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MtI:HL$HIJLD$PMBILT$(LkM9[H\$ M)IMcIMM9H|$LHLT$pHT$PLT$$HI HL$HLHH5"7$H|$PXH\$ Lt$(L9= L2H)HHL,@Af.Cw'DAtL]L;] MtEIL]HI9MII?IIItwIt:%f.cw'D AtHUH;U HtD HHUH=ef.{w$;tHuH;u Ht>HHuH5.f.sw% tL}L;}MtAIL}HDfD.Cw'DAtHEH;EHtDHHEHf.Sw'D+AtLEL;EMtE(ILEHD fD.Kw'DAtLML;MMtEILMHDCfD.Sw$;tHUH;UHt:HHUHI9DfD.[w$3tHMH;MHt1HHML{D%ȿfE.gw&[tLUL;UMtAILUIwD-fD.nw(AGtLmL;mMtAEILmIw0D5PfD.vw(EG0AtL]L;]MtEIL]IwHD=fD.~w(EOHAtH}H;}%HtDHH}Iw` ׾f.Nw&AO`tHUH;UHt HHUIwx-f.nw'A_xtLUL;UMtAILUIaf.^w+AtLmL;meMtAEILmII9H|$ HtMtLHh[]A\A]A^A_@H I9OtmD$IL>IuIID$Ht!-1@@hLML$HHHWHHHHtjHt2Mt%I1AAAAAAYHIMt%%1AAAAAAaHIMt%=0AAAAAAyHIMt%50AAAAAAqHIMt%0AAAAAAQHIMt&DN0AAAAAEAHIMt&D 0AAAAAEIIH(Mt&D/AAAAAEQMHyIt'D/AAA@A@EXMIt'D%/A@AAAAEaMI0t'D-O/A@0ABABEjMIHt'D5/A@HACACEsLH`t%D=.A@`@@DxLHxt# .A@xBBJMIŐt)-.AǀAEAEAmHMHnfL(L*LHLJHHZHJH8I8LPMPLXE@MX8LMHPIRLhAzMjL|$@1E1L|$@I)AOH|$ L1OtmHIH\$ NL9T$(LT$(Ht$H|$LT$(Ht$H|$LT$(LHLD$@E11LD$F@HHHH~H{HnHa HS3LH@H3cHH HHHHHHRHHHHHHHH=H|$ HHtMtLHof.DAWAVAUATUHSHHHc_ 1dH%(H$81HD$pHD$xHDŽ$Ht HHH1Ld$`HH|$pH$H)CHD$pH='1LH\$xHl$`IHD$HHD$hL)H}hLHAAH$Hcu H$IHT$0HLHcu H$HHIHL$8H$HHt$ L$L$ML$MLl$ H=ܖ1HH$D$L$H貖]LL$H$LHDŽ$HDŽ$LHDŽ$HDŽ$HDŽ$HDŽ$L\$@HD$(L$LLLd$(HcL$H$LeHcH$0LNL$H$HH$L$0I$HL,A4$HI7HwAD$(GEL$DOED$ IHDGDREVJANDZE^BAF`D H0HI0DNq(vDADFDQ DVDZE^zA~DbEfZLHA^A$L9AT$(VEL$DNED$ DFDEZ(A^DZE^B AFS1E11H$H LcLIэFAH90HHI}LaL$0MeHYI]HMLMQLPMYLXD9HHLDHDŽ$PHDŽ$XHDŽ$`HDŽ$pHDŽ$xEHDŽ$H$PL$pDE11Ht$Ll$fDEE1Ht#H$HH$HOH$HGHH$XLcLDk-&MLGMQLWMYL_H$x$H;$DŽ$DŽ$DŽ$Ht#H$H>H$HVH$HNHH$xIcHA0LLNL@LFLPLVDE9H$XH;$`%%DŽ$DŽ$DŽ$$H|$LH$XH|$LH$xKfDDAE9DLHDŽ$H$HDŽ$HDŽ$H$PHHHDŽ$A6HDŽ$HDŽ$LH$pLHAuLHHHT$PLLHH0HHT$HL=T,$L)L|$P$贷HM1Ht$HHH$HtH$HtH$pHtH$PHtH$0HtH$HtH|$(H|$@Hl$ HcH H|$8H|$0H|$pHtHH$8dH3%(HH[]A\A]A^A_1IH$HtH|$(9H|$@/Lt$ LLzH|$8H|$0H|$pHtLIH$0HtIH$pHtH$PHtqH$IHtZH$HtHfDImI[I4IHHDebug[LightGBM] [%s] InfoWarning[LightGBM] [Fatal] %s ] outside []_Map_base::atunknown exceptionField not found/__w/1/s/src/c_api.cppregressiongbdtserialcpuLightGBM_model.txtLightGBM_predict_result.txtgbdt_prediction.cpp'"basic_string::erasenonemulticlassCould not open %svector::reserveUsed indices of subsetvector::_M_default_appendbasic_string::substr ,num_classboostingobjective[%s]: does not tolerate element [#%i = Input data type error or field not foundUnknown data type in RowFunctionFromDenseMatricUnknown data type in RowFunctionFromCSRCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . Unknown data type in CSC matrixSize of feature_names error, should equal with total number of featuresFind whitespaces in feature_names, replace with underlinesDo not support non-ASCII characters in feature name.Do not support special JSON characters in feature name./__w/1/s/src/application/predictor.hppCheck failed: early_stop_freq > 0 at %s, line %d . Check failed: early_stop_margin >= 0 at %s, line %d . The number of features in data (%d) is not the same as it was in training data (%d).Skipped header "%s" in file %sCheck failed: num_used_row_indices > 0 at %s, line %d . used_row_indices should be sorted in SubsetThe number of columns should be greater than zero.The number of columns should be smaller than INT32_MAX.Check failed: inner_data.first < num_col at %s, line %d . Using self-defined objective functionContinued train from model is not supported for c_api, please use continued train with input scoreDo not support feature parallel in c apiOnly find one worker, will switch to serial tree learnerWarning: last line of %s has no end of line, still using this linePrediction results file %s cannot be foundCould not recognize the data format of data file %sCannot change num_class during trainingCannot change boosting during trainingCannot change metric during trainingSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE*Z25LGBM_BoosterPredictForCSCEUliE_*Z26RowFunctionFromDenseMatricPKviiiiEUliE_*Z26RowFunctionFromDenseMatricPKviiiiEUliE0_*Z26RowFunctionFromDenseMatricPKviiiiEUliE1_*Z26RowFunctionFromDenseMatricPKviiiiEUliE2_*Z30RowPairFunctionFromDenseMatricPKviiiiEUliE_*Z28RowPairFunctionFromDenseRowsPPKviiEUliE_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE0_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE1_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE2_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE0_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE1_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE2_St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN8LightGBM8BoostingEN8LightGBM8GBDTBaseENSt6thread10_Impl_baseESsNSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS5_mEEEUlvE_vEEEEZN8LightGBM9Predictor7PredictEPKcS2_bEUliRKSt6vectorISsSaISsEEE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE1_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE2_ZN8LightGBM9Predictor7PredictEPKcS2_bEUlS2_PSt6vectorISt4pairIidESaIS5_EEE_ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKSt8functionIFviRKSt6vectorISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKSt8functionIFviRKSt6vectorISsSaISsEEEERKS2_IFbiiEEEUlPKcmE_St19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS6_mEEEUlvE_vEEEESaISF_ELN9__gnu_cxx12_Lock_policyE2EE8?MbP????$@?𿚙?ffffff??{Gz?C8/__w/1/s/src/lightgbm_R.cppDon't support large string in R-packageCheck failed: out_len == len at %s, line %d . Check failed: len == out_len at %s, line %d . basic_string::_S_construct null not validStarted training...Finished trainingFinished initializing networkFinished RefitTreeFinished predictionUnknown parameter: %sconfig# Finished loading parameters%s is set=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%sFinished initializing prediction, total used %d iterationsFinished loading data in %f secondsFinished initializing trainingConfig file %s doesn't exist, will ignoreNo training/prediction data, application quitZN8LightGBM10TextReaderImE12ReadAllLinesEvEUlmPKcmE_ZN8LightGBM10TextReaderIiE12ReadAllLinesEvEUliPKcmE_ZN8LightGBM10TextReaderImE17ReadAllAndProcessERKSt8functionIFvmPKcmEEEUlS4_mE_ZN8LightGBM10TextReaderIiE17ReadAllAndProcessERKSt8functionIFviPKcmEEEUlS4_mE_.A/__w/1/s/src/boosting/gbdt.h/__w/1/s/src/boosting/rf.hppUsing GOSSCannot use bagging in GOSSdefaultvector::_M_fill_insertvector::_M_range_insertdartgossrfUnknown boosting type %sCheck failed: data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size()) at %s, line %d . Check failed: tree_idx >= 0 && static_cast(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . Check failed: num_tree_per_iteration_ == num_class_ at %s, line %d . /__w/1/s/src/boosting/goss.hppCheck failed: config_->top_rate + config_->other_rate <= 1.0f at %s, line %d . Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . Check failed: gradients == nullptr at %s, line %d . Check failed: hessians == nullptr at %s, line %d . RF mode do not support custom objective function, please use built-in objectives.Time for loading model: %f secondsUnknown model format or submodel type in model file %sCheck failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at %s, line %d . Check failed: config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f at %s, line %d . Check failed: train_data->metadata().init_score() == nullptr at %s, line %d . N8LightGBM4DARTEN8LightGBM4GOSSEN8LightGBM2RFEZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_?<.snapshot_iter_No object function providedStart training from score %lfregression_l1quantilemapeUse subset for baggingIteration:, training : , valid_%f seconds elapsed, finished iteration %d/__w/1/s/include/LightGBM/tree.hCheck failed: max_depth_ >= 0 at %s, line %d . /__w/1/s/src/boosting/gbdt.cppCheck failed: leaf_pred[i] < models_[model_index]->num_leaves() at %s, line %d . Check failed: tree_leaf_prediction.size() > 0 at %s, line %d . Check failed: static_cast(num_data_) == tree_leaf_prediction.size() at %s, line %d . Check failed: static_cast(models_.size()) == tree_leaf_prediction[0].size() at %s, line %d . Check failed: cur_left_cnt == bag_data_cnt at %s, line %d . Re-bagging, using %d data to trainDisabling boost_from_average in %s may cause the slow convergenceNumber of class for initial score errorEarly stopping at iteration %d, the best iteration round is %dOutput of best iteration round: %sCheck failed: num_tree_per_iteration_ == objective_function_->NumModelPerIteration() at %s, line %d . Cannot reset training data, since new training data has different bin mappersCheck failed: train_data != nullptr at %s, line %d . Stopped training because there are no more leaves that meet the split requirementsCannot add validation data, since it has different bin mappers with training data*ZN8LightGBM4GBDT12TrainOneIterEPKfS2_EUlS2_iE_N8LightGBM4GBDTE?Tree=truefalse"name":""version":""num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":"average_output":"objective":"", ",""feature_names":[""],"monotone_constraints":["tree_info":["tree_index":#include "gbdt.h"#include #include #include #include #include #include #include namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBMl2_rootroot_mean_squared_errorrmsemulticlassovaovrxentropycross_entropyxentlambdacross_entropy_lambdanullcustomnaregression_l2mean_absolute_errormaesoftmaxmulticlass_ova#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifmonotone_constraintsWrong line at model file: %snum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesvector::_M_emplace_back_auxWrong size of feature_infostree_sizesend of parametersversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=feature_names=monotone_constraints=feature_infos=tree_sizes=end of treesfeature importances:%lu parameters:v3Model format error, expect a tree here. met %s#include #include #include double (*PredictTreePtr[])(const double*) = { int early_stop_round_counter = 0;std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);for (int i = 0; i < num_iteration_for_pred_; ++i) {for (int k = 0; k < num_tree_per_iteration_; ++k) {output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { void GBDT::PredictLeafIndex(const double* features, double *output) const {int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;for (int i = 0; i < total_tree; ++i) {output[i] = (*PredictTreeLeafPtr[i])(features);double (*PredictTreeLeafByMapPtr[])(const std::unordered_map&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Unknown importance type: only support split=0 and gain=1mean_absolute_percentage_errorModel file doesn't specify the number of classesModel file doesn't specify the label indexModel file doesn't specify max_feature_idxModel file doesn't contain feature_namesWrong size of monotone_constraintsModel file doesn't contain feature_infosUnknown early stopping type: Binary early stopping needs predictions to be of length oneMulticlass early stopping needs predictions to be of length two or larger*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_/__w/1/s/src/io/bin.cppUsing OrderedSparseBin->ConstructHistogram() insteadCheck failed: max_bin > 0 at %s, line %d . Check failed: bin_upper_bound.size() <= static_cast(max_bin) at %s, line %d . Check failed: num_bin_ <= max_bin at %s, line %d . Met negative value in categorical features, will convert it to NaNMet categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zeroCheck failed: default_bin_ > 0 at %s, line %d . 8N8LightGBM10OrderedBinEN8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM21Dense4bitsBinIteratorEN8LightGBM13Dense4bitsBinEN8LightGBM8DenseBinIhEEN8LightGBM8DenseBinItEEN8LightGBM8DenseBinIjEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16OrderedSparseBinIhEEN8LightGBM16OrderedSparseBinItEEN8LightGBM16OrderedSparseBinIjEEN8LightGBM16DenseBinIteratorIhEEN8LightGBM16DenseBinIteratorItEEN8LightGBM16DenseBinIteratorIjEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE@p}?multi_loglossmulti_errorvoting[boosting: ] [objective: [metric: [tree_learner: [device_type: binary_loglossndcgmapkldivkullback_leiblerlambdarankmean_average_precisiondevice_typegpuUnknown device type %stasktrainconvert_modelrefitUnknown task type %stestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallelgbrtrandom_forestUnknown parameter %s /__w/1/s/src/io/config.cppNumber of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingMulticlass objective and metrics don't matchHistogram LRU queue was enabled (histogram_pool_size=%f). Will disable this to reduce communication costsAccuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leavesDo not support non-ASCII characters in config.Parameter %s should be of type int, got "%s"Check failed: valid.size() == valid_data_initscores.size() at %s, line %d .  naninfinfinityUnknown token %s in data file-+stodvalidnum_iterationslearning_ratenum_leavesnum_threadsmax_depthmin_data_in_leafmin_sum_hessian_in_leafpos_bagging_fractionneg_bagging_fractionbagging_freqbagging_seedfeature_fractionfeature_fraction_bynodefeature_fraction_seedearly_stopping_roundfirst_metric_onlylambda_l1lambda_l2min_gain_to_splitdrop_ratemax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotfeature_contriforcedsplits_filenameforcedbins_filenamerefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledverbositymax_binmax_bin_by_featuremin_data_in_binbin_construct_sample_cnthistogram_pool_sizedata_random_seedoutput_modelsnapshot_freqinput_modeloutput_resultinitscore_filenamevalid_data_initscoresmax_conflict_rateis_enable_sparsesparse_thresholduse_missingzero_as_missingtwo_roundlabel_columnweight_columngroup_columnignore_columncategorical_featurenum_iteration_predictpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageis_unbalancescale_pos_weightsigmoidboost_from_averagereg_sqrtfair_cpoisson_max_delta_steptweedie_variance_powermax_positionlambdamart_normlabel_gainmetric_freqis_provide_training_metricmulti_error_top_knum_machineslocal_listen_porttime_outmachine_list_filenamegpu_platform_idgpu_device_idgpu_use_dp[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [pos_bagging_fraction: [neg_bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_bynode: [feature_fraction_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [min_gain_to_split: [drop_rate: [max_drop: [skip_drop: [xgboost_dart_mode: [uniform_drop: [drop_seed: [top_rate: [other_rate: [min_data_per_group: [max_cat_threshold: [cat_l2: [cat_smooth: [max_cat_to_onehot: [top_k: [monotone_constraints: [feature_contri: [forcedsplits_filename: [forcedbins_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [histogram_pool_size: [data_random_seed: [output_model: [snapshot_freq: [input_model: [output_result: [initscore_filename: [valid_data_initscores: [pre_partition: [enable_bundle: [max_conflict_rate: [is_enable_sparse: [sparse_threshold: [use_missing: [zero_as_missing: [two_round: [save_binary: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [predict_raw_score: [predict_leaf_index: [predict_contrib: [num_iteration_predict: [pred_early_stop: [pred_early_stop_freq: [pred_early_stop_margin: [convert_model_language: [convert_model: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [max_position: [lambdamart_norm: [label_gain: [metric_freq: [is_provide_training_metric: [eval_at: [multi_error_top_k: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: config_filetask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorsshrinkage_rateetanum_leafmax_leavesmax_leaftree_typetree_learner_typenum_threadnthreadnthreadsn_jobsdevicerandom_statemin_data_per_leafmin_datamin_child_samplesmin_sum_hessian_per_leafmin_sum_hessianmin_hessianmin_child_weightpos_sub_rowpos_subsamplepos_baggingneg_sub_rowneg_subsampleneg_baggingsubsample_freqbagging_fraction_seedsub_featurecolsample_bytreesub_feature_bynodecolsample_bynodeearly_stopping_roundsearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphareg_lambdamin_split_gainrate_droptopkmcmonotone_constraintfeature_contribfcfpfeature_penaltyfsforced_splits_filenameforced_splits_fileforced_splitsverbosesubsample_for_binhist_pool_sizedata_seedmodel_outputmodel_outsave_periodmodel_inputmodel_inpredict_resultprediction_resultpredict_nameprediction_namepred_namename_predinit_score_filenameinput_init_scorevalid_data_init_scoresvalid_init_score_filevalid_init_scoreis_pre_partitionis_enable_bundleis_sparseuse_two_round_loadingis_save_binaryis_save_binary_filehas_headergroup_idquery_columnquery_idignore_featureblacklistcat_featurecategorical_columncat_columnis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribconvert_model_filenum_classesunbalanced_setsmetricsmetric_typesoutput_freqis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinelocal_portmachine_list_filemachine_listmlistworkersnodesParameter %s should be of type double, got "%s"Parameter %s should be "true"/"+" or "false"/"-", got "%s"/__w/1/s/src/io/config_auto.cppCheck failed: num_iterations >=0 at %s, line %d . Check failed: learning_rate >0.0 at %s, line %d . Check failed: num_leaves >1 at %s, line %d . Check failed: num_leaves <=131072 at %s, line %d . Check failed: min_data_in_leaf >=0 at %s, line %d . Check failed: min_sum_hessian_in_leaf >=0.0 at %s, line %d . Check failed: bagging_fraction >0.0 at %s, line %d . Check failed: bagging_fraction <=1.0 at %s, line %d . Check failed: pos_bagging_fraction >0.0 at %s, line %d . Check failed: pos_bagging_fraction <=1.0 at %s, line %d . Check failed: neg_bagging_fraction >0.0 at %s, line %d . Check failed: neg_bagging_fraction <=1.0 at %s, line %d . Check failed: feature_fraction >0.0 at %s, line %d . Check failed: feature_fraction <=1.0 at %s, line %d . Check failed: feature_fraction_bynode >0.0 at %s, line %d . Check failed: feature_fraction_bynode <=1.0 at %s, line %d . Check failed: lambda_l1 >=0.0 at %s, line %d . Check failed: lambda_l2 >=0.0 at %s, line %d . Check failed: min_gain_to_split >=0.0 at %s, line %d . Check failed: drop_rate >=0.0 at %s, line %d . Check failed: drop_rate <=1.0 at %s, line %d . Check failed: skip_drop >=0.0 at %s, line %d . Check failed: skip_drop <=1.0 at %s, line %d . Check failed: top_rate >=0.0 at %s, line %d . Check failed: top_rate <=1.0 at %s, line %d . Check failed: other_rate >=0.0 at %s, line %d . Check failed: other_rate <=1.0 at %s, line %d . Check failed: min_data_per_group >0 at %s, line %d . Check failed: max_cat_threshold >0 at %s, line %d . Check failed: cat_l2 >=0.0 at %s, line %d . Check failed: cat_smooth >=0.0 at %s, line %d . Check failed: max_cat_to_onehot >0 at %s, line %d . Check failed: top_k >0 at %s, line %d . Check failed: refit_decay_rate >=0.0 at %s, line %d . Check failed: refit_decay_rate <=1.0 at %s, line %d . Check failed: cegb_tradeoff >=0.0 at %s, line %d . Check failed: cegb_penalty_split >=0.0 at %s, line %d . Check failed: max_bin >1 at %s, line %d . Check failed: min_data_in_bin >0 at %s, line %d . Check failed: bin_construct_sample_cnt >0 at %s, line %d . Check failed: max_conflict_rate >=0.0 at %s, line %d . Check failed: max_conflict_rate <1.0 at %s, line %d . Check failed: sparse_threshold >0.0 at %s, line %d . Check failed: sparse_threshold <=1.0 at %s, line %d . Check failed: num_class >0 at %s, line %d . Check failed: scale_pos_weight >0.0 at %s, line %d . Check failed: sigmoid >0.0 at %s, line %d . Check failed: alpha >0.0 at %s, line %d . Check failed: fair_c >0.0 at %s, line %d . Check failed: poisson_max_delta_step >0.0 at %s, line %d . Check failed: tweedie_variance_power >=1.0 at %s, line %d . Check failed: tweedie_variance_power <2.0 at %s, line %d . Check failed: max_position >0 at %s, line %d . Check failed: metric_freq >0 at %s, line %d . Check failed: multi_error_top_k >0 at %s, line %d . Check failed: num_machines >0 at %s, line %d . Check failed: local_listen_port >0 at %s, line %d . Check failed: time_out >0 at %s, line %d . [cegb_penalty_feature_coupled: d~QJחA_/__w/1/s/src/io/dataset.cpptargetweightsnonameBianry file %s already exists.binSaving data to binary file %swtnum_features: %d num_total_features: %d num_groups: %d num_data: %d feature_names: monotone_constraints: %s, feature_penalty: %d, max_bin_by_feature: %lf, forced_bins: feature %d: NA, vector::_M_range_checkCheck failed: num_used_indices == num_data_ at %s, line %d . Check failed: num_data > 0 at %s, line %d . Cannot change forced bins after constructed Dataset handle.Check failed: static_cast(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d . Check failed: static_cast(num_total_features_) == io_config.feature_contri.size() at %s, line %d . Cannot change max_bin after constructed Dataset handle.Cannot change max_bin_by_feature after constructed Dataset handle.Cannot change bin_construct_sample_cnt after constructed Dataset handle.Cannot change min_data_in_bin after constructed Dataset handle.Cannot change use_missing after constructed Dataset handle.Cannot change zero_as_missing after constructed Dataset handle.Cannot change sparse_threshold after constructed Dataset handle.File %s exists, cannot save binary to itCannot write binary data to %s Cannot add features from other Dataset with a different number of rows/__w/1/s/include/LightGBM/feature_group.hCheck failed: static_cast(bin_mappers->size()) == num_feature at %s, line %d . Check failed: num_total_features_ == static_cast(bin_mappers->size()) at %s, line %d . There are no meaningful features, as all feature values are constant.Check failed: static_cast(num_total_features_) == io_config.max_bin_by_feature.size() at %s, line %d . Check failed: *(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end())) > 1 at %s, line %d . ______LightGBM_Binary_File_Token______ ffffff???Data file %s is emptyCannot open data file %sbin_upper_boundColumn_name:Using column %s as weightUsing column %s as labelCurrent query exceeds the range of the query file, please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctThe output cannot be monotone with respect to categorical featuresSize of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupCould not read binary data from %sBinary file error: token has the wrong sizeBinary file error: header has the wrong sizeBinary file error: header is incorrect/__w/1/s/src/io/dataset_loader.cppCheck failed: static_cast(dataset->num_total_features_) == config_.monotone_constraints.size() at %s, line %d . Check failed: static_cast(dataset->num_total_features_) == config_.feature_contri.size() at %s, line %d . Check failed: static_cast(dataset->num_total_features_) == config_.max_bin_by_feature.size() at %s, line %d . Check failed: *(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())) > 1 at %s, line %d . Binary file error: meta data has the wrong sizeBinary file error: meta data is incorrectBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %dInput file is not LightGBM binary fileCould not open %s. Will ignore.Check failed: forced_bins_json.is_array() at %s, line %d . Check failed: feature_num < num_total_features at %s, line %d . Feature %d is categorical. Will ignore forced bins for this feature.Check failed: static_cast(num_col) == config_.max_bin_by_feature.size() at %s, line %d . Check failed: dataset->num_total_features_ == static_cast(feature_names_.size()) at %s, line %d . Check failed: label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_ at %s, line %d . Check failed: weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_ at %s, line %d . Check failed: group_idx_ < 0 || group_idx_ < dataset->num_total_features_ at %s, line %d . Could not find label column %s in data file or data file doesn't contain headerCould not find ignore column %s in data fileCould not find weight column %s in data fileUsing column %s as group/query idCould not find group/query column %s in data fileCould not find categorical_feature %s in data filelabel_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column number %d as labelignore_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameweight_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column number %d as weightgroup_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column number %d as group/query idcategorical_feature is not a number, if you want to use a column name, please add the prefix "name:" to the column nameCould not recognize data format of %sUsing a query id without pre-partitioning the data file is not supported for parallel training. Please use an additional query file or pre-partition the data*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader23ExtractFeaturesFromFileEPKcPKNS_6ParserERKSt6vectorIiSaIiEEPNS_7DatasetEEUliRKS6_ISsSaISsEEE_ZN8LightGBM10TextReaderIiE9CountLineEvEUliPKcmE_ZN8LightGBM10TextReaderIiE18ReadAndFilterLinesERKSt8functionIFbiEEPSt6vectorIiSaIiEEEUliPKcmE_ZN8LightGBM10TextReaderIiE14SampleFromFileEPNS_6RandomEiPSt6vectorISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE23SampleAndFilterFromFileERKSt8functionIFbiEEPSt6vectorIiSaIiEEPNS_6RandomEiPS7_ISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE26ReadPartAndProcessParallelERKSt6vectorIiSaIiEERKSt8functionIFviRKS2_ISsSaISsEEEEEUliiE_HDFS support is not enabledrbwbhdfs://N8LightGBM17VirtualFileWriterEN8LightGBM17VirtualFileReaderEN8LightGBM9LocalFileE'%c' (%d)%.17g\\\"\b\f\n\r\t\u%04x\u2028\u2029Malformed commentUnexpected end of inputUnescaped in stringBad \u escape: Invalid escape character Expected JSON object, got Bad type for in /__w/1/s/src/io/json11.cppi != 0Parse error: expected Invalid in numberExpected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected trailing Unexpected end of input after start of commentUnexpected end of input inside multi-line commentUnexpected end of input in stringLeading 0s not permitted in numbersAt least one digit required in fractional partAt least one digit required in exponentExceeded maximum nesting depthjson11::Json json11::{anonymous}::JsonParser::expect(const string&, json11::Json)N6json119JsonValueEN6json115ValueILNS_4Json4TypeE1EdEEN6json1110JsonDoubleEN6json115ValueILNS_4Json4TypeE1EiEEN6json117JsonIntEN6json115ValueILNS_4Json4TypeE2EbEEN6json1111JsonBooleanEN6json115ValueILNS_4Json4TypeE3ESsEEN6json1110JsonStringEN6json115ValueILNS_4Json4TypeE4ESt6vectorIS1_SaIS1_EEEEN6json119JsonArrayEN6json115ValueILNS_4Json4TypeE5ESt3mapISsS1_St4lessISsESaISt4pairIKSsS1_EEEEEN6json1110JsonObjectEN6json115ValueILNS_4Json4TypeE0ENS_10NullStructEEEN6json118JsonNullESt23_Sp_counted_ptr_inplaceIN6json118JsonNullESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1111JsonBooleanESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonDoubleESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json117JsonIntESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonStringESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json119JsonArrayESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonObjectESaIS1_ELN9__gnu_cxx12_Lock_policyE2EElabel cannot be nullptrLoading query weights....queryLoading query boundaries....initLoading initial scores....weightLoading weights...Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query fileInitial score size doesn't match data sizeLength of label is not same with #dataLength of weights is not same with #dataSum of query counts is not same with #dataData partition error, data didn't match queriesCannot used query_id for parallel trainingWeights size doesn't match data sizeQuery size doesn't match data sizeInvalid initial score file. Redundant or insufficient columnsv~v*G*ǜu<7~u<7 Data file %s doesn't exist.Input format error when parsing as TSVInput format error when parsing as CSVInput format error when parsing as LibSVMData file %s couldn't be read.Data file %s should have at least one line.Data file %s only has one line.Unknown format of training data.Label should be the first column in a LibSVM fileData file %s doesn't contain a label column.N8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 d'@Bʚ;*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE2_%gnum_leaves=num_cat=split_feature=split_gain=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=shrinkage=if (fval <= || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":||"threshold":""decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","internal_value":"missing_type":"Zero","missing_type":"NaN","internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":num_catleaf_valueshrinkageleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typecat_boundariesif (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector cat_threshold = {(const std::unordered_map& arr) { /__w/1/s/include/LightGBM/utils/common.hCheck failed: strs.size() == static_cast(n) at %s, line %d . Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold field G label should be int type (met %f) for ranking task, for the gain of label, please set the label_gain parameterlabel (%d) excel the max range %d/__w/1/s/src/metric/dcg_calculator.cppCheck failed: eval_at->at(i) > 0 at %s, line %d . '}&_poissonbinary_erroraucgammagamma_deviancetweediehuberfair[%s:%s]: sum-of-weights = %f%s offset term = %fmap@ndcg@multi_error@/__w/1/s/src/metric/xentropy_metric.hpplabel_ Can't be NULL at %s, line %d . [%s:%s]: (metric) labels passed interval [0, 1] check[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) all weights must be positive[%s:%s]: (metric) at least one weight is negativeFor MAP metric, there should be query informationTotal groups: %d, total data: %dThe NDCG metric requires query information/__w/1/s/src/metric/regression_metric.hppCheck failed: label > 0 at %s, line %d . InitInitInitN8LightGBM6MetricEN8LightGBM12BinaryMetricINS_19BinaryLoglossMetricEEEN8LightGBM19BinaryLoglossMetricEN8LightGBM12BinaryMetricINS_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_10RMSEMetricEEEN8LightGBM10RMSEMetricEN8LightGBM16RegressionMetricINS_8L2MetricEEEN8LightGBM8L2MetricEN8LightGBM16RegressionMetricINS_14QuantileMetricEEEN8LightGBM14QuantileMetricEN8LightGBM16RegressionMetricINS_8L1MetricEEEN8LightGBM8L1MetricEN8LightGBM16RegressionMetricINS_15HuberLossMetricEEEN8LightGBM15HuberLossMetricEN8LightGBM16RegressionMetricINS_14FairLossMetricEEEN8LightGBM14FairLossMetricEN8LightGBM16RegressionMetricINS_13PoissonMetricEEEN8LightGBM13PoissonMetricEN8LightGBM16RegressionMetricINS_10MAPEMetricEEEN8LightGBM10MAPEMetricEN8LightGBM16RegressionMetricINS_11GammaMetricEEEN8LightGBM11GammaMetricEN8LightGBM16RegressionMetricINS_19GammaDevianceMetricEEEN8LightGBM19GammaDevianceMetricEN8LightGBM16RegressionMetricINS_13TweedieMetricEEEN8LightGBM13TweedieMetricEN8LightGBM18CrossEntropyMetricEN8LightGBM24CrossEntropyLambdaMetricEN8LightGBM25KullbackLeiblerDivergenceE-q=;& .>DA@|=?Trying to bind port %d...0.0.0.0Binding port %d succeededBinding port %d failedSet TCP_NODELAY failedListening...Socket accept error, code: %dSocket recv error, code: %dConnected to rank %dSocket construction errorSocket send error, code: %drank=Connecting to rank %d failed, waiting for %d millisecondsFinished linking network in %f secondsSet SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at leastSet SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at leastMachine list file %s doesn't existmachine_list size is larger than the parameter num_machines, ignoring redundant entriesCannot find any ip and port. Please check machine_list_filename or machines parameterWorld size is larger than the machine_list size, change world size to %dMachine list file doesn't contain the local machineNSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS4_iEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS5_iEEEESaISC_ELN9__gnu_cxx12_Lock_policyE2EEff?Local rank: %d, total number of machines: %dPlease initilize the network interface firstNSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS4_iEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS5_iEUlvE_vEEEESaIS9_ELN9__gnu_cxx12_Lock_policyE2EEnum_class:sigmoid: sqrtContains only one class[%s]: sum of weights is zero[%s]: sum of labels is zero[%s:%s]: havg = %f -> initscore = %f[%s:%s]: pavg=%f -> initscore=%f[%s:%s]: pavg = %f -> initscore = %fStarting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true. This may cause significantly different results comparing to the previous versions of LightGBM. Try to set boost_from_average=false, if your old models produce bad resultsNumber of positive: %d, number of negative: %d/__w/1/s/src/objective/xentropy_objective.hpp[%s:%s]: (objective) labels passed interval [0, 1] check[%s]: at least one weight is negative[%s]: at least one weight is non-positive[%s:%s]: min, max weights = %f, %f; ratio = %fLambdarank tasks require query informationSigmoid parameter %f should be greater than zeroCannot set is_unbalance and scale_pos_weight at the same timeCannot use sqrt transform in %s Regression, will auto disable it[%s]: at least one target label is negativeMet 'abs(label) < 1', will convert them to '1' in MAPE objective and metricObjective should contain num_class fieldUnknown objective type name: %sLabel must be in [0, %d), but found %d in label/__w/1/s/src/objective/regression_objective.hppCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . Sigmoid param %f should be greater than zeroCheck failed: threshold >= weighted_cdf[pos - 1] at %s, line %d . Check failed: threshold < weighted_cdf[pos] at %s, line %d . BoostFromScoreInitBoostFromScoreInitBoostFromScoreN8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM14LambdarankNDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC1ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC1ERKNS_6ConfigESt8functionIFbfEEEUlfE_?qq C<ư>II@@z?????vector::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: tree->num_leaves() <= data_partition_->num_leaves() at %s, line %d . GPU Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_GPU=1N8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEE<N8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEETotal Bins %dleftrightNo further splits with positive gain, best gain: %fTrained a tree with leaves = %d and max_depth = %d/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: inner_feature_index >= 0 at %s, line %d . Check failed: num_machines > 1 at %s, line %d . Check failed: bag_cnt == num_data_ at %s, line %d . Invalid categorical threshold split'Forced Split' will be ignored since the gain getting worse. Check failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d . /__w/1/s/src/treelearner/feature_histogram.hppCheck failed: offset == num_total_bin at %s, line %d . Check failed: cache_size_ >= 2 at %s, line %d . cegb_penalty_feature_lazy should be the same size as feature number.cegb_penalty_feature_coupled should be the same size as feature number.Check failed: num_features_ == train_data_->num_features() at %s, line %d . 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