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train_el.py
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train_el.py
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# -*- coding: utf-8 -*-
"""
@author: alexyang
@contact: alex.yang0326@gmail.com
@file: train_el.py.py
@time: 2019/5/17 11:03
@desc:
"""
import os
import gc
import time
import numpy as np
from keras import backend as K
from keras import optimizers
from config import ModelConfig, PROCESSED_DATA_DIR, VOCABULARY_TEMPLATE, MENTION_TO_ENTITY_FILENAME, \
ENTITY_DESC_FILENAME, EMBEDDING_MATRIX_TEMPLATE, LOG_DIR, PERFORMANCE_LOG
from models.linking_model import LinkModel
from utils.data_loader import LinkDataGenerator, load_data
from utils.io import pickle_load, format_filename, write_log
os.environ['CUDA_VISIBLE_DEVICES'] = "1"
def get_optimizer(op_type, learning_rate):
if op_type == 'sgd':
return optimizers.SGD(learning_rate)
elif op_type == 'rmsprop':
return optimizers.RMSprop(learning_rate)
elif op_type == 'adagrad':
return optimizers.Adagrad(learning_rate)
elif op_type == 'adadelta':
return optimizers.Adadelta(learning_rate)
elif op_type == 'adam':
return optimizers.Adam(learning_rate, clipnorm=5)
else:
raise ValueError('Optimizer Not Understood: {}'.format(op_type))
def train_link(model_name, batch_size=32, n_epoch=50, learning_rate=0.001, optimizer_type='adam',
embed_type=None, embed_trainable=True, callbacks_to_add=None,
use_relative_pos=False, n_neg=1, omit_one_cand=True, overwrite=False, swa_start=5,
early_stopping_patience=3, **kwargs):
config = ModelConfig()
config.model_name = model_name
config.batch_size = batch_size
config.n_epoch = n_epoch
config.learning_rate = learning_rate
config.optimizer = get_optimizer(optimizer_type, learning_rate)
config.embed_type = embed_type
if embed_type:
config.embeddings = np.load(format_filename(PROCESSED_DATA_DIR, EMBEDDING_MATRIX_TEMPLATE, type=embed_type))
config.embed_trainable = embed_trainable
else:
config.embeddings = None
config.embed_trainable = True
config.callbacks_to_add = callbacks_to_add or ['modelcheckpoint', 'earlystopping']
if 'swa' in config.callbacks_to_add:
config.swa_start = swa_start
config.early_stopping_patience = early_stopping_patience
config.vocab = pickle_load(format_filename(PROCESSED_DATA_DIR, VOCABULARY_TEMPLATE, level='char'))
config.vocab_size = len(config.vocab) + 2
config.mention_to_entity = pickle_load(format_filename(PROCESSED_DATA_DIR, MENTION_TO_ENTITY_FILENAME))
config.entity_desc = pickle_load(format_filename(PROCESSED_DATA_DIR, ENTITY_DESC_FILENAME))
config.exp_name = '{}_{}_{}_{}_{}_{}'.format(model_name, embed_type if embed_type else 'random',
'tune' if config.embed_trainable else 'fix', batch_size, optimizer_type,
learning_rate)
config.use_relative_pos = use_relative_pos
if config.use_relative_pos:
config.exp_name += '_rel'
config.n_neg = n_neg
if config.n_neg > 1:
config.exp_name += '_neg_{}'.format(config.n_neg)
config.omit_one_cand = omit_one_cand
if not config.omit_one_cand:
config.exp_name += '_not_omit'
if kwargs:
config.exp_name += '_' + '_'.join([str(k) + '_' + str(v) for k, v in kwargs.items()])
callback_str = '_' + '_'.join(config.callbacks_to_add)
callback_str = callback_str.replace('_modelcheckpoint', '').replace('_earlystopping', '')
config.exp_name += callback_str
# logger to log output of training process
train_log = {'exp_name': config.exp_name, 'batch_size': batch_size, 'optimizer': optimizer_type, 'epoch': n_epoch,
'learning_rate': learning_rate, 'other_params': kwargs}
print('Logging Info - Experiment: %s' % config.exp_name)
model_save_path = os.path.join(config.checkpoint_dir, '{}.hdf5'.format(config.exp_name))
model = LinkModel(config, **kwargs)
train_data_type, dev_data_type = 'train', 'dev'
train_generator = LinkDataGenerator(train_data_type, config.vocab, config.mention_to_entity, config.entity_desc,
config.batch_size, config.max_desc_len, config.max_erl_len,
config.use_relative_pos, config.n_neg, config.omit_one_cand)
dev_data = load_data(dev_data_type)
if not os.path.exists(model_save_path) or overwrite:
start_time = time.time()
model.train(train_generator, dev_data)
elapsed_time = time.time() - start_time
print('Logging Info - Training time: %s' % time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
train_log['train_time'] = time.strftime("%H:%M:%S", time.gmtime(elapsed_time))
model.load_best_model()
dev_text_data, dev_pred_mentions, dev_gold_mention_entities = [], [], []
for data in dev_data:
dev_text_data.append(data['text'])
dev_pred_mentions.append(data['mention_data'])
dev_gold_mention_entities.append(data['mention_data'])
print('Logging Info - Evaluate over valid data:')
r, p, f1 = model.evaluate(dev_text_data, dev_pred_mentions, dev_gold_mention_entities)
train_log['dev_performance'] = (r, p, f1)
swa_type = None
if 'swa' in config.callbacks_to_add:
swa_type = 'swa'
elif 'swa_clr' in config.callbacks_to_add:
swa_type = 'swa_clr'
if swa_type:
model.load_swa_model(swa_type)
print('Logging Info - Evaluate over valid data based on swa model:')
r, p, f1 = model.evaluate(dev_text_data, dev_pred_mentions, dev_gold_mention_entities)
train_log['swa_dev_performance'] = (r, p, f1)
train_log['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())
write_log(format_filename(LOG_DIR, PERFORMANCE_LOG, model_type='2step_el'), log=train_log, mode='a')
del model
gc.collect()
K.clear_session()
if __name__ == '__main__':
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=5, omit_one_cand=False,
use_relative_pos=True, score_func='cosine', margin=0.04, max_mention=True, add_cnn='after')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, omit_one_cand=False,
use_relative_pos=True, score_func='cosine', margin=0.04, max_mention=True, add_cnn='after')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, use_relative_pos=True,
score_func='cosine', margin=0.04, max_mention=True)
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=5, use_relative_pos=True,
score_func='cosine', margin=0.04, max_mention=True, add_cnn='after')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, use_relative_pos=True,
score_func='cosine', margin=0.04, max_mention=True, add_cnn='after')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, score_func='cosine', margin=0.04,
max_mention=True, encoder_type='self_attend_single_attend', ent_attend_type='add')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, use_relative_pos=True,
score_func='cosine', margin=0.04, max_mention=True, encoder_type='self_attend_single_attend',
ent_attend_type='mul')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, use_relative_pos=True,
score_func='cosine', margin=0.04, max_mention=True, add_cnn='after',
encoder_type='self_attend_single_attend', ent_attend_type='scaled_dot')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=5, use_relative_pos=True,
score_func='cosine', margin=0.04, max_mention=True)
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=5, score_func='cosine', margin=0.04,
max_mention=True)
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=4, score_func='cosine', margin=0.04,
max_mention=True, add_cnn='after')
train_link('2step_el', batch_size=32, embed_type='c2v', embed_trainable=False,
callbacks_to_add=['swa', 'modelcheckpoint', 'earlystopping'], n_neg=5, score_func='cosine', margin=0.04,
max_mention=True, add_cnn='after')