train_tensorflow.py 文件源码

def predict_tf_once(day,start_date = '2016-10-1'):
    all_dataset = get_dataset(day)
    all_dataset = map(lambda x:x.ix[start_date:start_date],all_dataset)
    y_p_features = map(lambda user_id:tf_percent_model.resample_x_y_(all_dataset,user_id)[0].reshape(-1),get_full_user_ids())
    y_p_features_df = pd.DataFrame(y_p_features,index = get_full_user_ids())
    percent = pd.DataFrame.from_csv('./features/tensorflow_model/percent_model/%d.csv'%day)
    #percent = pd.DataFrame.from_csv('./features/tensorflow_model/percent_model/%d.csv'%2)
    #%%
    percent = percent[map(lambda x:'percent#%d'%x,range(_feature_length))]
    t = pd.DataFrame(index = percent.index)
    t[pd.Timestamp(start_date)+pd.Timedelta('%dd'%(day-1))] = (np.array(y_p_features_df)*percent).sum(axis=1)
    t = t.T
    t.to_csv('./result/predict_part/%d.csv'%day)
    real = int(np.round((np.array(y_p_features_df)*percent).sum().sum()))
    print (day,real)
    return (day,real)