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E__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3IL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D;~@uHnD09@HknH|$@HH=R' W)D$,D$$D$+AEAD{@K8DS4@{0 &D[4C8K@Dt$+HDL$$DD$,HD\$D{0IŋT$ D4$D|$)L{(Lk(MtLL LmS@EL$$t$ HAHS(Lt$PHHC LHr$LLKHt$XHL$PL[LL$XLD$PHCH HsH|$`HD$`HH{LT$hL\$hLSt LLL}HA0I,$CDI$Ht@H](HtH>HFLeHE(MtHHAH|$8t H|$0nHD$xdH3%(u4HĈ[]A\A]A^A_HsD$,D$$D$+- LILc(MM$Il$xI9MH}I)IIOlmAHtHI9?MIItrItZItBIt*ItH}HtHH}HtHH}HtHH}HtHH}Ht~HH}HtlHH}HtZHI9H}Ht?H}LmHt-I}HtI}0HtI}HHtI}`HtI}xHtIHtII9yI|$xHtID$XHtI|$HHMt$8MtI|$(HAMl$MtI|$HALLcHC(MtHHAH|$8t H|$0L"LYI|$HHtHH"I븉fAWAAVAAULHATILUSHH$H$H$H$DD$8DL$ILIHHI>t}HtL!H$xdH3%(HĈ[]A\A]A^A_I|$H9D$<t$AGL9MIIItfItKIt0ItLI}Mn`LI0I}LI0I}LI0~I}LI0iI}LI0TI}LI0?I}vL9L%I}\I}0I}0JI}`I}`8II IIIII I IPIŀI}L9FMtLLt$ HHD$HD$IM^I)ILL9\$$Hl$@HT$(LeNIHM9IFL|$PLl$(IAEN4HHI9IFHl$0Ld$HHI$= 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 . /__w/1/s/src/boosting/goss.hppCheck failed: (config_->top_rate + config_->other_rate) <= (1.0f) at %s, line %d . Check failed: (num_tree_per_iteration_) == (num_class_) at %s, line %d . Skipped header "%s" in file %sbasic_string::_S_construct null not validCheck 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 . Check failed: hessians != nullptr && objective_function_ == nullptr at %s, line %d . Check failed: hessians == nullptr at %s, line %d . N3fmt2v712format_errorEN3fmt2v712system_errorEN8LightGBM4DARTEN8LightGBM4GOSSEN8LightGBM2RFEZN8LightGBM4GOSS7BaggingEiEUliiiPiS1_E_ZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_33333333)\(\p= ףp= x&1|?5^I ~:pΈҲ qh N]h㈵9ahVyc{-1Fw$zՔ!"Kga*m.:BGkI> O{ͷu9 idW>Pb6-?C,z MY@1eoT5$E!@pG8w=_$A|AѤ䥬4 )TTpi;wwIYePr&ޭԔ],`yOoy.P9kY 3333)\£p= x&I ~:h 9ah1F!"*m.:I>8??<.AMbP??8GBDT::UpdateScoreGBDT::BoostingNo object function providedGBDT::Train.snapshot_iter_GBDT::BaggingGBDT::BoostFromAverageStart training from score %lfregression_l1quantilemapeIteration:, training : , valid_Use subset for baggingnonecudaGBDT::TrainOneIter/__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 . %f seconds elapsed, finished iteration %dRe-bagging, using %d data to trainDisabling boost_from_average in %s may cause the slow convergenceCheck 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 . Number of class for initial score errorEarly stopping at iteration %d, the best iteration round is %dOutput of best iteration round: %sCheck failed: (static_cast(train_data_->num_total_features())) == (config->monotone_constraints.size()) at %s, line %d . Check failed: (static_cast(train_data_->num_total_features())) == (config->feature_contri.size()) at %s, line %d . Cannot use ``monotone_constraints`` in %s objective, please disable it.train_data Can't be NULL at %s, line %d . Check failed: (num_tree_per_iteration_) == (objective_function_->NumModelPerIteration()) at %s, line %d . Cannot reset training data, since new training data has different bin mappersStopped training because there are no more leaves that meet the split requirementsCannot add validation data, since it has different bin mappers with training data*ZN8LightGBM4GBDT7BaggingEiEUliiiPiS1_E_*ZN8LightGBM4GBDT12TrainOneIterEPKfS2_EUlS2_iE_N8LightGBM8BoostingEN8LightGBM8GBDTBaseEN8LightGBM4GBDTE?Tree=#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 LightGBMregressionl2_rootroot_mean_squared_errorrmsemaemulticlassmulticlassovaovrxentropycross_entropyxentlambdacross_entropy_lambdarank_xendcgxe_ndcgxe_ndcg_martxendcg_martcustomnaregression_l2mean_absolute_errorsoftmaxmulticlass_ovananinfinfinityUnknown token %s in data filebasic_string::substrtruefalse"name":""version":""num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":"objective":"", "average_output":",""feature_names":[""],"monotone_constraints":["feature_infos":},basic_string::erase{"min_value":"max_value":"values":[]}vector::reserve"values":["tree_info":["tree_index":"feature_importances":%lu#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifmonotone_constraintsWrong line at model file: %snum_classnum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesvector::_M_emplace_back_auxWrong size of feature_infosobjectivetree_sizes[linear_tree: end 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: 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);Model file %s is not available for writesUnknown 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_infosZN8LightGBM9ArrayArgsIiE8ArgMaxMTERKSt6vectorIiSaIiEEEUlimmE_$@d~QJחAu<7~u<7C _Map_base::atBinary early stopping needs predictions to be of length oneUnknown early stopping type: %sMulticlass early stopping needs predictions to be of length two or larger*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_/__w/1/s/src/io/bin.cppMultiValSparseBin::MergeData/__w/1/s/src/io/multi_val_dense_bin.hppCheck failed: (num_data_) == (num_used_indices) at %s, line %d . Check 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 zero/__w/1/s/src/io/multi_val_sparse_bin.hpp8N8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE??@ffffff?p}?multi_loglossserialcpuregresson_l1intermediatebasicmulti_errorauc_muvotingadvanced[boosting: [objective: [metric: [tree_learner: [device_type: binary_loglossmapkldivkullback_leiblerlambdarankmean_average_precisionboostinggbrtrandom_forestdevice_typeUnknown device type %stasktrainconvert_modelrefitUnknown task type %strainingpredictiontestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallel '"Unknown parameter %sUnknown parameter: %s AUC-mu matrix must have zeros on diagonal. Overwriting value in position %d of auc_mu_weights with 0.auc_mu_weights must have %d elements, but found %dAUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %d of auc_mu_weights.Number 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 costsDon't support forcedsplits in %s tree learnerAccuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=%d).Although "deterministic" is set, the results ran by GPU may be non-deterministic.CUDA currently requires double precision calculations.Linear tree learner only works with CPU.Linear tree learner must be serial.zero_as_missing must be false when fitting linear trees.Cannot use regression_l1 objective when fitting linear trees.min_data_in_leaf has been increased to 2 because this is required when path smoothing is active.Cannot use "intermediate" or "advanced" monotone constraints in distributed learning, auto set to "basic" method.Cannot use "intermediate" or "advanced" monotone constraints with feature fraction different from 1, auto set monotone constraints to "basic" method.Monotone penalty greater than tree depth. Monotone features won't be used.Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. Will set min_data_in_leaf to 1.%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=%sParameter %s should be of type int, got "%s"save_binary parameter set to true because task is save_binary??[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [deterministic: [force_col_wise: [force_row_wise: [histogram_pool_size: [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: [extra_trees: [extra_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [linear_lambda: [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: [monotone_penalty: [feature_contri: [forcedsplits_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [path_smooth: [interaction_constraints: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [data_random_seed: [is_enable_sparse: [enable_bundle: [use_missing: [zero_as_missing: [feature_pre_filter: [pre_partition: [two_round: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [forcedbins_filename: [precise_float_parser: [objective_seed: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [lambdarank_norm: [label_gain: [eval_at: [multi_error_top_k: [auc_mu_weights: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: [num_gpu: -+config_fileconfigtask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalidvalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationnum_iterationsn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorsmax_itershrinkage_ratelearning_rateetanum_leafnum_leavesmax_leavesmax_leafmax_leaf_nodestree_typetree_learner_typenum_threadnum_threadsnthreadnthreadsn_jobsdevicerandom_statehist_pool_sizehistogram_pool_sizemin_data_per_leafmin_data_in_leafmin_datamin_child_samplesmin_samples_leafmin_sum_hessian_per_leafmin_sum_hessian_in_leafmin_sum_hessianmin_hessianmin_child_weightpos_sub_rowpos_bagging_fractionpos_subsamplepos_baggingneg_sub_rowneg_bagging_fractionneg_subsampleneg_baggingsubsample_freqbagging_freqbagging_fraction_seedbagging_seedsub_featurefeature_fractioncolsample_bytreesub_feature_bynodefeature_fraction_bynodecolsample_bynodeextra_treeextra_treesearly_stopping_roundsearly_stopping_roundearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphalambda_l1l1_regularizationreg_lambdalambda_l2l2_regularizationmin_split_gainmin_gain_to_splitrate_dropdrop_ratetopkmcmonotone_constraintmonotonic_cstmonotone_constraining_methodmonotone_constraints_methodmc_methodmonotone_splits_penaltymonotone_penaltyms_penaltymc_penaltyfeature_contribfeature_contrifcfpfeature_penaltyfsforcedsplits_filenameforced_splits_filenameforced_splits_fileforced_splitsverboseverbositymodel_inputinput_modelmodel_inmodel_outputoutput_modelmodel_outsave_periodsnapshot_freqlinear_treeslinear_treemax_binsmax_binsubsample_for_binbin_construct_sample_cntdata_seeddata_random_seedis_sparseis_enable_sparseis_enable_bundleis_pre_partitiontwo_rounduse_two_round_loadinghas_headerlabel_columnweight_columngroup_columngroup_idquery_columnquery_idignore_featureignore_columnblacklistcat_featurecategorical_featurecategorical_columncat_columncategorical_featuresis_save_binaryis_save_binary_fileis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribpredict_resultoutput_resultprediction_resultpredict_nameprediction_namepred_namename_predconvert_model_filenum_classesis_unbalanceunbalanced_setsmetricsmetric_typesoutput_freqmetric_freqis_provide_training_metricis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinenum_machineslocal_portlocal_listen_portmachine_list_filemachine_list_filenamemachine_listmlistworkersstoddeterministicforce_col_wiseforce_row_wisemax_depthfeature_fraction_seedextra_seedfirst_metric_onlylinear_lambdamax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotrefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledpath_smoothinteraction_constraintssaved_feature_importance_typemax_bin_by_featuremin_data_in_binuse_missingzero_as_missingfeature_pre_filterforcedbins_filenameprecise_float_parserstart_iteration_predictnum_iteration_predictpredict_disable_shape_checkpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageobjective_seedscale_pos_weightsigmoidboost_from_averagereg_sqrtfair_cpoisson_max_delta_steptweedie_variance_powerlambdarank_truncation_levellambdarank_normlabel_gainmulti_error_top_kauc_mu_weightstime_outgpu_platform_idgpu_device_idgpu_use_dpnum_gpu[monotone_constraints_method: [cegb_penalty_feature_coupled: [saved_feature_importance_type: [lambdarank_truncation_level: Parameter %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: (linear_lambda) >= (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: (monotone_penalty) >= (0.0) 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: (path_smooth) >= (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: (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: (lambdarank_truncation_level) > (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 . Check failed: (num_gpu) > (0) at %s, line %d . _Dataset::sparse_bin_histogramtargetinit_scoreDataset::InitTrainDataset::dense_bin_histogramwtnum_features: %d num_total_features: %d num_groups: %d num_data: %d feature_names: max_bin_by_feature: %s, %d, forced_bins: feature %d: %lf, NA, /__w/1/s/src/io/dataset.cppUsing Sparse Multi-Val BinUsing Dense Multi-Val BinnonameDataset::FastFeatureBundlingBinary file %s already exists.binSaving data to binary file %sDFind the same feature name (%s) in Dataset::AddFeaturesFrom, change its name to (%s)Dataset::ConstructHistogramsMultiValDataset::sparse_bin_histogram_mergeDataset::sparse_bin_histogram_moveDataset::PushDataToMultiValBin/__w/1/s/include/LightGBM/feature_group.hCheck failed: is_multi_val_ at %s, line %d . Check failed: other->is_multi_val_ at %s, line %d . Check failed: (num_used_indices) == (num_data_) at %s, line %d . Dataset::GetMultiBinFromSparseFeaturesBug. There should be only one multi-val group.Dataset::GetMultiBinFromSparseFeatures: sparse rate %fDataset::GetMultiBinFromAllFeaturesCheck failed: static_cast(most_freq_bins.size()) == ncol at %s, line %d . Dataset::GetMultiBinFromAllFeatures: sparse rate %fDataset::TestMultiThreadingMethodCannot set both of `force_col_wise` and `force_row_wise` to `true` at the same timeinit for col-wise cost %f seconds, init for row-wise cost %f secondsAuto-choosing col-wise multi-threading, the overhead of testing was %f seconds. You can set `force_col_wise=true` to remove the overhead.Auto-choosing row-wise multi-threading, the overhead of testing was %f seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`.Check failed: (num_data) > (0) at %s, line %d . File %s exists, cannot save binary to itCannot write binary data to %s 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.Using sparse features with CUDA is currently not supported.Check failed: (static_cast(bin_mappers->size())) == (num_feature) at %s, line %d . 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 . Check failed: (static_cast(bin_mappers->size())) == (1) at %s, line %d . Cannot add features from other Dataset with a different number of rowsCan only add features from other Dataset if both or neither have raw data.Check failed: num_groups_ == static_cast(features_in_group.size()) at %s, line %d . ______LightGBM_Binary_File_Token______ *ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE_*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE0_?Read %.1f GBs from %s.Data file %s is emptyCannot open data file %sbin_upper_boundColumn_name: ,Using column %s as weightUsing column %s as labelMaking second pass...Load from binary file %sCurrent 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 featuresUsing too small ``bin_construct_sample_cnt`` may encounter unexpected errors and poor accuracy.Size of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupDataset max_bin %d != config %dDataset min_data_in_bin %d != config %dDataset use_missing %d != config %dDataset zero_as_missing %d != config %dDataset bin_construct_sample_cnt %d != config %dDataset label_idx %d != config %dConfig label_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config weight_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config group_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config ignore_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Dataset max_bin_by_feature does not match with configRecommend use integer for label index when loading data from binary for sanity check.Warning: last line of %s has no end of line, still using this lineCould 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_.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: %dBinary file error: row %d of raw data 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.Size of feature_names error, should equal with total number of featuresFeature (%s) appears more than one time.Found whitespace in feature_names, replace with underlinesDo not support special JSON characters in feature name.Check failed: (static_cast(num_col)) == (config_.max_bin_by_feature.size()) 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 %sCheck 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 . Construct bin mappers from text data time %.2f secondsUsing a query id without pre-partitioning the data file is not supported for distributed training. Please use an additional query file or pre-partition the dataSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader23ExtractFeaturesFromFileEPKcPKNS_6ParserERKSt6vectorIiSaIiEEPNS_7DatasetEEUliRKS6_ISsSaISsEEE_St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EENSt6thread10_Impl_baseENSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS5_mEEEUlvE_vEEEEZN8LightGBM10TextReaderIiE9CountLineEvEUliPKcmE_ZN8LightGBM10TextReaderIiE12ReadAllLinesEvEUliPKcmE_ZN8LightGBM10TextReaderIiE18ReadAndFilterLinesERKSt8functionIFbiEEPSt6vectorIiSaIiEEEUliPKcmE_ZN8LightGBM10TextReaderIiE14SampleFromFileEPNS_6RandomEiPSt6vectorISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE23SampleAndFilterFromFileERKSt8functionIFbiEEPSt6vectorIiSaIiEEPNS_6RandomEiPS7_ISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE26ReadPartAndProcessParallelERKSt6vectorIiSaIiEERKSt8functionIFviRKS2_ISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKSt8functionIFviRKSt6vectorISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderIiE17ReadAllAndProcessERKSt8functionIFviPKcmEEEUlS4_mE_ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKSt8functionIFviRKSt6vectorISsSaISsEEEERKS2_IFbiiEEEUlPKcmE_St19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS6_mEEEUlvE_vEEEESaISF_ELN9__gnu_cxx12_Lock_policyE2EE>?rbwbhdfs://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.cppParse 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 stringCheck failed: (i) != (0) at %s, line %d . Leading 0s not permitted in numbersAt least one digit required in fractional partAt least one digit required in exponentExceeded maximum nesting depthN6json119JsonValueEN6json115ValueILNS_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_policyE2EELoading query weights...label cannot be nullptr.queryLoading query boundaries....weightLoading weights....initLoading initial scores...Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query filePlease note that `init_score` is not saved in binary file. If you need it, please set it again after loading Dataset.Data partition error, data didn't match queriesCannot used query_id for distributed trainingNumber of queries in %s: %i. Average number of rows per query: %f.Weights size doesn't match data sizeQuery size doesn't match data sizeInitial score size doesn't match data sizeLength of weights is not same with #dataLength of label is not same with #dataSum of query counts is not same with #dataInvalid initial score file. Redundant or insufficient columnsZN8LightGBM10TextReaderImE12ReadAllLinesEvEUlmPKcmE_ZN8LightGBM10TextReaderImE17ReadAllAndProcessERKSt8functionIFvmPKcmEEEUlS4_mE_v~v*G*CData file %s doesn't exist. /__w/1/s/src/io/parser.cppno conversion to double for: %sconvert to double got underflow or overflow: %sData file %s couldn't be read.Check failed: (max_col_idx) > (0) at %s, line %d . Input 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 line.Data file %s only has one line.Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported.Label should be the first column in a LibSVM fileData file %s doesn't contain a label column.-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G ni %Cp(W^j87.h3DJ!jzd)غ`Y3)$oVyԶӥlwHޔ3 M^Pa,\":1H cm̻, `%N *᮴ fMȟP(rbISz%Taw$jDk(sK2 CgN?͓A"xuRCkxk SVk#췆,2{S̰?h@ѡ¹ #2( ԫlQ?2 g٧=wA͙CV@@r%kf5(HeC2ڂj@>ԾDwZ6q>D[Z † yXr9Mnb-!= 4f|N bf'bK=`?wo!M8UHj`FS*~mBDt.SU'~U5yX4/UKoy^F {fg@'᷂g$m\,nsXjPu9BRm;Ӧ{d JU\HH/&$ڔ;X=vnd KvT }]/L|]C5;ӯmJ{ A5bKc]BcyBY{[o>B,S*xP(d$5V6w?nY{U(ω/3!v]?Skuz(YI\ٻ-qdϪMyUDrĆ"u(1k62c}1]{$crmYGBtRl'.gSۣ(Ͳ"a_ Wky;t60˜D۾D-HUJM-u]cxZb4|qM=5] Y`tK?p8+'Fc{BձL;J_ r{~TC#OaԔdӍ@EHP$W $֭; ץLL!Lϟ^gi GvsA)XRqhf@q0U@HL|jPZ٦cmJ2NY)?@|Y+X0t<6Rj'Dܹ^:)Ht!d4[I4 B@OQ]=Q* rGVieg!Y,S>ih0:BA9JFE߃2kde2?/n{S 6__,t7#8H,,Z"sM`>&1RpIFw3k.dsƣz=N\ b OInHãۉZuZF)#X񗳻,.X}jtWN¨labM w.t V$ ilc?ʲ|0]|k:BzkSҘl{qrIdGϛ= NJ9QX*r(eN2)b"=s_R?Z}5&ϰʵݓCx LKKe^y JFMENKPq/^LZN'sv]oj:6U .tE+ɋ ;D7@n j B-#JF"GV+~xYZUhaέ[ù?Brgɟ Th;#)!e ri>[0 Ho^+Ʊ 68c%8Z~HWQZ-elE1Xd˞?/"=~Frj[ŸG$bGט#?: 쎬$0hS-JyR] X`Unp" ŗ{`=jP|}B`wY78Uf/Flk=;K## ^!J5T.wAP~ dDKN^> ;ZꊭJgpꀍMy%0CXn Snʋ}4Ud^w*J6"݃:B5yrjē<u8c$S^ӄ2l1+ϟCb.2:‡ yh.L K7Soe(E.D?H9iMZDs00 h1=|6+ Liv2=I?Sr3܀N@a&1Zdp= ףp=@PM (l2@<KԆDU]ʡZ@=J͜m#, 7 4"&EA+p@_v <ji zEz ꎀ֘ErPG+ڦG$g_mA!z j+R-DvSG6e,Bb~:MBާe/눟Uc%kqkD+Sq`1FUAck-~<ƢDRs\dj:EE='WTKxT6ݧjWQDme%Jd ]=U B$ /Sm mhDi}n܁7{S⻅xtm'z81{D&v't#qINgV-ae(R63s4aF]'A1qRuqg~ӄbhx{RVp @v`5ГjCMĸJpz3zr\L.YOtsyob+EV݊6+>m7̶Ȣ" @K ж%dD.\G_,>%tw(N/opk UMlcNTGtT%k$MT¶)s$t-ɒez|/~e>"t*U_':7h*-Ciu+-s)b);{Is!6p$ӌ#@J286>C &|ca/5_([cрyb2üק+GٍQO3VnG/ X'a'͜x8Vc GtlXC 7T%`i *.GATW3LR?#wXHv W>~;Υ5]JBϲzΕIB롦`f8Gc^s b/6{+ջC-;eUkԜv,n% DHdW*͖>'ukq~X1[DLhrE=k0bЏij<)).%I qo(TJ2qn1'ƚp}x=MNF!&8#Xlo(,nGEyۤ̂}f;^@J6VchN"uO>ZS U1(\Qӑf-@s]Ύhr#j9NDGC"ɦq"|+TO9zc%C1X=c5u|&<%ˋ#w"_uv6]IYyX7 1 ?jgν߽B`A֋mSx@I̮Hh[-^zyx2_`&;\oJW`Wζ]y<$ˢhm} T]&Tpƌ$gDבv@6VM6+zCZ[l1r'0N1J<6^F&t~WOH8oꖐcZ mM`34,9gAwG`I,9b7]:%˅t׼dqӨMSf-bg`=ހx SaVfQp[y2Fqk󗿗φ}l]=c1Ü`w;մ-xF\* 4zWF׬XҘ#Tw,)Udwsj=>*b5*g81~8<<^p8G vڍԨ +eṗt;?Ȅ w UyϿՓ/8';J0F.D.>vJ'D(0Tx>ݔ20:?巧`ޥV֑f%Οk;i‡F k)XsۓPҸsUrsONdP#b}$lr]ΖKO|UraօuERAQ;H=_ Xfѷ?ϻӛa՟ȂS|n{ch i->!Qai&ޓv:k\me 3RYg+@8H۔PFfנ`2$^`#㏜ūksd|F ,.JDw`zd؋J|l_b-]:y4yx`K6C>'=z·)f&4*cR04`g|A8?,6)1c}e5C.?9ϛdpÂz}U&N(&۲oOFkȒ˻zܝYTWdeT-~)p$w6vkwąYڛSu᪢R|Dե-帟ߦ&/xs$^;V-k4y˼׷&62$C1]?Ɣ}t_|Hi"RD¢Ag k`ŗ`鸶8>G#g$ v6ΛpD’s繹;Hw(J2R l (_S#Y7yHD"'k6-w꺔R̆9'd҉>+Zꌤ11eU%>_Un* du.=Ķ{sLuZ(eMq3{?_xۏ|Vsﭚ'vcY~S|qޝh b!q&Ui ;+*\[z@g7._⼺;1awl}9U"Suu\TҒsi$$wÿ-ԴJbڗ;5 H~tڝXv%QӮR;uD'm,{tPd弥a}J]I62w[Mľ٬:|\ 5$KB.ˈPo ̼$ 믾ۗ6A_paw̫BzՔֵil7#GGŧ+eX;On ףp= ף@P$ (k@C*焑 1_.@v:k #NJbxz&n2xW ?hӠ@aQYȥo: '΄ x9? 6Ngɖ"E@|op+ŝL67V߄\l: {Η H½ Pv1P?%O7и'ƫCưo\{2~]n0b/5E=!֕C)L{QF$+v؈Sﶓh髤8AqfcQlN@< ePKПDǕh"!9oj%p E"&'O֪"Nj+w 3;LզIxŠ'V:qCN'1c$_E^69uD]ȩdLJ:݈MXd슠p`~Ȍ8eްݫ--{9.ڇz贙#"׬+֫* eǃR?Vʸ&ϫ ^xa Z^9wuܠLqm_:JIxX{-KqB]e wet _jhvjE¿;JЕ(aʩ]D&r<uX,iM.wzU?7ߎ~S^=lX![qgpdڲX4A4>QԞ2#Ek0SF|n_OIwmU c*O~MQ5Fe B‹_FiYW7/-k}{x 2cPME fzBΨ?]ߌG#٨l*CwyT^Tj4'RE]dB!ܺ~IriE֒Pś[[26hd#Dvja5ӨŹ7hdZk"!"k*=]S5Ȍ:Bxri %ь[e8y/JXg].\*߈=9tauGѹ"+_vI}Azdұ_F;#J ,(ن"y(+EWAu-/ӷ .|]|%94›.²}DKa2(x5˲az®k[rc? <<Ϗ( CYxķapeFYy "WˇuξTSܷ.$*(VyuUD`גjU'9pbCFTfAXjiQ.A"ZҪ/ iyMh, XEa75 .|BǼ]R5VS!{Z֨Y dp>ԅ.7JŇԱ YJ^M:0ܵ$~sީq]V ukP/bin_offsets_.back() at %s, line %d . 333333?>%s:%d: assertion failed: %stoo large exponentnegative valueinvalid digit countempty buffersign == '+' || sign == '-'is_digit(*p)shift >= 0exp >= 0unaligned bigintsk is out of rangeshifting error detected"leaf_const":"leaf_features":["leaf_coeff":["leaf_features":[], "leaf_coeff":[] 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":string pointer is nullFailed to parse double: %sn is too largenum_catleaf_valueshrinkageis_linearleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typeleaf_constnum_featuresleaf_featuresleaf_coeffcat_boundariesexponent out of rangeNANINFinvalid type specifiernumber is too bigvalue is negativeinvalid number of digitsnegative widthwidth is not integerargument not foundinvalid format stringnegative precisionprecision is not integer0123456789ABCDEFinvalid fill character '{'invalid fillmissing precision specifiermissing '}' in format stringinvalid argument typeunknown format specifier{}{:.17g}{:g}num_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=is_linear=leaf_const=num_features=leaf_features=leaf_coeff=shrinkage=/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format-inl.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/core.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format.hbigits_.capacity() <= bigits_capacityif (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) { unmatched '}' in format string/__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 fieldcannot switch from manual to automatic argument indexingcannot switch from automatic to manual argument indexingformat specifier requires numeric argumentinvalid format specifier for charformat specifier requires signed argumentprecision not allowed for this argument typeNumerical conversion failed. Buffer is too small.@P ؑpP int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer&) [with T = double]fmt::v7::detail::bigint::~bigint()fmt::v7::detail::bigint& fmt::v7::detail::bigint::operator<<=(int)void fmt::v7::detail::bigint::assign_pow10(int)int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer&) [with T = long double]-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G ni %Cp(W^j87.h3DJ!jzd)غ`Y3)$oVyԶӥlwHޔ3 M^Pa,\":1H cm̻, `%N *᮴ fMȟP(rbISz%Taw$jDk(sK2 CgN?͓A"xuRCkxk SVk#췆,2{S̰?h@ѡ¹ #2( ԫlQ?2 g٧=wA͙CV@@r%kf5(HeC2ڂj@>ԾDwZ6q>D[Z † yXr9Mnb-!= 4f|N bf'bK=`?wo!M8UHj`FS*~mBDt.SU'~U5yX4/UKoy^F {fg@'᷂g$m\,nsXjPu9BRm;Ӧ{d JU\HH/&$ڔ;X=vnd KvT }]/L|]C5;ӯmJ{ A5bKc]BcyBY{[o>B,S*xP(d$5V6w?nY{U(ω/3!v]?Skuz(YI\ٻ-qdϪMyUDrĆ"u(1k62c}1]{$crmYGBtRl'.gSۣ(Ͳ"a_ Wky;t60˜D۾D-HUJM-u]cxZb4|qM=5] Y`tK?p8+'Fc{BձL;J_ r{~TC#OaԔdӍ@EHP$W $֭; ץLL!Lϟ^gi GvsA)XRqhf@q0U@HL|jPZ٦cmJ2NY)?@|Y+X0t<6Rj'Dܹ^:)Ht!d4[I4 B@OQ]=Q* rGVieg!Y,S>ih0:BA9JFE߃2kde2?/n{S 6__,t7#8H,,Z"sM`>&1RpIFw3k.dsƣz=N\ b OInHãۉZuZF)#X񗳻,.X}jtWN¨labM w.t V$ ilc?ʲ|0]|k:BzkSҘl{qrIdGϛ= NJ9QX*r(eN2)b"=s_R?Z}5&ϰʵݓCx LKKe^y JFMENKPq/^LZN'sv]oj:6U .tE+ɋ ;D7@n j B-#JF"GV+~xYZUhaέ[ù?Brgɟ Th;#)!e ri>[0 Ho^+Ʊ 68c%8Z~HWQZ-elE1Xd˞?/"=~Frj[ŸG$bGט#?: 쎬$0hS-JyR] X`Unp" ŗ{`=jP|}B`wY78Uf/Flk=;K## ^!J5T.wAP~ dDKN^> ;ZꊭJgpꀍMy%0CXn Snʋ}4Ud^w*J6"݃:B5yrjē<u8c$S^ӄ2l1+ϟCb.2:‡ yh.L K7Soe(E.D?H9iMZDs00 h1=|6+ Liv2=I?Sr3܀N@a&1Zdp= ףp=@PM (l2@<KԆDU]ʡZ@=J͜m#, 7 4"&EA+p@_v <ji zEz ꎀ֘ErPG+ڦG$g_mA!z j+R-DvSG6e,Bb~:MBާe/눟Uc%kqkD+Sq`1FUAck-~<ƢDRs\dj:EE='WTKxT6ݧjWQDme%Jd ]=U B$ /Sm mhDi}n܁7{S⻅xtm'z81{D&v't#qINgV-ae(R63s4aF]'A1qRuqg~ӄbhx{RVp @v`5ГjCMĸJpz3zr\L.YOtsyob+EV݊6+>m7̶Ȣ" @K ж%dD.\G_,>%tw(N/opk UMlcNTGtT%k$MT¶)s$t-ɒez|/~e>"t*U_':7h*-Ciu+-s)b);{Is!6p$ӌ#@J286>C &|ca/5_([cрyb2üק+GٍQO3VnG/ X'a'͜x8Vc GtlXC 7T%`i *.GATW3LR?#wXHv W>~;Υ5]JBϲzΕIB롦`f8Gc^s b/6{+ջC-;eUkԜv,n% DHdW*͖>'ukq~X1[DLhrE=k0bЏij<)).%I qo(TJ2qn1'ƚp}x=MNF!&8#Xlo(,nGEyۤ̂}f;^@J6VchN"uO>ZS U1(\Qӑf-@s]Ύhr#j9NDGC"ɦq"|+TO9zc%C1X=c5u|&<%ˋ#w"_uv6]IYyX7 1 ?jgν߽B`A֋mSx@I̮Hh[-^zyx2_`&;\oJW`Wζ]y<$ˢhm} T]&Tpƌ$gDבv@6VM6+zCZ[l1r'0N1J<6^F&t~WOH8oꖐcZ mM`34,9gAwG`I,9b7]:%˅t׼dqӨMSf-bg`=ހx SaVfQp[y2Fqk󗿗φ}l]=c1Ü`w;մ-xF\* 4zWF׬XҘ#Tw,)Udwsj=>*b5*g81~8<<^p8G vڍԨ +eṗt;?Ȅ w UyϿՓ/8';J0F.D.>vJ'D(0Tx>ݔ20:?巧`ޥV֑f%Οk;i‡F k)XsۓPҸsUrsONdP#b}$lr]ΖKO|UraօuERAQ;H=_ Xfѷ?ϻӛa՟ȂS|n{ch i->!Qai&ޓv:k\me 3RYg+@8H۔PFfנ`2$^`#㏜ūksd|F ,.JDw`zd؋J|l_b-]:y4yx`K6C>'=z·)f&4*cR04`g|A8?,6)1c}e5C.?9ϛdpÂz}U&N(&۲oOFkȒ˻zܝYTWdeT-~)p$w6vkwąYڛSu᪢R|Dե-帟ߦ&/xs$^;V-k4y˼׷&62$C1]?Ɣ}t_|Hi"RD¢Ag k`ŗ`鸶8>G#g$ v6ΛpD’s繹;Hw(J2R l (_S#Y7yHD"'k6-w꺔R̆9'd҉>+Zꌤ11eU%>_Un* du.=Ķ{sLuZ(eMq3{?_xۏ|Vsﭚ'vcY~S|qޝh b!q&Ui ;+*\[z@g7._⼺;1awl}9U"Suu\TҒsi$$wÿ-ԴJbڗ;5 H~tڝXv%QӮR;uD'm,{tPd弥a}J]I62w[Mľ٬:|\ 5$KB.ˈPo ̼$ 믾ۗ6A_paw̫BzՔֵil7#GGŧ+eX;On ףp= ף@P$ (k@C*焑 1_.@v:k #NJbxz&n2xW ?hӠ@aQYȥo: '΄ x9? 6Ngɖ"E@|op+ŝL67V߄\l: {Η H½ Pv1P?%O7и'ƫCưo\{2~]n0b/5E=!֕C)L{QF$+v؈Sﶓh髤8AqfcQlN@< ePKПDǕh"!9oj%p E"&'O֪"Nj+w 3;LզIxŠ'V:qCN'1c$_E^69uD]ȩdLJ:݈MXd슠p`~Ȍ8eްݫ--{9.ڇz贙#"׬+֫* eǃR?Vʸ&ϫ ^xa Z^9wuܠLqm_:JIxX{-KqB]e wet _jhvjE¿;JЕ(aʩ]D&r<uX,iM.wzU?7ߎ~S^=lX![qgpdڲX4A4>QԞ2#Ek0SF|n_OIwmU c*O~MQ5Fe B‹_FiYW7/-k}{x 2cPME fzBΨ?]ߌG#٨l*CwyT^Tj4'RE]dB!ܺ~IriE֒Pś[[26hd#Dvja5ӨŹ7hdZk"!"k*=]S5Ȍ:Bxri %ь[e8y/JXg].\*߈=9tauGѹ"+_vI}Azdұ_F;#J ,(ن"y(+EWAu-/ӷ .|]|%94›.²}DKa2(x5˲az®k[rc? <<Ϗ( CYxķapeFYy "WˇuξTSܷ.$*(VyuUD`גjU'9pbCFTfAXjiQ.A"ZҪ/ iyMh, XEa75 .|BǼ]R5VS!{Z֨Y dp>ԅ.7JŇԱ YJ^M:0ܵ$~sީq]V ukP/ (0) at %s, line %d . '}&_] outside [auc[%s:%s]: sum-of-weights = %f%s offset term = %fmap@ndcg@poissonbinary_errorgammagamma_deviancetweediehuberfairmulti_error@[%s]: does not tolerate element [#%i = /__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_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM22AveragePrecisionMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM11AucMuMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_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...Connected to rank %dSocket construction errorrank=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 leastSocket accept error, %s (code: %d)Socket recv error, %s (code: %d)Socket send error, %s (code: %d)Machine 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 initialize 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 zeroRanking tasks require query information[%s:%s]: havg = %f -> initscore = %f[%s:%s]: pavg=%f -> initscore=%f[%s:%s]: pavg = %f -> initscore = %fNumber 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 = %fSigmoid 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 negativeSome label values are < 1 in absolute value. MAPE is unstable with such values, so LightGBM rounds them to 1.0 when calculating MAPE.Objective 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 . BoostFromScoreInitBoostFromScoreInitBoostFromScoreN8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM16RankingObjectiveEN8LightGBM14LambdarankNDCGEN8LightGBM10RankXENDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC1ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC1ERKNS_6ConfigESt8functionIFbfEEEUlfE_ZN8LightGBM9ArrayArgsIfE8ArgMaxMTERKSt6vectorIfSaIfEEEUlimmE_ZN8LightGBM9ArrayArgsIdE8ArgMaxMTERKSt6vectorIdSaIdEEEUlimmE_?qq C<ư>0y$k CII@@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 . CUDA Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_CUDA=1GPU Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_GPU=1N8LightGBM15CUDATreeLearnerEN8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_N8LightGBM26FeatureParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEE/__w/1/s/external_libs/eigen/Eigen/src/Core/util/Memory.h(size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator."/__w/1/s/external_libs/eigen/Eigen/src/Core/PlainObjectBase.h((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0/__w/1/s/external_libs/eigen/Eigen/src/LU/FullPivLU.hm_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseCoeffsBase.h/__w/1/s/external_libs/eigen/Eigen/src/Core/PermutationMatrix.hi>=0 && j>=0 && irows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."/__w/1/s/src/treelearner/linear_tree_learner.h(!(RowsAtCompileTime!=Dynamic) || (rows==RowsAtCompileTime)) && (!(ColsAtCompileTime!=Dynamic) || (cols==ColsAtCompileTime)) && (!(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic) || (rows<=MaxRowsAtCompileTime)) && (!(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic) || (cols<=MaxColsAtCompileTime)) && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array."/__w/1/s/external_libs/eigen/Eigen/src/Core/MapBase.h(dataPtr == 0) || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))/__w/1/s/external_libs/eigen/Eigen/src/Core/Block.hstartRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols(i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)/__w/1/s/external_libs/eigen/Eigen/src/Core/AssignEvaluator.hdst.rows() == src.rows() && dst.cols() == src.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/util/BlasUtil.hm_lu.rows()<=NumTraits::highest() && m_lu.cols()<=NumTraits::highest()/__w/1/s/external_libs/eigen/Eigen/src/Core/Redux.hthis->rows()>0 && this->cols()>0 && "you are using an empty matrix"/__w/1/s/external_libs/eigen/Eigen/src/Core/Visitor.hrow >= 0 && row < rows() && col >= 0 && col < cols()rows()==other.rows() && cols()==other.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/ProductEvaluators.hdst.rows() == dstRows && dst.cols() == dstCols/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseBinaryOp.haLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)/__w/1/s/external_libs/eigen/Eigen/src/Core/SolveTriangular.hderived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols()))/__w/1/s/external_libs/eigen/Eigen/src/Core/Product.hlhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.hdst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/SolverBase.hderived().m_isInitialized && "Solver is not initialized."(Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b"No further splits with positive gain, best gain: %fTrained a tree with leaves = %d and depth = %dDetected that num_threads changed during training (from %d to %d), it may cause unexpected errors.index >= 0 && index < size()v == T(Value)incr==1SerialTreeLearner::Train+0+`+0+,0+`+0+0+0+0+`+0+0+0+0+0,0+0+0+0+0+0+0+P,@,0++0+0+0++0+0++0+0+0++0+0+0+0+0+0+0+0+p+`,p++ , +0+,p++ +++++p,,,+,,,0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+`+0+0+0+0+0++`++0+0+0+0+0+0+0+0+0+0+0+0+0+0+0++p++ ++++ + +p++ +++ +++++0+0+,Eigen::DenseCoeffsBase::Scalar& Eigen::DenseCoeffsBase::operator()(Eigen::Index) [with Derived = Eigen::Matrix; Eigen::DenseCoeffsBase::Scalar = double; Eigen::Index = long int]void Eigen::PlainObjectBase::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = const Eigen::CwiseUnaryOp, const Eigen::Inverse > > >; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op&) [with DstXprType = Eigen::Matrix; SrcXprType = Eigen::CwiseUnaryOp, const Eigen::Inverse > > >; T1 = double; T2 = double]static void Eigen::internal::generic_product_impl::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix; Lhs = Eigen::CwiseUnaryOp, const Eigen::Inverse > > >; Rhs = Eigen::Matrix; Eigen::internal::generic_product_impl::Scalar = double]Eigen::CwiseNullaryOp::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op; PlainObjectType = Eigen::Matrix; Eigen::Index = long int]typename Eigen::internal::traits::Scalar Eigen::DenseBase::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op; Derived = Eigen::CwiseBinaryOp, const Eigen::Transpose, 1, -1, false> >, const Eigen::Block, -1, 1, true> >; typename Eigen::internal::traits::Scalar = double]Eigen::CwiseBinaryOp::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op; LhsType = const Eigen::Transpose, 1, -1, false> >; RhsType = const Eigen::Block, -1, 1, true>; Eigen::CwiseBinaryOp::Lhs = Eigen::Transpose, 1, -1, false> >; Eigen::CwiseBinaryOp::Rhs = Eigen::Block, -1, 1, true>]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, 1, -1, false>; Eigen::MapBase::PointerType = const double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, 1, true>; Eigen::MapBase::PointerType = const double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op&) [with DstXprType = Eigen::Matrix; SrcXprType = Eigen::CwiseBinaryOp, const Eigen::CwiseNullaryOp, const Eigen::Matrix >, const Eigen::Product > >, Eigen::Matrix, 1> >; T1 = double; T2 = double]Eigen::CwiseNullaryOp::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op; PlainObjectType = Eigen::Matrix; Eigen::Index = long int]const PermutationQType& Eigen::FullPivLU::permutationQ() const [with _MatrixType = Eigen::Matrix; Eigen::FullPivLU::PermutationQType = Eigen::PermutationMatrix<-1, -1, int>]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase&) const [with int Side = 1; OtherDerived = Eigen::Block, -1, -1, false>; _MatrixType = const Eigen::Block, -1, -1, false>; unsigned int _Mode = 2u]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block, -1, -1, false>; SrcXprType = Eigen::Matrix; Functor = Eigen::internal::sub_assign_op]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, 1, -1, false> >, -1, 1, true>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]static void Eigen::internal::generic_product_impl::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix; Lhs = Eigen::Block, -1, -1, false>; Rhs = Eigen::Block, -1, -1, false>; Eigen::internal::generic_product_impl::Scalar = double]typename Eigen::internal::traits::Scalar Eigen::DenseBase::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op; Derived = Eigen::CwiseBinaryOp, const Eigen::Transpose, -1, -1, false>, 1, -1, false> >, const Eigen::Block, -1, -1, false>, -1, 1, true> >; typename Eigen::internal::traits::Scalar = double]Eigen::CwiseBinaryOp::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op; LhsType = const Eigen::Transpose, -1, -1, false>, 1, -1, false> >; RhsType = const Eigen::Block, -1, -1, false>, -1, 1, true>; Eigen::CwiseBinaryOp::Lhs = Eigen::Transpose, -1, -1, false>, 1, -1, false> >; Eigen::CwiseBinaryOp::Rhs = Eigen::Block, -1, -1, false>, -1, 1, true>]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, -1, false>, 1, -1, false>; Eigen::MapBase::PointerType = const double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block, -1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, -1, false>, -1, 1, true>; Eigen::MapBase::PointerType = const double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op&) [with DstXprType = Eigen::Matrix; SrcXprType = Eigen::Product, -1, -1, false>, Eigen::Block, -1, -1, false>, 1>; T1 = double; T2 = double]Eigen::Product::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block, -1, -1, false>; _Rhs = Eigen::Block, -1, -1, false>; int Option = 1; Eigen::Product::Lhs = Eigen::Block, -1, -1, false>; Eigen::Product::Rhs = Eigen::Block, -1, -1, false>]Eigen::Product::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block, -1, -1, false>; _Rhs = Eigen::Block, -1, -1, false>; int Option = 0; Eigen::Product::Lhs = Eigen::Block, -1, -1, false>; Eigen::Product::Rhs = Eigen::Block, -1, -1, false>]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, -1, false>; Eigen::MapBase::PointerType = const double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = const Eigen::Matrix; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::gemm_pack_rhs::operator()(Scalar*, const DataMapper&, Index, Index, Index, Index) [with Scalar = double; Index = long int; DataMapper = Eigen::internal::blas_data_mapper; int nr = 4; bool Conjugate = false; bool PanelMode = true]Eigen::internal::blas_data_mapper::blas_data_mapper(Scalar*, Index, Index) [with Scalar = double; Index = long int; int StorageOrder = 0; int AlignmentType = 0]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase&) const [with int Side = 1; OtherDerived = Eigen::Block, -1, -1, false>; _MatrixType = const Eigen::Block, -1, -1, false>; unsigned int _Mode = 5u]void Eigen::DenseBase::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, 1, -1, false>; Eigen::Index = long int]Eigen::Product::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::PermutationMatrix<-1, -1, int>; _Rhs = Eigen::CwiseNullaryOp, Eigen::Matrix >; int Option = 2; Eigen::Product::Lhs = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Product::Rhs = Eigen::CwiseNullaryOp, Eigen::Matrix >]const PermutationPType& Eigen::FullPivLU::permutationP() const [with _MatrixType = Eigen::Matrix; Eigen::FullPivLU::PermutationPType = Eigen::PermutationMatrix<-1, -1, int>]Eigen::Index Eigen::FullPivLU::rank() const [with _MatrixType = Eigen::Matrix; Eigen::Index = long int]void Eigen::SolverBase::_check_solve_assertion(const Rhs&) const [with bool Transpose_ = false; Rhs = Eigen::MatrixBase, Eigen::Matrix > >; Derived = Eigen::FullPivLU >]Eigen::CwiseNullaryOp::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_identity_op; PlainObjectType = Eigen::Matrix; Eigen::Index = long int]Eigen::CwiseBinaryOp::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op; LhsType = const Eigen::CwiseNullaryOp, const Eigen::Matrix >; RhsType = const Eigen::Product > >, Eigen::Matrix, 1>; Eigen::CwiseBinaryOp::Lhs = Eigen::CwiseNullaryOp, const Eigen::Matrix >; Eigen::CwiseBinaryOp::Rhs = Eigen::Product > >, Eigen::Matrix, 1>]Eigen::CwiseNullaryOp::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op; PlainObjectType = const Eigen::Matrix; Eigen::Index = long int]Eigen::Product::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Inverse > >; _Rhs = Eigen::Matrix; int Option = 1; Eigen::Product::Lhs = Eigen::Inverse > >; Eigen::Product::Rhs = Eigen::Matrix]Eigen::Product::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::CwiseUnaryOp, const Eigen::Inverse > > >; _Rhs = Eigen::Matrix; int Option = 0; Eigen::Product::Lhs = Eigen::CwiseUnaryOp, const Eigen::Inverse > > >; Eigen::Product::Rhs = Eigen::Matrix]const Eigen::Inverse > Eigen::FullPivLU::inverse() const [with _MatrixType = Eigen::Matrix]Derived& Eigen::PermutationBase::applyTranspositionOnTheRight(Eigen::Index, Eigen::Index) [with Derived = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block, -1, -1, false>, -1, 1, true>; SrcXprType = Eigen::CwiseBinaryOp, const Eigen::CwiseNullaryOp, const Eigen::Matrix >, const Eigen::Block, -1, 1, true>, -1, 1, false> >; Functor = Eigen::internal::sub_assign_op]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, -1, false>, -1, 1, true>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = Eigen::Block, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::CwiseNullaryOp::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op; PlainObjectType = const Eigen::Matrix; Eigen::Index = long int]static void Eigen::internal::Assignment, Eigen::internal::sub_assign_op, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::run(DstXprType&, const SrcXprType&, const Eigen::internal::sub_assign_op&) [with DstXprType = Eigen::Block, -1, -1, false>; Lhs = Eigen::Block, -1, 1, true>, -1, 1, false>; Rhs = Eigen::Block, 1, -1, false>, 1, -1, false>; int Options = 0; Scalar = double; Eigen::internal::Assignment, Eigen::internal::sub_assign_op, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::SrcXprType = Eigen::Product, -1, 1, true>, -1, 1, false>, Eigen::Block, 1, -1, false>, 1, -1, false>, 0>]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, 1, true>, -1, 1, false>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block, -1, 1, true>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, 1, true>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, 1, -1, false>, 1, -1, false>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block, 1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, 1, -1, false>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::DenseBase::swap(const Eigen::DenseBase&) [with OtherDerived = Eigen::Block, -1, 1, true>; Derived = Eigen::Block, -1, 1, true>]void Eigen::DenseBase::swap(const Eigen::DenseBase&) [with OtherDerived = Eigen::Block, 1, -1, false>; Derived = Eigen::Block, 1, -1, false>]typename Eigen::internal::traits::Scalar Eigen::DenseBase::maxCoeff(IndexType*, IndexType*) const [with IndexType = long int; Derived = Eigen::CwiseUnaryOp, const Eigen::Block, -1, -1, false> >; typename Eigen::internal::traits::Scalar = double]Eigen::MapBase::MapBase(Eigen::MapBase::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block, -1, -1, false>; Eigen::MapBase::PointerType = double*; Eigen::Index = long int]Eigen::Block::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Matrix; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]typename Eigen::internal::traits::Scalar Eigen::DenseBase::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op; Derived = Eigen::Block, const Eigen::Matrix >, -1, 1, true>; typename Eigen::internal::traits::Scalar = double]Eigen::internal::variable_if_dynamic::variable_if_dynamic(T) [with T = long int; int Value = 1]typename Eigen::internal::traits::Scalar Eigen::DenseBase::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_max_op; Derived = Eigen::PartialReduxExpr, const Eigen::Matrix >, Eigen::internal::member_sum, 0>; typename Eigen::internal::traits::Scalar = double]void Eigen::FullPivLU::computeInPlace() [with _MatrixType = Eigen::Matrix]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op&) [with DstXprType = Eigen::Matrix; SrcXprType = Eigen::Matrix; T1 = double; T2 = double]void Eigen::PlainObjectBase::resize(Eigen::Index) [with Derived = Eigen::Matrix; Eigen::Index = long int]void Eigen::PlainObjectBase::resize(Eigen::Index) [with Derived = Eigen::Matrix; Eigen::Index = long int]Eigen::DenseCoeffsBase::Scalar& Eigen::DenseCoeffsBase::operator()(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix; Eigen::DenseCoeffsBase::Scalar = double; Eigen::Index = long int]void* Eigen::internal::aligned_malloc(std::size_t)void Eigen::PlainObjectBase::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix; Eigen::Index = long int]N8LightGBM17LinearTreeLearnerEj@<SerialTreeLearner::ConstructHistogramsSerialTreeLearner::BeforeFindBestSplit/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: (num_machines) > (1) at %s, line %d . Check failed: (bag_cnt) == (num_data_) at %s, line %d . share_state_ Can't be NULL at %s, line %d . Check failed: (data_partition_->num_leaves()) >= (tree->num_leaves()) at %s, line %d . SerialTreeLearner::BeforeTrainInvalid categorical threshold split'Forced Split' will be ignored since the gain getting worse.cegb_penalty_feature_lazy should be the same size as feature number.cegb_penalty_feature_coupled should be the same size as feature number.SerialTreeLearner::FindBestSplitsFromHistogramsGet historical Histogram for leaf %d failed, will skip the ``RecomputeBestSplitForLeaf``Check failed: (best_split_info.left_count) > (0) at %s, line %d . Check failed: (best_split_info.right_count) > (0) at %s, line %d . /__w/1/s/src/treelearner/feature_histogram.hppCheck failed: (cache_size_) >= (2) at %s, line %d . Check failed: (train_data->num_features()) > (0) at %s, line %d . Number of data points in the train set: %d, number of used features: %dCheck failed: (num_features_) == (train_data_->num_features()) at %s, line %d . DataPartition::Splitvector::_M_insert_auxleftrightSerialTreeLearner::SplitInnerTotal Bins %dSt22_Maybe_get_result_typeILb1ESt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEESt22_Weak_result_type_implISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEEN8LightGBM15BasicConstraintESt17_Weak_result_typeISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEEN8LightGBM11TreeLearnerEN8LightGBM17FeatureConstraintEN8LightGBM15ConstraintEntryEN8LightGBM20BasicConstraintEntryEN8LightGBM26AdvancedFeatureConstraintsEN8LightGBM19LeafConstraintsBaseEN8LightGBM23AdvancedConstraintEntryEN8LightGBM20BasicLeafConstraintsEN8LightGBM27IntermediateLeafConstraintsEN8LightGBM23AdvancedLeafConstraintsEN8LightGBM17SerialTreeLearnerEZN8LightGBM13DataPartition5SplitEiPKNS_7DatasetEiPKjibiEUliiiPiS6_E_St5_BindIFSt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEPS2_St12_PlaceholderILi1EESC_ILi2EESC_ILi3EESC_ILi4EESC_ILi5EESC_ILi6EEEEZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS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exceptionField not found/__w/1/s/src/c_api.cpppm_ != nullptrUsed indices of subsets->rwcount & writer_maskThe number of features in data (%d) is not the same as it was in training data (%d). You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.Feature (%s) is missed in data file. If it is weight/query/group/ignore_column, you can ignore this warning.Input data type error or field not foundUnknown indptr type in LGBM_BoosterFreePredictSparseUnknown data type in LGBM_BoosterFreePredictSparseUnknown data type in RowFunctionFromDenseMatricCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . Unknown data type in CSC matrix/__w/1/s/include/LightGBM/utils/yamc/yamc_shared_lock.hpp/__w/1/s/include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp0 < (s->rwcount & reader_mask)Unknown data type in RowFunctionFromCSR(s->rwcount & reader_mask) < reader_maskCannot change forced bins after constructed Dataset handle.Cannot change data_random_seed after constructed Dataset handle.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 categorical_feature after constructed Dataset handle.Cannot change feature_pre_filter after constructed Dataset handle.Cannot change is_enable_sparse after constructed Dataset handle.Cannot change pre_partition after constructed Dataset handle.Cannot change enable_bundle after constructed Dataset handle.Cannot change header after constructed Dataset handle.Cannot change two_round after constructed Dataset handle.Cannot change label_column after constructed Dataset handle.Cannot change weight_column after constructed Dataset handle.Cannot change group_column after constructed Dataset handle.Cannot change ignore_column after constructed Dataset handle.Reducing `min_data_in_leaf` with `feature_pre_filter=true` may cause unexpected behaviour for features that were pre-filtered by the larger `min_data_in_leaf`. You need to set `feature_pre_filter=false` to dynamically change the `min_data_in_leaf`.Cannot change linear_tree after constructed Dataset handle.Cannot change precise_float_parser after constructed Dataset handle.LGBM_GetSampleCount output is nullptrCheck failed: (num_used_row_indices) > (0) at %s, line %d . used_row_indices should be sorted in SubsetLGBM_SampleIndices output is nullptrThe 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 learnerPrediction results file %s cannot be createdCould not recognize the data format of data file %sCannot change num_class during trainingCannot change boosting during trainingCannot change metric during training/__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 . Predicting SHAP feature contributions is not implemented for linear trees.Unknown data type in PredictSparseUnknown col_ptr type in PredictSparseCSCUnknown indptr type in PredictSparseCSRUnknown matrix type in LGBM_BoosterPredictSparseOutputstatic bool yamc::rwlock::ReaderPrefer::release_rlock(yamc::rwlock::ReaderPrefer::state*)yamc::shared_lock::~shared_lock() [with Mutex = yamc::alternate::basic_shared_mutex]static void yamc::rwlock::ReaderPrefer::acquire_rlock(yamc::rwlock::ReaderPrefer::state*)static void yamc::rwlock::ReaderPrefer::release_wlock(yamc::rwlock::ReaderPrefer::state*)*Z31LGBM_BoosterPredictSparseOutputEUllE_*Z25LGBM_BoosterPredictForCSCEUliE_*Z30RowPairFunctionFromDenseMatricPKviiiiEUliE_*Z28RowPairFunctionFromDenseRowsPPKviiEUliE_SsZN8LightGBM9Predictor7PredictEPKcS2_bbbEUliRKSt6vectorISsSaISsEEE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPS3_ISt13unordered_mapIidSt4hashIiESt8equal_toIiESaIS4_IKidEEESaISI_EEE1_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE2_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE3_ZN8LightGBM9Predictor7PredictEPKcS2_bbbEUlS2_PSt6vectorISt4pairIidESaIS5_EEE_Z33RowFunctionFromDenseMatric_helperIfESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE_Z33RowFunctionFromDenseMatric_helperIfESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE0_Z33RowFunctionFromDenseMatric_helperIdESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE_Z33RowFunctionFromDenseMatric_helperIdESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE0_Z29IterateFunctionFromCSC_helperIfiESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIflESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIdiESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIdlESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z25RowFunctionFromCSR_helperIifiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIiflESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIidiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIidlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIlfiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIlflESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIldiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIldlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_{Gz?;Tsi8psXzlh{ X8KT T, \   * C \ u 0 x   z ( . 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