train_tensorflow.py 文件源码

def create_features(user_id,is_exp,
                         feature_cloumn_func = lambda day:get_feature_cloumn(None,day,has_user_type=False),
                         load_exp_func = load_user_exp_model,
                         load_func = load_user_model,
                         is_exp_power = False
                        ):
    print user_id
    dataset = get_month_by_id(user_id)
    result = []
    for day in range(1,32):
        feature_column = feature_cloumn_func(day)
        x_ = dataset[feature_column]
        trainer = xgb.XGBRegressor()
        if is_exp:
            if is_exp_power:
                x_ = exp_power(x_)
            load_exp_func(trainer,day,user_id)
        else:
            load_func(trainer,day,user_id)
        y_p = trainer.predict(x_)
        y_p = pd.Series(y_p,name='y_p#%d'%(day-1))
        if not is_exp:
            y_p = np.exp(y_p)
        result.append(y_p)
    result = pd.DataFrame(result).T
    result.index = dataset.index
    for day in range(31):
        result['real#%d'%day] = dataset['y#%d'%day].apply(np.exp)
    sys.stdout.flush()
    return result