def threadPredict(model,test_data,test_Xc,start_index,end_index,test_loc_dic):
start_time = time.time()
pre_result = predict(model, test_Xc[start_index:end_index])
end_time = time.time()
print "predict from %d to %d. \n cost time :%lf s\n" % (start_index, end_index, end_time - start_time)
start_time1 = time.time()
result = transformResult(pre_result, test_loc_dic)
if mu.acquire(True):
wr = WriteResult()
wr.WriteResultAnswer(test_data[start_index:end_index], result,Config.ResultDataPath+"submission_1.csv")
mu.release()
end_time1 = time.time()
print "transform index from %d to %d. \n cost time :%lf s\n" % (start_index, end_index, end_time1 - start_time1)
def main():
print "Start......"
rf = ReadCsvFile()
# train_data = rf.ReadTrainFile()
preP = PreProcess()
# train_X,train_lab,loc_dic = preP.preProcessTrainData(train_data)
# train_Xc = preP.getFeatureScaler(train_X)
# print "Train model"
# TrainModel(train_Xc,train_lab)
# train_lab = None
# loc_dic = None
# train_data = None
# train_X = None
# train_Xc = None
# with open(Config.ResultDataPath+"result.csv","w") as fp:
# print "清空文件","result.csv"
model = LoadModel()
# print "Read data predict ... ... "
test_data = rf.ReadTestFile()
test_X,test_loc_dic = preP.preProcessTestData(test_data)
test_Xc = preP.getFeatureScaler(test_X)
test_X = None
length = len(test_Xc)
print "test data len:",length
print "predict result ... ... "
for i in xrange(104,length):
pre_result = None
start_index = i * 100
end_index = (i+1) * 100
if end_index > length:
end_index = length
start_time = time.time()
pre_result = predict(model,test_Xc[start_index:end_index])
end_time = time.time()
print "predict from %d to %d. \n cost time :%lf s\n" % (start_index, end_index,end_time-start_time)
start_time = time.time()
result = transformResult(pre_result, test_loc_dic)
logging.info("predict from %d to %d. \ncost time :%lf s" % (start_index, end_index,end_time-start_time))
wr = WriteResult()
wr.WriteResultAnswer(test_data[start_index:end_index], result,Config.ResultDataPath+"result_1.csv")
end_time = time.time()
logging.info("transform index from %d to %d. \ncost time :%lf s" % (start_index, end_index,end_time-start_time))
print "transform index from %d to %d. \n cost time :%lf s\n" % (start_index, end_index,end_time-start_time)