def roberta_pair_task(config):
tokenizer = BertTokenizer.from_pretrained(config.tokenizer_file,
do_lower_case=config.do_lower_case)
processor = DataProcessor(config)
config.class_list = processor.get_labels()
config.num_labels = len(config.class_list)
train_examples = processor.get_train_examples()
dev_examples = processor.get_dev_examples()
augment_examples = processor.read_data_augment(config.data_augment_method)
cur_model = MODEL_CLASSES[config.use_model]
model = cur_model(config)
logging.info("self config %s", config_to_json_string(config))
model_example, dev_evaluate, predict_label = cross_validation(
config=config,
model=model,
tokenizer=tokenizer,
train_examples=train_examples,
dev_examples=dev_examples,
pattern=config.pattern,
train_enhancement=augment_examples if config.data_augment else None,
test_examples=None)
logging.info("dev_evaluate: {}".format(dev_evaluate))
if config.pattern == 'full_train':
model_save(config, model_example)
return dev_evaluate
def chip2019_extract(config):
config.stop_word_valid = False
processor = DataProcessor(config)
config.class_list = processor.get_labels()
config.num_labels = len(config.class_list)
original_chip2019_examples = processor.get_original_chip2019_examples()
if config.reverse_tag: # 交换
reverse_test_examples = sentence_reverse(original_chip2019_examples)
all_test_examples = [original_chip2019_examples, reverse_test_examples]
else:
all_test_examples = [original_chip2019_examples]
cur_model = MODEL_CLASSES[config.use_model]
tokenizer = BertTokenizer.from_pretrained(
config.tokenizer_file, do_lower_case=config.do_lower_case)
model = cur_model(config)
modle_file = os.path.join(config.save_path[0],
config.save_file[0] + '.pkl')
if not os.path.isfile(modle_file) or config.retrain_model:
print("{} not exit.".format(modle_file))
# 不存在模型文件
# 读取训练数据
config.batch_size = 16
train_examples = processor.get_train_examples()
dev_examples = processor.get_dev_examples()
if config.data_augment:
augment_examples = processor.read_data_augment(
config.data_augment_method)
else:
augment_examples = None
model_example, dev_evaluate, predict_label = cross_validation(
config=config,
model=model,
tokenizer=tokenizer,
train_examples=train_examples,
dev_examples=dev_examples,
pattern=config.pattern,
train_enhancement=augment_examples,
test_examples=None)
model_save(config, model_example)
model_load(config, model, device='cpu')
model.to(config.device)
config.batch_size = 512
single_model_predict = []
for test_examples in all_test_examples:
_, _, predict_label = cross_validation(config=config,
model=model,
tokenizer=tokenizer,
train_examples=None,
dev_examples=None,
pattern='predict',
train_enhancement=None,
test_examples=test_examples)
single_model_predict.append(predict_label)
predict_prob = combined_result(single_model_predict, pattern='average')
save_file = os.path.join(config.other_data_dir,
config.chip2019_augment_save_file)
print('save_file{}'.format(save_file))
examples_extract(original_chip2019_examples,
predict_prob,
save_file,
sel_prob=config.prob_range,
random_state=config.seed)
def test_task(config):
print('cude: {}'.format(torch.cuda.is_available()))
print('cur device {}'.format(config.device.type))
start_time = time.time()
processor = DataProcessor(config)
config.class_list = processor.get_labels()
config.num_labels = len(config.class_list)
test_examples = processor.get_test_examples(config.test_data_dir)
# 交换
if config.reverse_tag:
reverse_test_examples = sentence_reverse(test_examples)
all_examples = [test_examples, reverse_test_examples]
else:
all_examples = [test_examples]
cur_model = MODEL_CLASSES[config.use_model]
print('loading data time: {:.6f}s'.format(time.time() - start_time))
all_predict = []
for i in range(config.model_num):
model_time_s = time.time()
print('the model of {} starting...'.format(config.models_name[i]))
tokenizer = BertTokenizer.from_pretrained(
config.tokenizer_file[i], do_lower_case=config.do_lower_case)
model = cur_model(config, num=i)
model_load(config, model, num=i, device='cpu')
model.to(config.device)
print("\tloading pre-train model, cost time {:.6f}s".format(
time.time() - model_time_s))
single_model_predict = []
for e_idx, t_examples in enumerate(all_examples):
example_time = time.time()
_, _, predict_label = cross_validation(config=config,
model=model,
tokenizer=tokenizer,
train_examples=None,
dev_examples=None,
pattern='predict',
train_enhancement=None,
test_examples=t_examples)
single_model_predict.append(predict_label)
print("\ttest dataset:{}, cost time {:.6f}s, total time {:.6f}s".
format(e_idx + 1,
time.time() - example_time,
time.time() - start_time))
print("# time {:.6f}s, total time {:.6f}s".format(
time.time() - model_time_s,
time.time() - start_time))
predict_prob = combined_result(single_model_predict, pattern='average')
all_predict.append(predict_prob)
final_predict_label = combined_result(all_predict, pattern='average')
final_predict_label = np.asarray(
final_predict_label >= config.prob_threshold, dtype=np.int)
index = list(
pd.read_csv(os.path.join(config.test_data_dir, 'test.csv'),
encoding='utf-8')['id'])
df_upload = pd.DataFrame({'id': index, 'label': final_predict_label})
df_upload.to_csv(config.save_data_path, index=False)
print('\ntotal time {:.6f}s'.format(time.time() - start_time))
