Create train.py
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missQian
GitHub
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import os
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import math
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from model import *
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from io_utils import read_pickle, write_lines, read_lines
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from vocab import Vocab
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class Trainer(object):
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def __init__(self):
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logger.info('Loading data...')
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self.train_list, self.dev_list, self.test_list, self.token_vocab, self.char_vocab, self.ent_type_vocab, \
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self.ent_ref_vocab, self.tri_type_vocab, self.arg_type_vocab, self.action_vocab, \
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self.pos_vocab = read_pickle(data_config['inst_pl_file'])
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logger.info('Sent size train: %d, dev: %d, test:%d'%(len(self.train_list), len(self.dev_list), len(self.test_list)))
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logger.info('Loading pretrained from: %s'%data_config['vec_npy'])
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pretrained_vec = np.load(data_config['vec_npy'])
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self.unk_idx = self.token_vocab[Vocab.UNK]
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joint_config['n_chars'] = len(self.char_vocab)
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self.trans_model = MainModel(self.token_vocab.get_vocab_size(),
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self.action_vocab,
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self.ent_type_vocab,
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self.tri_type_vocab,
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self.arg_type_vocab,
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self.pos_vocab,
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pretrained_vec=pretrained_vec)
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logger.info("Model:%s"%type(self.trans_model))
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self.event_eval = EventEval()
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# model = pm.global_collection()
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# for param in model.parameters_list():
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# print(param.name(), param.dim())
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#
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# for param in model.lookup_parameters_list():
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# print(param.name(), param.dim())
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def unk_replace_singleton(self, unk_idx, unk_ratio, words):
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noise = words[:]
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bernoulli = np.random.binomial(n=1, p=unk_ratio, size=len(words))
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for i, idx in enumerate(words):
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if self.token_vocab.is_singleton(idx) and bernoulli[i] == 1:
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noise[i] = unk_idx
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return noise
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def iter_batch(self, inst_list, shuffle=True):
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batch_size = joint_config['batch_size']
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if shuffle:
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random.shuffle(inst_list)
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inst_len = len(inst_list)
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plus_n = 0 if (inst_len % batch_size) == 0 else 1
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num_batch = (len(inst_list) // batch_size) + plus_n
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start = 0
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for i in range(num_batch):
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batch_inst = inst_list[start: start + batch_size]
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start += batch_size
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yield batch_inst
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def train_batch(self):
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loss_all = 0.
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batch_num = 0
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for batch_inst in self.iter_batch(self.train_list, shuffle=True):
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dy.renew_cg()
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loss_minibatch = []
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for inst in batch_inst:
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words = inst['word_indices']
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if joint_config['unk_ratio'] > 0:
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words = self.unk_replace_singleton(self.unk_idx, joint_config['unk_ratio'], words)
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loss_rep = self.trans_model(words, inst['char_indices'], inst['action_indices'],
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inst['actions'], inst['tri_indices'], inst['ent_indices'],
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inst['arg_indices'], inst['sent_range'],
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inst['pos_indices'])
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loss_minibatch.append(loss_rep)
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batch_loss = dy.esum(loss_minibatch) / len(loss_minibatch)
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loss_all += batch_loss.value()
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batch_loss.backward()
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self.trans_model.update()
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batch_num += 1
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logger.info('loss %.5f ' % (loss_all / float(batch_num)))
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def eval(self, inst_list, write_ent_file=None, is_write_ent=False, mtype='dev'):
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#logger.info('~~~~~~Empty buffer times %d'%self.trans_model.shift_reduce.empty_times)
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self.event_eval.reset()
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sent_num_eval = 0
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total_loss = 0.
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ent_lines = []
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eval_lines = []
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for inst in inst_list:
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_, pred_ents, pred_tris, pred_args = self.trans_model.decode(
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inst['word_indices'], inst['char_indices'], inst['action_indices'],
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inst['actions'], inst['tri_indices'], inst['ent_indices'],
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inst['arg_indices'], inst['sent_range'],
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inst['pos_indices'], mtype=mtype)
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# total_loss += loss
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self.event_eval.update(pred_ents, inst['ent_indices'],
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pred_tris, inst['tri_indices'],
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pred_args, inst['arg_indices'],
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eval_arg=True, words=inst['nlp_words'])
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ent_line = str(sent_num_eval) + ' '
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ent_str_list = []
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for ent in pred_ents:
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ent_str_list.append(str(ent[0])+'#'+str(ent[1])+'#'+self.ent_type_vocab.get_token(ent[2]))
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ent_line += ','.join(ent_str_list)
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ent_lines.append(ent_line)
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sent_num_eval += 1
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# eval_lines.append(' '.join(inst['nlp_words']))
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# eval_lines.append(str(pred_ents))
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# eval_lines.append(str(pred_tris))
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# eval_lines.append(str(pred_args))
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# write_lines('data_files/dev_test_prediction/test_pred.txt',eval_lines)
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if write_ent_file is not None and is_write_ent:
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write_lines(write_ent_file, ent_lines)
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# file.flush()
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#logger.info('~~~~~~Empty buffer times %d' % self.trans_model.shift_reduce.empty_times)
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def train(self, save_model=True):
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logger.info(joint_config['msg_info'])
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best_f1_tri_typed, best_f1_arg_typed = 0, 0
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best_epoch = 0
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adjust_lr = False
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stop_patience = joint_config['patience']
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stop_count = 0
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eval_best_arg = True # for other task than event
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t_cur = 1
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t_i = 4
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t_mul = 2
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lr_max, lr_min = joint_config['init_lr'], joint_config['minimum_lr']
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for epoch in range(joint_config['num_epochs']):
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anneal_lr = lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * t_cur / t_i))
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self.trans_model.set_lr(anneal_lr)
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logger.info('--------------------------------------')
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logger.info('Epoch : %d'%epoch)
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logger.info('LR : %.5f' % self.trans_model.get_lr())
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self.train_batch()
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self.eval(self.dev_list, mtype='dev')
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(p_ent, r_ent, f_ent), (p_ent_typed, r_ent_typed, f_ent_typed), \
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(p_tri, r_tri, f_tri), (p_tri_typed, r_tri_typed, f_tri_typed), \
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(p_arg, r_arg, f_arg), (p_arg_typed, r_arg_typed, f_arg_typed) = self.event_eval.report()
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logger.info('Entity P:%.5f, R:%.5f, F:%.5f' % (p_ent, r_ent, f_ent))
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logger.info('Entity typed P:%.5f, R:%.5f, F:%.5f' % (p_ent_typed, r_ent_typed, f_ent_typed))
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logger.info('Trigger P:%.5f, R:%.5f, F:%.5f' % (p_tri, r_tri, f_tri))
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logger.info('Trigger typed P:%.5f, R:%.5f, F:%.5f' % (p_tri_typed, r_tri_typed, f_tri_typed))
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logger.info('Argument P:%.5f, R:%.5f, F:%.5f' % (p_arg, r_arg, f_arg))
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logger.info('Argument typed P:%.5f, R:%.5f, F:%.5f' % (p_arg_typed, r_arg_typed, f_arg_typed))
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if t_cur == t_i:
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t_cur = 0
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t_i *= t_mul
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t_cur += 1
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if not eval_best_arg:
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continue
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if f_arg_typed >= best_f1_arg_typed :
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best_f1_arg_typed = f_arg_typed
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best_f1_tri_typed = f_tri_typed
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best_epoch = epoch
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stop_count = 0
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#logger.info('Saving model %s'%model_save_file)
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#self.trans_model.save_model(model_save_file)
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if save_model:
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logger.info('Saving model %s' % data_config['model_save_file'])
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self.trans_model.save_model(data_config['model_save_file'])
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else:
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stop_count += 1
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if stop_count >= stop_patience:
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logger.info('Stop training, Arg performance did not improved for %d epochs'%stop_count)
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break
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if adjust_lr:
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self.trans_model.decay_lr(joint_config['decay_lr'])
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logger.info('@@@@ Adjusting LR: %.5f @@@@@@' % self.trans_model.get_lr())
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if self.trans_model.get_lr() < joint_config['minimum_lr']:
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adjust_lr = False
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# if epoch > 0 and epoch % 6 == 0:
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# self.trans_model.decay_lr(joint_config['decay_lr'])
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# logger.info('@@@@ Adjusting LR: %.5f @@@@@@' % self.trans_model.get_lr())
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best_msg = '*****Best epoch: %d Tri and Arg typed F:%.5f, F:%.5f ******' % (best_epoch,
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best_f1_tri_typed,
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best_f1_arg_typed)
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logger.info(best_msg)
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return best_msg, best_f1_arg_typed
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def test(self, fname):
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self.trans_model.load_model(fname)
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self.eval(self.test_list, mtype='test')
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(p_ent, r_ent, f_ent), (p_ent_typed, r_ent_typed, f_ent_typed), \
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(p_tri, r_tri, f_tri), (p_tri_typed, r_tri_typed, f_tri_typed), \
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(p_arg, r_arg, f_arg), (p_arg_typed, r_arg_typed, f_arg_typed) = self.event_eval.report()
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logger.info('Entity P:%.5f, R:%.5f, F:%.5f' % (p_ent, r_ent, f_ent))
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logger.info('Entity typed P:%.5f, R:%.5f, F:%.5f' % (p_ent_typed, r_ent_typed, f_ent_typed))
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logger.info('Trigger P:%.5f, R:%.5f, F:%.5f' % (p_tri, r_tri, f_tri))
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logger.info('Trigger typed P:%.5f, R:%.5f, F:%.5f' % (p_tri_typed, r_tri_typed, f_tri_typed))
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logger.info('Argument P:%.5f, R:%.5f, F:%.5f' % (p_arg, r_arg, f_arg))
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logger.info('Argument typed P:%.5f, R:%.5f, F:%.5f' % (p_arg_typed, r_arg_typed, f_arg_typed))
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def make_dir(path):
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if not os.path.exists(path):
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os.makedirs(path)
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if __name__ == '__main__':
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trainer = Trainer()
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trainer.train(save_model=True)
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logger.info('---------------Test Results---------------')
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ckp_path = data_config['model_save_file']
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trainer.test(ckp_path)
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