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Cannot create file: () Please check that wether you need to basic_string::append/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/split_string.cc%s: __pos (which is %zu) > this->size() (which is %zu)delim != NULLresult != NULLbasic_string::substrSplitStringToSetUsingSplitStringUsingvector::_M_default_append/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/levenshtein_distance.cc!source.empty()!target.empty()!str.empty()!list.empty()ldistanceFindSimilarFind zD/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/file_util.hbuflenError: invoke fwrite().Error: invoke fread().CHECK_EQ failed ret!score_func.empty()!loss_func.empty()num_featurescalelinearUnknow score function: CHECK_NE failed truebias: i_v_filenameCannot open file: with mode: file_ptrError: invoke fclose().[ ERROR ] /home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/data/model_parameters.ccCannot allocate enough memory for current model parameters. Parameter size: Cannot Load model from the file: %sSetBestModelReadDataFromDiskReadStringFromFileDeserializeSerializeToTXTWriteDataToDiskWriteStringToFileOpenFileOrDieSerializeModelinitialInitialize????????????????/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/ReadDataFromDiskxlearn/src/data/model_parameters>0?std::future_error: /home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/loss/loss.ccCHECK_GE failed end_idxstart_idxpred.empty()pred.size()matrix->row_lengthsquaredcross-entropyvector::_M_fill_insertenqueue on stopped ThreadPoolvector::reservePredictpred_threadSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKN6xLearn7DMatrixEPNS9_5ModelEPSt6vectorIfSaIfEEPNS9_5ScoreEbmmESC_SE_SI_SK_bmmEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKN6xLearn7DMatrixEPNS9_5ModelEPSt6vectorIfSaIfEEPNS9_5ScoreEbmmESC_SE_SI_SK_bmmEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EENSt13__future_base13_State_baseV2ENSt13__future_base7_ResultIvEEN6xLearn4LossENSt13__future_base16_Task_state_baseIFvvEEESt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceISt13packaged_taskIFvvEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEZN10ThreadPool7enqueueISt5_BindIFPFvPKN6xLearn7DMatrixEPNS2_5ModelEPSt6vectorIfSaIfEEPNS2_5ScoreEbmmES5_S7_SB_SD_bmmEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSK_DpOSL_EUlvE_NSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKN6xLearn7DMatrixEPNS2_5ModelEPSt6vectorIfSaIfEEPNS2_5ScoreEbmmES5_S7_SB_SD_bmmEEvEESaIiEFvvEEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKN6xLearn7DMatrixEPNS3_5ModelEPSt6vectorIfSaIfEEPNS3_5ScoreEbmmES6_S8_SC_SE_bmmEEvEESaIiEFvvEEESL_LN9__gnu_cxx12_Lock_policyE2EE_/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/loss/squared_loss.ccstartlabel.empty()CalcGradsq_gradient_threadsq_evalute_threadEvaluteNSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKN6xLearn7DMatrixEPNS9_5ModelEPNS9_5ScoreEbPfjjESC_SE_SG_bSH_mmEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKN6xLearn7DMatrixEPNS9_5ModelEPNS9_5ScoreEbPfjjESC_SE_SG_bSH_mmEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PfmmESD_SD_SE_mmEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PfmmESD_SD_SE_mmEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEEN6xLearn11SquaredLossEZN10ThreadPool7enqueueISt5_BindIFPFvPKSt6vectorIfSaIfEES6_PfmmES6_S6_S7_mmEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSE_DpOSF_EUlvE_ZN10ThreadPool7enqueueISt5_BindIFPFvPKN6xLearn7DMatrixEPNS2_5ModelEPNS2_5ScoreEbPfjjES5_S7_S9_bSA_mmEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSH_DpOSI_EUlvE_NSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKSt6vectorIfSaIfEES6_PfmmES6_S6_S7_mmEEvEESaIiEFvvEEENSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKN6xLearn7DMatrixEPNS2_5ModelEPNS2_5ScoreEbPfjjES5_S7_S9_bSA_mmEEvEESaIiEFvvEEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKSt6vectorIfSaIfEES7_PfmmES7_S7_S8_mmEEvEESaIiEFvvEEESF_LN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKN6xLearn7DMatrixEPNS3_5ModelEPNS3_5ScoreEbPfjjES6_S8_SA_bSB_mmEEvEESaIiEFvvEEESI_LN9__gnu_cxx12_Lock_policyE2EE?/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/loss/cross_entropy_loss.ccce_gradient_threadCalcGradce_evalute_threadEvaluteNSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKN6xLearn7DMatrixEPNS9_5ModelEPNS9_5ScoreEbPfmmESC_SE_SG_bSH_jjEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKN6xLearn7DMatrixEPNS9_5ModelEPNS9_5ScoreEbPfmmESC_SE_SG_bSH_jjEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEEN6xLearn16CrossEntropyLossEZN10ThreadPool7enqueueISt5_BindIFPFvPKN6xLearn7DMatrixEPNS2_5ModelEPNS2_5ScoreEbPfmmES5_S7_S9_bSA_jjEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSH_DpOSI_EUlvE_NSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKN6xLearn7DMatrixEPNS2_5ModelEPNS2_5ScoreEbPfmmES5_S7_S9_bSA_jjEEvEESaIiEFvvEEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKN6xLearn7DMatrixEPNS3_5ModelEPNS3_5ScoreEbPfmmES6_S8_SA_bSB_jjEEvEESaIiEFvvEEESI_LN9__gnu_cxx12_Lock_policyE2EE/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/loss/metric.hpositive_vec.size()negative_vec.size()Y.size()accprecrecallf1maemapermsdaucauc_accum_threadAccumulateCalcAUCrmsd_accum_threadAccumulatemae_accum_threadAccumulatemae_accum_threadAccumulatef1_accum_threadAccumulaterecall_accum_threadAccumulateprec_accum_threadAccumulateacc_accum_threadAccumulate   NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PN6xLearn9AUCMetric4InfoEmmESD_SD_SH_mmEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PN6xLearn9AUCMetric4InfoEmmESD_SD_SH_mmEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PjSE_mmESD_SD_SE_SE_mmEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PjSE_mmESD_SD_SE_SE_mmEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PjmmESD_SD_SE_mmEEvEESaIiEFvvEE6_M_runEvEUlvE_vEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFS8_IFPFvPKSt6vectorIfSaIfEESD_PjmmESD_SD_SE_mmEEvEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEEN6xLearn6MetricEN6xLearn9AccMetricEN6xLearn10PrecMetricEN6xLearn12RecallMetricEN6xLearn8F1MetricEN6xLearn9AUCMetricEN6xLearn9MAEMetricEN6xLearn10MAPEMetricEN6xLearn10RMSDMetricEZN10ThreadPool7enqueueISt5_BindIFPFvPKSt6vectorIfSaIfEES6_PjmmES6_S6_S7_mmEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSE_DpOSF_EUlvE_ZN10ThreadPool7enqueueISt5_BindIFPFvPKSt6vectorIfSaIfEES6_PjS7_mmES6_S6_S7_S7_mmEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSE_DpOSF_EUlvE_ZN10ThreadPool7enqueueISt5_BindIFPFvPKSt6vectorIfSaIfEES6_PN6xLearn9AUCMetric4InfoEmmES6_S6_SA_mmEEJEEESt6futureINSt9result_ofIFT_DpT0_EE4typeEEOSH_DpOSI_EUlvE_NSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKSt6vectorIfSaIfEES6_PjmmES6_S6_S7_mmEEvEESaIiEFvvEEENSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKSt6vectorIfSaIfEES6_PjS7_mmES6_S6_S7_S7_mmEEvEESaIiEFvvEEENSt13__future_base11_Task_stateISt5_BindIFS1_IFPFvPKSt6vectorIfSaIfEES6_PN6xLearn9AUCMetric4InfoEmmES6_S6_SA_mmEEvEESaIiEFvvEEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKSt6vectorIfSaIfEES7_PjmmES7_S7_S8_mmEEvEESaIiEFvvEEESF_LN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKSt6vectorIfSaIfEES7_PjS8_mmES7_S7_S8_S8_mmEEvEESaIiEFvvEEESF_LN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFS2_IFPFvPKSt6vectorIfSaIfEES7_PN6xLearn9AUCMetric4InfoEmmES7_S7_SB_mmEEvEESaIiEFvvEEESI_LN9__gnu_cxx12_Lock_policyE2EE?;?@AQB?K$tIEncountered a too-long line. Please check the data./home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/reader/parser.cclibsvmlibffmcsvParseParseAddNodeParseN6xLearn6ParserEN6xLearn12LibsvmParserEN6xLearn9FFMParserEN6xLearn9CSVParserEget_line_from_buxlearn/src/reade/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/reader/file_splitor.ccnum_blocksError: invoke fseek().Error: invoke ftell().%s_%dw+map_ptr_readMAP_FAILEDmap_ptr_writeOpenFileOrDieGetFileSizesplitr/file_splitor.cFail to return to the head of file.fgets(line.get(), kMaxLineSize, file)/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/scoped_ptr.hEncountered a too-long line: Cannot find the '\n' char. Please check the data./home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/reader/reader.ccBinary file (%s.bin) found. Skip converting text to binary.Binary file (%s.bin) NOT found. Convert text file to binary file.Cannot set shuffle for OndiskReader.modestatic_cast(f.tellg())i >= 0 ,File format error!.binFile: doesn't exists.!filename.empty()memorydmatrix!vec.empty()row.size()norm.size()InitializeWriteDataToDiskWriteVectorToFileWriteVectorToFileSerializeReadVectorFromFileReadVectorFromFileDeserializeHashFileReadDataFromDiskInitializeCloseC& scoped_array::operator[](std::ptrdiff_t) const [with C = char; std::ptrdiff_t = long int]GetLineOpenFileOrDie ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,   :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: N6xLearn6ReaderEN6xLearn11InmemReaderEN6xLearn12OndiskReaderEN6xLearn12FromDMReaderEcheck_file_formaUnknow file formFirst check if the text file has been already converted to binarsgdadagradftrlUnknow optimization method: N6xLearn5ScoreEN6xLearn11LinearScoreE?xlearn/src/score/linear_score.ccN6xLearn7FMScoreEN6xLearn8FFMScoreE@ ---------------------------------------- Training task ------------------------------------- USAGE: xlearn_train [OPTIONS] e.g., xlearn_train train_data.txt -s 0 -v validate_data.txt -r 0.1 OPTIONS: -s : Type of machine learning model (default 0) for classification task: 0 -- linear model (GLM) 1 -- factorization machines (FM) 2 -- field-aware factorization machines (FFM) for regression task: 3 -- linear model (GLM) 4 -- factorization machines (FM) 5 -- field-aware factorization machines (FFM) -x : The metric can be 'acc', 'prec', 'recall', 'f1', 'auc' (classification), and 'mae', 'mape', 'rmsd (rmse)' (regression). On defaurt, xLearn will not print any evaluation metric information. -p : Choose the optimization method, including 'sgd', adagrad', and 'ftrl'. On default, we use the adagrad optimization. -v : Path of the validation data file. This option will be empty by default, and in this way, the xLearn will not perform validation. -m : Path of the model checkpoint file. On default, the model file name will be. set to 'train_file' + '.model'. If we set this value to 'none', the xLearn will not dump the model checkpoint after training. -pre : Path of the pre-trained model. This can be used for online learning. -t : Path of the txt model checkpoint file. On default, this option is empty and xLearn will not dump the txt model. -l : Path of the log file. Using '/tmp/xlearn_log/' by default. -k : Number of the latent factor used by fm and ffm tasks. Using 4 by default. Note that, we will get the same model size when setting k to 1 and 4. This is because we use SSE instruction and the memory need to be aligned. So even you assign k = 1, we still fill some dummy zeros from k = 2 to 4. -r : Learning rate for stochastic gradient descent. Using 0.2 by default. xLearn uses adaptive gradient descent (AdaGrad) for optimization problem, and the learning rate will be changed adaptively. -b : Lambda for L2 regular. Using 0.00002 by default. We can disable the regular term by setting this value to 0.0 -alpha : Used by ftrl. -beta : Used by ftrl. -lambda_1 : Used by ftrl. -lambda_2 : Used by ftrl. -u : Hyper parameter used for initialize model parameters. Using 0.66 by default. -e : Number of epoch for training. Using 10 by default. Note that, xLearn will perform early-stopping by default, so this value is just a upper bound. -f : Number of folds for cross-validation. Using 5 by default. -nthread : Number of thread for multi-thread training. -block : Block size fot on-disk training. -sw : Size of stop window for early-stopping. Using 2 by default. -seed : Random Seed to shuffle data set. --disk : Open on-disk training for large-scale machine learning problems. --cv : Open cross-validation in training tasks. If we use this option, xLearn will ignore the validation file (-t). --dis-lock-free : Disable lock-free training. Lock-free training can accelerate training but the result is non-deterministic. Our suggestion is that you can open this flag if the training data is big and sparse. --dis-es : Disable early-stopping in training. By default, xLearn will use early-stopping in training tasks, except for training in cross-validation. --no-norm : Disable instance-wise normalization. By default, xLearn will use instance-wise normalization for both training and prediction. --no-bin : Do not generate bin file for training and test data file. --quiet : Don't print any evaluation information during the training and just train the model quietly. ---------------------------------------------------------------------------------------------- -------------------------------------- Prediction task --------------------------------------- USAGE: xlearn_predict [OPTIONS] e.g., xlearn_predict ./test_data.txt ./model_file -o ./out.txt OPTIONS: -o : Path of the output file. On default, this value will be set to 'test_file' + '.out' -nthread : Number of thread for multi-thread learning. -l : Path of the log file. Using '/tmp/xlearn_log' by default. -block : Block size fot on-disk prediction. --sign : Converting output to 0 and 1. --sigmoid : Converting output to 0~1 (problebility). --disk : On-disk prediction. --no-norm : Disable instance-wise normalization. By default, xLearn will use instance-wise normalization for both training and prediction. ----------------------------------------------------------------------------------------------The --cv (cross-validation) has been set, and xLearn will ignore the validation file: %sValidation file not found, xLearn has already disable (-x %s) option.The -x: %s metric can only be used in classification tasks. xLearn will ignore this option.The -x: %s metric can only be used in regression tasks. xLearn will ignore this option.Training data file: %s does not exist.Validation data file: %s does not exist.Training dataset is None, please check!The thread number must be greater than zero: %d.The task can only be 'binary' or 'reg'.Unknow optimization method: %s.Invalid size of K: %d. Size of K must be greater than zero.Invalid size of folds: %d. Size of folds must be greater than zero.Invalid number of epoch: %d. Number of epoch must be greater than zero.Test set file: %s does not exist.Test dataset is None, please check!Model file: %s does not exist.-s can only be [0 - 5] : for classification task: 0 -- linear model (GLM) 1 -- factorization machines (FM) 2 -- field-aware factorization machines (FFM) for regression task: 3 -- linear model (GLM) 4 -- factorization machines (FM) 5 -- field-aware factorization machines (FFM)Unknow metric: %s -x can only be: acc prec recall f1 auc mae mape rmsd rmse noneUnknow optimization method: %s -o can only be: adagrad and ftrl. Validation set file: %s dose not exists.Illegal -k '%i'. -k must be geater than zero.Illegal -r : '%f'. -r must be greater than zero.Illegal -b : '%f' -b must be greater than or equal to zero.Illegal -u : '%f'. -u must be greater than zero.Illegal -e : '%i'. -e must be greater than zero.Illegal -f : '%i'. -f must be greater than zero.Illegal -nthread : '%i'. -nthread must be greater than zero.Illegal -block : '%i'. -block must be greater than zero.Illegal -sw : '%i'. -sw must be greater than or equal to 1.Illegal -seed : '%i'. -seed must be greater than or equal to 1.Illegal -alpha : '%f'. -alpha must be greater than zero.Illegal -beta : '%f'. -beta cannot be less than zero.Illegal -lambda_1 : '%f'. -lambda_1 cannot be less than zero.Illegal -lambda_2 : '%f'. -lambda_2 cannot be less than zero.Unknow argument '%s'. Do you mean '%s' ?[ WARNING ] nonermseunknowUnknow evaluation metric: %s..model.out-s-x-p-v-m-t-l-k-r-b-u-e-f-pre-nthread-block-sw-seed--disk--cv--dis-lock-free--dis-es--no-norm--no-bin--quiet-alpha-beta-lambda_1-lambda_2-o--sign--sigmoiddccxc0c(dTransform DMatrix not from file doesn't support cross-validation. xLearn has already disable theOn-disk training doesn't support cross-validation. xLearn has already disable thCross-validation early-stopping. xLearn has already close early-The --cv (cross-validation) has been set, and xLearn will ignore the --quiet optearn will not dump model checkpoValidation file(dataset) not found, xLearn has already disable eBoth of --sign and --sigmoid have been set. xLearn has already disable --sign anThe test file and model file musAverage %s: %.6freader.empty()Train Test [------------]%.2f[ % ]reader_list.empty()AccuarcyPrecisionRecallF1AUCMAEMAPERMSD[ ACTION ] Cross-validation: %d/%lu:/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/solver/trainer.ccEarly-stopping at epoch %d, best %s: %f/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/format_print.hcolumn.size()width.size()calc_metriccalc_gradientprint_rowY@/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/solver/inference.ccin.size()out.size()vector::_M_range_insertThe test loss is: %.6fsignsigmoid????????????????/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/base/system.hlocaltime_r(¤t_time, &broken_down_time) == &broken_down_timeTime cost for saving model: %.2f (sec)Time cost for saving txt model: %.2f (sec)/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/solver/inference.hClear the xLearn environment ...xLearn uses %i threads for training task./home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/solver/solver.cchyper_param_.train_set_file.empty()Cannot open initialze dataset %dFeature index is too large (overflow).Time cost for reading problem: %.2f (sec)Time cost for model initial: %.2f (sec)/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/loss/loss.hxLearn uses %i threads for prediction task.hyper_param_.model_file.empty()Time cost for loading model: %.2f (sec)hyper_param_.test_set_file.empty()USERUSERNAME%04d%02d%02d-%02d%02d%02d!file_base.empty()%s.%s.%s.%s.%uCannot create reader: Cannot create score: Cannot create loss: _train%s.ERROR_predict%s.WARN%s.INFOfilename.compare("none")TXT Model file: %sreaderout.empty()Start training work.Start inference work.Start to init Readerhyper_param_.num_foldsSplit file into parts.Number of Reader: Cannot open the file %sInit Reader: hyper_param_.train_datasetNumber of feature: Number of Feature: %dNumber of field: Number of Field: %dNumber parameters: %.2f GB%.2f MB%.2f KBModel size: %sInitialize score function.scorepoolInitialize loss function.Initialize evaluation metric.Load model from %sLoss function: %sScore function: %sNumber of K: %dNumber of field: %dInitialize model.hyper_param_.test_datasetInitialize Reader: /home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/reader/reader.h/home/runner/work/ml-test-builds/ml-test-builds/xlearn/src/solver/trainer.hInitializeSaveTxtModelSaveModelInitializeinit_predictInitializeInitializeSetBlockSizeinit_trainget_log_fileprint_current_timeNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN10ThreadPoolC4EmEUlvE_EEEEEExlearn/src/solveStart to train .Finish Cross-ValStart to save moStart to save txStart to predictClear the xLearn environment ...Read Problem ...Feature index is too large (overInitialize model>>P?; (H8pTX2h9^Y 0r`rPvd(X($X@#/ (8!$=H!SH$X%Y%@fx'f((Di)j)l*n8*rh*8v*dw*+.6d:::<48=\x??(BB<8EEGXHhLpN8T(ZPH[hefgh(j<HkPld8nX`htxXhLtH 8l8Ș`تXdh<X8xHH  x!T!x!!!h@"(""x %X*$*,$*h'h+'H.4(X/-5`/; #;4#;H#<\#($(>$H>%H?8%H@l%8B%xB%B%HC&DX&G&8H&H'8I('xK'L(L(XM(M(P,)Y)c*fh*g*Hh*i +j`+k+l+hn\,Hq,u0-x-(.Ȁt.. /L2H2ؓ67(/80H0X,0@0ءT0h0X|0h0x00001( 1(T1(1(1(2X2H$3x83x333( 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