通过源码分析GBDT是怎么实现early stopping的

程序员文章站 2024-03-15 19:55:30

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GBDT文档：Early stopping of Gradient Boosting

文章目录

源码分析
不同参数效果比较

`validation_fraction=0.2`, `n_iter_no_change=5`
`validation_fraction=0.2`, `n_iter_no_change=10`
`validation_fraction=0.2`, `n_iter_no_change=20`
`validation_fraction=0.1`, `n_iter_no_change=20`

手动实现early_stop

源码分析

有无early stopping的比较

    gbes = ensemble.GradientBoostingClassifier(n_estimators=n_estimators,
                                               validation_fraction=0.2,
                                               n_iter_no_change=5, tol=0.01,
                                               random_state=0)
    gb = ensemble.GradientBoostingClassifier(n_estimators=n_estimators,
                                             random_state=0)

打开scikit-learn源码，看到sklearn.ensemble._gb.GradientBoostingClassifier

尝试用Example作为调试入口：examples/ensemble/plot_gradient_boosting_early_stopping.py

尝试失败。Cython和cpp项目就是难搞。

看到他的基类sklearn.ensemble._gb.BaseGradientBoosting#fit

sklearn/ensemble/_gb.py:424

        if self.n_iter_no_change is not None:
            stratify = y if is_classifier(self) else None
            X, X_val, y, y_val, sample_weight, sample_weight_val = (
                train_test_split(X, y, sample_weight,
                                 random_state=self.random_state,
                                 test_size=self.validation_fraction,
                                 stratify=stratify))
            if is_classifier(self):
                if self.n_classes_ != np.unique(y).shape[0]:
                    # We choose to error here. The problem is that the init
                    # estimator would be trained on y, which has some missing
                    # classes now, so its predictions would not have the
                    # correct shape.
                    raise ValueError(
                        'The training data after the early stopping split '
                        'is missing some classes. Try using another random '
                        'seed.'
                    )
        else:
            X_val = y_val = sample_weight_val = None

这波操作是在整理训练集与测试集

        n_stages = self._fit_stages(
            X, y, raw_predictions, sample_weight, self._rng, X_val, y_val,
            sample_weight_val, begin_at_stage, monitor, X_idx_sorted)

这应该是热启动的操作

begin_at_stage = self.estimators_.shape[0]

_fit_stages是boosting的全过程

# fit the boosting stages
n_stages = self._fit_stages(
    X, y, raw_predictions, sample_weight, self._rng, X_val, y_val,
    sample_weight_val, begin_at_stage, monitor, X_idx_sorted)

进入sklearn.ensemble._gb.BaseGradientBoosting#_fit_stages
sklearn/ensemble/_gb.py:516

for i in range(begin_at_stage, self.n_estimators):

一次boosting迭代

    # fit next stage of trees
    raw_predictions = self._fit_stage(
        i, X, y, raw_predictions, sample_weight, sample_mask,
        random_state, X_idx_sorted, X_csc, X_csr)

sklearn.ensemble._gb.BaseGradientBoosting#_fit_stage

看了下_fit_stage函数（与_fit_stages不同，是拟合单个boosting过程），放几段关键的代码

            residual = loss.negative_gradient(y, raw_predictions_copy, k=k,
                                              sample_weight=sample_weight)

            # induce regression tree on residuals
            tree = DecisionTreeRegressor(...
            
            tree.fit(X, residual, sample_weight=sample_weight,
                     check_input=False, X_idx_sorted=X_idx_sorted)

            # update tree leaves
            loss.update_terminal_regions(
                tree.tree_, X, y, residual, raw_predictions, sample_weight,
                sample_mask, learning_rate=self.learning_rate, k=k)

            # add tree to ensemble
            self.estimators_[i, k] = tree

感觉都是些基操，和书上看的拟合负梯度操作一致，oob的操作需要学习下

对于self.estimators_这个成员变量我很好奇，于是调查了一下。

    def _clear_state(self):
        """Clear the state of the gradient boosting model. """
        if hasattr(self, 'estimators_'):
            self.estimators_ = np.empty((0, 0), dtype=np.object)

    def _resize_state(self):
            self.estimators_ = np.resize(self.estimators_,
                                     (total_n_estimators, self.loss_.K))

回到for循环的部分。
看到一个有意思的地方：

    if monitor is not None:
        early_stopping = monitor(i, self, locals())
        if early_stopping:
            break

monitor是fit中用户传入的一个函数。

    # By calling next(y_val_pred_iter), we get the predictions
    # for X_val after the addition of the current stage
    validation_loss = loss_(y_val, next(y_val_pred_iter),
                            sample_weight_val)

    # Require validation_score to be better (less) than at least
    # one of the last n_iter_no_change evaluations
    if np.any(validation_loss + self.tol < loss_history):
        loss_history[i % len(loss_history)] = validation_loss
    else:
        break

这里就是early_stop最关键的地方了。

loss_history是一个长度为n_iter_no_change的向量，刚开始用np.full填充为inf，然后通过区域操作往里面放最新的loss。

这个操作还是很巧妙的。

不同参数效果比较

GBDT文档：Early stopping of Gradient Boosting