如何在回归问题中将 objective 函数与 MultiOutputRegressor 一起使用

Question

我正在研究时间序列回归问题，预测未来 5 天的股价，我认为这是多个连续输出（多元回归）。

因此，我在 sklearn 中使用 MultiOutputRegressor 来做我想做的事情。

但是未来前三天的预测值比未来第四天和第五天的预测值更重要。

因此，我想用一个权重来惩罚前三天，谁知道如何解决客户 objective 功能的这个问题？或者有其他方法解决这个问题

数据和代码如下

{"date": {"0": "2003-06-30", "1": "2003-07-01", "2": "2003-07-02", "3": "2003-07-03", "4": "2003-07-04", "5": "2003-07-07", "6": "2003-07-08", "7": "2003-07-09", "8": "2003-07-10", "9": "2003-07-11"}, "open": {"0": 37.1, "1": 37.09, "2": 38.17, "3": 40.6, "4": 39.1, "5": 39.6, "6": 42.0, "7": 41.3, "8": 41.2, "9": 39.6}, "max": {"0": 37.4, "1": 38.1, "2": 38.82, "3": 40.6, "4": 39.26, "5": 41.0, "6": 42.0, "7": 41.3, "8": 41.2, "9": 39.97}, "min": {"0": 36.92, "1": 37.09, "2": 38.1, "3": 38.81, "4": 38.75, "5": 39.6, "6": 40.7, "7": 40.81, "8": 40.05, "9": 39.3}, "close": {"0": 37.08, "1": 38.05, "2": 38.69, "3": 39.0, "4": 39.26, "5": 41.0, "6": 41.19, "7": 41.22, "8": 40.05, "9": 39.91}, "stock_id": {"0": 50, "1": 50, "2": 50, "3": 50, "4": 50, "5": 50, "6": 50, "7": 50, "8": 50, "9": 50}, "target_next_1_day": {"0": 38.05, "1": 38.69, "2": 39.0, "3": 39.26, "4": 41.0, "5": 41.19, "6": 41.22, "7": 40.05, "8": 39.91, "9": 40.66}, "target_next_2_day": {"0": 38.69, "1": 39.0, "2": 39.26, "3": 41.0, "4": 41.19, "5": 41.22, "6": 40.05, "7": 39.91, "8": 40.66, "9": 40.19}, "target_next_3_day": {"0": 39.0, "1": 39.26, "2": 41.0, "3": 41.19, "4": 41.22, "5": 40.05, "6": 39.91, "7": 40.66, "8": 40.19, "9": 40.85}, "target_next_4_day": {"0": 39.26, "1": 41.0, "2": 41.19, "3": 41.22, "4": 40.05, "5": 39.91, "6": 40.66, "7": 40.19, "8": 40.85, "9": 39.8}, "target_next_5_day": {"0": 41.0, "1": 41.19, "2": 41.22, "3": 40.05, "4": 39.91, "5": 40.66, "6": 40.19, "7": 40.85, "8": 39.8, "9": 39.92}}

data = pd.DataFrame(data)
X = data[['open', 'max', 'min', 'close']]
Y = data[['target_next_1_day', 'target_next_2_day', 'target_next_3_day', 'target_next_4_day', 'target_next_5_day']]

def customer_obj(target, predict):
    gradient = ????
    hess = ????
    return gradient, hess
clfpre = xgb.XGBRegressor(n_estimators=5, objective =customer_obj)
clf = MultiOutputRegressor(clfpre).fit(X.values, Y.values)

Answer 1

MultiOutputRegressor只是为每个目标变量建立独立的模型。因此，创建自定义 objective 函数对 MultiOutputRegressor 没有用处。

您的customer_obj只能说明一个单独的目标变量。例如，您可以将默认值 objective reg:squarederror 更改为 平均绝对误差 .

来自Documentation:

Multi target regression

This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

如何在回归问题中将 objective 函数与 MultiOutputRegressor 一起使用

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