vendor/lib_lightgbm.so in lightgbm-0.1.6 vs vendor/lib_lightgbm.so in lightgbm-0.1.7

- old
+ new

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Mu IM)ALsMuB|6 IIt|ItbItHIt.ItLsIB|6 LsIB|6 LsIB|6 LsIB|6 @@ -2010,11 +2020,11 @@ MFLCB|6 MNLKB|6 MVLSB|6 MnLkB|6 IFIHCB|6 -XL$HT$eH5LL{H{uL%H$CH~H;=4H=4HO1Hf{DMLAPMMN AEEAbM.LAUSLD$lLT$mL$1A/L$LD$LT$0LL$(tHfI6DDA A A{@1A/uHNLpLsI9T/*uvHHH9HCvL9L[#L؀<*LXu|/usHHCfD@L37HHC+HT$H5GL{H{uL$H$CH~H;=4 HC1HxH9H{| +XL$HT$eH5LlY{H{uLkH$CH~H;= 4H=%4HO1HM{DMLAPMMN AEEAbM.LAUSLD$lLT$mL$1A/L$LD$LT$0LL$(tHfI6DDA A A{@1A/uHNLpLsI9T/*uvHHH9HCvL9L[#L؀<*LXu|/usHHCfD@L37HHC+HT$H5LW{H{uLMiH$CH~H;=>4 HC1HxH9H{| HPII)AHHS| PIHIItgItLIt1ItHCH| HHS< LRLHS< LJLHS< @@ -2026,18 +2036,19 @@ HtfIVMFHSB|6 tRLCB|6 LMVt?LSB|6 LMNt,LKB|6 LM^tIL[LB|6 -]fsHйsHL$HT$oH5Lr{H{uL"H$CH~H;=4H=+4HW ADEHsHT$@H|$8H5w{H{u -Ht$8!H$CH~H;=4@HT$0H|$(H5'{H{u -Ht$(F!H$CH~H;=G4)1L$HT$dH5LX{H{uL H$CH~H;=4"L$HT$nH5L{H{uL H$CH~H;=4@L$HT$fH5L{H{uLR L$CHIyHCH|$ HL$`DLD $  L1/ L$01H$ L H$H56H LHHLL$0L$ LH$H5L LLHLcH$ QH$0L$8DŽ$8HDŽ$@HDŽ$XH|$HL$HL$P茛<}<"AD$H$L$ H$L$HL$Lt$D$8HT$@Ld$HH|$Hҟ{H <:LD$T$8HLLD$ L$@MH$L\$H\$0Ht$(LvMcHx?MMmMt<I} 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$EH=H$8IHt=H$ LHy7L1\D@AXEXEZEkDEjIzH$(HH=L$ H$II 7H$8HCU=9ID[ EKDK CHKrDS EZD[ eL$0H$ IIy6LS[gAL$Ht$_HI|$i6L$Ht$hHIK6H$Ht$jHH}-6FD@ADFoH$Ht$kHHy5aHH$Ht$]Hx5CDk AUS E^AkAnEf A|$A~ H$(HH;AF HAN +D^ASVDH$Ht$aHH}L5HC PS %AWAVAUATUHHSHH(dH%(H$1辏{PЀ <-<t<f<n* <"y <{ -<[<tL$LDȹ  L1L$L$LLLH1LXL=4HLIwH$H0{H{ CH$H$HH]HEt;L-a4H{MH$HtADKA H$HxL9|L$IzL9t7H=4LAL_ DH H$dH34%(HvH([]A\A]A^A_L$DL'D $  L1tL$1L$LeH$H5{LNLLHHH$H$H{L$M3L-4IvMAA MLASMIN 9AD9EAM&LAT$f.HkHHL$L$LnH$H5LWLLHHL$HHDŽ$HDŽ$HDŽ$Ld$HH$<]HL$* Ht$]H$L$Lt$\L$Ht$ HD$YDA Az@{H9VHHSD8A]s A,aH謋HkHLL$L;$Mt7L$I@ML$HDŽ$MPHDŽ$IL$L$Mt'H=Ԓ4M_A ʃ{ -L;HSI7HDDA HHHSE1A/tKHSI7HDA A EJAEE1A/uHNHBHCH9DDA/A*HHH9HSvqL9LKLʀ<*LJu|/uHHS{A@ubL;&fHHHSLl$pHT$VH5PLv{H{uL HL$pCLHy1Lt$`L H51LH=4LHL$H8LI{H{CH$L$HH]LEt8L=Ð4HSM -H$HtCk H$LHLD$`LIxH|$H5'LL\{H{uL H$CH~H;=4E1jLZL9L[O| +<[<tL$LDȹ  L13L$L$LLLEH.1LX'L=4HLIwH$H0&{H{ CH$H$HH]HEt;L-4H{MH$HtADKA H$HxL9|L$IzL9t7H=4LAL9 DH &H$dH34%(HvH([]A\A]A^A_L$DLgD $  L12L$1L$LFH$H5LnDLLHHH$H$H{2L$M3L-4IvMAA MLASMIN 9AD9EAM&LAT$f.HkHHL$L$LEH$H5ŏLwCLLHHL$HHDŽ$HDŽ$HDŽ$Ld$HH$<]HL$* Ht$]H$L$Lt$\L$Ht$ HD$YDA Az@{H9VHHSD8A]s A,aH謋HkHLL$L;$Mt7L$I@ML$HDŽ$MPHDŽ$IL$L$Mt'H=4M_A ʃ{ +L;HSI7HDDA HHHSE1A/tKHSI7HDA A EJAEE1A/uHNHBHCH9DDA/A*HHH9HSvqL9LKLʀ<*LJu|/uHHS{A@ubL;&fHHHSLl$pHT$VH5L@{H{uLDRHL$pCLHy.1Lt$`L Hu1L!H= 4LHL$H8LI!{H{CH$L$HH]LEt8L=4HSM +H$HtCk H$LH.LD$`LIx-H|$0H5gLL|?{H{uL*QH$CH~H;=4E1jLZL9L[O| DMIIALZL9L[ | IItwIt^ItEIt,ItIL[B< IL[B< IL[B< IL[B< @@ -2049,17 +2060,14 @@ 6MCLCB| "MKLKB| M{L{B| MSLSB| IL[B< -XL$HT$_H5Lr{H{uL -H$CH~H;=4H=+4HOg 1L{I7LVH=4IG  ADEAMLAS HT$ H|$H55{H{u -Ht$T H$CH~H;=U4Z?L$HT$^H5Lf{H{uL H$CH~H;=4 -fH|$L%L$L$LFH$H53LLLHHH$|.L$HL蔉LHH$H$HH$H$HDŽ$HDŽ$HDŽ$Ht$H$H${<}<"L$L$Ll$pH$LT$LL$ Ll$@HL$HH|$H͈{H<:Lt$ HLLt$(L$MH$HT$IHl$8LGH|$0LjHLDx?MMvMt<I~ HLgL9LHFauI)II}MvMuHt$LIHl$8HI9Iu H|$0HFI9HHD$8IFu$LL$8MM)IIDЅLL$L$HDŽ$HDŽ$L`0LX(Lh0Mt]L5n4I|$MAD?EAL$Mt#MMe7A$׃T{2HV<}<,MH>L$I{H;=4<"L|$L}Hk1LGL L u4HHL$L$ILL踿L$H$I{H$LHy#L$Ht$ IzHt$HH$H|$t?H$L\$MkHL$LD$@HH|$LH$HL$HyM}LAWMIu 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f.AWAVAUATUHSHHH$H|$`H$dH%(H$1HDŽ$H$$HDŽ$H$H$H +f  r@$&$LeI9D$E L$AL  L1Lt$ HT$LL/H 1L L%q|4L8Ll$pLIl$L|$PH($H$L|$xƄ$foL$pM t6IwL5?4MKL|$xMtAHl$PH}L9xLT$ IzL9Ll$hMt'H-߁4MEH7AED΃t^HH$dH3%(H[]A\A]A^A_HH$/foD$`HLl$hvM}LAWHMU LAEEArIMLQcLD $  L1./Ll$Ld$0Lt$pL|$tb$E1@YL$H$I f  DBAEr/uLAHxH$L9DLA/A*|HIL9H$vM9L$LЀ<*LPu|/uHH$$ADHH$H5LL,$H$uLs>HD$0Ƅ$HxH;=cy4LPM9L$n| Hx_MI)AH$Hx|9 8IIItnItQIt4ItH$H|9 H$H|9 H$H|9 H$H|9 @@ -2071,14 +2079,14 @@ LO4L$|9 HGH$|9 LWL$|9 L_L$|9 HWHH$|9 -?HT$H5L$H$uL&LD$pƄ$IxH;=&u4H=@{4HOeHt$PVfDL\$@H5LLL\$$H$u -Ht$HD$@Ƅ$HxH;=t4UH=z4HWtAD -EL H|$PHT$H5H|$|$H$u -Ht$HD$PƄ$HxH;=t4sDHt$PMLARMIG AAMLAPEe AD$AE AGADOEOLl$pHHT$ LHzH|$hHtmHHAuDvEuH=by4HwtOAD>EfLYH=3y4Lgt1A $JL=DuENEDMABDXEZE_ AKAO H|$xHHtH|$PLH8HHD$PHt$HxHl$@Ht$HH}Ll$pHt$HI}ApDNEH"HL$0Ht$HHypDFAD@AWAVAUATIUHSHHHHHDŽ$HDŽ$H$H$HxD$HHGHGCH$HL$-Ht$@Ll$pL|$,Lt$0H$HL$Ht$?f r@$H}I$H9GH4$LH{H;{Ht+LD$pHGLLL$xHD$xLOHD$pHH{LT$xMt'H=v4MZOA ȃ$BH$H$HfD HH$E1ۀ/tBH$Hf  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learnerSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE*Z25LGBM_BoosterPredictForCSCEUliE_*Z26RowFunctionFromDenseMatricPKviiiiEUliE_*Z26RowFunctionFromDenseMatricPKviiiiEUliE0_*Z26RowFunctionFromDenseMatricPKviiiiEUliE1_*Z26RowFunctionFromDenseMatricPKviiiiEUliE2_*Z30RowPairFunctionFromDenseMatricPKviiiiEUliE_*Z28RowPairFunctionFromDenseRowsPPKviiEUliE_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE0_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE1_*Z18RowFunctionFromCSRPKviPKiS0_illEUliE2_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE0_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE1_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE2_St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN8LightGBM8BoostingEN8LightGBM8GBDTBaseENSt6thread10_Impl_baseESsNSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS5_mEEEUlvE_vEEEEZN8LightGBM9Predictor7PredictEPKcS2_bEUliRKSt6vectorISsSaISsEEE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE1_ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE2_ZN8LightGBM9Predictor7PredictEPKcS2_bEUlS2_PSt6vectorISt4pairIidESaIS5_EEE_ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKSt8functionIFviRKSt6vectorISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKSt8functionIFviRKSt6vectorISsSaISsEEEERKS2_IFbiiEEEUlPKcmE_St19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS6_mEEEUlvE_vEEEESaISF_ELN9__gnu_cxx12_Lock_policyE2EE8?MbP????$@?𿚙?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 . +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<int>(valid_score_updater_.size()) at %s, line %d . Check failed: tree_idx >= 0 && static_cast<size_t>(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . @@ -2936,20 +2928,20 @@ Check failed: static_cast<size_t>(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=#include "gbdt.h"#include <LightGBM/metric.h>#include <ctime>#include <sstream>#include <chrono>#include <string>#include <vector>#include <utility>namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBM#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endiftruefalse"name":""version":""num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":"average_output":"objective":"", -",""feature_names":[""],"monotone_constraints":["tree_info":["tree_index":monotone_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 <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>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<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& 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<int, double>& 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<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Unknown importance type: only support split=0 and gain=1Model 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.cppvector::_M_range_checkUsing OrderedSparseBin->ConstructHistogram() insteadCheck failed: max_bin > 0 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 <LightGBM/metric.h>#include <ctime>#include <sstream>#include <chrono>#include <string>#include <vector>#include <utility>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 <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>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<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& 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<int, double>& 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<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& 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<size_t>(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}?multiclassovacustommulti_loglossmulti_errorvoting[boosting: ] -[objective: [metric: [tree_learner: [device_type: rmsexentropycross_entropyxentlambdacross_entropy_lambdaregression_l2l2_rootroot_mean_squared_errormean_absolute_errormaesoftmaxmulticlass_ovaovrnullnabinary_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_leavesmean_absolute_percentage_errorDo 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 . +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 . @@ -3004,52 +2996,53 @@ 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_vector<bool>::_M_range_check/__w/1/s/src/io/dataset.cpptargetweightsnonameBianry file %s already exists.binSaving data to binary file %swtnum_features: %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, Check failed: num_used_indices == num_data_ at %s, line %d . -Cannot change forced bins after constructed Dataset handle.Check failed: static_cast<size_t>(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d . +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<size_t>(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d . Check failed: static_cast<size_t>(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 Check failed: num_data > 0 at %s, line %d . -Cannot add features from other Dataset with a different number of rows/__w/1/s/include/LightGBM/feature_group.hCheck failed: static_cast<int>(bin_mappers->size()) == num_feature 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<int>(bin_mappers->size()) == num_feature at %s, line %d . +Check failed: num_total_features_ == static_cast<int>(bin_mappers->size()) at %s, line %d . There are no meaningful features, as all feature values are constant.Check failed: static_cast<size_t>(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, +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<size_t>(dataset->num_total_features_) == config_.monotone_constraints.size() at %s, line %d . Check failed: static_cast<size_t>(dataset->num_total_features_) == config_.feature_contri.size() at %s, line %d . Check failed: static_cast<size_t>(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: label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_ at %s, line %d . +Feature %d is categorical. Will ignore forced bins for this feature.Check failed: static_cast<size_t>(num_col) == config_.max_bin_by_feature.size() at %s, line %d . +Check failed: dataset->num_total_features_ == static_cast<int>(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 . -Check failed: buffer_size >= 0 at %s, line %d . -Check failed: static_cast<size_t>(num_col) == config_.max_bin_by_feature.size() at %s, line %d . -Could not find label column %s in data file +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 nameUsing 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 dataCould not recognize data format of %s*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<7Data file %s doesn't existData file %s couldn't be readInput format error when parsing as TSVInput format error when parsing as CSVInput format error when parsing as LibSVMData file %s should have at least one lineData file %s only has one lineLabel should be the first column in a LibSVM fileData file %s doesn't contain a label columnUnknown format of training dataN8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 +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<int>(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { /__w/1/s/include/LightGBM/utils/common.hCheck failed: strs.size() == static_cast<size_t>(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 . @@ -3066,2034 +3059,2025 @@ 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 . 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