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exception;unknown;unknown;0;0;;The 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 . /Users/runner/runners/2.160.1/work/1/s/src/c_api.cppCheck failed: num_used_row_indices > 0 at %s, line %d . Used indices of subsetused_row_indices should be sorted in 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 matrixregressiongbdtserialcpuLightGBM_model.txtLightGBM_predict_result.txtgbdt_prediction.cppunordered_map::at: key not foundWarning[LightGBM] [%s] [LightGBM] [Fatal] %s Size of feature_names error, should equal with total number of featuresDo not support non-ASCII characters in feature name.Do not support special JSON characters in feature name.Find whitespaces in feature_names, replace with underlinesContinued train from model is not supported for c_api, please use continued train with input scorefeatureDo not support feature parallel in c apiOnly 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 trainingobjectivenoneCheck failed: early_stop_freq > 0 at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/application/predictor.hppCheck failed: early_stop_margin >= 0 at %s, line %d . binarymulticlassPrediction results file %s cannot be foundCould not recognize the data format of data file %sThe number of features in data (%d) is not the same as it was in training data (%d).Could not open %sSkipped header "%s" in file %sDebug Warning: last line of %s has no end of line, still using this linethread constructor failedInfo[%s]: does not tolerate element [#%i = ] outside [, ]Don't support large string in R-packageCheck failed: len == out_len at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/lightgbm_R.cppgroupqueryinit_scoreCheck failed: out_len == len at %s, line %d . No training/prediction data, application quitconfigConfig file %s doesn't exist, will ignoreFinished loading parametersFinished loading data in %f secondsFinished initializing networkFinished initializing trainingStarted training...cppFinished trainingFinished RefitTreeFinished predictionFinished initializing prediction, total used %d iterations%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=%sTime for loading model: %f secondsdartgossrftreeUnknown boosting type %sUnknown model format or submodel type in model file %s/dev/urandomCheck failed: data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size()) at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/boosting/gbdt.hCheck failed: tree_idx >= 0 && static_cast(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . Check failed: config_->top_rate + config_->other_rate <= 1.0f at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/boosting/goss.hppCheck failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . Cannot use bagging in GOSSUsing GOSSCheck failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/boosting/rf.hppCheck 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 . 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.Check failed: train_data != nullptr at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/boosting/gbdt.cppCannot 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: %sIteration:, training : , 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 baggingNumber of class for initial score error:Check failed: max_depth_ >= 0 at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/include/LightGBM/tree.hr{"name":"","version":""num_class":,"num_tree_per_iteration":"label_index":"max_feature_idx":"average_output":truefalse, "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=1v3regression_l2mean_squared_errormsel2l2_rootroot_mean_squared_errorrmsemean_absolute_errorl1maesoftmaxmulticlassovamulticlass_ovaovaovrxentropycross_entropyxentlambdacross_entropy_lambdamean_absolute_percentage_errornullcustomnawwbUnknown early stopping type: Multiclass early stopping needs predictions to be of length two or largerBinary early stopping needs predictions to be of length oneUsing OrderedSparseBin->ConstructHistogram() insteadCheck failed: max_bin > 0 at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/io/bin.cppCheck 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 . Do not support non-ASCII characters in config.Unknown parameter %sgbrtrandom_foresttasktraintrainingpredictpredictiontestconvert_modelrefitrefit_treeUnknown task type %sdevice_typegpuUnknown device type %stree_learnerfeature_paralleldatadata_parallelvotingvoting_parallelUnknown tree learner type %sseedCheck failed: valid.size() == valid_data_initscores.size() at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/io/config.cppNumber of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass 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: binary_loglossndcglambdarankmapmean_average_precisionkldivkullback_leiblerParameter %s should be of type int, got "%s"validnum_iterationsCheck failed: num_iterations >=0 at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/io/config_auto.cpplearning_rateCheck failed: learning_rate >0.0 at %s, line %d . num_leavesCheck failed: num_leaves >1 at %s, line %d . Check failed: num_leaves <=131072 at %s, line %d . num_threadsmax_depthmin_data_in_leafCheck failed: min_data_in_leaf >=0 at %s, line %d . min_sum_hessian_in_leafCheck failed: min_sum_hessian_in_leaf >=0.0 at %s, line %d . bagging_fractionCheck failed: bagging_fraction >0.0 at %s, line %d . Check failed: bagging_fraction <=1.0 at %s, line %d . pos_bagging_fractionCheck failed: pos_bagging_fraction >0.0 at %s, line %d . Check failed: pos_bagging_fraction <=1.0 at %s, line %d . neg_bagging_fractionCheck failed: neg_bagging_fraction >0.0 at %s, line %d . Check failed: neg_bagging_fraction <=1.0 at %s, line %d . bagging_freqbagging_seedfeature_fractionCheck failed: feature_fraction >0.0 at %s, line %d . Check failed: feature_fraction <=1.0 at %s, line %d . feature_fraction_bynodeCheck failed: feature_fraction_bynode >0.0 at %s, line %d . Check failed: feature_fraction_bynode <=1.0 at %s, line %d . feature_fraction_seedearly_stopping_roundfirst_metric_onlymax_delta_steplambda_l1Check failed: lambda_l1 >=0.0 at %s, line %d . lambda_l2Check failed: lambda_l2 >=0.0 at %s, line %d . min_gain_to_splitCheck failed: min_gain_to_split >=0.0 at %s, line %d . drop_rateCheck failed: drop_rate >=0.0 at %s, line %d . Check failed: drop_rate <=1.0 at %s, line %d . max_dropskip_dropCheck failed: skip_drop >=0.0 at %s, line %d . Check failed: skip_drop <=1.0 at %s, line %d . xgboost_dart_modeuniform_dropdrop_seedtop_rateCheck failed: top_rate >=0.0 at %s, line %d . Check failed: top_rate <=1.0 at %s, line %d . other_rateCheck failed: other_rate >=0.0 at %s, line %d . Check failed: other_rate <=1.0 at %s, line %d . min_data_per_groupCheck failed: min_data_per_group >0 at %s, line %d . max_cat_thresholdCheck failed: max_cat_threshold >0 at %s, line %d . cat_l2Check failed: cat_l2 >=0.0 at %s, line %d . cat_smoothCheck failed: cat_smooth >=0.0 at %s, line %d . max_cat_to_onehotCheck failed: max_cat_to_onehot >0 at %s, line %d . top_kCheck failed: top_k >0 at %s, line %d . feature_contriforcedsplits_filenameforcedbins_filenamerefit_decay_rateCheck failed: refit_decay_rate >=0.0 at %s, line %d . Check failed: refit_decay_rate <=1.0 at %s, line %d . cegb_tradeoffCheck failed: cegb_tradeoff >=0.0 at %s, line %d . cegb_penalty_splitCheck failed: cegb_penalty_split >=0.0 at %s, line %d . cegb_penalty_feature_lazycegb_penalty_feature_coupledverbositymax_binCheck failed: max_bin >1 at %s, line %d . max_bin_by_featuremin_data_in_binCheck failed: min_data_in_bin >0 at %s, line %d . bin_construct_sample_cntCheck failed: bin_construct_sample_cnt >0 at %s, line %d . histogram_pool_sizedata_random_seedoutput_modelsnapshot_freqinput_modeloutput_resultinitscore_filenamevalid_data_initscorespre_partitionenable_bundlemax_conflict_rateCheck failed: max_conflict_rate >=0.0 at %s, line %d . Check failed: max_conflict_rate <1.0 at %s, line %d . is_enable_sparsesparse_thresholdCheck failed: sparse_threshold >0.0 at %s, line %d . Check failed: sparse_threshold <=1.0 at %s, line %d . use_missingzero_as_missingtwo_roundsave_binaryheaderlabel_columnweight_columngroup_columnignore_columncategorical_featurepredict_raw_scorepredict_leaf_indexpredict_contribnum_iteration_predictpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageCheck failed: num_class >0 at %s, line %d . is_unbalancescale_pos_weightCheck failed: scale_pos_weight >0.0 at %s, line %d . sigmoidCheck failed: sigmoid >0.0 at %s, line %d . boost_from_averagereg_sqrtalphaCheck failed: alpha >0.0 at %s, line %d . fair_cCheck failed: fair_c >0.0 at %s, line %d . poisson_max_delta_stepCheck failed: poisson_max_delta_step >0.0 at %s, line %d . tweedie_variance_powerCheck failed: tweedie_variance_power >=1.0 at %s, line %d . Check failed: tweedie_variance_power <2.0 at %s, line %d . max_positionCheck failed: max_position >0 at %s, line %d . lambdamart_normlabel_gainmetric_freqCheck failed: metric_freq >0 at %s, line %d . is_provide_training_metriceval_atmulti_error_top_kCheck failed: multi_error_top_k >0 at %s, line %d . num_machinesCheck failed: num_machines >0 at %s, line %d . local_listen_portCheck failed: local_listen_port >0 at %s, line %d . time_outCheck failed: time_out >0 at %s, line %d . machine_list_filenamemachinesgpu_platform_idgpu_device_idgpu_use_dp[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [pos_bagging_fraction: [neg_bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_bynode: [feature_fraction_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [min_gain_to_split: [drop_rate: [max_drop: [skip_drop: [xgboost_dart_mode: [uniform_drop: [drop_seed: [top_rate: [other_rate: [min_data_per_group: [max_cat_threshold: [cat_l2: [cat_smooth: [max_cat_to_onehot: [top_k: [monotone_constraints: [feature_contri: [forcedsplits_filename: [forcedbins_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [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: config_filetask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorsshrinkage_rateetanum_leafmax_leavesmax_leaftree_typetree_learner_typenum_threadnthreadnthreadsn_jobsdevicerandom_seedrandom_statemin_data_per_leafmin_datamin_child_samplesmin_sum_hessian_per_leafmin_sum_hessianmin_hessianmin_child_weightsub_rowsubsamplebaggingpos_sub_rowpos_subsamplepos_baggingneg_sub_rowneg_subsampleneg_baggingsubsample_freqbagging_fraction_seedsub_featurecolsample_bytreesub_feature_bynodecolsample_bynodeearly_stopping_roundsearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphareg_lambdalambdamin_split_gainrate_droptopkmcmonotone_constraintfeature_contribfcfpfeature_penaltyfsforced_splits_filenameforced_splits_fileforced_splitsverbosesubsample_for_binhist_pool_sizedata_seedmodel_outputmodel_outsave_periodmodel_inputmodel_inpredict_resultprediction_resultpredict_nameprediction_namepred_namename_predinit_score_filenameinit_score_fileinput_init_scorevalid_data_init_scoresvalid_init_score_filevalid_init_scoreis_pre_partitionis_enable_bundlebundleis_sparseenable_sparsesparsetwo_round_loadinguse_two_round_loadingis_save_binaryis_save_binary_filehas_headerlabelweightgroup_idquery_columnquery_idignore_featureblacklistcat_featurecategorical_columncat_columnis_predict_raw_scorepredict_rawscoreraw_scoreis_predict_leaf_indexleaf_indexis_predict_contribcontribconvert_model_filenum_classesunbalanceunbalanced_setsmetricsmetric_typesoutput_freqtraining_metricis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinelocal_portportmachine_list_filemachine_listmlistworkersnodesParameter %s should be of type double, got "%s"naninfinfinityUnknown token %s in data file-+Parameter %s should be "true"/"+" or "false"/"-", got "%s"nonameCheck failed: num_data > 0 at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/io/dataset.cppCheck failed: num_total_features_ == static_cast(bin_mappers->size()) at %s, line %d . There are no meaningful features, as all feature values are constant.Check failed: static_cast(num_total_features_) == io_config.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 rows______LightGBM_Binary_File_Token______ Check failed: static_cast(bin_mappers->size()) == num_feature at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/include/LightGBM/feature_group.hname: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 incorrectCheck failed: static_cast(dataset->num_total_features_) == config_.monotone_constraints.size() at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/io/dataset_loader.cppCheck 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 featuresData file %s is emptySize of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupCheck 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 . 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_boundQuery id exceeds the range of the query file, please ensure the query file is correctHDFS support is not enabledrbhdfs://Unexpected trailing Expected JSON object, got Bad type for in Exceeded maximum nesting depthExpected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected end of inputLeading 0s not permitted in numbersInvalid in numberAt least one digit required in fractional partAt least one digit required in exponentexpect/Users/runner/runners/2.160.1/work/1/s/src/io/json11.cppi != 0Parse error: expected , got Unexpected end of input in stringUnescaped in stringBad \u escape: Invalid escape character Unexpected end of input after start of commentUnexpected end of input inside multi-line commentMalformed comment'%c' (%d)(%d)%.17g%d\\\"\b\f\n\r\t\u%04x\u2028\u2029[: Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query fileData partition error, data didn't match queriesWeights size doesn't match data sizeQuery size doesn't match data sizeInitial score size doesn't match data sizeCannot used query_id for parallel traininglabel cannot be nullptrLength of label is not same with #dataLength of weights is not same with #dataSum of query counts is not same with #data.weightLoading weights....initLoading initial scores...Invalid initial score file. Redundant or insufficient columns.queryLoading query boundaries...Loading query weights...Data file %s doesn't exist.Data file %s couldn't be read.Data file %s should have at least one line.Data file %s only has one line.Unknown format of training data.Data file %s doesn't contain a label column.Label should be the first column in a LibSVM fileInput format error when parsing as LibSVMInput format error when parsing as TSVInput format error when parsing as CSVnum_leaves=num_cat=split_feature=split_gain=threshold=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=shrinkage="num_leaves":"num_cat":"shrinkage":"tree_structure":{"leaf_value":"tree_structure":"split_index":"split_feature":"split_gain":"threshold":"||"decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","missing_type":"Zero","missing_type":"NaN","internal_value":"internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_weight":"leaf_count":if (fval <= ) { || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast(fval); }if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast(fval); }if (int_fval >= 0 && int_fval < 32 * () && (((cat_threshold[ + int_fval / 32] >> (int_fval & 31)) & 1))) {double PredictTree(const double* arr) { return ;const std::vector cat_threshold = {};double fval = 0.0f; int int_fval = 0; }(const std::unordered_map& arr) { fval = arr[]; } else { fval = arr.count() > 0 ? arr.at() : 0.0f;Tree model should contain num_leaves fieldnum_catTree model should contain num_cat fieldleaf_valueTree model string format error, should contain leaf_value fieldshrinkageleft_childTree model string format error, should contain left_child fieldright_childTree model string format error, should contain right_child fieldsplit_featureTree model string format error, should contain split_feature fieldthresholdTree model string format error, should contain threshold fieldsplit_gaininternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typecat_boundariesTree model should contain cat_boundaries field.cat_thresholdTree model should contain cat_threshold field%gCheck failed: strs.size() == static_cast(n) at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/include/LightGBM/utils/common.hCheck failed: eval_at->at(i) > 0 at %s, line %d . /Users/runner/runners/2.160.1/work/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 parameterlabel (%d) excel the max range %dhuberfairpoissonbinary_erroraucgammagamma_deviancetweediendcg@The NDCG metric requires query informationmap@For MAP metric, there should be query informationTotal groups: %d, total data: %dmulti_error@label_ Can't be NULL at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/metric/xentropy_metric.hpp[%s:%s]: (metric) labels passed interval [0, 1] checkInit[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: sum-of-weights = %f[%s:%s]: (metric) all weights must be positive[%s:%s]: (metric) at least one weight is negative%s offset term = %fCheck failed: label > 0 at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/metric/regression_metric.hppMachine list file doesn't contain the local machineFinished linking network in %f secondsMachine 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 %dTrying to bind port %d...Binding port %d succeededBinding port %d failedListening...Connecting to rank %d failed, waiting for %d millisecondsConnected to rank %dSocket construction errorSet 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 leastSet TCP_NODELAY failed0.0.0.0Socket accept error, code: %dSocket recv error, code: %dSocket send error, code: %dLocal rank: %d, total number of machines: %dPlease initilize the network interface firstUnknown objective type name: %s sqrtCheck failed: threshold >= weighted_cdf[pos - 1] at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/objective/regression_objective.hppCheck failed: threshold < weighted_cdf[pos] at %s, line %d . Check failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . Cannot use sqrt transform in %s Regression, will auto disable it[%s]: at least one target label is negative[%s]: sum of labels is zeroSigmoid parameter %f should be greater than zeroCannot set is_unbalance and scale_pos_weight at the same timeStarting 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 resultsContains only one classNumber of positive: %d, number of negative: %d[%s:%s]: pavg=%f -> initscore=%fBoostFromScoresigmoid:Sigmoid param %f should be greater than zeroLambdarank tasks require query informationLabel must be in [0, %d), but found %d in labelnum_class:/Users/runner/runners/2.160.1/work/1/s/src/objective/xentropy_objective.hpp[%s:%s]: (objective) labels passed interval [0, 1] check[%s]: at least one weight is negative[%s]: sum of weights is zero[%s:%s]: pavg = %f -> initscore = %f[%s]: at least one weight is non-positive[%s:%s]: min, max weights = %f, %f; ratio = %f[%s:%s]: havg = %f -> initscore = %fMet 'abs(label) < 1', will convert them to '1' in MAPE objective and metricsqrtObjective should contain num_class fieldGPU 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 . /Users/runner/runners/2.160.1/work/1/s/src/treelearner/serial_tree_learner.hNumber of data: %d, number of used features: %dCheck failed: num_features_ == train_data_->num_features() at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/treelearner/serial_tree_learner.cppNo further splits with positive gain, best gain: %fTrained a tree with leaves = %d and max_depth = %dCheck failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d . Check failed: inner_feature_index >= 0 at %s, line %d . leftrightCheck failed: bag_cnt == num_data_ at %s, line %d . Check failed: num_machines > 1 at %s, line %d . Total Bins %dCheck failed: offset == num_total_bin at %s, line %d . /Users/runner/runners/2.160.1/work/1/s/src/treelearner/feature_histogram.hppCheck failed: cache_size_ >= 2 at %s, line %d . cegb_penalty_feature_coupled should be the same size as feature number.cegb_penalty_feature_lazy should be the same size as feature number.'Forced Split' will be ignored since the gain getting worse. Invalid categorical threshold split HLXQX! Q!Q aQaQ %(pP!h ( %'`('`+`(,(0)0)@5p*8+9D+@=,R$.V.Z@/Pk 1l\1o2P3 4`6p67l8P8@H909 ::@8;P;@<p|<<P|==d>>@H?0??`@8AAB`\BBDCC DDDPXEE(FFF`GGXHHHI J J|KXLL4MM`DNNHO@OPP Pp \Q R lRR0Sp@S #hTP$T'T0PU2U7U`HU0J(VU W ^LX^XP`Xpbo0?oA@p0DpGpJpQqTq fTsjs sttttPvtP|LuLvЋv v@(w\wxxy`yPyPy(zdzzP<|h||}0X}h"3Ё@>IxPJLR P[^ ephPkHm|s} БP ԉP<@h@p؊P@@<hp 4P4@"P%Г`(0M4Plopr slvp|(\@Ĝ,LlP ؝`DPd О @P`|`pT$ 0`D`"x&,@@-l 0̣P2:, BXPGPJNT@U@X[4`cpfPn̦qx(@zH|h }@ħЉh lPD p4|ج,0-8@;X`<x D0Lx Q̮PUd]^DpePPܲp<`4PT00\@lp`dp$`@`;`>A `EKQTмPp PT` @ < P!0#& p+.1\3>, Dt`N `PRTpX,P]t_PilH0o{0\`|0 l``pP @ P ` 7 ; R PP_ \@d i k l 4`q @v dy  P hP  В p P `  \   40 L d@    ` 4 `     4 Pp `     P H   P  l p   x  % Hp( 7 ; 0? C PG H S H V `W Y _ p`b Pc e ,f hh  o t  x | І l  @  x  0      h 4 ` ! ! &" '- |'`; '; '> (C (pG P)K )O * T p*X *\ 0+c +e +g +j ,Й H, - . 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