Sklearn pass fit() parameters to xgboost in pipeline

Question

Similar to How to pass a parameter to only one part of a pipeline object in scikit learn? I want to pass parameters to only one part of a pipeline. Usually, it should work fine like:

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

and executed like

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

but it fails with:

    /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

Whereas a

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

works just fine.

Answer 1

For the early stopping rounds, you must always specify the validation set given by the argument eval_set. Here is how the error in your code can be fixed.

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

Answer 2

I recently used the following steps to use the eval metric and eval_set parameters for Xgboost.

1. create the pipeline with the pre-processing/feature transformation steps:

This was made from a pipeline defined earlier which includes the xgboost model as the last step.

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

2. Fit this Pipeline

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

3. Create your eval_set by applying the transformations to the test set

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

4. Add your xgboost step back into the Pipeline

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

5. Fit the new pipeline by passing the Parameters

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

6. Persist the Pipeline if you wish to.

joblib.dump(pipeline_temp, save_path)

Answer 3

This is the solution: https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13755/xgboost-early-stopping-and-other-issues both early_stooping_rounds and the watchlist / eval_set need to be passed. Unfortunately, this does not work for me, as the variables on the watchlist would require a preprocessing step which is only applied in the pipeline / I would need to apply this step manually.