/
run.py
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run.py
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from config import FLAGS
from dataset import QA_dataset
import os
from datetime import date,timedelta
import tensorflow as tf
from model.QA_CNN import model_fn
from model.QA_CNN import cnn_model_fn
from model.QA_CNN import cnn_quantum_fn
import evaluation
import pickle
import logging
import shutil
import numpy as np
if FLAGS.model_type == "fnn":
model_params = {
"num_classes":FLAGS.num_classes,
"embedding_size":FLAGS.embedding_size,
"learning_rate":FLAGS.learning_rate,
"trainable":FLAGS.trainable,
"optim_type":FLAGS.optim_type
}
elif FLAGS.model_type == 'cnn':
model_params = {
'query_length' : 40,
'app_name_length': 40,
'trainable': False,
'filter_sizes': [3,4,5],
'num_filters':64,
'optim_type':'adam',
'embedding_size':FLAGS.embedding_size,
'learning_rate':0.001,
'batch_size':64,
"trainable":FLAGS.trainable,
"num_classes":FLAGS.num_classes
}
else:
pass
def prepare():
logger = logging.getLogger('QA')
data_path = FLAGS.data_path
train_file = os.path.join(data_path,'train.txt')
test_file = os.path.join(data_path,'test.txt')
dev_file = os.path.join(data_path,'test.txt')
logger.info('checking the data file')
for dir_path in [FLAGS.vocab_dir,FLAGS.model_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
data_set = QA_dataset(train_file,dev_file,test_file,FLAGS)
data_set.get_alphabet([data_set.train_set,data_set.test_set])
# with open ('index_to_word','w',encoding = 'utf-8') as fout:
# for index in data_set.index_to_word:
# line = str(index) + '\t' + data_set.index_to_word[index] + '\n'
# fout.write(line)
data_set.process_pairs()
embeddings = data_set.get_embedding(FLAGS.embedding_dir,data_set.word_dict,dim = FLAGS.embedding_size)
print("alphabet size{}:".format(len(data_set.word_dict)))
para = {'embeddings':embeddings}
logger.info('save the embedding')
with open(os.path.join(FLAGS.vocab_dir,'vocab.data'),'wb') as fout:
pickle.dump(para, fout)
logger.info('Done with preparing')
def train():
if FLAGS.dt_dir == "":
FLAGS.dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')
FLAGS.model_dir = FLAGS.model_dir + FLAGS.dt_dir
if FLAGS.clear_existing_model:
print("clear the exist model")
if os.path.exists(FLAGS.model_dir):
shutil.rmtree(FLAGS.model_dir,ignore_errors=True)
logger = logging.getLogger('QA')
logger.info("load vocab")
with open(os.path.join(FLAGS.vocab_dir,'vocab.data'),'rb') as fin:
vocab = pickle.load(fin)
logger.info('loading the dataset')
data_set = QA_dataset(None,None,None,FLAGS)
model_params["vocab_size"] = len(vocab['embeddings'])
model_params["embeddings"] = vocab["embeddings"]
config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':FLAGS.num_threads}),
log_step_count_steps=FLAGS.log_steps, save_summary_steps = FLAGS.log_steps)
QA_CNN = tf.estimator.Estimator(model_fn = cnn_model_fn, model_dir = FLAGS.model_dir, params = model_params, config=config)
train_spec = tf.estimator.TrainSpec(input_fn=lambda: data_set.input_fn(FLAGS.train_tf_records, num_epochs=FLAGS.num_epochs, batch_size=FLAGS.batch_size,perform_shuffle = True),max_steps = 20000)
eval_spec = tf.estimator.EvalSpec(input_fn=lambda: data_set.input_fn(FLAGS.test_tf_records, num_epochs=1, batch_size=FLAGS.batch_size), steps=None, start_delay_secs=1000, throttle_secs=1200)
tf.estimator.train_and_evaluate(QA_CNN, train_spec, eval_spec)
def predict():
logger = logging.getLogger('QA')
logger.info('load vocab')
data_path = FLAGS.data_path
train_file = os.path.join(data_path,'train.txt')
test_file = os.path.join(data_path,'test.txt')
if FLAGS.dt_dir == "":
FLAGS.dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')
FLAGS.model_dir = FLAGS.model_dir + FLAGS.dt_dir
with open(os.path.join(FLAGS.vocab_dir,'vocab.data'),'rb') as fin:
vocab = pickle.load(fin)
model_params["vocab_size"] = len(vocab['embeddings'])
model_params["embeddings"] = vocab["embeddings"]
data_set = QA_dataset(None,None,test_file,FLAGS)
config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':FLAGS.num_threads}),
log_step_count_steps=FLAGS.log_steps, save_summary_steps=FLAGS.log_steps)
QA_CNN = tf.estimator.Estimator(model_fn = cnn_model_fn, model_dir=FLAGS.model_dir, params=model_params, config=config)
preds = QA_CNN.predict(input_fn=lambda: data_set.input_fn(FLAGS.test_tf_records, num_epochs=1, batch_size=FLAGS.batch_size), predict_keys=["prob",'score'])
# list_pred = list(map(lambda x:x['prob'],preds))
a = list(map(lambda x:(x['prob'],x['score']),preds))
list_pred, score = zip(*a)
random_pred = np.random.rand(len(data_set.test_set))
print('random:{}\n'.format(evaluation.evaluationBypandas(data_set.test_set,random_pred)))
print(evaluation.evaluationBypandas(data_set.test_set,score))
# data_set.test_set['pred'] = list_pred
print(data_set.test_set.head())
data_set.test_set.to_csv('pred.txt',sep = '\t',index = None,header = None)
def main(_):
logger = logging.getLogger('QA')
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s')
if FLAGS.log_path:
file_handler = logging.FileHandler(FLAGS.log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
# os.environ['CUDA_DEVICE_ORDER'] = "PCI_BUS_ID"
# os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
if FLAGS.task_type == 'prepare':
prepare()
elif FLAGS.task_type == 'train':
prepare()
train()
elif FLAGS.task_type == 'infer':
predict()
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()