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main.py
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#-*-coding:utf-8-*-
import numpy as np
import logging
import sys
import os
import h5py
import argparse
from qa.model_factory import ModelFactory
from qa.data_process import Vocab, DataGenerator, ModelParam
np.random.seed(1337)
log = logging.getLogger("output")
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='log.txt',
filemode='a')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s [line:%(lineno)d] %(levelname)s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
parser = argparse.ArgumentParser(description='Dynamic-Clip Attention')
parser.add_argument('-t','--task',type=str,default="wikiqa",help='task: trecqa or wikiqa')
parser.add_argument('-m',"--model",type=str,default="listwise",help='model: listwise, k_max or k_threhold')
parser.add_argument('-d',"--hidden_dim",type=int,default=300,help='dimension of the hidden layer')
parser.add_argument('-e',"--epoch",type=int,default=10,help='max epoch')
parser.add_argument('-l',"--lr",type=float,default=0.001,help='learning rate')
parser.add_argument('-k_q',"--k_value_ques",type=float,default=5,help='k value in k_max or k_threshold for question attention')
parser.add_argument('-k_a',"--k_value_ans",type=float,default=5,help='k value in k_max or k_threshold for answer attention')
parser.add_argument('-b',"--batch_size",type=int,default=3,help='batch_size')
parser.add_argument('-p',"--pre_train",type=int,default=5,help='pre_train')
global_mark = "wikiqa" + "_" +"listwise"
def result_log(str):
ff = open("./result_note.txt",'a')
ff.write(str+"\n")
ff.close()
def specific_load_epoch(model,epoch,prefix):
assert os.path.exists('model/'+prefix+'_weights_epoch_%s.h5' %
epoch), 'Weights at epoch %s not found' % epoch
filename = 'model/'+prefix+'_weights_epoch_%s.h5' % epoch
h5_file = h5py.File(filename,'r')
weight = []
for i in range(len(h5_file.keys())):
weight.append(h5_file['weight'+str(i)][:])
model.set_weights(weight)
def specific_save_epoch(model,epoch,prefix,global_mark_copy):
if not os.path.exists('model/'):
os.makedirs('model/')
filename = 'model/'+prefix+'_weights_epoch_%s.h5' % (str(epoch)+"_"+global_mark_copy)
h5_file = h5py.File(filename,'w')
weight = model.get_weights()
for i in range(len(weight)):
h5_file.create_dataset('weight'+str(i),data=weight[i])
h5_file.close()
def prog_bar(so_far, total, n_bars=20):
n_complete = int(so_far * n_bars / total)
if n_complete >= n_bars - 1:
sys.stderr.write('\r[' + '=' * n_bars + ']')
else:
s = '\r[' + '=' * (n_complete - 1) + '>' + '.' * \
(n_bars - n_complete) + ']'
sys.stderr.write(s)
def task_data_ready(task,model_param):
if task == "wikiqa":
vocab_all = Vocab("./data/wikiqa/vocab_wiki.txt", max_size=80000)
data_generator = DataGenerator(vocab_all, model_param,"./data/wikiqa/wiki_answer_train.pkl")
embedding_file = "./data/wikiqa/wikiqa_glovec.txt"
dev_data = data_generator.EvaluateGenerate("./data/wikiqa/wiki_dev.pkl")
test_data = data_generator.EvaluateGenerate("./data//wikiqa/wiki_test.pkl")
elif task == "trecqa":
vocab_all = Vocab("./data/trecqa/vocab_trec.txt", max_size=80000,)
data_generator = DataGenerator(vocab_all, model_param,"./data/trecqa/trec_answer_train.pkl")
embedding_file = "./data/trecqa/trecqa_glovec.txt"
dev_data = data_generator.EvaluateGenerate("./data/trecqa/trec_dev.pkl")
test_data = data_generator.EvaluateGenerate("./data/trecqa/trec_test.pkl")
return vocab_all,data_generator,embedding_file,dev_data,test_data
def main(args):
global_mark = args.task + "_" + args.model
print str(args.pre_train)+" model"
if args.task == "wikiqa":
model_param = ModelParam(hidden_dim=args.hidden_dim, enc_timesteps=25, dec_timesteps=90, batch_size=args.batch_size, random_size=15, lr=args.lr, k_value_ques=args.k_value_ques,k_value_ans=args.k_value_ans)
elif args.task == "trecqa":
model_param = ModelParam(hidden_dim=args.hidden_dim, enc_timesteps=30, dec_timesteps=70, batch_size=args.batch_size, random_size=40, lr=args.lr, k_value_ques=args.k_value_ques,k_value_ans=args.k_value_ans)
logging.info(model_param.__str__())
vocab_all,data_generator,embedding_file,dev_data,test_data = task_data_ready(args.task,model_param)
train_model, predict_model = ModelFactory.get_model(model_param, embedding_file, vocab_all.NumIds(),model_type=args.model)
def data_evaluate(epoch,small_evaluate_data,flag):
c_1_j = 0
c_2_j = 0
for i, d in enumerate(small_evaluate_data.values()):
prog_bar(i, len(small_evaluate_data))
question = d["question"]
answers = d["answer"]
question_len = d["ques_len"]
ans_len = d["ans_len"]
sims = predict_model.predict([question,answers,question_len,ans_len],batch_size=len(question))
sims = sims[:,0]
rank_index = np.argsort(sims).tolist()[::-1]
score = 0.0
count = 0.0
for i in range(1,len(sims)+1):
if d["label"][rank_index[i-1]] == 1:
count += 1
score += count / i
for i in range(1,len(sims)+1):
if d["label"][rank_index[i-1]] == 1:
c_2_j += 1/float(i)
break
c_1_j += score / count
MAP = c_1_j / float(len(small_evaluate_data))
MRR = c_2_j / float(len(small_evaluate_data))
print ""
logging.info(global_mark + " evaluate on "+ flag +" data at epoch "+str(epoch)+' MAP: %f' % MAP)
logging.info(" evaluate on "+ flag + " data at epoch "+str(epoch)+' MRR: %f' % MRR)
result_log(global_mark+" evaluate on "+ flag + " data at epoch "+str(epoch)+' MAP: %f' % MAP)
result_log(global_mark+" evaluate on "+ flag + " data at epoch "+str(epoch)+' MRR: %f' % MRR)
return MAP,MRR
##############dynamic-clip attention load listwise model as pre-train model
if args.model != "listwise":
reload_epoch = args.pre_train
assert os.path.exists('model/train_weights_epoch_%s.h5' % (str(reload_epoch)+"_"+args.task+"_listwise")), "please pre-train listwise approach"
specific_load_epoch(train_model,str(reload_epoch)+"_"+args.task+"_listwise","train")
##############
best_epoch = 0
best_map = 0
score_list = []
for i in range(1,args.epoch+1):
if args.task == "wikiqa":
train_filename = "./data/wikiqa/wiki_train.pkl"
questions, answers, label, question_len ,answer_len = data_generator.wikiQaGenerate(train_filename,"basic")
elif args.task == "trecqa":
train_filename = "./data/trecqa/trec_train.pkl"
questions, answers, label, question_len ,answer_len = data_generator.trecQaGenerate(train_filename,"basic")
logging.info('Fitting epoch %d' % i)
train_model.fit([questions, answers,question_len,answer_len], label,nb_epoch=1, batch_size=model_param.batch_size, validation_split=0, verbose=1,shuffle=True)
specific_save_epoch(train_model,i, prefix="train",global_mark_copy = global_mark)
specific_save_epoch(predict_model,i, prefix="predict",global_mark_copy = global_mark)
####evaluate
dev_map,dev_mrr = data_evaluate(i,dev_data,"dev")
test_map,test_mrr = data_evaluate(i,test_data,"test")
if dev_map > best_map:
best_map = dev_map
best_epoch = i
score_list = [dev_map,dev_mrr,test_map,test_mrr]
#######best result
logging.info("best model at epoch "+str(best_epoch))
logging.info("the dev score of best model: MAP_"+str(score_list[0])+" MRR_"+str(score_list[1]))
logging.info("the test score of best model: MAP_"+str(score_list[2])+" MRR_"+str(score_list[3]))
if __name__ == "__main__":
args = parser.parse_args()
main(args)