train_tensorflow.py 文件源码

def predict_tf_all(path = None):
    result_list = []
    p = m_Pool(31)
    result_list = p.map(predict_tf_once,range(1,32))
    p.close()
    p.join()
    print 'writing...'
    result_df = pd.DataFrame(index = range(1))
    for day,result in result_list:
        day_s = str(day)
        if len(day_s)<=1:
            day_s = '0'+day_s
        result_df['201610'+day_s] = result
    result_df = result_df.T
    result_df.columns = ['predict_power_consumption']
    if path == None:
        date = str(pd.Timestamp(time.ctime())).replace(' ','_').replace(':','_')
        path = './result/'+date+'.csv'
    result_df.to_csv(path,index_label='predict_date')

    l = map(lambda day:pd.DataFrame.from_csv('./result/predict_part/%d.csv'%day),range(1,32))
    t = pd.concat(l)
    t.to_csv('./result/predict_part/'+date+'.csv')