python - GridSearchCV 在管道中将 fit_params 传递给 XGBRegressor 产生 "ValueError: need more than 1 value to unpack"

Question

无论内容如何，​​将 fit_params 传递到包含 XGBRegressor 的管道都会返回错误

训练数据集已经过热编码并被拆分以用于管道

train_X, val_X, train_y, val_y = train_test_split(final_train, y, random_state = 0)

创建一个 Imputer -> XGBRegressor 管道。设置XGBRegressor的参数和拟合参数

pipe = Pipeline(steps=[("Imputer", Imputer()),
                      ("XGB", XGBRegressor())])

xgb_hyperparams = {'XGB__n_estimators': [1000, 2000, 3000],
                   'XGB__learning_rate': [0.01, 0.03, 0.05, 0.07],
                   'XGB__max_depth': [3, 4, 5]}

fit_parameters = {'XGB__early_stopping_rounds': 5,
              'XGB__eval_metric': 'mae',
              'XGB__eval_set': [(val_X, val_y)],
              'XGB__verbose': False}

grid_search = GridSearchCV(pipe,
                          xgb_hyperparams,
                          #fit_params=fit_parameters,
                          scoring='neg_mean_squared_error',
                          cv=5,
                          n_jobs=1,
                          verbose=3)

grid_search.fit(train_X, train_y, fit_params=fit_parameters)

这会产生以下输出:

Fitting 5 folds for each of 36 candidates, totalling 180 fits
[CV] XGB__learning_rate=0.01, XGB__n_estimators=1000, XGB__max_depth=3 
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-76-0751db18c046> in <module>()
----> 1 grid_search.fit(train_X, train_y, fit_params=fit_parameters)

/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc in fit(self, X, y, groups, **fit_params)
    638                                   error_score=self.error_score)
    639           for parameters, (train, test) in product(candidate_params,
--> 640                                                    cv.split(X, y, groups)))
    641 
    642         # if one choose to see train score, "out" will contain train score info

/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    777             # was dispatched. In particular this covers the edge
    778             # case of Parallel used with an exhausted iterator.
--> 779             while self.dispatch_one_batch(iterator):
    780                 self._iterating = True
    781             else:

/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
    623                 return False
    624             else:
--> 625                 self._dispatch(tasks)
    626                 return True
    627 

/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
    586         dispatch_timestamp = time.time()
    587         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588         job = self._backend.apply_async(batch, callback=cb)
    589         self._jobs.append(job)
    590 

/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in apply_async(self, func, callback)
    109     def apply_async(self, func, callback=None):
    110         """Schedule a func to be run"""
--> 111         result = ImmediateResult(func)
    112         if callback:
    113             callback(result)

/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in __init__(self, batch)
    330         # Don't delay the application, to avoid keeping the input
    331         # arguments in memory
--> 332         self.results = batch()
    333 
    334     def get(self):

/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
    129 
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132 
    133     def __len__(self):

/usr/local/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
    456             estimator.fit(X_train, **fit_params)
    457         else:
--> 458             estimator.fit(X_train, y_train, **fit_params)
    459 
    460     except Exception as e:

/usr/local/lib/python2.7/site-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
    246             This estimator
    247         """
--> 248         Xt, fit_params = self._fit(X, y, **fit_params)
    249         if self._final_estimator is not None:
    250             self._final_estimator.fit(Xt, y, **fit_params)

/usr/local/lib/python2.7/site-packages/sklearn/pipeline.pyc in _fit(self, X, y, **fit_params)
    195                                 if step is not None)
    196         for pname, pval in six.iteritems(fit_params):
--> 197             step, param = pname.split('__', 1)
    198             fit_params_steps[step][param] = pval
    199         Xt = X

ValueError: need more than 1 value to unpack

Answer 1

我不认为问题出在 xgboost 上。这是将 fit_params 传递给 fit 方法的方式中的错误。你需要的是 grid_search.fit(train_X, train_y, **fit_parameters)