Sklearn将拟合参数传递给管道中的xgboost

Question

类似于如何在scikit中仅将参数传递给管道对象的一部分？我想将参数传递给管道的一部分.通常,它应该工作正常,如:

estimator = XGBClassifier()
pipeline = Pipeline([
        ('clf', estimator)
    ])

并执行像

pipeline.fit(X_train, y_train, clf__early_stopping_rounds=20)

但它失败了:

    /usr/local/lib/python3.5/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
        114         """
        115         Xt, yt, fit_params = self._pre_transform(X, y, **fit_params)
    --> 116         self.steps[-1][-1].fit(Xt, yt, **fit_params)
        117         return self
        118 

    /usr/local/lib/python3.5/site-packages/xgboost-0.6-py3.5.egg/xgboost/sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose)
        443                               early_stopping_rounds=early_stopping_rounds,
        444                               evals_result=evals_result, obj=obj, feval=feval,
    --> 445                               verbose_eval=verbose)
        446 
        447         self.objective = xgb_options["objective"]

    /usr/local/lib/python3.5/site-packages/xgboost-0.6-py3.5.egg/xgboost/training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, learning_rates, xgb_model, callbacks)
        201                            evals=evals,
        202                            obj=obj, feval=feval,
    --> 203                            xgb_model=xgb_model, callbacks=callbacks)
        204 
        205 

    /usr/local/lib/python3.5/site-packages/xgboost-0.6-py3.5.egg/xgboost/training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
         97                                end_iteration=num_boost_round,
         98                                rank=rank,
    ---> 99                                evaluation_result_list=evaluation_result_list))
        100         except EarlyStopException:
        101             break

    /usr/local/lib/python3.5/site-packages/xgboost-0.6-py3.5.egg/xgboost/callback.py in callback(env)
        196     def callback(env):
        197         """internal function"""
    --> 198         score = env.evaluation_result_list[-1][1]
        199         if len(state) == 0:
        200             init(env)

    IndexError: list index out of range

而a

estimator.fit(X_train, y_train, early_stopping_rounds=20)

工作得很好.

Answer 1

对于早期停止轮次,必须始终指定参数eval_set给出的验证集.以下是如何修复代码中的错误.

pipeline.fit(X_train, y_train, clf__early_stopping_rounds=20, clf__eval_set=[(test_X, test_y)])

Answer 2

这是解决方案:https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13755/xgboost-early-stopping-and-other-issues early_stooping_rounds和监视列表/需要传递eval_set.不幸的是,这对我不起作用,因为监视列表中的变量需要预处理步骤,该步骤仅应用于管道/我需要手动应用此步骤.

Answer 3

我最近使用以下步骤为 Xgboost 使用 eval 指标和 eval_set 参数。

1. 使用预处理/特征转换步骤创建管道：

这是由之前定义的管道制成的，其中包括作为最后一步的 xgboost 模型。

pipeline_temp = pipeline.Pipeline(pipeline.cost_pipe.steps[:-1])  

2.适合这个管道

X_trans = pipeline_temp.fit_transform(X_train[FEATURES],y_train)

3. 通过将转换应用于测试集来创建您的 eval_set

eval_set = [(X_trans, y_train), (pipeline_temp.transform(X_test), y_test)]

4. 将您的 xgboost 步骤重新添加到管道中

 pipeline_temp.steps.append(pipeline.cost_pipe.steps[-1])

5. 通过传递参数来适应新的管道

pipeline_temp.fit(X_train[FEATURES], y_train,
             xgboost_model__eval_metric = ERROR_METRIC,
             xgboost_model__eval_set = eval_set)

6. 如果您愿意，可以保留管道。

joblib.dump(pipeline_temp, save_path)