dict)
# word_alphabet, char_alphabet, pos_alphabet, type_alphabet, max_sent_length = create_alphabets(alphabet_path,
# train_path, data_paths=[dev_path, test_path], max_vocabulary_size=50000, embedd_dict=word_dict)
max_sent_length = max(max_sent_length, args.position_embed_num)
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()
# ===== the reading
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_stacked_data.read_stacked_data_to_variable(
path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=(not is_train),
prior_order=prior_order,
lang_id=lang_id,
len_thresh=(args.train_len_thresh if is_train else 100000))
return one_data
data_train = _read_one(train_path, True)
num_data = sum(data_train[1])
data_dev = _read_one(dev_path, False)
data_test = _read_one(test_path, False)
# =====
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_stacked_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
logger.info('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_stacked_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
logger.info('character OOV: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table() if use_word_emb else None
char_table = construct_char_embedding_table()
window = 3
network = StackPtrNet(word_dim,
num_words,
char_dim,
num_chars,
pos_dim,
num_pos,
num_filters,
window,
mode,
input_size_decoder,
hidden_size,
encoder_layers,
decoder_layers,
num_types,
arc_space,
type_space,
args.pool_type,
args.multi_head_attn,
args.num_head,
max_sent_length,
args.trans_hid_size,
args.d_k,
args.d_v,
train_position=args.train_position,
embedd_word=word_table,
embedd_char=char_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
biaffine=True,
use_word_emb=use_word_emb,
pos=use_pos,
char=use_char,
prior_order=prior_order,
use_con_rnn=use_con_rnn,
skipConnect=skipConnect,
grandPar=grandPar,
sibling=sibling,
use_gpu=use_gpu,
dec_max_dist=args.dec_max_dist,
dec_use_neg_dist=args.dec_use_neg_dist,
dec_use_encoder_pos=args.dec_use_encoder_pos,
dec_use_decoder_pos=args.dec_use_decoder_pos,
dec_dim_feature=args.dec_dim_feature,
dec_drop_f_embed=args.dec_drop_f_embed,
enc_clip_dist=args.enc_clip_dist,
enc_use_neg_dist=args.enc_use_neg_dist,
input_concat_embeds=args.input_concat_embeds,
input_concat_position=args.input_concat_position,
position_dim=args.position_dim)
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, input_size_decoder, hidden_size,
encoder_layers, decoder_layers, num_types, arc_space, type_space,
args.pool_type, args.multi_head_attn, args.num_head,
max_sent_length, args.trans_hid_size, args.d_k, args.d_v
]
kwargs = {
'train_position': args.train_position,
'use_word_emb': use_word_emb,
'use_con_rnn': use_con_rnn,
'p_in': p_in,
'p_out': p_out,
'p_rnn': p_rnn,
'biaffine': True,
'pos': use_pos,
'char': use_char,
'prior_order': prior_order,
'skipConnect': skipConnect,
'grandPar': grandPar,
'sibling': sibling,
'dec_max_dist': args.dec_max_dist,
'dec_use_neg_dist': args.dec_use_neg_dist,
'dec_use_encoder_pos': args.dec_use_encoder_pos,
'dec_use_decoder_pos': args.dec_use_decoder_pos,
'dec_dim_feature': args.dec_dim_feature,
'dec_drop_f_embed': args.dec_drop_f_embed,
'enc_clip_dist': args.enc_clip_dist,
'enc_use_neg_dist': args.enc_use_neg_dist,
'input_concat_embeds': args.input_concat_embeds,
'input_concat_position': args.input_concat_position,
'position_dim': args.position_dim
}
json.dump({
'args': arguments,
'kwargs': kwargs
},
open(arg_path, 'w'),
indent=4)
if use_word_emb and 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)
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)
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, %d), input_dec=%d, hidden=%d, arc_space=%d, type_space=%d"
% (mode, encoder_layers, decoder_layers, input_size_decoder,
hidden_size, arc_space, type_space))
logger.info(
"train: cov: %.1f, (#data: %d, batch: %d, clip: %.2f, label_smooth: %.2f, unk_repl: %.2f)"
% (cov, num_data, batch_size, clip, label_smooth, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
logger.info('prior order: %s, grand parent: %s, sibling: %s, ' %
(prior_order, grandPar, sibling))
logger.info('skip connect: %s, beam: %d' % (skipConnect, beam))
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
# lrate decay
patient = 0
decay = 0
max_decay = args.max_decay
double_schedule_decay = args.double_schedule_decay
# lrate schedule
step_num = 0
use_warmup_schedule = args.use_warmup_schedule
warmup_factor = (lr + 0.) / num_batches
if use_warmup_schedule:
logger.info("Use warmup lrate for the first epoch, from 0 up to %s." %
(lr, ))
#
for epoch in range(1, num_epochs + 1):
logger.info(
'Epoch %d (%s, optim: %s, learning rate=%.6f, eps=%.1e, decay rate=%.2f '
'(schedule=%d, patient=%d, decay=%d (%d, %d))): ' %
(epoch, mode, opt, lr, eps, decay_rate, schedule, patient, decay,
max_decay, double_schedule_decay))
train_err_arc_leaf = 0.
train_err_arc_non_leaf = 0.
train_err_type_leaf = 0.
train_err_type_non_leaf = 0.
train_err_cov = 0.
train_total_leaf = 0.
train_total_non_leaf = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
# lrate schedule (before each step)
step_num += 1
if use_warmup_schedule and epoch <= 1:
cur_lrate = warmup_factor * step_num
# set lr
for param_group in optim.param_groups:
param_group['lr'] = cur_lrate
# train
input_encoder, input_decoder = conllx_stacked_data.get_batch_stacked_variable(
data_train, batch_size, unk_replace=unk_replace)
word, char, pos, heads, types, masks_e, lengths_e = input_encoder
stacked_heads, children, sibling, stacked_types, skip_connect, masks_d, lengths_d = input_decoder
optim.zero_grad()
loss_arc_leaf, loss_arc_non_leaf, \
loss_type_leaf, loss_type_non_leaf, \
loss_cov, num_leaf, num_non_leaf = network.loss(word, char, pos, heads, stacked_heads, children, sibling,
stacked_types, label_smooth,
skip_connect=skip_connect, mask_e=masks_e,
length_e=lengths_e, mask_d=masks_d, length_d=lengths_d)
loss_arc = loss_arc_leaf + loss_arc_non_leaf
loss_type = loss_type_leaf + loss_type_non_leaf
loss = loss_arc + loss_type + cov * loss_cov
loss.backward()
clip_grad_norm(network.parameters(), clip)
optim.step()
num_leaf = num_leaf.data[0]
num_non_leaf = num_non_leaf.data[0]
train_err_arc_leaf += loss_arc_leaf.data[0] * num_leaf
train_err_arc_non_leaf += loss_arc_non_leaf.data[0] * num_non_leaf
train_err_type_leaf += loss_type_leaf.data[0] * num_leaf
train_err_type_non_leaf += loss_type_non_leaf.data[0] * num_non_leaf
train_err_cov += loss_cov.data[0] * (num_leaf + num_non_leaf)
train_total_leaf += num_leaf
train_total_non_leaf += num_non_leaf
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)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf +
train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
log_info = 'train: %d/%d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, err, err_arc, err_arc_leaf,
err_arc_non_leaf, err_type, err_type_leaf,
err_type_non_leaf, err_cov, 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)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf + train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
logger.info(
'train: %d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time: %.2fs'
% (num_batches, err, err_arc, err_arc_leaf, err_arc_non_leaf,
err_type, err_type_leaf, err_type_non_leaf, err_cov,
time.time() - start_time))
################################################################################################
if epoch % args.check_dev != 0:
continue
# 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_stacked_data.iterate_batch_stacked_variable(
data_dev, batch_size):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_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_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_stacked_data.iterate_batch_stacked_variable(
data_test, batch_size):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_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.load_state_dict(torch.load(model_name))
lr = lr * decay_rate
optim = generate_optimizer(opt, lr, network.parameters())
patient = 0
decay += 1
if decay % double_schedule_decay == 0:
schedule *= 2
else:
patient += 1
logger.info(
'----------------------------------------------------------------------------------------------------------------------------'
)
logger.info(
'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))
logger.info(
'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))
logger.info(
'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))
logger.info(
'----------------------------------------------------------------------------------------------------------------------------'
)
logger.info(
'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))
logger.info(
'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))
logger.info(
'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))
logger.info(
'============================================================================================================================'
)
if decay == max_decay:
break
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
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('--punctuation',
nargs='+',
type=str,
help='List of punctuations')
args_parser.add_argument('--beam',
type=int,
default=1,
help='Beam size for decoding')
args_parser.add_argument('--gpu', action='store_true', help='Using GPU')
args_parser.add_argument(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
args = args_parser.parse_args()
logger = get_logger("Analyzer")
test_path = args.test
model_path = args.model_path
model_name = args.model_name
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_stacked_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)
use_gpu = args.gpu
prior_order = args.prior_order
beam = args.beam
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
logger.info('use gpu: %s, beam: %d' % (use_gpu, beam))
punct_set = None
punctuation = args.punctuation
if punctuation is not None:
punct_set = set(punctuation)
logger.info("punctuations(%d): %s" %
(len(punct_set), ' '.join(punct_set)))
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
pred_writer.start('tmp/analyze_pred')
gold_writer.start('tmp/analyze_gold')
network = torch.load(model_name)
if use_gpu:
network.cuda()
else:
network.cpu()
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
test_ucorrect_stack_leaf = 0.0
test_ucorrect_stack_non_leaf = 0.0
test_lcorrect_stack_leaf = 0.0
test_lcorrect_stack_non_leaf = 0.0
test_leaf = 0
test_non_leaf = 0
sent = 0
network.eval()
start_time = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(
data_test, 1):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks, lengths = input_encoder
stacked_heads, children, stacked_types, skip_connect, mask_d, lengths_d = input_decoder
heads_pred, types_pred, children_pred, stacked_types_pred = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
mask_d = mask_d.data
mask_leaf = torch.eq(children, stacked_heads).float()
mask_non_leaf = (1.0 - mask_leaf)
mask_leaf = mask_leaf * mask_d
mask_non_leaf = mask_non_leaf * mask_d
num_leaf = mask_leaf.sum()
num_non_leaf = mask_non_leaf.sum()
ucorr_stack = torch.eq(children_pred, children).float()
lcorr_stack = ucorr_stack * torch.eq(stacked_types_pred,
stacked_types).float()
ucorr_stack_leaf = (ucorr_stack * mask_leaf).sum()
ucorr_stack_non_leaf = (ucorr_stack * mask_non_leaf).sum()
lcorr_stack_leaf = (lcorr_stack * mask_leaf).sum()
lcorr_stack_non_leaf = (lcorr_stack * mask_non_leaf).sum()
test_ucorrect_stack_leaf += ucorr_stack_leaf
test_ucorrect_stack_non_leaf += ucorr_stack_non_leaf
test_lcorrect_stack_leaf += lcorr_stack_leaf
test_lcorrect_stack_non_leaf += lcorr_stack_non_leaf
test_leaf += num_leaf
test_non_leaf += num_non_leaf
# ------------------------------------------------------------------------------------------------
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('\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))
print(
'============================================================================================================================'
)
print(
'Stack leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%'
% (test_ucorrect_stack_leaf, test_lcorrect_stack_leaf, test_leaf,
test_ucorrect_stack_leaf * 100 / test_leaf,
test_lcorrect_stack_leaf * 100 / test_leaf))
print(
'Stack non_leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%'
% (test_ucorrect_stack_non_leaf, test_lcorrect_stack_non_leaf,
test_non_leaf, test_ucorrect_stack_non_leaf * 100 / test_non_leaf,
test_lcorrect_stack_non_leaf * 100 / test_non_leaf))
print(
'============================================================================================================================'
)
def main():
args_parser = argparse.ArgumentParser(description='Tuning with stack pointer parser')
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('--decoder_input_size', type=int, default=256, help='Number of input units in decoder RNN.')
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('--encoder_layers', type=int, default=1, help='Number of layers of encoder RNN')
args_parser.add_argument('--decoder_layers', type=int, default=1, help='Number of layers of decoder 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('--lemma', action='store_true', help='use lemma 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('--lemma_dim', type=int, default=50, help='Dimension of Lemma embeddings')
args_parser.add_argument('--opt', choices=['adam', 'sgd', 'adamax'], help='optimization algorithm')
args_parser.add_argument('--learning_rate', type=float, default=0.001, help='Learning rate')
args_parser.add_argument('--decay_rate', type=float, default=0.75, help='Decay rate of learning rate')
args_parser.add_argument('--max_decay', type=int, default=9, help='Number of decays before stop')
args_parser.add_argument('--double_schedule_decay', type=int, default=5, help='Number of decays to double schedule')
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('--coverage', type=float, default=0.0, help='weight for coverage loss')
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('--label_smooth', type=float, default=1.0, help='weight of label smoothing method')
args_parser.add_argument('--skipConnect', action='store_true', help='use skip connection for decoder RNN.')
args_parser.add_argument('--grandPar', action='store_true', help='use grand parent.')
args_parser.add_argument('--sibling', action='store_true', help='use sibling.')
args_parser.add_argument('--prior_order', choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'], help='prior order of children.', required=False)
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('--beam', type=int, default=1, help='Beam size for decoding')
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('--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('--test2')
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 = args_parser.parse_args()
logger = get_logger("PtrParser")
print('SEMANTIC DEPENDENCY PARSER with POINTER NETWORKS')
print('CUDA?', torch.cuda.is_available())
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
test_path2 = args.test2
model_path = args.model_path
model_name = args.model_name
num_epochs = args.num_epochs
batch_size = args.batch_size
input_size_decoder = args.decoder_input_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
encoder_layers = args.encoder_layers
decoder_layers = args.decoder_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
cov = args.coverage
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
label_smooth = args.label_smooth
unk_replace = args.unk_replace
prior_order = args.prior_order
skipConnect = args.skipConnect
grandPar = args.grandPar
sibling = args.sibling
beam = args.beam
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_dim = args.pos_dim
use_lemma = args.lemma
lemma_dim = args.lemma_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)
word_alphabet, char_alphabet, pos_alphabet, type_alphabet, lemma_alphabet = conllx_stacked_data.create_alphabets(alphabet_path, train_path, data_paths=[dev_path, test_path, test_path2],
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()
num_lemmas = lemma_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("LEMMA Alphabet Size: %d" % num_lemmas)
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conllx_stacked_data.read_stacked_data_to_variable(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, lemma_alphabet, use_gpu=use_gpu, prior_order=prior_order)
num_data = sum(data_train[1])
data_dev = conllx_stacked_data.read_stacked_data_to_variable(dev_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, lemma_alphabet, use_gpu=use_gpu, volatile=True, prior_order=prior_order)
data_test = conllx_stacked_data.read_stacked_data_to_variable(test_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, lemma_alphabet, use_gpu=use_gpu, volatile=True, prior_order=prior_order)
data_test2 = conllx_stacked_data.read_stacked_data_to_variable(test_path2, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, lemma_alphabet, use_gpu=use_gpu, volatile=True, prior_order=prior_order)
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_stacked_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)
print(torch.__version__)
return torch.from_numpy(table)
def construct_lemma_embedding_table():
scale = np.sqrt(3.0 / lemma_dim)
table = np.empty([lemma_alphabet.size(), lemma_dim], dtype=np.float32)
table[conllx_stacked_data.UNK_ID, :] = np.zeros([1, lemma_dim]).astype(np.float32) if freeze else np.random.uniform(-scale, scale, [1, lemma_dim]).astype(np.float32)
oov = 0
for lemma, index in lemma_alphabet.items():
if lemma in word_dict:
embedding = word_dict[lemma]
elif lemma.lower() in word_dict:
embedding = word_dict[lemma.lower()]
else:
embedding = np.zeros([1, lemma_dim]).astype(np.float32) if freeze else np.random.uniform(-scale, scale, [1, lemma_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('LEMMA OOV: %d' % oov)
print(torch.__version__)
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_stacked_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()
lemma_table = construct_lemma_embedding_table()
window = 3
network = NewStackPtrNet(word_dim, num_words, lemma_dim, num_lemmas, char_dim, num_chars, pos_dim, num_pos, num_filters, window,
mode, input_size_decoder, hidden_size, encoder_layers, decoder_layers,
num_types, arc_space, type_space,
embedd_word=word_table, embedd_char=char_table, embedd_lemma=lemma_table, p_in=p_in, p_out=p_out, p_rnn=p_rnn,
biaffine=True, pos=use_pos, char=use_char, lemma=use_lemma, prior_order=prior_order,
skipConnect=skipConnect, grandPar=grandPar, sibling=sibling)
def save_args():
arg_path = model_name + '.arg.json'
arguments = [word_dim, num_words, lemma_dim, num_lemmas, char_dim, num_chars, pos_dim, num_pos, num_filters, window,
mode, input_size_decoder, hidden_size, encoder_layers, decoder_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, 'lemma': use_lemma, 'prior_order': prior_order,
'skipConnect': skipConnect, 'grandPar': grandPar, 'sibling': sibling}
json.dump({'args': arguments, 'kwargs': kwargs}, open(arg_path, 'w'), indent=4)
if freeze:
network.word_embedd.freeze()
if use_gpu:
print('CUDA IS AVAILABLE')
network.cuda()
else:
print('CUDA IS NOT AVAILABLE', use_gpu)
save_args()
pred_writer = CoNLLXWriter(word_alphabet, lemma_alphabet, char_alphabet, pos_alphabet, type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, lemma_alphabet, char_alphabet, pos_alphabet, type_alphabet)
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)
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'
lemma_status = 'enabled' if use_lemma else 'disabled'
logger.info("Embedding dim: word=%d (%s), lemma=%d (%s) char=%d (%s), pos=%d (%s)" % (word_dim, word_status, lemma_dim, lemma_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, %d), input_dec=%d, hidden=%d, arc_space=%d, type_space=%d" % (mode, encoder_layers, decoder_layers, input_size_decoder, hidden_size, arc_space, type_space))
logger.info("train: cov: %.1f, (#data: %d, batch: %d, clip: %.2f, label_smooth: %.2f, unk_repl: %.2f)" % (cov, num_data, batch_size, clip, label_smooth, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" % (p_in, p_out, p_rnn))
logger.info('prior order: %s, grand parent: %s, sibling: %s, ' % (prior_order, grandPar, sibling))
logger.info('skip connect: %s, beam: %d' % (skipConnect, beam))
logger.info(opt_info)
num_batches = num_data / batch_size + 1
#dev_ucorrect = 0.0
dev_bestLF1 = 0.0
dev_bestUF1 = 0.0
dev_bestUprecision = 0.0
dev_bestLprecision = 0.0
dev_bestUrecall = 0.0
dev_bestLrecall = 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_pred = 0
test_total_gold = 0
#test_total_nopunc = 0
test_total_inst = 0
#test_total_root = 0
test_LF1 = 0.0
test_UF1 = 0.0
test_Uprecision = 0.0
test_Lprecision = 0.0
test_Urecall = 0.0
test_Lrecall = 0.0
test2_ucorrect = 0.0
test2_lcorrect = 0.0
test2_total_pred = 0
test2_total_gold = 0
test2_total_inst = 0
test2_LF1 = 0.0
test2_UF1 = 0.0
test2_Uprecision = 0.0
test2_Lprecision = 0.0
test2_Urecall = 0.0
test2_Lrecall = 0.0
patient = 0
decay = 0
max_decay = args.max_decay
double_schedule_decay = args.double_schedule_decay
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 (%d, %d))): ' % (
epoch, mode, opt, lr, eps, decay_rate, schedule, patient, decay, max_decay, double_schedule_decay))
train_err_cov = 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):
input_encoder, input_decoder = conllx_stacked_data.get_batch_stacked_variable(data_train, batch_size, unk_replace=unk_replace)
word, lemma, char, pos, heads, types, masks_e, lengths_e = input_encoder
stacked_heads, children, sibling, stacked_types, skip_connect, previous, next, masks_d, lengths_d = input_decoder
#print('HEADSSS', heads)
optim.zero_grad()
loss_arc, \
loss_type, \
loss_cov, num = network.loss(word, lemma, char, pos, heads, stacked_heads, children, sibling, stacked_types, previous, next, label_smooth,
skip_connect=skip_connect, mask_e=masks_e, length_e=lengths_e, mask_d=masks_d, length_d=lengths_d)
loss = loss_arc + loss_type + cov * loss_cov
loss.backward()
clip_grad_norm(network.parameters(), clip)
optim.step()
train_err_arc += loss_arc.data[0] * num
train_err_type += loss_type.data[0] * num
train_err_cov += loss_cov.data[0] * num
train_total += num
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
err_arc = train_err_arc / train_total
err_type = train_err_type / train_total
err_cov = train_err_cov / train_total
err = err_arc + err_type + cov * err_cov
print('train: %d loss: %.4f, arc: %.4f, type: %.4f, coverage: %.4f, time: %.2fs' % (
num_batches, err, err_arc, err_type, err_cov, time.time() - start_time))
print('======EVALUATING PERFORMANCE ON DEV======')
# evaluate performance on dev data
network.eval()
#pred_filename = 'tmp/%spred_dev%d' % (str(uid), epoch)
pred_filename = '%spred_dev%d' % (str(uid), epoch)
pred_filename = os.path.join(model_path, pred_filename)
pred_writer.start(pred_filename)
#gold_filename = 'tmp/%sgold_dev%d' % (str(uid), epoch)
gold_filename = '%sgold_dev%d' % (str(uid), epoch)
gold_filename = os.path.join(model_path, gold_filename)
gold_writer.start(gold_filename)
dev_ucorr = 0.0
dev_lcorr = 0.0
dev_total_gold = 0
dev_total_pred = 0
dev_total_inst = 0.0
start_time_dev = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(data_dev, batch_size):
input_encoder, _ = batch
word, lemma, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(word, lemma, char, pos, mask=masks, length=lengths, beam=beam, leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
lemma = lemma.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, lemma, pos, heads_pred, types_pred, lengths, symbolic_root=True)
gold_writer.write(word, lemma, pos, heads, types, lengths, symbolic_root=True)
#stats, stats_nopunc, stats_root, num_inst = parser.evalF1(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
ucorr, lcorr, total_gold, total_pred, num_inst = parser.evalF1(word, lemma, pos, heads_pred, types_pred, heads, types, word_alphabet, lemma_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
dev_ucorr += ucorr
dev_lcorr += lcorr
dev_total_gold += total_gold
dev_total_pred += total_pred
dev_total_inst += num_inst
end_time_dev = time.time()
lasted_time_dev=end_time_dev-start_time_dev
pred_writer.close()
gold_writer.close()
dev_Uprecision=0.
dev_Lprecision=0.
if dev_total_pred!=0:
dev_Uprecision=dev_ucorr * 100 / dev_total_pred
dev_Lprecision=dev_lcorr * 100 / dev_total_pred
dev_Urecall=dev_ucorr * 100 / dev_total_gold
dev_Lrecall=dev_lcorr * 100 / dev_total_gold
if dev_Uprecision ==0. and dev_Urecall==0.:
dev_UF1=0
else:
dev_UF1=2*(dev_Uprecision*dev_Urecall)/(dev_Uprecision+dev_Urecall)
if dev_Lprecision ==0. and dev_Lrecall==0.:
dev_LF1=0
else:
dev_LF1=2*(dev_Lprecision*dev_Lrecall)/(dev_Lprecision+dev_Lrecall)
print('CUR DEV %d: ucorr: %d, lcorr: %d, tot_gold: %d, tot_pred: %d, Uprec: %.2f%%, Urec: %.2f%%, Lprec: %.2f%%, Lrec: %.2f%%, UF1: %.2f%%, LF1: %.2f%%' % (
epoch, dev_ucorr, dev_lcorr, dev_total_gold, dev_total_pred, dev_Uprecision, dev_Urecall, dev_Lprecision, dev_Lrecall, dev_UF1, dev_LF1))
#if dev_lcorrect_nopunc < dev_lcorr_nopunc or (dev_lcorrect_nopunc == dev_lcorr_nopunc and dev_ucorrect_nopunc < dev_ucorr_nopunc):
if dev_bestLF1 < dev_LF1:
dev_bestLF1 = dev_LF1
dev_bestUF1 = dev_UF1
dev_bestUprecision = dev_Uprecision
dev_bestLprecision = dev_Lprecision
dev_bestUrecall = dev_Urecall
dev_bestLrecall = dev_Lrecall
best_epoch = epoch
patient = 0
# torch.save(network, model_name)
torch.save(network.state_dict(), model_name)
print('======EVALUATING PERFORMANCE ON TEST======')
#pred_filename = 'tmp/%spred_test%d' % (str(uid), epoch)
pred_filename = '%spred_test%d' % (str(uid), epoch)
pred_filename = os.path.join(model_path, pred_filename)
pred_writer.start(pred_filename)
#gold_filename = 'tmp/%sgold_test%d' % (str(uid), epoch)
gold_filename = '%sgold_test%d' % (str(uid), epoch)
gold_filename = os.path.join(model_path, gold_filename)
gold_writer.start(gold_filename)
test_ucorrect = 0.0
test_lcorrect = 0.0
test_total_pred = 0
test_total_gold = 0
test_total_inst = 0
start_time_test = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(data_test, batch_size):
input_encoder, _ = batch
word, lemma, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(word, lemma, char, pos, mask=masks, length=lengths, beam=beam, leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
lemma = lemma.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, lemma, pos, heads_pred, types_pred, lengths, symbolic_root=True)
gold_writer.write(word, lemma, pos, heads, types, lengths, symbolic_root=True)
ucorr, lcorr, total_gold, total_pred, num_inst = parser.evalF1(word, lemma, pos, heads_pred, types_pred, heads, types, word_alphabet, lemma_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total_gold += total_gold
test_total_pred += total_pred
test_total_inst += num_inst
end_time_test = time.time()
lasted_time_test=end_time_test-start_time_test
pred_writer.close()
gold_writer.close()
test_Uprecision=0.
test_Lprecision=0.
if test_total_pred!=0:
test_Uprecision=test_ucorrect * 100 / test_total_pred
test_Lprecision=test_lcorrect * 100 / test_total_pred
test_Urecall=test_ucorrect * 100 / test_total_gold
test_Lrecall=test_lcorrect * 100 / test_total_gold
if test_Uprecision ==0. and test_Urecall==0.:
test_UF1=0
else:
test_UF1=2*(test_Uprecision*test_Urecall)/(test_Uprecision+test_Urecall)
if test_Lprecision ==0. and test_Lrecall==0.:
test_LF1=0
else:
test_LF1=2*(test_Lprecision*test_Lrecall)/(test_Lprecision+test_Lrecall)
print('======EVALUATING PERFORMANCE ON TEST 2======')
#pred_filename = 'tmp/%spred_test%d' % (str(uid), epoch)
pred_filename2 = '%spred_test_two%d' % (str(uid), epoch)
pred_filename2 = os.path.join(model_path, pred_filename2)
pred_writer.start(pred_filename2)
#gold_filename = 'tmp/%sgold_test%d' % (str(uid), epoch)
gold_filename2 = '%sgold_test_two%d' % (str(uid), epoch)
gold_filename2 = os.path.join(model_path, gold_filename2)
gold_writer.start(gold_filename2)
test2_ucorrect = 0.0
test2_lcorrect = 0.0
test2_total_pred = 0
test2_total_gold = 0
test2_total_inst = 0
start_time_test2 = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(data_test2, batch_size):
input_encoder, _ = batch
word, lemma, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(word, lemma, char, pos, mask=masks, length=lengths, beam=beam, leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
lemma = lemma.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, lemma, pos, heads_pred, types_pred, lengths, symbolic_root=True)
gold_writer.write(word, lemma, pos, heads, types, lengths, symbolic_root=True)
ucorr, lcorr, total_gold, total_pred, num_inst = parser.evalF1(word, lemma, pos, heads_pred, types_pred, heads, types, word_alphabet, lemma_alphabet, pos_alphabet, lengths, punct_set=punct_set, symbolic_root=True)
test2_ucorrect += ucorr
test2_lcorrect += lcorr
test2_total_gold += total_gold
test2_total_pred += total_pred
test2_total_inst += num_inst
end_time_test2 = time.time()
lasted_time_test2=end_time_test2-start_time_test2
pred_writer.close()
gold_writer.close()
test2_Uprecision=0.
test2_Lprecision=0.
if dev_total_pred!=0:
test2_Uprecision=test2_ucorrect * 100 / test2_total_pred
test2_Lprecision=test2_lcorrect * 100 / test2_total_pred
test2_Urecall=test2_ucorrect * 100 / test2_total_gold
test2_Lrecall=test2_lcorrect * 100 / test2_total_gold
if test2_Uprecision ==0. and test2_Urecall==0.:
test2_UF1=0.
else:
test2_UF1=2*(test2_Uprecision*test2_Urecall)/(test2_Uprecision+test2_Urecall)
if test2_Lprecision ==0 and test2_Lrecall==0:
test2_LF1=0.
else:
test2_LF1=2*(test2_Lprecision*test2_Lrecall)/(test2_Lprecision+test2_Lrecall)
else:
#if dev_ucorr_nopunc * 100 / dev_total_nopunc < dev_ucorrect_nopunc * 100 / dev_total_nopunc - 5 or patient >= schedule:
if dev_LF1 < dev_bestLF1 - 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())
patient = 0
decay += 1
if decay % double_schedule_decay == 0:
schedule *= 2
else:
patient += 1
print('----------------------------------------------------------------------------------------------------------------------------')
print('TIME DEV: ', lasted_time_dev, 'NUM SENTS DEV: ', dev_total_inst, 'SPEED DEV: ', dev_total_inst/lasted_time_dev)
print('DEV: Uprec: %.2f%%, Urec: %.2f%%, Lprec: %.2f%%, Lrec: %.2f%%, UF1: %.2f%%, LF1: %.2f%% (epoch: %d)' % (
dev_bestUprecision, dev_bestUrecall, dev_bestLprecision, dev_bestLrecall, dev_bestUF1, dev_bestLF1, best_epoch))
print('----------------------------------------------------------------------------------------------------------------------------')
print('TIME TEST: ', lasted_time_test, 'NUM SENTS TEST: ', test_total_inst, 'SPEED TEST: ', test_total_inst/lasted_time_test)
print('TEST: ucorr: %d, lcorr: %d, tot_gold: %d, tot_pred: %d, Uprec: %.2f%%, Urec: %.2f%%, Lprec: %.2f%%, Lrec: %.2f%%, UF1: %.2f%%, LF1: %.2f%% (epoch: %d)' % (
test_ucorrect, test_lcorrect, test_total_gold, test_total_pred, test_Uprecision, test_Urecall, test_Lprecision, test_Lrecall, test_UF1, test_LF1, best_epoch))
print('----------------------------------------------------------------------------------------------------------------------------')
print('TIME TEST2: ', lasted_time_test2, 'NUM SENTS TEST: ', test2_total_inst, 'SPEED TEST2: ', test2_total_inst/lasted_time_test2)
print('TEST2: ucorr: %d, lcorr: %d, tot_gold: %d, tot_pred: %d, Uprec: %.2f%%, Urec: %.2f%%, Lprec: %.2f%%, Lrec: %.2f%%, UF1: %.2f%%, LF1: %.2f%% (epoch: %d)' % (
test2_ucorrect, test2_lcorrect, test2_total_gold, test2_total_pred, test2_Uprecision, test2_Urecall, test2_Lprecision, test2_Lrecall, test2_UF1, test2_LF1, best_epoch))
print('============================================================================================================================')
#exit(0)
if decay == max_decay:
break
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
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('--decoder_input_size', type=int, default=256, help='Number of input units in decoder RNN.')
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('--encoder_layers',
type=int,
default=1,
help='Number of layers of encoder RNN')
#args_parser.add_argument('--decoder_layers', type=int, default=1, help='Number of layers of decoder RNN')
args_parser.add_argument('--num_filters',
type=int,
default=50,
help='Number of filters in CNN')
# NOTE: action='store_true' is just to set ON
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')
# NOTE: arg MUST be one of choices(when specified)
args_parser.add_argument('--opt',
choices=['adam', 'sgd', 'adamax'],
help='optimization algorithm')
args_parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Learning rate')
args_parser.add_argument('--decay_rate',
type=float,
default=0.75,
help='Decay rate of learning rate')
args_parser.add_argument('--max_decay',
type=int,
default=9,
help='Number of decays before stop')
args_parser.add_argument('--double_schedule_decay',
type=int,
default=5,
help='Number of decays to double schedule')
args_parser.add_argument('--clip',
type=float,
default=1.0,
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('--coverage',
type=float,
default=0.0,
help='weight for coverage loss')
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('--label_smooth',
type=float,
default=1.0,
help='weight of label smoothing method')
args_parser.add_argument('--skipConnect',
action='store_true',
help='use skip connection for decoder RNN.')
args_parser.add_argument('--grandPar',
action='store_true',
help='use grand parent.')
args_parser.add_argument('--sibling',
action='store_true',
help='use sibling.')
args_parser.add_argument(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
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('--beam',
type=int,
default=1,
help='Beam size for decoding')
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)
# TODO: to include in logging process
args_parser.add_argument('--pos_embedding',
choices=[1, 2, 4],
type=int,
help='Embedding method for korean POS tag',
default=2)
args_parser.add_argument('--pos_path', help='path for pos embedding dict')
args_parser.add_argument('--elmo',
action='store_true',
help='use elmo embedding.')
args_parser.add_argument('--elmo_path',
help='path for elmo embedding model.')
args_parser.add_argument('--elmo_dim',
type=int,
help='dimension for elmo embedding model')
#args_parser.add_argument('--fine_tune_path', help='fine tune starting from this state_dict')
args_parser.add_argument('--model_version',
help='previous model version to load')
#hoon : bert
args_parser.add_argument(
'--bert', action='store_true',
help='use elmo embedding.') # true if use bert(hoon)
args_parser.add_argument(
'--etri_train',
help='path for etri data of bert') # etri train path(hoon)
args_parser.add_argument(
'--etri_dev', help='path for etri data of bert') # etri dev path(hoon)
args_parser.add_argument('--bert_path',
help='path for bert embedding model.') # yjyj
args_parser.add_argument('--bert_dim',
type=int,
help='dimension for bert embedding model') # yjyj
args_parser.add_argument('--bert_learning_rate',
type=float,
default=5e-5,
help='Bert Learning rate')
args_parser.add_argument('--decode',
choices=['mst', 'greedy'],
help='decoding algorithm',
required=True) #yj
args_parser.add_argument('--objective',
choices=['cross_entropy', 'crf'],
default='cross_entropy',
help='objective function of training procedure.')
args = args_parser.parse_args()
logger = get_logger("PtrParser")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
model_path = args.model_path + uid + '/' # for numerous experiments
model_name = args.model_name
num_epochs = args.num_epochs
batch_size = args.batch_size
#input_size_decoder = args.decoder_input_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
encoder_layers = args.encoder_layers
#decoder_layers = args.decoder_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
cov = args.coverage
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
label_smooth = args.label_smooth
unk_replace = args.unk_replace
prior_order = args.prior_order
skipConnect = args.skipConnect
grandPar = args.grandPar
sibling = args.sibling
beam = args.beam
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
# QUESTION: pretrained vector for char?
char_path = args.char_path
use_pos = False
pos_embedding = args.pos_embedding
pos_path = args.pos_path
pos_dict = None
pos_dim = args.pos_dim # NOTE pretrain 있을 경우 pos_dim은 그거 따라감
if pos_path is not None:
pos_dict, pos_dim = utils.load_embedding_dict(
word_embedding,
pos_path) # NOTE 임시적으로 word_embedding(NNLM)이랑 같은 형식
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)
use_elmo = args.elmo
elmo_path = args.elmo_path
elmo_dim = args.elmo_dim
#fine_tune_path = args.fine_tune_path
#bert(hoon)
use_bert = args.bert
#bert yj
bert_path = args.bert_path
bert_dim = args.bert_dim
bert_lr = args.bert_learning_rate
etri_train_path = args.etri_train
etri_dev_path = args.etri_dev
obj = args.objective
decoding = args.decode
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets/')
model_name = os.path.join(model_path, model_name)
# min_occurence=1
data_paths = [dev_path, test_path] if test_path else [dev_path]
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_stacked_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() # 30268
num_chars = char_alphabet.size() # 3545
num_pos = pos_alphabet.size() # 46
num_types = type_alphabet.size() # 39
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 is a list of tuple containing tensors, etc ...
data_train = conllx_stacked_data.read_stacked_data_to_variable(
train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=1,
prior_order=prior_order,
elmo=use_elmo,
bert=use_bert,
etri_path=etri_train_path)
num_data = sum(data_train[2])
data_dev = conllx_stacked_data.read_stacked_data_to_variable(
dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order,
elmo=use_elmo,
bert=use_bert,
etri_path=etri_dev_path)
if test_path:
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order,
elmo=use_elmo)
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)
# NOTE: UNK 관리!
table[conllx_stacked_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:
# NOTE: words not in pretrained are set to random
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_stacked_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, char_dim]).astype(np.float32)
oov = 0
#for char, index, in char_alphabet.items():
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)
def construct_pos_embedding_table():
if pos_dict is None:
return None
scale = np.sqrt(3.0 / char_dim)
table = np.empty([num_pos, pos_dim], dtype=np.float32)
for pos, index in list(pos_alphabet.items()):
if pos in pos_dict:
embedding = pos_dict[pos]
else:
embedding = np.random.uniform(-scale, scale,
[1, char_dim]).astype(np.float32)
table[index, :] = embedding
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
char_table = construct_char_embedding_table()
pos_table = construct_pos_embedding_table()
window = 3
# yj 수정
# network = StackPtrNet(word_dim, num_words, char_dim, num_chars, pos_dim, num_pos, num_filters, window,
# mode, input_size_decoder, hidden_size, encoder_layers, decoder_layers,
# num_types, arc_space, type_space, pos_embedding,
# embedd_word=word_table, embedd_char=char_table, embedd_pos=pos_table, p_in=p_in, p_out=p_out,
# p_rnn=p_rnn, biaffine=True, pos=use_pos, char=use_char, elmo=use_elmo, prior_order=prior_order,
# skipConnect=skipConnect, grandPar=grandPar, sibling=sibling, elmo_path=elmo_path, elmo_dim=elmo_dim,
# bert = use_bert, bert_path=bert_path, bert_dim=bert_dim)
network = BiRecurrentConvBiAffine(word_dim,
num_words,
char_dim,
num_chars,
pos_dim,
num_pos,
num_filters,
window,
mode,
hidden_size,
encoder_layers,
num_types,
arc_space,
type_space,
embedd_word=word_table,
embedd_char=char_table,
embedd_pos=pos_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
biaffine=True,
pos=use_pos,
char=use_char,
elmo=use_elmo,
elmo_path=elmo_path,
elmo_dim=elmo_dim,
bert=use_bert,
bert_path=bert_path,
bert_dim=bert_dim)
# if fine_tune_path is not None:
# pretrained_dict = torch.load(fine_tune_path)
# model_dict = network.state_dict()
# # select
# #model_dict['pos_embedd.weight'] = pretrained_dict['pos_embedd.weight']
# model_dict['word_embedd.weight'] = pretrained_dict['word_embedd.weight']
# #model_dict['char_embedd.weight'] = pretrained_dict['char_embedd.weight']
# network.load_state_dict(model_dict)
model_ver = args.model_version
if model_ver is not None:
savePath = args.model_path + model_ver + 'network.pt'
network.load_state_dict(torch.load(savePath))
logger.info('Load model: %s' % (model_ver))
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, encoder_layers, num_types,
arc_space, type_space, pos_embedding
]
kwargs = {
'p_in': p_in,
'p_out': p_out,
'p_rnn': p_rnn,
'biaffine': True,
'pos': use_pos,
'char': use_char,
'elmo': use_elmo,
'bert': use_bert
}
json.dump({
'args': arguments,
'kwargs': kwargs
},
open(arg_path, 'w', encoding="utf-8"),
indent=4)
with open(arg_path + '.raw_args', 'w', encoding="utf-8") as f:
f.write(str(args))
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 name, param in params if param.requires_grad]
params = [param for name, param in params]
if True:
return AdamW(params, lr=lr, betas=betas, weight_decay=gamma)
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)
# 우선 huggingface 기본 bert option으로 수정
def generate_bert_optimizer(t_total, bert_lr, model):
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
gamma
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters, lr=bert_lr, eps=1e-8)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=0,
t_total=t_total)
return scheduler, optimizer
lr = learning_rate
if use_bert:
scheduler, optim = generate_bert_optimizer(
len(data_train) * num_epochs, lr, network)
#optim = generate_optimizer(opt, lr, network.named_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, %d), input_dec=%d, hidden=%d, arc_space=%d, type_space=%d" % (mode, encoder_layers, decoder_layers, input_size_decoder, hidden_size, arc_space, type_space))
logger.info(
"train: cov: %.1f, (#data: %d, batch: %d, clip: %.2f, label_smooth: %.2f, unk_repl: %.2f)"
% (cov, num_data, batch_size, clip, label_smooth, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
logger.info('prior order: %s, grand parent: %s, sibling: %s, ' %
(prior_order, grandPar, sibling))
logger.info('skip connect: %s, beam: %d' % (skipConnect, beam))
logger.info(opt_info)
num_batches = int(num_data / batch_size + 1) # kwon
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 = args.max_decay
double_schedule_decay = args.double_schedule_decay
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):
# load data
input_encoder, _ = conllx_stacked_data.get_batch_stacked_variable(
data_train,
batch_size,
pos_embedding,
unk_replace=unk_replace,
elmo=use_elmo,
bert=use_bert)
word_elmo = None
if use_elmo:
word, char, pos, heads, types, masks, lengths, word_elmo, word_bert = input_encoder
else:
word, char, pos, heads, types, masks, lengths, word_bert = input_encoder
#stacked_heads, children, sibling, stacked_types, skip_connect, masks_d, lengths_d = input_decoder
optim.zero_grad()
# yjyj
loss_arc, loss_type, bert_word_feature_ids, bert_morp_feature_ids = network.loss(
word,
char,
pos,
heads,
types,
mask=masks,
length=lengths,
input_word_bert=word_bert)
# loss_arc_leaf, loss_arc_non_leaf, \
# loss_type_leaf, loss_type_non_leaf, \
# loss_cov, num_leaf, num_non_leaf = network.loss(word, char, pos, heads, stacked_heads, children, sibling, stacked_types, label_smooth, skip_connect=skip_connect, mask_e=masks_e, \
# length_e=lengths_e, mask_d=masks_d, length_d=lengths_d, input_word_elmo = word_elmo, input_word_bert = word_bert)
# loss_arc = loss_arc_leaf + loss_arc_non_leaf
# loss_type = loss_type_leaf + loss_type_non_leaf
# loss = loss_arc + loss_type + cov * loss_cov # cov is set to 0 by default
loss = loss_arc + loss_type
loss.backward()
clip_grad_norm_(network.parameters(), clip)
optim.step()
if use_bert:
pass
#bert_optim.step()
#scheduler.step()
num_inst = word.size(
0) if obj == 'crf' else masks.data.sum() - word.size(0)
train_err += loss.item() * num_inst
train_err_arc += loss_arc.item() * num_inst
train_err_type += loss_type.item() * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# yjyj
# num_leaf = num_leaf.item()
# num_non_leaf = num_non_leaf.item()
# train_err_arc_leaf += loss_arc_leaf.item() * num_leaf
# train_err_arc_non_leaf += loss_arc_non_leaf.item() * num_non_leaf
#
# train_err_type_leaf += loss_type_leaf.item() * num_leaf
# train_err_type_non_leaf += loss_type_non_leaf.item() * num_non_leaf
#
# train_err_cov += loss_cov.item() * (num_leaf + num_non_leaf)
# train_total_leaf += num_leaf
# train_total_non_leaf += num_non_leaf
#
# time_ave = (time.time() - start_time) / batch
# time_left = (num_batches - batch) * time_ave
# update log
# 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))
# yjyj
# if batch % 10 == 0:
# sys.stdout.write("\b" * num_back)
# sys.stdout.write(" " * num_back)
# sys.stdout.write("\b" * num_back)
# err_arc_leaf = train_err_arc_leaf / train_total_leaf
# err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
# err_arc = err_arc_leaf + err_arc_non_leaf
#
# err_type_leaf = train_err_type_leaf / train_total_leaf
# err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
# err_type = err_type_leaf + err_type_non_leaf
#
# err_cov = train_err_cov / (train_total_leaf + train_total_non_leaf)
#
# err = err_arc + err_type + cov * err_cov
# log_info = 'train: %d/%d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time left (estimated): %.2fs' % (
# batch, num_batches, err, err_arc, err_arc_leaf, err_arc_non_leaf, err_type, err_type_leaf, err_type_non_leaf, err_cov, 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)
# err_arc_leaf = train_err_arc_leaf / train_total_leaf
# err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
# err_arc = err_arc_leaf + err_arc_non_leaf
#
# err_type_leaf = train_err_type_leaf / train_total_leaf
# err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
# err_type = err_type_leaf + err_type_non_leaf
#
# err_cov = train_err_cov / (train_total_leaf + train_total_non_leaf)
#
# err = err_arc + err_type + cov * err_cov
# print('train: %d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time: %.2fs' % (
# num_batches, err, err_arc, err_arc_leaf, err_arc_non_leaf, err_type, err_type_leaf, err_type_non_leaf, err_cov, time.time() - start_time))
# evaluate performance on dev data
network.eval()
pred_filename = model_path + 'tmp/pred_dev%d' % (epoch)
pred_writer.start(pred_filename)
gold_filename = model_path + 'tmp/gold_dev%d' % (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_stacked_data.iterate_batch_stacked_variable(
data_dev, batch_size, pos_embedding, type='dev',
elmo=use_elmo):
input_encoder, _ = batch
#@TODO 여기 input word elmo랑 input word bert 처리
if use_elmo:
word, char, pos, heads, types, masks, lengths, word_elmo, word_bert = input_encoder
heads_pred, types_pred = decode(
word,
char,
pos,
mask=masks,
length=lengths,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
# heads_pred, types_pred, _, _ = network.decode(word, char, pos, input_word_elmo=word_elmo, mask=masks,
# length=lengths, beam=beam,
# leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS, input_word_bert=word_bert)
else:
word, char, pos, heads, types, masks, lengths, word_bert = input_encoder
heads_pred, types_pred, bert_word_feature_ids, bert_morp_feature_ids = decode(
word,
char,
pos,
mask=masks,
length=lengths,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS,
input_word_bert=word_bert)
# heads_pred, types_pred, _, _ = network.decode(word, char, pos, mask=masks, length=lengths, beam=beam,
# leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS, input_word_bert=word_bert)
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,
bert_word_feature_ids=bert_word_feature_ids,
bert_morp_feature_ids=bert_morp_feature_ids)
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 * 1.5 + dev_lcorrect_nopunc < dev_ucorr_nopunc * 1.5 + dev_lcorr_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)
# save embedding to txt
# FIXME format!
#with open(model_path + 'embedding.txt', 'w') as f:
# for word, idx in word_alphabet.items():
# embedding = network.word_embedd.weight[idx, :]
# f.write('{}\t{}\n'.format(word, embedding))
if test_path:
pred_filename = model_path + 'tmp/%spred_test%d' % (str(uid),
epoch)
pred_writer.start(pred_filename)
gold_filename = model_path + '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_stacked_data.iterate_batch_stacked_variable(
data_test, batch_size, pos_embedding, type='dev'):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
# yjyj
# heads_pred, types_pred, _, _ = network.decode(word, char, pos, mask=masks, length=lengths, beam=beam, leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
heads_pred, types_pred = decode(
word,
char,
pos,
mask=masks,
length=lengths,
leading_symbolic=conllx_stacked_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
# = generate_optimizer(opt, lr, network.named_parameters())
optim = generate_bert_optimizer(opt, lr, network)
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(str(best_dev_Punc.encode('utf-8')) + '\n')
f.write(str(best_dev_noPunc.encode('utf-8')) + '\n')
f.write(str(best_dev_Root.encode('utf-8')))
f.close()
save_result()
def stackptr(model_path, model_name, test_path, punct_set, use_gpu, logger,
args):
pos_embedding = args.pos_embedding
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_stacked_data.create_alphabets(
alphabet_path,
None,
pos_embedding,
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)
beam = args.beam
ordered = args.ordered
display_inst = args.display
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()
prior_order = kwargs['prior_order']
logger.info('use gpu: %s, beam: %d, order: %s (%s)' %
(use_gpu, beam, prior_order, ordered))
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
pos_embedding,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order,
is_test=True)
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet, pos_embedding)
logger.info('model: %s' % model_name)
# kwargs???�로??embedidng 추�?
word_path = os.path.join(model_path, 'embedding.txt')
word_dict, word_dim = utils.load_embedding_dict('NNLM', word_path)
def get_embedding_table():
table = np.empty([len(word_dict), word_dim])
for idx, (word, embedding) in enumerate(word_dict.items()):
try:
table[idx, :] = embedding
except:
print(word)
return torch.from_numpy(table)
word_table = get_embedding_table()
kwargs['embedd_word'] = word_table
args[1] = len(word_dict) # word_dim
network = StackPtrNet(*args, **kwargs)
# word_embedidng?� ??불러?�기
model_dict = network.state_dict()
pretrained_dict = torch.load(model_name)
model_dict.update({
k: v
for k, v in pretrained_dict.items() if k != 'word_embedd.weight'
})
network.load_state_dict(model_dict)
if use_gpu:
network.cuda()
else:
network.cpu()
network.eval()
if not ordered:
pred_writer.start(model_path + '/tmp/inference.txt')
else:
pred_writer.start(model_path + '/tmp/inference_ordered_temp.txt')
sent = 0
start_time = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(
data_test, 1, pos_embedding, type='dev'):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks, lengths = input_encoder
stacked_heads, children, siblings, stacked_types, skip_connect, mask_d, lengths_d = input_decoder
heads_pred, types_pred, children_pred, stacked_types_pred = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
ordered=ordered,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
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)
pred_writer.close()
def stackptr(model_path, model_name, test_path, punct_set, use_gpu, logger, args):
pos_embedding = args.pos_embedding
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_stacked_data.create_alphabets\
(alphabet_path,None, pos_embedding,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)
beam = args.beam
ordered = args.ordered
use_bert = args.bert
bert_path = args.bert_path
bert_feature_dim = args.bert_feature_dim
if use_bert:
etri_test_path = args.etri_test
else:
etri_test_path = None
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()
prior_order = kwargs['prior_order']
logger.info('use gpu: %s, beam: %d, order: %s (%s)' % (use_gpu, beam, prior_order, ordered))
data_test = conllx_stacked_data.read_stacked_data_to_variable(test_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, pos_embedding,
use_gpu=use_gpu, volatile=True, prior_order=prior_order, is_test=False,
bert=use_bert, etri_path=etri_test_path)
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet, type_alphabet, pos_embedding)
logger.info('model: %s' % model_name)
word_path = os.path.join(model_path, 'embedding.txt')
word_dict, word_dim = utils.load_embedding_dict('NNLM', word_path)
def get_embedding_table():
table = np.empty([len(word_dict), word_dim])
for idx,(word, embedding) in enumerate(word_dict.items()):
try:
table[idx, :] = embedding
except:
print(word)
return torch.from_numpy(table)
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_stacked_data.UNK_ID, :] = 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.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 = get_embedding_table()
word_table = construct_word_embedding_table()
# kwargs['embedd_word'] = word_table
# args[1] = len(word_dict) # word_dim
network = StackPtrNet(*args, **kwargs, bert=use_bert, bert_path=bert_path, bert_feature_dim=bert_feature_dim)
network.load_state_dict(torch.load(model_name))
"""
model_dict = network.state_dict()
pretrained_dict = torch.load(model_name)
model_dict.update({k:v for k,v in list(pretrained_dict.items())
if k != 'word_embedd.weight'})
network.load_state_dict(model_dict)
"""
if use_gpu:
network.cuda()
else:
network.cpu()
network.eval()
if not ordered:
pred_writer.start(model_path + '/inference.txt')
else:
pred_writer.start(model_path + '/RL_B[test].txt')
sent = 1
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_total_inst = 0.0
sys.stdout.write('Start!\n')
start_time = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(data_test, 1, pos_embedding, type='dev', bert=use_bert):
if sent % 100 == 0:
####
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))
sys.stdout.write('[%d/%d]\n' %(sent, int(data_test[2][0])))
####
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks_e, lengths, word_bert = input_encoder
stacked_heads, children, sibling, stacked_types, skip_connect, previous, nexts, masks_d, lengths_d = input_decoder
heads_pred, types_pred, _, _ = network.decode(word, char, pos, previous, nexts, stacked_heads, mask_e=masks_e, mask_d=masks_d,
length=lengths, beam=beam, leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS, input_word_bert=word_bert)
"""
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
"""
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.test_write(word, pos, heads_pred, types_pred, lengths, symbolic_root=True)
###########
stats, stats_nopunc, _, 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_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
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_total_inst += num_inst
end_time = time.time()
################
pred_writer.close()
print('\nFINISHED!!\n', end_time - start_time)
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))
def evaluate(beam, pred_path, gold_path):
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
test_ucorrect_stack_leaf = 0.0
test_ucorrect_stack_non_leaf = 0.0
test_lcorrect_stack_leaf = 0.0
test_lcorrect_stack_non_leaf = 0.0
test_leaf = 0
test_non_leaf = 0
pred_writer.start(pred_path)
gold_writer.start(gold_path)
sent = 0
start_time = time.time()
for batch in conllx_stacked_data.iterate_batch_stacked_variable(data_test, 1):
sys.stdout.write('%d, ' % sent)
sys.stdout.flush()
sent += 1
input_encoder, input_decoder = batch
word, char, pos, heads, types, masks, lengths = input_encoder
stacked_heads, children, siblings, stacked_types, skip_connect, mask_d, lengths_d = input_decoder
heads_pred, types_pred, children_pred, stacked_types_pred = network.decode(word, char, pos, mask=masks, length=lengths, beam=beam, ordered=ordered,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
stacked_heads = stacked_heads.data
children = children.data
stacked_types = stacked_types.data
children_pred = torch.from_numpy(children_pred).long()
stacked_types_pred = torch.from_numpy(stacked_types_pred).long()
if use_gpu:
children_pred = children_pred.cuda()
stacked_types_pred = stacked_types_pred.cuda()
mask_d = mask_d.data
mask_leaf = torch.eq(children, stacked_heads).float()
mask_non_leaf = (1.0 - mask_leaf)
mask_leaf = mask_leaf * mask_d
mask_non_leaf = mask_non_leaf * mask_d
num_leaf = mask_leaf.sum()
num_non_leaf = mask_non_leaf.sum()
ucorr_stack = torch.eq(children_pred, children).float()
lcorr_stack = ucorr_stack * torch.eq(stacked_types_pred, stacked_types).float()
ucorr_stack_leaf = (ucorr_stack * mask_leaf).sum()
ucorr_stack_non_leaf = (ucorr_stack * mask_non_leaf).sum()
lcorr_stack_leaf = (lcorr_stack * mask_leaf).sum()
lcorr_stack_non_leaf = (lcorr_stack * mask_non_leaf).sum()
test_ucorrect_stack_leaf += ucorr_stack_leaf
test_ucorrect_stack_non_leaf += ucorr_stack_non_leaf
test_lcorrect_stack_leaf += lcorr_stack_leaf
test_lcorrect_stack_non_leaf += lcorr_stack_non_leaf
test_leaf += num_leaf
test_non_leaf += num_non_leaf
# ------------------------------------------------------------------------------------------------
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('\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))
print('============================================================================================================================')
print('Stack leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%' % (
test_ucorrect_stack_leaf, test_lcorrect_stack_leaf, test_leaf,
test_ucorrect_stack_leaf * 100 / test_leaf, test_lcorrect_stack_leaf * 100 / test_leaf))
print('Stack non_leaf: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%' % (
test_ucorrect_stack_non_leaf, test_lcorrect_stack_non_leaf, test_non_leaf,
test_ucorrect_stack_non_leaf * 100 / test_non_leaf, test_lcorrect_stack_non_leaf * 100 / test_non_leaf))
print('============================================================================================================================')
