在具有大数据帧的XGBoost上运行BayesianOptimization时，为什么会出错？

如何解决在具有大数据帧的XGBoost上运行BayesianOptimization时，为什么会出错？

当我在较小的数据帧上运行带有XGBoost的BayesianOptimization时，一切工作都很好，但是当我使用较大的数据帧时，会遇到各种错误。我最近一次运行它收到了

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\target_space.py in probe(self,params)
    190         try:
--> 191             target = self._cache[_hashable(x)]
    192         except KeyError:

KeyError: (0.5414533613708468,0.4244312334264955,2.505215712179838,3.29866527650986,6937.176867016293,0.833081120152225)

During handling of the above exception,another exception occurred:

XGBoostError                              Traceback (most recent call last)
<ipython-input-2-ef2efb90fddb> in <module>
     53 # Use the expected improvement acquisition function to handle negative numbers
     54 # Optimally needs quite a few more initiation points and number of iterations
---> 55 xgb_bo.maximize(init_points=10,n_iter=100,acq='ei')
     56 print(xgb_bo.max)

~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\bayesian_optimization.py in maximize(self,init_points,n_iter,acq,kappa,xi,**gp_params)
    172                 iteration += 1
    173 
--> 174             self.probe(x_probe,lazy=False)
    175 
    176         self.dispatch(Events.OPTIMIZATION_END)

~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\bayesian_optimization.py in probe(self,params,lazy)
    110             self._queue.add(params)
    111         else:
--> 112             self._space.probe(params)
    113             self.dispatch(Events.OPTIMIZATION_STEP)
    114 

~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\target_space.py in probe(self,params)
    192         except KeyError:
    193             params = dict(zip(self._keys,x))
--> 194             target = self.target_func(**params)
    195             self.register(x,target)
    196         return target

<ipython-input-2-ef2efb90fddb> in xgb_evaluate(max_depth,gamma,colsample_bytree,subsample,eta,min_child_weight)
     38              }
     39     # Used around 1000 boosting rounds in the full model
---> 40     cv_result = xgb.cv(params,dtrain,num_boost_round=100,nfold=3)
     41 
     42 

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\training.py in cv(params,num_boost_round,nfold,stratified,folds,metrics,obj,feval,maximize,early_stopping_rounds,fpreproc,as_pandas,verbose_eval,show_stdv,seed,callbacks,shuffle)
    496                            evaluation_result_list=None))
    497         for fold in cvfolds:
--> 498             fold.update(i,obj)
    499         res = aggcv([f.eval(i,feval) for f in cvfolds])
    500 

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\training.py in update(self,iteration,fobj)
    224     def update(self,fobj):
    225         """"Update the boosters for one iteration"""
--> 226         self.bst.update(self.dtrain,fobj)
    227 
    228     def eval(self,feval):

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\core.py in update(self,fobj)
   1367             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,1368                                                     ctypes.c_int(iteration),-> 1369                                                     dtrain.handle))
   1370         else:
   1371             pred = self.predict(dtrain,output_margin=True,training=True)

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\core.py in _check_call(ret)
    188     """
    189     if ret != 0:
--> 190         raise XGBoostError(py_str(_LIB.XGBGetLastError()))
    191 
    192 
    >
    >XGBoostError: [07:47:25] C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/tree/updater_gpu_hist.cu:952: Exception in gpu_hist: bad allocation: temporary_buffer::allocate: get_temporary_buffer failed

Before that,I've received 

>OSError: [WinError -529697949] Windows Error 0xe06d7363

Here's the code that I am running.

        
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    
    number_of_rows = 10000000
    
    print('reading data')
    train_file = "D:\\reading_data/training_data.csv"
    df = pd.read_csv(train_file,sep=',',nrows=number_of_rows)
    df = df.replace([np.inf,-np.inf],np.nan).dropna(axis=0)
    print('done reading data')
    
    import xgboost as xgb
    from bayes_opt import BayesianOptimization
    from sklearn.metrics import mean_squared_error
    
    from sklearn.model_selection import train_test_split
    
    X_train,X_test,y_train,y_test = train_test_split(df.drop(['Symbol','new_date','old_dep_var'],axis=1),df['old_dep_var'],test_size=0.25)
    del(df)
    dtrain = xgb.DMatrix(X_train,label=y_train)
    del(X_train)
    dtest = xgb.DMatrix(X_test)
    del(X_test)
    
    def xgb_evaluate(max_depth,min_child_weight):
        params = {'objective': 'reg:squarederror','eval_metric': 'rmse','max_depth': int(max_depth),'subsample': 0.8,'eta': 0.1,'gamma': gamma,'colsample_bytree': colsample_bytree,'tree_method':'gpu_hist','min_child_weight': 100
                 }
        cv_result = xgb.cv(params,nfold=3)
        
        
        return -1.0 * cv_result['test-rmse-mean'].iloc[-1]
    
    xgb_bo = BayesianOptimization(xgb_evaluate,{'max_depth': (2,10),'gamma': (0,'colsample_bytree': (0.1,0.9),'subsample': (0.2,0.95),'eta': (0.05,0.5),'min_child_weight': (10,10000)
                                                })
    xgb_bo.maximize(init_points=10,acq='ei')
    print(xgb_bo.max)

我尝试过：1）重新安装贝叶斯优化，2）创建/使用新的conda环境，3）安装并运行Outbyte PC Repair来更新所有驱动程序，修复所有损坏的系统文件并消除恶意软件。

我不知道还能尝试什么。任何帮助将不胜感激。