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HHEH"H%HH@]HT$UH@HH$HH@]HT$UH@HHHHPHnH;uHAH nHHEH "H$HH@]HT$UH@HHW$HH@]H4H(HMHr7H (HHHrHIH'H+HHv@foH(H(H HtJHH+HHrLAH'I+HAHw?IԸWHfH *H PH(靸H(H HtJHH+HHrLAH'I+HAHw?IDWHvfvH W:H K0H( wH(H݋Hr7H HHHrHIH'H+HHv&谷fo@fqH(H\$WH L HT$0H MH PRH s^H GH3Ht)CuHHC u HHPHHt)CuHHC u HHPHHt'CuHH{ u HHPH\$8H _@SH HHt6H|$0CuHH{ u HHPH|$0H [H(H HtJHH+HHrLAH'I+HAHw#IWHfNH(CH(H EHtJHIH+HHrLAH'I+HAHw#I脵WHH(̋eH%X8H c@SH eH%X(HHHtHA)0HH [H [̋^eH%XH C@SH 8eH%X0HH Ht>HSH+HHrLAH'I+HAHwIq3HHCHCH [ @SH eH%XHHH Ht>HSH+HHrLAH'I+HAHwI3HHCHCH [H @SH 8eH%X`HH Ht:HSH+HrLAH'I+HAHwIu3HHCHCH [ H fԨ .FV|ک  <Pjj̪ЕޕJԖRԗJ0l4tR\ƜNBΞ:^ΠZء"H̢JΣܣ•:`ޓ@ntd@,ڔΔ.Hhīګ*$ڤΤ  to 9s N֦̦ަ6|>DVL0* ƥBz^`r$j^VLB8.TK`D t@0)>>>>?P??@@@  ` "й2    L "<`33 "й3 api-ms-win-core-synch-l1-2-0.dllkernel32.dllInitializeConditionVariableSleepConditionVariableCSWakeAllConditionVariable ` 0OA@HM @bad allocationM @bad array new lengthUnknown exceptionDebugInfoWarning[LightGBM] [Fatal] %s [LightGBM] [%s] '"Size of feature_names error, should equal with total number of featuresFind whitespaces in feature_names, replace with underlinesnoned:\a\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 . 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 long8a | ||ywNOPP@xwhZ[" " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " "  ^0y0y@ yxw(Pyy`yyw[{zzPzw]{{ {@{w([xxxxwX`@ЊxwRywp\yyyyw^0$  !!  !   % + 1 `VpywPO`{`{P{@{wT{{{@{wXyyxxwZ@|PA@^Y { {{yw@Xzzyyw8 8Q`y`y@PyxwPxxxww0`xxЈxxwa0ywPN{{{ywZ0z0z zywDon'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 . d```Pxwdyw8eyw d00` xw %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=%sconfigf PPW[0i 0}`p0>?@PAA`B0 `p@PT Tlnd:\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 . No object function providedCheck 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 %sXh `W[p 0 0}`p0>?@PAA`B0 `pЈPkT ln0gpPW[di 0}`p0>?@PAA`B0 `p@Pk Tlnbad 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 dataCheck 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 longPi@`@ В @@hp?PFP`PW[di 0}`p0>?@PAA`B0 `p@PkT TlniA@(i@j4falsetruev2{","name":""version":","num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":, "average_output":", "objective":""],",""feature_names":[""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=feature_infos=Tree=tree_sizes=end of treesfeature importances:= parameters:end of parametersfeature_namesWrong 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_namesfeature_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=1kpJpJK`JxwkJJJJxwkJJJJxwMulticlass early stopping needs predictions to be of length two or largerBinary early stopping needs predictions to be of length oneUnknown early stopping type: D:\a\1\s\src\io\bin.cppCheck failed: max_bin > 0 at %s, line %d . Check failed: right_max_bin > 0 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() insteadu!0`/P] p``]Kt#P1 2%rK" " " " " " " " " " " " " " " " PnЬpp@u LLKK8pPprPp4P]ФPPPP@5@m0".P @P] P `]Kxq" .P] p  `]KtLLK%n0aaKK@s#p6 2%uL`LKK@rPK" " " " "  oP#4 2%nP`M^a_]@`P]MW@TPRM Y[`]p]oP!/`@P]ФPPPPp0sp2p0PP]ФPPPPп`3`p  pnanullParameter %s should be of type int, got "%s"Unknown parameter %s gbrtrandom_foresttasktraintrainingpredictpredictiontestconvert_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 . multiclassovasoftmaxmulticlass_ovaovaovrcustomNumber of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingmulti_loglossmulti_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_rowsubsamplebaggingbagging_freqsubsample_freqbagging_seedbagging_fraction_seedfeature_fractionsub_featurecolsample_bytreeearly_stopping_roundearly_stopping_roundsearly_stoppingmax_delta_stepmax_tree_outputmax_leaf_outputlambda_l1reg_alphalambda_l2reg_lambdalambdamin_gain_to_splitmin_split_gaindrop_raterate_droptop_ktopkmonotone_constraintsmcmonotone_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_fileenable_load_from_binary_fileload_from_binary_filebinary_loadload_binaryheaderhas_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_seedmax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotrefit_decay_ratemax_binmin_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_positionlabel_gaintime_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: 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: feature_fraction >0.0 at %s, line %d . Check failed: feature_fraction <=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: 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: 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: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_seed: [early_stopping_round: [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: [refit_decay_rate: [verbosity: [max_bin: [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: [enable_load_from_binary_file: [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: [label_gain: [metric_freq: [is_provide_training_metric: [eval_at: [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 . Cannot change max_bin 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.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 %sname: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 . 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_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 %syddidywz0e0e`i eewxee0iexwxffgf@{wzee iexwwddPjdxw0w`f`f hPfewweeheeww0f0fg f@{wxpepeK`exwX}0qpp|4u@up}o" " |o" " rbhdfs://HDFS support is not enabledwb: %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 8pк@`]Jw P0~~pp~P]J  @~`]J  8`]ЈJ`]J  @~`]%`]~pк@`]J  P%`]~0`]J  0`]J0 w`P@`]~  @~`0%`]x0} ~@}}J  @~Є``]@`]Jw  `" " " " `]J  u%`]%`]Using weights in data file, ignoring the additional weights fileData partition error, data didn't match queriesUsing query id in data file, ignoring the additional query fileQuery size doesn't match data sizeWeights size doesn't match data sizeCannot used query_id for parallel trainingInitial score size doesn't match data sizeLength of label is not same with #datalabel cannot be nullptrSum of query counts is not same with #dataLength of weights is not same with #dataLoading weights....weightLoading initial scores....init.queryInvalid initial score file. Redundant or insufficient columnsLoading query weights...Loading query boundaries...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@`" "  ``PwwРwwhew  wwh@Пewnum_cat=num_leaves=split_gain=split_feature=decision_type=threshold=right_child=left_child=leaf_count=leaf_value=internal_count=internal_value=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_count":"internal_value":"right_child":"left_child":"leaf_count":"leaf_index":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_valueTree model string format error, should contain left_child fieldleft_childTree model string format error, should contain right_child fieldright_childTree model string format error, should contain split_feature fieldsplit_featureTree model string format error, should contain threshold fieldthresholdinternal_countsplit_gainleaf_countinternal_valuecat_boundariesdecision_typecat_thresholdTree model should contain cat_boundaries field.shrinkageTree model should contain cat_threshold fieldD:\a\1\s\include\LightGBM/utils/common.h%gCheck failed: strs.size() == static_cast(n) at %s, line %d .   0ewСww d'@Bʚ;`` PewD:\a\1\s\src\metric\dcg_calculator.cpplabel should be int type (met %f) for ranking task, for the gain of label, please set the label_gain parameterCheck failed: eval_at->at(i) > 0 at %s, line %d . label (%d) excel the max range %d8 0rmsel1l2fairhubergammapoissonCheck failed: label > 0 at %s, line %d . d:\a\1\s\src\metric\regression_metric.hpptweediegamma-deviancebinary_errorbinary_loglossndcg@aucmap@The NDCG metric requires query informationTotal groups: %d, total data: %dFor MAP metric, there should be query informationd:\a\1\s\src\metric\xentropy_metric.hppxentropyInitlabel_ Can't be NULL at %s, line %d . [%s:%s]: (metric) weights not allowed to be negative[%s:%s]: (metric) labels passed interval [0, 1] check[%s:%s]: sum-of-weights = %f[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) all weights must be positivexentlambda[%s:%s]: (metric) at least one weight is negativekldivregression_l2%s offset term = %fmsemean_squared_errorroot_mean_squared_errorl2_rootmaemean_absolute_errorlambdarankndcgmean_average_precisionmapcross_entropy_lambdacross_entropymean_absolute_percentage_errorkullback_leiblergamma_deviance(@ 0``кp 00@`0p00P0`@0@ 0p ` 0P@ 0X00 00` 0``P 0 x@0`00@0pP@0p00P00`P 0 ` 0кp 0X 0 0Ȗ0P 0`0" " " " x 00P 0 0`0@0px@ 0p БP 0`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 milliseconds0i0Please initilize the network interface firstLocal rank: %d, total number of machines: %d sqrtsqrtCheck failed: threshold >= weighted_cdf[pos - 1] at %s, line %d . d:\a\1\s\src\objective\regression_objective.hppCannot use sqrt transform in %s Regression, will auto disable itCheck failed: threshold < weighted_cdf[pos] at %s, line %d . [%s]: sum of labels is zero[%s]: at least one target label is negativeMet 'abs(label) < 1', will convert them to '1' in MAPE objective and metricCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . 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=%fLambdarank tasks require query informationSigmoid param %f should be greater than zeroLabel must be in [0, %d), but found %d in labelObjective should contain num_class fieldd:\a\1\s\src\objective\xentropy_objective.hppnum_class:[%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 = %f0pJЈJ00ЈЋ@   p JJ` ` Ј00J  Ч@0JJЈJ00ЈЋw w  w xw8p0JJ@ЈJ00Ј# P  @! JJ# # ЈJP! ! еJJЈJ00ЈЋ0  `  JJЈJ00JЇ 8pЈ@ЈJ00ЈЋ@@0ЈЈPPЈJ00ЈЋ- P) + P+ JJ, ЈJ00Ј`+ +     JJ  ЈJ  0@JJ@ЈJ00ЈHЈЈJ00ЈЋp0JJ@ЈJ00Јآ" " " JJЈJ00ЈЇ"  ) $ 0& & JJ ( ЈJ00Ј& &     xwЬ  ! " # `' %   0V S ' 0, / 0 1 M @4  0  !  # `' %   0V S @ 0,  0   @4  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=1Check failed: tree->num_leaves() <= data_partition_->num_leaves() at %s, line %d . 8P p ! " # `' %   0V S  0, / 0  M @4  Ȯ  ! " # `' %   0V S ' 0, / 0 1 M @4  x` " " " " " " " " " "     ywInvalid categorical threshold split'Forced Split' will be ignored since the gain getting worse. Check failed: cache_size_ >= 2 at %s, line %d . d:\a\1\s\src\treelearner\feature_histogram.hppCheck failed: offset == num_total_bin at %s, line %d . Total Bins %dD:\a\1\s\src\treelearner\serial_tree_learner.cppNumber of data: %d, number of used features: %dNo further splits with positive gain, best gain: %fCheck failed: num_features_ == train_data_->num_features() at %s, line %d . Check failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d . 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