main.py
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import os
import re
import time
from argparse import ArgumentParser
from sklearn.externals import joblib
from keras import backend as K
# ugly hack to prevent built-in parser module from loading
import sys; sys.meta_path.append(sys.meta_path.pop(0))
from parser import Parser
from utils import (
ConllLoader,
ConllSaver,
ConllSemanticLoader,
ConllSemanticSaver,
print_summary,
ensure_deterministic,
em_score,
EmbeddingSaver,
TxtLoader)
def valid_params(params):
if 'deprel' in params.targets and 'head' not in params.targets:
raise KeyError('You have to predict "head" in order to predict "deprel".')
if 'train' in params.mode and len(params.targets) != len(params.loss_weights):
raise KeyError('loss_weights and targets must be the same length.')
if __name__ == '__main__':
ensure_deterministic()
parser = ArgumentParser()
parser.add_argument(
"--mode", dest="mode", help="Mode of parser (train or predict)",
choices=['train', 'autotrain', 'multitrain', 'evaluate', 'predict', 'multipredict'])
parser.add_argument("--train", dest="train", help="Annotated CONLLu train file", metavar="FILE")
parser.add_argument("--valid", dest="valid", help="Annotated CONLLu valid file", metavar="FILE")
parser.add_argument("--test", dest="test", help="Unannotated CONLLu test file", metavar="FILE")
parser.add_argument("--embed", dest="embed_file", help="External embeddings for forms", metavar="FILE")
parser.add_argument("--model", dest="model_file", default="model.pkl", help="Load/Save model file", metavar="FILE")
parser.add_argument("--pred", dest="pred_file", help="CONLLu output pred file", metavar="FILE")
parser.add_argument("--train_embed", action="store_true", dest="train_embed", default=False)
parser.add_argument("--train_partial", action="store_true", dest="train_partial", default=False)
parser.add_argument("--full_tree", dest="full_tree", default='# conversion_status = complete')
parser.add_argument("--partial_tree", dest="partial_tree", default='# conversion_status = no_tree')
parser.add_argument("--features", dest="features", nargs='+',
help="Which features to use: form, lemma, upostag, xpostag, feats, char",
choices=['form', 'lemma', 'upostag', 'xpostag', 'feats', 'char'],
default=['form', 'char'])
parser.add_argument("--targets", dest="targets", nargs="+",
help="Which targets to predict: head, deprel, lemma, upostag, xpostag, feats, sent, semrel",
choices=['head', 'deprel', 'lemma', 'upostag', 'xpostag', 'feats', 'sent', 'semrel'],
default=['head', 'deprel', 'lemma', 'upostag', 'feats'])
parser.add_argument("--loss_weights", type=float, dest="loss_weights", nargs='+',
help="Importance of each loss", default=[0.2, 0.8, 0.05, 0.05, 0.2])
parser.add_argument("--form_embed", type=int, dest="form_embed", default=100)
parser.add_argument("--pos_embed", type=int, dest="pos_embed", default=32)
parser.add_argument("--xpos_embed", type=int, dest="xpos_embed", default=32)
parser.add_argument("--feat_embed", type=int, dest="feat_embed", default=32)
parser.add_argument("--char_embed", type=int, dest="char_embed", default=64)
parser.add_argument("--char_max_len", type=int, dest="char_max_len", default=30)
parser.add_argument("--lstm_layers", type=int, dest="lstm_layers", default=2)
parser.add_argument("--lstm_hidden_size", type=int, dest="lstm_hidden_size", default=512)
parser.add_argument("--lstm_dropout", type=float, dest="lstm_dropout", default=0.25)
parser.add_argument("--head_hidden_size", type=int, dest="head_hidden_size", default=512)
parser.add_argument("--deprel_hidden_size", type=int, dest="deprel_hidden_size", default=128)
parser.add_argument("--lemma_hidden_size", type=int, dest="lemma_hidden_size", default=256)
parser.add_argument("--pos_hidden_size", type=int, dest="pos_hidden_size", default=64)
parser.add_argument("--xpos_hidden_size", type=int, dest="xpos_hidden_size", default=128)
parser.add_argument("--feat_hidden_size", type=int, dest="feat_hidden_size", default=128)
parser.add_argument("--semrel_hidden_size", type=int, dest="semrel_hidden_size", default=64)
parser.add_argument("--dense_dropout", type=float, dest="dense_droput", default=0.25)
parser.add_argument("--input_dropout", type=float, dest="input_droput", default=0.25)
parser.add_argument("--batch_size", type=int, dest="batch_size", default=2500)
parser.add_argument("--epochs", type=int, dest="epochs", default=400)
parser.add_argument("--lr", type=float, dest="learning_rate", default=0.002)
parser.add_argument("--cycle_loss_n", type=int, dest="cycle_loss_n", default=3)
parser.add_argument("--cycle_loss_weight", type=float, dest="cycle_loss_weight", default=1.0)
parser.add_argument("--verbose", type=int, dest="verbose", default=1)
parser.add_argument("--force_trees", action="store_true", dest="force_trees", default=False)
parser.add_argument("--evaluate", action="store_true", dest="evaluate", default=False)
parser.add_argument("--continue", action="store_true", dest="continue_training", default=False)
parser.add_argument("--lower", action="store_true", dest="lower", default=False)
parser.add_argument("--freeze", action="store_true", dest="freeze", default=False)
parser.add_argument("--save_probs", action="store_true", dest="save_probs", default=False)
parser.add_argument("--reload_params", action="store_true", dest="reload_params", default=False)
params = parser.parse_args()
valid_params(params)
if 'semrel' in params.targets:
loader = ConllSemanticLoader()
saver = ConllSemanticSaver()
else:
loader = ConllLoader()
saver = ConllSaver()
if params.mode == 'train':
print('Load train data', time.strftime("%Y-%m-%d %H:%M:%S"))
train_data = loader.load(params.train)
print('Start training', time.strftime("%Y-%m-%d %H:%M:%S"))
if params.continue_training:
print('Load model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = joblib.load(params.model_file)
if params.reload_params:
parser.params = params
w = parser.model.get_weights()
parser.model = parser.create()
parser.model.set_weights(w)
del w
else:
print('Initiate model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = Parser(params)
parser.fit(train_data)
parser.model.summary()
print('Save model', time.strftime("%Y-%m-%d %H:%M:%S"))
joblib.dump(parser, params.model_file)
if params.evaluate:
print('Predict on train', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_train = parser.predict(train_data)
print_summary(pred_train, train_data)
if params.valid is not None:
print('Load valid data', time.strftime("%Y-%m-%d %H:%M:%S"))
valid_data = loader.load(params.valid)
print('Predict on valid', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_valid = parser.predict(valid_data)
print_summary(pred_valid, valid_data)
elif params.mode == 'multitrain':
if params.evaluate and params.valid is not None:
print('Load valid data', time.strftime("%Y-%m-%d %H:%M:%S"))
valid_data = loader.load(params.valid)
if params.continue_training:
print('Load model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = joblib.load(params.model_file)
if params.reload_params:
parser.params = params
w = parser.model.get_weights()
parser.model = parser.create()
parser.model.set_weights(w)
del w
else:
print('Initiate model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = Parser(params)
print('Start training', time.strftime("%Y-%m-%d %H:%M:%S"))
for file_name in os.listdir(params.train):
if 'conll' not in file_name:
continue
print('Load train data:', file_name, time.strftime("%Y-%m-%d %H:%M:%S"))
train_data = loader.load(params.train + file_name)
parser.fit(train_data)
if params.evaluate and params.valid is not None:
print('Predict on valid', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_valid = parser.predict(valid_data)
print_summary(pred_valid, valid_data)
print('Save model', time.strftime("%Y-%m-%d %H:%M:%S"))
joblib.dump(parser, params.model_file)
if params.evaluate:
print('Predict on train', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_train = parser.predict(train_data)
print_summary(pred_train, train_data)
if params.valid is not None:
print('Predict on valid', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_valid = parser.predict(valid_data)
print_summary(pred_valid, valid_data)
elif params.mode == 'autotrain':
print('Load train data', time.strftime("%Y-%m-%d %H:%M:%S"))
train_data = loader.load(params.train)
print('Load valid data', time.strftime("%Y-%m-%d %H:%M:%S"))
valid_data = loader.load(params.valid)
print('Start training', time.strftime("%Y-%m-%d %H:%M:%S"))
print('Initiate model', time.strftime("%Y-%m-%d %H:%M:%S"))
threshold = 0.001
decreases = 2
patience = 5
decrease_factor = 2
epochs_per_eval = 5
iters = params.epochs//epochs_per_eval
if params.continue_training:
print('Load model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = joblib.load(params.model_file)
if params.reload_params:
parser.params = params
w = parser.model.get_weights()
parser.model = parser.create()
parser.model.set_weights(w)
del w
else:
print('Initiate model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = Parser(params)
parser.params.epochs = epochs_per_eval
scores = []
score_func = em_score
for i in range(iters):
print('Train iter', i, time.strftime("%Y-%m-%d %H:%M:%S"))
parser.fit(train_data)
pred_valid = parser.predict(valid_data)
score = score_func(pred_valid, valid_data)
print_summary(pred_valid, valid_data)
print('summary', patience, decreases, threshold, score, time.strftime("%Y-%m-%d %H:%M:%S"))
if len(scores) and score - max(scores) < threshold:
if patience == 0:
if decreases > 0:
prev_lr = K.get_value(parser.model.optimizer.lr)
K.set_value(parser.model.optimizer.lr, prev_lr/decrease_factor)
print('lr change', prev_lr, K.get_value(parser.model.optimizer.lr))
threshold /= decrease_factor
decreases -= 1
patience = 5
else:
break
else:
patience -= 1
else:
patience = 5
scores.append(score)
print('Finished training', time.strftime("%Y-%m-%d %H:%M:%S"))
parser.model.summary()
print(scores)
print('Save model', time.strftime("%Y-%m-%d %H:%M:%S"))
joblib.dump(parser, params.model_file)
if params.evaluate:
print('Predict on train', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_train = parser.predict(train_data)
print_summary(pred_train, train_data)
if params.valid is not None:
print('Predict on valid', time.strftime("%Y-%m-%d %H:%M:%S"))
pred_valid = parser.predict(valid_data)
print_summary(pred_valid, valid_data)
elif params.mode == 'evaluate':
print('Load model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = joblib.load(params.model_file)
if params.reload_params:
parser.params = params
w = parser.model.get_weights()
parser.model = parser.create()
parser.model.set_weights(w)
del w
print('Load data', time.strftime("%Y-%m-%d %H:%M:%S"))
test_data = loader.load(params.test)
print('Predict', time.strftime("%Y-%m-%d %H:%M:%S"))
pred = parser.predict(test_data)
print_summary(pred, test_data)
elif params.mode == 'predict':
if params.test.endswith('.txt'):
loader = TxtLoader(semantic=('semrel' in params.targets))
print('Load model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = joblib.load(params.model_file)
if params.reload_params:
parser.params = params
w = parser.model.get_weights()
parser.model = parser.create()
parser.model.set_weights(w)
del w
print('Load data', time.strftime("%Y-%m-%d %H:%M:%S"))
test_data = loader.load(params.test)
print('Predict', time.strftime("%Y-%m-%d %H:%M:%S"))
pred = parser.predict(test_data)
print('Save predictions', time.strftime("%Y-%m-%d %H:%M:%S"))
saver.save(params.pred_file, pred)
if 'sent' in parser.params.targets:
EmbeddingSaver().save(re.sub(r'\..+$', r'.vec', params.pred_file), pred)
elif params.mode == 'multipredict':
print('Load model', time.strftime("%Y-%m-%d %H:%M:%S"))
parser = joblib.load(params.model_file)
if params.reload_params:
parser.params = params
w = parser.model.get_weights()
parser.model = parser.create()
parser.model.set_weights(w)
del w
for file_name in os.listdir(params.test):
if 'conll' not in file_name:
continue
print('Load data:', file_name, time.strftime("%Y-%m-%d %H:%M:%S"))
test_data = loader.load(params.test + file_name)
print('Predict:', file_name, time.strftime("%Y-%m-%d %H:%M:%S"))
pred = parser.predict(test_data)
print('Save predictions:', file_name, time.strftime("%Y-%m-%d %H:%M:%S"))
saver.save(params.pred_file + file_name, pred)