问题描述
我正在尝试在非常大的数据中安装xgboost回归器。如果没有改善，我希望在50棵树中使用早期停止功能，并希望每10棵树中打印评估指标（我将RMSE作为主要指标）。
我当前的代码如下：
#Building a training DMatrix from my training dataset
xgb_tr=xgb.DMatrix(data=x_train[predictors],label=x_train['target'].values,feature_names=predictors)
#Building a testinng DMatrix from my testing dataset
xgb_te=xgb.DMatrix(data=x_test[predictors],label=x_test['target'].values,feature_names=predictors)
params_xgb={
'objective':'reg:linear','eval_metric':'rmse'
}
best_xgb=xgb.train(params_xgb,xgb_tr,evals=[(xgb_tr,'training'),(xgb_te,'test')],num_boost_round=3000,early_stopping_rounds=50,verbose_eval=10)
我期望的是这样的（这是lgbm模型的输出）：
Training until validation scores don't improve for 50 rounds
[10] train's rmse: 1.18004 valid's rmse: 1.10737
[20] train's rmse: 1.16906 valid's rmse: 1.09693
[30] train's rmse: 1.15957 valid's rmse: 1.08851
[40] train's rmse: 1.14905 valid's rmse: 1.07874
[50] train's rmse: 1.14026 valid's rmse: 1.07104
[60] train's rmse: 1.13104 valid's rmse: 1.06248
[70] train's rmse: 1.12265 valid's rmse: 1.05476
[80] train's rmse: 1.114 valid's rmse: 1.04638
[90] train's rmse: 1.10739 valid's rmse: 1.04018
[100] train's rmse: 1.10001 valid's rmse: 1.03354
但是我却收到了令人费解的错误消息：
---------------------------------------------------------------------------
XGBoostError Traceback (most recent call last)
<ipython-input-26-827da738fc42> in <module>
1 evals_results = {}
----> 2 best_xgb=xgb.train(params_xgb,3 xgb_tr,4 evals=[(xgb_tr,5 num_boost_round=3000,/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/xgboost/training.py in train(params,dtrain,num_boost_round,evals,obj,feval,maximize,early_stopping_rounds,evals_result,verbose_eval,xgb_model,callbacks,learning_rates)
210 callbacks.append(callback.reset_learning_rate(learning_rates))
211
--> 212 return _train_internal(params,213 num_boost_round=num_boost_round,214 evals=evals,/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/xgboost/training.py in _train_internal(params,callbacks)
72 # Skip the first update if it is a recovery step.
73 if version % 2 == 0:
---> 74 bst.update(dtrain,i,obj)
75 bst.save_rabit_checkpoint()
76 version += 1
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/xgboost/core.py in update(self,iteration,fobj)
1106
1107 if fobj is None:
-> 1108 _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,ctypes.c_int(iteration),1109 dtrain.handle))
1110 else:
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/xgboost/core.py in _check_call(ret)
174 """
175 if ret != 0:
--> 176 raise XGBoostError(py_str(_LIB.XGBGetLastError()))
177
178
XGBoostError: [17:24:56] src/tree/updater_histmaker.cc:311: fv=inf,hist.last=inf
Stack trace:
[bt] (0) 1 libxgboost.dylib 0x0000000116ac6319 dmlc::LogMessageFatal::~LogMessageFatal() + 57
[bt] (1) 2 libxgboost.dylib 0x0000000116b8bef4 xgboost::tree::CQHistMaker::HistEntry::Add(float,xgboost::detail::GradientPairInternal<float>) + 772
[bt] (2) 3 libxgboost.dylib 0x0000000116b8b6b3 xgboost::tree::CQHistMaker::UpdateHistCol(std::__1::vector<xgboost::detail::GradientPairInternal<float>,std::__1::allocator<xgboost::detail::GradientPairInternal<float> > > const&,xgboost::common::Span<xgboost::Entry const,-1ll> const&,xgboost::MetaInfo const&,xgboost::RegTree const&,std::__1::vector<unsigned int,std::__1::allocator<unsigned int> > const&,unsigned int,std::__1::vector<xgboost::tree::CQHistMaker::HistEntry,std::__1::allocator<xgboost::tree::CQHistMaker::HistEntry> >*) + 643
[bt] (3) 4 libxgboost.dylib 0x0000000116b8d639 xgboost::tree::GlobalProposalHistMaker::CreateHist(std::__1::vector<xgboost::detail::GradientPairInternal<float>,xgboost::DMatrix*,xgboost::RegTree const&) + 1433
[bt] (4) 5 libxgboost.dylib 0x0000000116b834c4 xgboost::tree::HistMaker::Update(std::__1::vector<xgboost::detail::GradientPairInternal<float>,xgboost::RegTree*) + 388
[bt] (5) 6 libxgboost.dylib 0x0000000116b82df0 xgboost::tree::HistMaker::Update(xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float> >*,std::__1::vector<xgboost::RegTree*,std::__1::allocator<xgboost::RegTree*> > const&) + 144
[bt] (6) 7 libxgboost.dylib 0x0000000116b26296 xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float> >*,int,std::__1::vector<std::__1::unique_ptr<xgboost::RegTree,std::__1::default_delete<xgboost::RegTree> >,std::__1::allocator<std::__1::unique_ptr<xgboost::RegTree,std::__1::default_delete<xgboost::RegTree> > > >*) + 1766
[bt] (7) 8 libxgboost.dylib 0x0000000116b22566 xgboost::gbm::GBTree::doboost(xgboost::DMatrix*,xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float> >*,xgboost::ObjFunction*) + 310
[bt] (8) 9 libxgboost.dylib 0x0000000116ac27cc xgboost::LearnerImpl::UpdateOneIter(int,xgboost::DMatrix*) + 1532
有人遇到这样的错误吗？如果没有，是否有更好的方法通过此回调在回归中实现XGBoost算法？
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