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QHHEH讘HM`襛HH0]HT$UH0HHM`耛HuH0]HT$UH0HHM@HHPH!H;uHAH HHEH.H"HH0]HT$UH0HHHH0]HT$UH0HHMHHHPHH;uHAH AHHEH螗H蒚HpH0]HT$UH0HHjHHH0]@UH HXHe8HHH@HHHBHM0|H ]HT$UH@HHHHPHH;uHAH nHHEH˖Hp这HH@]HT$UH@HHp藙HH@]H`tkHTNHT$UH`HHHHPHH;uHAH HHEHHHH`]HT$UH`HHHH`]HT$UH@HHHHPH~H;uHAH .HHEH苕HHH@]HT$UH@HHWHH@]HT$UH@HHHHPHH;uHAH HHEHHHH H@]HT$UH@HHǗH H@]HiH(H HtJHH+HHrLAH'I+HAHw?IԚWHfffH GH ;PH(靚H(H ]HtJHaH+HHrLAH'I+HAHw?IDWHfH H 0H( ?H\$WH L HT$0H M+H P™H MH WrHCHt)CuHHC u HHPHHt)CuHHC u HHPHHt'CuHH{ u HHPH\$8H _@SH HHt6H|$0CuHH{ u HHPH|$0H [H(H HtJHH+HHrLAH'I+HAHw#IdWHnH({H(H eHtJHiH+HHrLAH'I+HAHw#IWH.H( ̋eH%X8H 3@SH eH%X(HHHtH60HH [gH [̋~eH%XH '@SH XeH%X0HH Ht>HSH+HHrLAH'I+HAHwI3HHCHCH [@SH eH%XHHH Ht>HSH+HHrLAH'I+HAHwIa3HHCHCH [@SH XeH%X`HH Ht:HSH+HrLAH'I+HAHwI3HHCHCH [H lfUJkbkzkkkkkkkl"l6lTdlllllllm&m```Waa*bdbbcBc`ccc dbdddeVeeefXfff X(U6U^UUUUUV&VHVfVVVVVW"WPWtWWWWWWWWafffmmmmnjnVn@n2nnmnnnXg@g&ggngggvgndg9 so tf`iji(ihhiiingninnn|irinhiXjiidj2i@j(j jDhPhNjiihvhhh*hhhgghhhhhFi~h 0 at %s, line %d . Check failed: early_stop_margin >= 0 at %s, line %d . binarymulticlass Prediction results file %s cannot be foundCould not recognize the data format of data file %s ,gbdtContinued train from model is not supported for c_api, please use continued train with input scorefeatureDo not support feature parallel in c apiserialOnly find one worker, will switch to serial tree learnerUsing self-defined objective functionnum_classCannot change num_class during trainingboostingCannot change boosting during trainingmetricCannot change metric during trainingobjectiveregressioncpuLightGBM_model.txtLightGBM_predict_result.txtgbdt_prediction.cppunknown exceptionD:\a\1\s\src\c_api.cppCheck failed: inner_data.first < num_col at %s, line %d . Check failed: num_used_row_indices > 0 at %s, line %d . Used indices of subsetInput data type error or field not foundField not foundUnknown data type in RowFunctionFromDenseMatricUnknown data type in RowFunctionFromCSRCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . Unknown data type in CSC matrixCould not open %sSkipped header "%s" in file %sinvalid unordered_map keyWarning: last line of %s has no end of line, still using this lineinvalid vector subscriptvector too longinvalid string position], ] outside [[%s]: does not tolerate element [#%i = string too longlist too longmap/set too longК00 @HPNba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba  @00@ ЙС p``pP@8 H`hЪP```P0AH H >>H  >H H H <;H H H (``P  Р`p@pC`CPp 8868M @0p00 PPP@Don't support large string in R-packageD:\a\1\s\src\lightgbm_R.cppCheck failed: len == out_len at %s, line %d . groupqueryinit_scoreCheck failed: out_len == len at %s, line %d . p @@@0   @ %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=%sUnknown parameter: %s%s is set=%s, %s=%s will be ignored. Current value: %s=%sconfig @3&0)`(2w{p+`/ 0  i kpllpmn 10 @ p     `   Ћt) P%p0 qw{  0  i kpllpmn 10 @ p      `   Ћ"@t P `P`PTPP  0 qw{   0  i kpllpmn 10 @ p      `   t@t Pd:\a\1\s\src\boosting\gbdt.hCheck failed: data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size()) at %s, line %d . Check failed: tree_idx >= 0 && static_cast(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . treed:\a\1\s\src\boosting\goss.hppCheck failed: config_->top_rate + config_->other_rate <= 1.0f at %s, line %d . Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . Cannot use bagging in GOSSUsing GOSSd:\a\1\s\src\boosting\rf.hppCheck 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: num_tree_per_iteration_ == num_class_ at %s, line %d . RF mode do not support custom objective function, please use built-in objectives.Check failed: gradients == nullptr at %s, line %d . Check failed: hessians == nullptr at %s, line %d . Time for loading model: %f secondsdartgossrfUnknown boosting type %sUnknown model format or submodel type in model file %sbad cast:D:\a\1\s\include\LightGBM/tree.hCheck failed: max_depth_ >= 0 at %s, line %d . Number of class for initial score errorD:\a\1\s\src\boosting\gbdt.cppCheck failed: train_data != nullptr at %s, line %d . Cannot add validation data, since it has different bin mappers with training dataNo object function providedCheck failed: cur_left_cnt == bag_data_cnt at %s, line %d . Re-bagging, using %d data to train%f seconds elapsed, finished iteration %d.snapshot_iter_Check failed: tree_leaf_prediction.size() > 0 at %s, line %d . Check failed: static_cast(num_data_) == tree_leaf_prediction.size() at %s, line %d . Check failed: static_cast(models_.size()) == tree_leaf_prediction[0].size() at %s, line %d . Check failed: leaf_pred[i] < models_[model_index]->num_leaves() at %s, line %d . Start training from score %lfregression_l1quantilemapeDisabling boost_from_average in %s may cause the slow convergenceStopped training because there are no more leaves that meet the split requirementsEarly stopping at iteration %d, the best iteration round is %dOutput of best iteration round: %s : , training Iteration: , valid_Cannot reset training data, since new training data has different bin mappersCheck failed: num_tree_per_iteration_ == objective_function_->NumModelPerIteration() at %s, line %d . Use subset for baggingvector too long X 0H 0P``0`@g0Ш qw{  0  i kpllpmn 10 @ p      `   Ћt@t PC`Cp`falsetruev3{","name":""version":","num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":, "average_output":", "objective":""],",""feature_names":["],"monotone_constraints":["tree_info":["tree_index":}#include "gbdt.h"#include #include #include #include #include #include #include #include #include #include namespace LightGBM {double (*PredictTreePtr[])(const double*) = { , PredictTree };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); ++early_stop_round_counter;if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))return; early_stop_round_counter = 0;void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map&) = { ByMapoutput[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);if (average_output_) {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*) = { Leafvoid 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&) = { LeafByMapvoid GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);} // namespace LightGBM#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=average_outputfeature_names=monotone_constraints=feature_infos=Tree=tree_sizes=end of treesfeature importances:= parameters:end of parametersfeature_namesmonotone_constraintsWrong line at model file: %sModel file doesn't specify the number of classesnum_tree_per_iterationlabel_indexModel file doesn't specify the label indexmax_feature_idxModel file doesn't specify max_feature_idxWrong size of feature_namesModel file doesn't contain feature_namesWrong size of monotone_constraintsfeature_infosWrong size of feature_infosModel file doesn't contain feature_infostree_sizesModel format error, expect a tree here. met %sparameters:Unknown importance type: only support split=0 and gain=1vvvv(@v@vv0vpvpvv`vMulticlass early stopping needs predictions to be of length two or largerBinary early stopping needs predictions to be of length oneUnknown early stopping type: ` y`PyP}py Qpd`@ e eN gp@h0iO @0D:\a\1\s\src\io\bin.cppCheck failed: max_bin > 0 at %s, line %d . Check failed: bin_upper_bound.size() <= static_cast(max_bin) at %s, line %d . Check failed: num_bin_ <= max_bin at %s, line %d . Met negative value in categorical features, will convert it to NaNMet categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zeroCheck failed: default_bin_ > 0 at %s, line %d . Using OrderedSparseBin->ConstructHistogram() instead`R$c $PP!`wdUfPgYX0wba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba ba M@@0@0 `wpw K`@0`L0`j`@Pk0lS05 %b$%$5*@,.1'@&`w@c8PSpE5bP5 6@5PEp:<>B86`wpbЁ`wY@`wpw UlPgXp`wpwvba ba ba ba ba `UiPgYnanullParameter %s should be of type int, got "%s"Do not support non-ascii characters in config.Unknown parameter %s gbrtrandom_forestregression_l2mean_squared_errormsel2l2_rootroot_mean_squared_errorrmsemean_absolute_errorl1maesoftmaxmulticlassovamulticlass_ovaovaovrxentropycross_entropyxentlambdacross_entropy_lambdamean_absolute_percentage_errorcustombinary_loglossndcglambdarankmapmean_average_precisionmulti_loglosskldivkullback_leiblertasktraintrainingpredictpredictiontestconvert_modelrefitrefit_treeUnknown task type %sdevice_typegpuUnknown device type %stree_learnerfeature_paralleldatadata_parallelvotingvoting_parallelUnknown tree learner type %sseedD:\a\1\s\src\io\config.cppCheck failed: valid.size() == valid_data_initscores.size() at %s, line %d . Number of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingmulti_errorMulticlass 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_leaves] [boosting: [objective: [metric: [tree_learner: [device_type: invalid stod argumentstod argument out of rangenaninfinfinityUnknown token %s in data fileParameter %s should be of type double, got "%s"-+Parameter %s should be "true"/"+" or "false"/"-", got "%s"config_filetask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalidvalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationsnum_iterationn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorslearning_rateshrinkage_rateetanum_leavesnum_leafmax_leavesmax_leaftree_typetree_learner_typenum_threadsnum_threadnthreadnthreadsn_jobsdevicerandom_seedrandom_statemin_data_in_leafmin_data_per_leafmin_datamin_child_samplesmin_sum_hessian_in_leafmin_sum_hessian_per_leafmin_sum_hessianmin_hessianmin_child_weightbagging_fractionsub_rowsubsamplebaggingpos_bagging_fractionpos_sub_rowpos_subsamplepos_baggingneg_bagging_fractionneg_sub_rowneg_subsampleneg_baggingbagging_freqsubsample_freqbagging_seedbagging_fraction_seedfeature_fractionsub_featurecolsample_bytreefeature_fraction_bynodesub_feature_bynodecolsample_bynodeearly_stopping_roundearly_stopping_roundsearly_stoppingn_iter_no_changemax_delta_stepmax_tree_outputmax_leaf_outputlambda_l1reg_alphalambda_l2reg_lambdalambdamin_gain_to_splitmin_split_gaindrop_raterate_droptop_ktopkmcmonotone_constraintfeature_contrifeature_contribfcfpfeature_penaltyforcedsplits_filenamefsforced_splits_filenameforced_splits_fileforced_splitsverbosityverbosebin_construct_sample_cntsubsample_for_binhistogram_pool_sizehist_pool_sizedata_random_seeddata_seedoutput_modelmodel_outputmodel_outsnapshot_freqsave_periodinput_modelmodel_inputmodel_inoutput_resultpredict_resultprediction_resultpredict_nameprediction_namepred_namename_predinitscore_filenameinit_score_filenameinit_score_fileinput_init_scorevalid_data_initscoresvalid_data_init_scoresvalid_init_score_filevalid_init_scorepre_partitionis_pre_partitionenable_bundleis_enable_bundlebundleis_enable_sparseis_sparseenable_sparsesparsetwo_roundtwo_round_loadinguse_two_round_loadingsave_binaryis_save_binaryis_save_binary_fileheaderhas_headerlabel_columnlabelweight_columnweightgroup_columngroup_idquery_columnquery_idignore_columnignore_featureblacklistcategorical_featurecat_featurecategorical_columncat_columnpredict_raw_scoreis_predict_raw_scorepredict_rawscoreraw_scorepredict_leaf_indexis_predict_leaf_indexleaf_indexpredict_contribis_predict_contribcontribconvert_model_filenum_classesis_unbalanceunbalanceunbalanced_setsmetricsmetric_typesmetric_freqoutput_freqis_provide_training_metrictraining_metricis_training_metrictrain_metriceval_atndcg_eval_atndcg_atmap_eval_atmap_atnum_machinesnum_machinelocal_listen_portlocal_portportmachine_list_filenamemachine_list_filemachine_listmlistmachinesworkersnodesmax_depthfeature_fraction_seedfirst_metric_onlymax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotforcedbins_filenamerefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledmax_binmax_bin_by_featuremin_data_in_binmax_conflict_ratesparse_thresholduse_missingzero_as_missingnum_iteration_predictpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languagescale_pos_weightsigmoidboost_from_averagereg_sqrtalphafair_cpoisson_max_delta_steptweedie_variance_powermax_positionlambdamart_normlabel_gainmulti_error_top_ktime_outgpu_platform_idgpu_device_idgpu_use_dpD:\a\1\s\src\io\config_auto.cppCheck failed: num_iterations >=0 at %s, line %d . Check failed: learning_rate >0.0 at %s, line %d . Check failed: num_leaves >1 at %s, line %d . Check failed: num_leaves <=131072 at %s, line %d . Check failed: min_data_in_leaf >=0 at %s, line %d . Check failed: min_sum_hessian_in_leaf >=0.0 at %s, line %d . Check failed: bagging_fraction >0.0 at %s, line %d . Check failed: bagging_fraction <=1.0 at %s, line %d . Check failed: pos_bagging_fraction >0.0 at %s, line %d . Check failed: pos_bagging_fraction <=1.0 at %s, line %d . Check failed: neg_bagging_fraction >0.0 at %s, line %d . Check failed: neg_bagging_fraction <=1.0 at %s, line %d . Check failed: feature_fraction >0.0 at %s, line %d . Check failed: feature_fraction <=1.0 at %s, line %d . Check failed: feature_fraction_bynode >0.0 at %s, line %d . Check failed: feature_fraction_bynode <=1.0 at %s, line %d . Check failed: lambda_l1 >=0.0 at %s, line %d . Check failed: lambda_l2 >=0.0 at %s, line %d . Check failed: min_gain_to_split >=0.0 at %s, line %d . Check failed: drop_rate >=0.0 at %s, line %d . Check failed: drop_rate <=1.0 at %s, line %d . Check failed: skip_drop >=0.0 at %s, line %d . Check failed: skip_drop <=1.0 at %s, line %d . Check failed: top_rate >=0.0 at %s, line %d . Check failed: top_rate <=1.0 at %s, line %d . Check failed: other_rate >=0.0 at %s, line %d . Check failed: other_rate <=1.0 at %s, line %d . Check failed: min_data_per_group >0 at %s, line %d . Check failed: max_cat_threshold >0 at %s, line %d . Check failed: cat_l2 >=0.0 at %s, line %d . Check failed: cat_smooth >=0.0 at %s, line %d . Check failed: max_cat_to_onehot >0 at %s, line %d . Check failed: top_k >0 at %s, line %d . Check failed: refit_decay_rate >=0.0 at %s, line %d . Check failed: refit_decay_rate <=1.0 at %s, line %d . Check failed: cegb_tradeoff >=0.0 at %s, line %d . Check failed: cegb_penalty_split >=0.0 at %s, line %d . Check failed: max_bin >1 at %s, line %d . Check failed: min_data_in_bin >0 at %s, line %d . Check failed: bin_construct_sample_cnt >0 at %s, line %d . Check failed: max_conflict_rate >=0.0 at %s, line %d . Check failed: max_conflict_rate <1.0 at %s, line %d . Check failed: sparse_threshold >0.0 at %s, line %d . Check failed: sparse_threshold <=1.0 at %s, line %d . Check failed: num_class >0 at %s, line %d . Check failed: scale_pos_weight >0.0 at %s, line %d . Check failed: sigmoid >0.0 at %s, line %d . Check failed: alpha >0.0 at %s, line %d . Check failed: fair_c >0.0 at %s, line %d . Check failed: poisson_max_delta_step >0.0 at %s, line %d . Check failed: tweedie_variance_power >=1.0 at %s, line %d . Check failed: tweedie_variance_power <2.0 at %s, line %d . Check failed: max_position >0 at %s, line %d . Check failed: metric_freq >0 at %s, line %d . Check failed: multi_error_top_k >0 at %s, line %d . Check failed: num_machines >0 at %s, line %d . Check failed: local_listen_port >0 at %s, line %d . Check failed: time_out >0 at %s, line %d . [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: [cegb_penalty_feature_coupled: [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: D:\a\1\s\include\LightGBM/feature_group.hCheck failed: static_cast(bin_mappers->size()) == num_feature at %s, line %d . ______LightGBM_Binary_File_Token______ nonameD:\a\1\s\src\io\dataset.cppCheck failed: num_data > 0 at %s, line %d . There are no meaningful features, as all feature values are constant.Check failed: static_cast(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d . Check failed: static_cast(num_total_features_) == io_config.feature_contri.size() at %s, line %d . 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 . 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.Cannot change forced bins after constructed Dataset handle.Check failed: num_used_indices == num_data_ at %s, line %d . targetweightsBianry file %s already exists.binFile %s exists, cannot save binary to itCannot write binary data to %s Saving data to binary file %swtnum_features: %d num_total_features: %d num_groups: %d num_data: %d feature_names: %s, monotone_constraints: %d, feature_penalty: %lf, max_bin_by_feature: forced_bins: feature %d: NA, Cannot add features from other Dataset with a different number of rowsinvalid vector subscriptP @  0 ` `  P p p  ` h  @!    @H0 0     p      @ @ `w0 name:Using column %s as labelCould not find label column %s in data file or data file doesn't contain headerlabel_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 labelCould not find ignore column %s in data fileignore_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column %s as weightCould not find weight column %s in data fileweight_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 weightUsing column %s as group/query idCould not find group/query column %s in data filegroup_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 idCould not find categorical_feature %s in data filecategorical_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 %sCould not read binary data from %sBinary file error: token has the wrong sizeInput file is not LightGBM binary fileBinary file error: header has the wrong sizeBinary file error: header is incorrectD:\a\1\s\src\io\dataset_loader.cppCheck failed: static_cast(dataset->num_total_features_) == config_.monotone_constraints.size() at %s, line %d . Check failed: static_cast(dataset->num_total_features_) == config_.feature_contri.size() at %s, line %d . Check failed: static_cast(dataset->num_total_features_) == config_.max_bin_by_feature.size() at %s, line %d . Check failed: *(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())) > 1 at %s, line %d . Binary file error: meta data has the wrong sizeBinary file error: meta data is incorrectCurrent query exceeds the range of the query file, please ensure the query file is correctBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %dColumn_Check failed: static_cast(num_col) == config_.max_bin_by_feature.size() at %s, line %d . The output cannot be monotone with respect to categorical featuresCheck failed: buffer_size >= 0 at %s, line %d . Data file %s is emptySize of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupQuery id exceeds the range of the query file, please ensure the query file is correctCheck 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 . Cannot open data file %sCould 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.bin_upper_boundhdfs://rbwbHDFS support is not enabled#P"8#ba ba "ba ba : %d%.17g\"\\\f\b\r\n\u%04x\t\u2029\u2028'%c' (%d)[Unexpected end of input after start of comment(%d)Malformed commentUnexpected end of input inside multi-line commentUnexpected end of input in stringUnexpected end of inputUnescaped in stringInvalid escape character Bad \u escape: in numberLeading 0s not permitted in numbersAt least one digit required in fractional partInvalid , got At least one digit required in exponentExceeded maximum nesting depthParse error: expected Expected ':' in object, got Expected '"' in object, got Expected ',' in list, got Expected ',' in object, got Unexpected trailing Expected value, got h)@_^^p\(v(()( #(+aP""@`@"v((((("'_p_`_P_(v((((("h'vPZ(v((((("0.Pkj`l@)kjY%@_^^p\(v((((( #)kjY$`\[ZZ(v(((((p#%`\[ZZ(v(()(p#+0` ``_("((((("'kjX%a!!a!!v((((("+kj0lp&_p_`_P_(v((((")ba ba ba ba (v(((((,kjY(kjYUsing query id in data file, ignoring the additional query fileUsing weights in data file, ignoring the additional weights fileWeights size doesn't match data sizeData partition error, data didn't match queriesInitial score size doesn't match data sizeQuery size doesn't match data sizelabel cannot be nullptrCannot used query_id for parallel trainingLength of weights is not same with #dataLength of label is not same with #data.weightSum of query counts is not same with #data.initLoading weights...Invalid initial score file. Redundant or insufficient columnsLoading initial scores...Loading query boundaries....queryLoading query weights...Input format error when parsing as TSVInput format error when parsing as CSVInput format error when parsing as LibSVMLabel should be the first column in a LibSVM fileData file %s couldn't be readData file %s doesn't existData file %s only has one lineData file %s should have at least one lineUnknown format of training dataData file %s doesn't contain a label column.p`@/p`/ba ba 0pPp1FFHpF4HH0HGp d'@Bʚ;p2FFHF83GG@HGp2@G@G`H0Gp 1GGpHFH1GGPHpGpp3@F@FH0Fnum_cat=num_leaves=split_gain=split_feature=decision_type=threshold=right_child=left_child=leaf_weight=leaf_value=internal_value=leaf_count=internal_count=internal_weight=cat_threshold=cat_boundaries="num_leaves":shrinkage="shrinkage":"num_cat":"tree_structure":{"leaf_value":"split_index":"tree_structure":"split_gain":"split_feature":"threshold":"||"threshold":"decision_type":"==","default_left":true,"decision_type":"<=","missing_type":"None","default_left":false,"missing_type":"NaN","missing_type":"Zero","internal_weight":"internal_value":"left_child":"internal_count":"leaf_index":"right_child":"leaf_count":"leaf_weight":if (fval <= ) { || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast(fval); }if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast(fval); }) && (((cat_threshold[if (int_fval >= 0 && int_fval < 32 * (double PredictTree + int_fval / 32] >> (int_fval & 31)) & 1))) {;(const double* arr) { const std::vector cat_threshold = {return double fval = 0.0f; }; }int int_fval = 0; ];(const std::unordered_map& arr) { } else { fval = arr[) > 0 ? arr.at() : 0.0f;Tree model should contain num_leaves fieldfval = arr.count(Tree model should contain num_cat fieldnum_catTree model string format error, should contain leaf_value fieldleaf_valueleft_childshrinkageright_childTree model string format error, should contain left_child fieldsplit_featureTree model string format error, should contain right_child fieldthresholdTree model string format error, should contain split_feature fieldsplit_gainTree model string format error, should contain threshold fieldinternal_valueinternal_countleaf_weightinternal_weightdecision_typeleaf_countTree model should contain cat_boundaries field.cat_boundariesTree model should contain cat_threshold fieldcat_thresholdD:\a\1\s\include\LightGBM/utils/common.h%gCheck failed: strs.size() == static_cast(n) at %s, line %d . Check failed: eval_at->at(i) > 0 at %s, line %d . D:\a\1\s\src\metric\dcg_calculator.cpplabel (%d) excel the max range %dlabel should be int type (met %f) for ranking task, for the gain of label, please set the label_gain parameter89]`] :`|`]~F`wPA0mpfjp]j5xPP;Pvpsp]s:xPX?yP@8`z0P`p;e]`]p]^07@yСPx=wШPh7`z0P` Ez PPPCxP5wPP@ xP@8Ђ5xPPx:`wP(<@yСPCz PP860n `]7]`] HGz`PF0n `]<` P@n`]@=\ba ba ba ba P<wШP(=\`]0xE\`]0@wPPG xP@PDyP@7n`]@9z`Paucbinary_errorFor MAP metric, there should be query informationmap@multi_error@Total groups: %d, total data: %dThe NDCG metric requires query informationndcg@fairhubergammapoissonCheck failed: label > 0 at %s, line %d . d:\a\1\s\src\metric\regression_metric.hpptweediegamma_deviancelabel_ Can't be NULL at %s, line %d . d:\a\1\s\src\metric\xentropy_metric.hpp[%s:%s]: (metric) labels passed interval [0, 1] checkInit[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: (metric) all weights must be positive[%s:%s]: sum-of-weights = %f%s offset term = %f[%s:%s]: (metric) at least one weight is negativeHP Socket construction errorSet SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at leastSet TCP_NODELAY failedSet SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at leastSocket error: Winsock.dll version errorSocket error: WSAStartup errorGetAdaptersinfo error: allocating memoryError code %d, when getting local host name0.0.0.0GetAdaptersinfo error: code %dSocket send error, code: %dSocket accept error, code: %dMachine list file doesn't contain the local machineSocket recv error, code: %dMachine list file %s doesn't existFinished linking network in %f secondsmachine_list size is larger than the parameter num_machines, ignoring redundant entriesrank=World size is larger than the machine_list size, change world size to %dCannot find any ip and port. Please check machine_list_filename or machines parameterBinding port %d succeededTrying to bind port %d...Listening...Binding port %d failedConnected to rank %dConnecting to rank %d failed, waiting for %d millisecondsLocal rank: %d, total number of machines: %dPlease initilize the network interface first`H N S @s s U  { s vaaS  T (Ns  q Pr r vv" r vaar  T  KP# ba ba ba vv" (vaa@# ba @I   P vv"  vaa`  K    Cannot set is_unbalance and scale_pos_weight at the same timeSigmoid parameter %f should be greater than zeroContains only one classStarting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true. This may cause significantly different results comparing to the previous versions of LightGBM. Try to set boost_from_average=false, if your old models produce bad resultsBoostFromScoreNumber of positive: %d, number of negative: %dsigmoid:[%s:%s]: pavg=%f -> initscore=%fLabel must be in [0, %d), but found %d in labelObjective should contain num_class fieldSigmoid param %f should be greater than zeronum_class:sqrtLambdarank tasks require query informationd:\a\1\s\src\objective\regression_objective.hpp sqrtCheck failed: threshold < weighted_cdf[pos] at %s, line %d . Check failed: threshold >= weighted_cdf[pos - 1] at %s, line %d . [%s]: at least one target label is negativeCannot use sqrt transform in %s Regression, will auto disable itCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . [%s]: sum of labels is zerod:\a\1\s\src\objective\xentropy_objective.hppMet 'abs(label) < 1', will convert them to '1' in MAPE objective and metric[%s]: at least one weight is negative[%s:%s]: (objective) labels passed interval [0, 1] check[%s:%s]: pavg = %f -> initscore = %f[%s]: sum of weights is zero[%s:%s]: min, max weights = %f, %f; ratio = %f[%s]: at least one weight is non-positiveUnknown objective type name: %s[%s:%s]: havg = %f -> initscore = %fJ@V S S S U v" U vaaS  T N. ) P+ , vv" + , aav`, - Po S po o vv" U vaaS  T XOP P p @ Os  q  Н vv" r vaar  T hI@@ < 0= = vv" @  @ ``v= P> 0Qpp S p `p vv" U vaaS  T HJN  A B M vv" (vaav@# M P0o S V  o U P^ V vaaS  T 0M `  М p p vaaS  T HK  Ф  vv"  vaa0 ` I@  q  0 vv" r vaar  T R`7 p2 4 `5 vv" 6  7 ``v@5 p5 Check failed: tree->num_leaves() <= data_partition_->num_leaves() at %s, line %d . d:\a\1\s\src\treelearner\serial_tree_learner.hGPU Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_GPU=1S @m r s u y w    y }   `    P R @ r @ u y w    y p   `     hT/ ( r s u y w    y `)   ` p,   P 'Forced Split' will be ignored since the gain getting worse. d:\a\1\s\src\treelearner\feature_histogram.hppInvalid categorical threshold splitTotal Bins %dCheck failed: cache_size_ >= 2 at %s, line %d . cegb_penalty_feature_coupled should be the same size as feature number.Check failed: offset == num_total_bin at %s, line %d . Number of data: %d, number of used features: %dcegb_penalty_feature_lazy should be the same size as feature number.Check failed: num_features_ == train_data_->num_features() at %s, line %d . D:\a\1\s\src\treelearner\serial_tree_learner.cppTrained a tree with leaves = %d and max_depth = %dNo further splits with positive gain, best gain: %fCheck failed: inner_feature_index >= 0 at %s, line %d . Check failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d . rightleftCheck failed: num_machines > 1 at %s, line %d . 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