vendor/lib_lightgbm.so in lightgbm-0.1.9 vs vendor/lib_lightgbm.so in lightgbm-0.2.0

- old
+ new

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tureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE0_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE1_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZNSt14_Function_base13_Base_managerIZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEE2_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation_ZTIZN8LightGBM16Featu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mricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2__ZN8LightGBM25VotingParallelTreeLearnerINS_14GPUTreeLearnerEEC1EPKNS_6Conf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RowFunctionFromCSRCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . -Unknown data type in CSC matrixSize of feature_names error, should equal with total number of featuresFind whitespaces in feature_names, replace with underlinesDo not support non-ASCII characters in feature name.Do not support special JSON characters in feature name./__w/1/s/src/application/predictor.hppCheck failed: early_stop_freq > 0 at %s, line %d . -Check failed: early_stop_margin >= 0 at %s, line %d . -The number of features in data (%d) is not the same as it was in training data (%d).Skipped header "%s" in file %sCheck failed: num_used_row_indices > 0 at %s, line %d . -used_row_indices should be sorted in SubsetThe number of columns should be greater than zero.The number of columns should be smaller than INT32_MAX.Check failed: inner_data.first < num_col at %s, line %d . -Using self-defined objective functionContinued train from model is not supported for c_api, -please use continued train with input scoreDo not support feature parallel in c apiOnly find one worker, will switch to serial tree learnerWarning: last line of %s has no end of line, still using this linePrediction results file %s cannot be foundCould not recognize the data format of data file %sCannot change num_class during trainingCannot change boosting during trainingCannot change metric during 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support large string in R-packageCheck failed: out_len == len at %s, line %d . -Check failed: len == out_len at %s, line %d . -basic_string::_S_construct null not validStarted training...Finished trainingFinished initializing networkFinished RefitTreeFinished predictionUnknown parameter: %sconfig# - Finished loading parameters%s is set=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%sFinished initializing prediction, total used %d iterationsFinished loading data in %f secondsFinished initializing trainingConfig file %s doesn't exist, will ignoreNo training/prediction data, application quitZN8LightGBM10TextReaderImE12ReadAllLinesEvEUlmPKcmE_ZN8LightGBM10TextReaderIiE12ReadAllLinesEvEUliPKcmE_ZN8LightGBM10TextReaderImE17ReadAllAndProcessERKSt8functionIFvmPKcmEEEUlS4_mE_ZN8LightGBM10TextReaderIiE17ReadAllAndProcessERKSt8functionIFviPKcmEEEUlS4_mE_.A/__w/1/s/src/boosting/gbdt.h/__w/1/s/src/boosting/rf.hppUsing GOSSCannot use bagging in GOSSdefaultvector::_M_fill_insertvector::_M_range_insertdartgossrfUnknown boosting type %sCheck failed: data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()) at %s, line %d . +ML\$@H|$ M)1E1ALL\$H|$I$Ht$H\$@IT$HT$HML$LL$PSII\$HL$0LT$(TfH[L9T$0H\$@IUHT$HMMLL$P MtIH|$HIzHt$PIrILT$(HL$0I]H;L9H|$@HsHt$HHCHD$P MtI:HL$HIJLD$PMBILT$(LkM9[H\$ M)IMcIMM9H|$LHLT$`VHT$PLT$$HI HL$HLHyYH5(H|$PH]H\$ Lt$(L9= L"fH)HHL,@Af.Cw'DAtL]L;] MtEIL]HI9MII?IIItwIt:%ef.cw'D AtHUH;U HtD +HHUH=Uef.{w$;tHuH;u Ht>HHuH5ef.sw% tL}L;}MtAIL}HDdfD.Cw'DAtHEH;EHtDHHEHdf.Sw'D+AtLEL;EMtE(ILEHD odfD.Kw'DAtLML;MMtEILMHD3dfD.Sw$;tHUH;UHt:HHUHI9DcfD.[w$3tHMH;MHt1HHML{D%cfE.gw&[tLUL;UMtAILUIwD-}cfD.nw(AGtLmL;mMtAEILmIw0D5@cfD.vw(EG0AtL]L;]MtEIL]IwHD=cfD.~w(EOHAtH}H;}%HtDHH}Iw` bf.Nw&AO`tHUH;UHt +HHUIwx-bf.nw'A_xtLUL;UMtAILUIQbf.^w+AtLmL;meMtAEILmII9H|$ Ht.MtL.Hh[]A\A]A^A_@H +I9OtmD$IL~oIuIID$Ht!-N@@hLML$HHHWHHHHtjHt2Mt%AAAAAAYHIMt%%AAAAAAaHIMt%=uAAAAAAyHIMt%5CAAAAAAqHIMt%AAAAAAQHIMt&DAAAAAEAHIMt&D AAAAAEIIH(Mt&DrAAAAAEQMHyIt'D?AAA@A@EXMIt'D%A@AAAAEaMI0t'D-A@0ABABEjMIHt'D5A@HACACEsLH`t%D=A@`@@DxLHxt# RA@xBBJMIŐt)-#AǀAEAEAmHMHnfL(L*LHLJHHZHJH8I8LPMPLXE@MX8LMHPIRLhAzMjL|$@1E1L|$@I)AOH|$ L1OtmHIdH\$ NL9T$(LT$(Ht$H|$1?LT$(Ht$H|$?LT$(LH]LD$@E11LD$F@HHHmHl~HlHlHl Hl3LHlHlcHHlHHlHHplHH`lRHHPlHH@lHH0lHH l=H|$ HHt(MtL|(H$jaf.DAWAVAUATUHSHXHc_ 1dH%(H$H1Ht$PHDŽ$HDŽ$HDŽ$Ht HiHH1Ld$pHH$H$H)uH$H=&r1LH$Hl$pIHD$XHD$xBdLqOH}0LHAA&H$Hcu H$IHT$8HLjHcu H$HHIHL$@wjH$HHt$(=L$L$ML$MLl$(H=Ȟ1HH$D$L$vcH螞9NL%L$H$LHDŽ$HDŽ$LHDŽ$HDŽ$HDŽ$HDŽ$L\$HHD$0FL$LLLd$0pFHcL$H$ La1HcH$@LJ1L$H$ IHH$IL$@IHpL)HL,A0HpI7HwA@(GEPDWE@ DGDZIHpE_JAOBAGDbEgeD LApH0HI0HDNHvD(DVD,D^DbEgzAZA_BAGAL9AP(VEHDNEP DVDEZ(A_BAGDb EgR1E11H$ H LcLIэFAH90HHI}LAL$@MEHYI]HMLMYLXMaL`D9HHVDHDŽ$`HDŽ$hHDŽ$pHDŽ$HDŽ$EHDŽ$H$`L$DE11Ht$Ll$ f.EE1Ht#H$HH$HOL$LGHH$hHcHDk->LLOLPLWLXL_H$$H;$DŽ$DŽ$DŽ$Ht#H$H>H$HVH$HNHH$McLA0IHFMHLNMPLVDE9H$hH;$p%cDŽ$DŽ$DŽ$$H|$L$H$hfH|$ L$H$CfDDAE9<LHDŽ$H$HDŽ$HDŽ$H$`HHHDŽ$A7HDŽ$HDŽ$ELH$LHAuELHHsHT$`LLHxL5WHHT$HLt$`L)ȉ$^LEHL$P1Ht$XHAH$Ht H$Ht H$Ht H$`Ht H$@Htp H$ Ht^ H|$0"H|$H"Hl$(HmHELH|$@k"H|$8a"H$Ht H$HdH3%(HX[]A\A]A^A_1IH$ HtH|$0"H|$H!L|$(LLKH|$@!H|$8!H$Ht{L#aIH$@Ht^IH$HtGH$`Ht5H$IHtH$Ht KIfnI\I5IAWAVAUATUSHHdH%(H$1Ht$\(H=(t *fH=͟(ffH}6H=(tffH=(ffHV6DDC LcE9D~ +DEE/HsEH1AHhLnHHc/Lc1MdM|$KHc09HLAEt~AtLAt#LcOLcaKlLuO<Ic9HLLc Hc,OdMt$M<Ic9HLHLc Hc,OdMt$M<Ic9HLHA9Lc Hc,OdLNMt$Nc$M<Jc,OtIcM~LvNc$9HLIJc,O|LcIGD9ILL Mc1D9LMHVLc$Hc,O|IGL IcA9IMHA9kHO[HhIL9D,IBEIcLHB4HHcIH9HCM)L9s +ILHHHH)H9 s +HHH8H(D@0HcK II)EKH)H96HLB?HHLIH?H:IHJ|?H)CH8@H`LPDhDXIM)DN\M)I9LHQ?HIHII?I:HLM ׃?L)L`hHcHxjHcHWHcs HGHcs H7HhHHcqP HD$0Dx@ \H@ HDxDxH@(HHtLD$0E`@[H@ HD`D`H@(HHtzLHhL$Ht$*ϙHLꉃ^c4Hck HHHH9wLHHL)HHH(HHLMLeLX ALx@AIICHAtM6IIIItxItNIt$MAGLeI)KD3IGLxxMAGII)I8KDIGMAGII)I8KD+IGLAGIH)I8ID;IGLAGIH)I8IDIGLAGIH)I8IDIGLIAGH)I8ID+IGI5MW8MwpLMIIMMMPMH)I)I)AGLID3HIGAGHHKD IG@AƇMKDMH)H)H)I)I)IIGxAƇID;IILJAƇ0IDILJ(AƇhID3ILJ`AGKD IGAGKDIGIHHHteLc{ IHHM9wN$LHL)HML8HHIIwLh AHP@HIEHAeH/HHHHtuHtLHt#HBIwH)ID-HBHPxHBHH)H8IDHBIBHI)H8KDHBIBHI)H8KD HBIBHI)H8KD5HBIBHI)H8KDHBIHBI)H8KD=HBH-Lb8HjpILLLLHLPHI)H)H)BMKDMHBBHMID=HB@ƂLIDHI)I)I)H)H)HHBxƂKD HHǂƂ0KDHǂ(ƂhKDHǂ`BID=HBBIDHBHHHHt bLLt$HT$0HD$0HD$8HD$@AuLHLcLH0LLHLPHAn8LHLhM)IHcLL9sLvIMLPLt$L$1H=X$LL$L$L$OL}X:AF8E1E1E1E1MH AIHI9PEifD.5EfWfD.A9f.H=~(H$HDŽ$H$H$H}5LD$AI0II8E9x8HT$0LLLHHHB4*HHcM LORL\$0MLHH0LHC<+EP(LEHcEH4HuI@ HHH9PDfEWfE.DfE.HDfD.=Gil I9PA*f.AfWf.Af.HC(H$H}HDŽ$H$H$3afH9PfWDf.fD.HDefD.TF @fEWfA.XEfE.L ~(H$0H}HDŽ$8H$@L$0(3fD.ADf.Lc(H$0H}HDŽ$8H$@L$02AffD.DfD. cH:fD.5GfWAAf.f. 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IM9LcJ 1HHD$HHT$PL{HD$PHt$HSHI9ULN L1HHD$HLL$P{LT$PL\$HLIL9$L$LL1HIN, 8{P觼Bf蛼H=1-HHHHH$HH9HF߁H|$xƄ$HCHH$ZH$HHHH$0L$MtH$HAHH1HLv̻H蔻MtL藂H$蚥HH$舥H${HHÐH$HHMHH;HH$Ht#뜐;H胾HLCHMtLHH[LightGBM] [Fatal] %s +[LightGBM] [%s] DebugRead %.1f GBs from %s.WarningInfoStarted training...Finished traininggbdtCould not open %svector::_M_default_append_Map_base::atbasic_string::substr ,vector::reserveUnknown parameter: %sconfig#basic_string::erase + Finished loading parametersregressionserialcpubasicLightGBM_model.txtLightGBM_predict_result.txtgbdt_prediction.cppvector::_M_fill_insertnonemulticlassFinished RefitTreeFinished predictionLoading train file...Finished initializing network%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=%s%s is set=%s, %s=%s will be ignored. Current value: %s=%sFeature (%s) is missed in data file. If it is weight/query/group/ignore_column, you can ignore this warning.Loading validation file #%zu...Number of data points in validation set #%zu: %zuFinished initializing prediction, total used %d iterationsSkipped header "%s" in file %sWarning: last line of %s has no end of line, still using this linePrediction 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). +You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.Feature (%s) appears more than one time.Config file %s doesn't exist, will ignoreNo training/prediction data, application quit/__w/1/s/src/application/predictor.hppCheck failed: (early_stop_freq) > (0) at %s, line %d . +Check failed: (early_stop_margin) >= (0) at %s, line %d . +Finished loading data in %f secondsFinished initializing trainingSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EENSt6thread10_Impl_baseENSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS5_mEEEUlvE_vEEEEZN8LightGBM9Predictor7PredictEPKcS2_bbEUliRKSt6vectorISsSaISsEEE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPS3_ISt13unordered_mapIidSt4hashIiESt8equal_toIiESaIS4_IKidEEESaISI_EEE1_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE2_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE3_ZN8LightGBM9Predictor7PredictEPKcS2_bbEUlS2_PSt6vectorISt4pairIidESaIS5_EEE_ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKSt8functionIFviRKSt6vectorISsSaISsEEEEEUliiE_ZN8LightGBM10TextReaderImE12ReadAllLinesEvEUlmPKcmE_ZN8LightGBM10TextReaderIiE12ReadAllLinesEvEUliPKcmE_ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKSt8functionIFviRKSt6vectorISsSaISsEEEERKS2_IFbiiEEEUlPKcmE_ZN8LightGBM10TextReaderImE17ReadAllAndProcessERKSt8functionIFvmPKcmEEEUlS4_mE_ZN8LightGBM10TextReaderIiE17ReadAllAndProcessERKSt8functionIFviPKcmEEEUlS4_mE_St19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM14PipelineReader4ReadEPKciRKSt8functionIFmS6_mEEEUlvE_vEEEESaISF_ELN9__gnu_cxx12_Lock_policyE2EE>?MbP????$@?ffffff??{Gz?C.AScoreUpdater::AddScore/__w/1/s/src/boosting/gbdt.h/__w/1/s/src/boosting/rf.hppUsing GOSSCannot use bagging in GOSSdefaultvector::_M_range_insertdartgossrfUnknown boosting type %sCheck failed: data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()) at %s, line %d . Check failed: tree_idx >= 0 && static_cast<size_t>(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: num_tree_per_iteration_ == num_class_ at %s, line %d . -/__w/1/s/src/boosting/goss.hppCheck failed: config_->top_rate + config_->other_rate <= 1.0f at %s, line %d . -Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . -Check failed: gradients == nullptr at %s, line %d . -Check failed: hessians == nullptr at %s, line %d . -RF mode do not support custom objective function, please use built-in objectives.Time for loading model: %f secondsUnknown model format or submodel type in model file %sCheck failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at %s, line %d . +/__w/1/s/src/boosting/goss.hppCheck failed: (config_->top_rate + config_->other_rate) <= (1.0f) at %s, line %d . +Check failed: (num_tree_per_iteration_) == (num_class_) at %s, line %d . +basic_string::_S_construct null not validCheck failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d . +Check failed: (gradients) == (nullptr) at %s, line %d . +Check failed: (hessians) == (nullptr) at %s, line %d . +RF mode do not support custom objective function, please use built-in objectives.Time for loading model: %f secondsUnknown model format or submodel type in model file %sCheck failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at %s, line %d . Check failed: config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f at %s, line %d . -Check failed: train_data->metadata().init_score() == nullptr at %s, line %d . -N8LightGBM4DARTEN8LightGBM4GOSSEN8LightGBM2RFEZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_?<.snapshot_iter_No object function providedStart training from score %lfregression_l1quantilemapeUse subset for baggingIteration:, training : , valid_%f seconds elapsed, finished iteration %d/__w/1/s/include/LightGBM/tree.hCheck failed: max_depth_ >= 0 at %s, line %d . -/__w/1/s/src/boosting/gbdt.cppCheck failed: leaf_pred[i] < models_[model_index]->num_leaves() at %s, line %d . -Check failed: tree_leaf_prediction.size() > 0 at %s, line %d . -Check failed: static_cast<size_t>(num_data_) == tree_leaf_prediction.size() at %s, line %d . -Check failed: static_cast<size_t>(models_.size()) == tree_leaf_prediction[0].size() at %s, line %d . -Check failed: cur_left_cnt == bag_data_cnt at %s, line %d . -Re-bagging, using %d data to trainDisabling boost_from_average in %s may cause the slow convergenceNumber of class for initial score errorEarly stopping at iteration %d, the best iteration round is %dOutput of best iteration round: -%sCheck failed: num_tree_per_iteration_ == objective_function_->NumModelPerIteration() at %s, line %d . -Cannot reset training data, since new training data has different bin mappersCheck failed: train_data != nullptr at %s, line %d . -Stopped training because there are no more leaves that meet the split requirementsCannot add validation data, since it has different bin mappers with training data*ZN8LightGBM4GBDT12TrainOneIterEPKfS2_EUlS2_iE_N8LightGBM4GBDTE?Tree=truefalse"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 <LightGBM/metric.h>#include <ctime>#include <sstream>#include <chrono>#include <string>#include <vector>#include <utility>namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBMl2_rootroot_mean_squared_errorrmsemulticlassovaovrxentropycross_entropyxentlambdacross_entropy_lambdanullcustomnaregression_l2mean_absolute_errormaesoftmaxmulticlass_ova#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifmonotone_constraintsWrong line at model file: %snum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesvector::_M_emplace_back_auxWrong size of feature_infostree_sizesend of parametersversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=feature_names=monotone_constraints=feature_infos=tree_sizes=end of treesfeature importances:%lu -parameters:v3Model format error, expect a tree here. met %s#include <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>double (*PredictTreePtr[])(const double*) = { int early_stop_round_counter = 0;std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);for (int i = 0; i < num_iteration_for_pred_; ++i) {for (int k = 0; k < num_tree_per_iteration_; ++k) {output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { void GBDT::PredictLeafIndex(const double* features, double *output) const {int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;for (int i = 0; i < total_tree; ++i) {output[i] = (*PredictTreeLeafPtr[i])(features);double (*PredictTreeLeafByMapPtr[])(const std::unordered_map<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Unknown importance type: only support split=0 and gain=1mean_absolute_percentage_errorModel file doesn't specify the number of classesModel file doesn't specify the label indexModel file doesn't specify max_feature_idxModel file doesn't contain feature_namesWrong size of monotone_constraintsModel file doesn't contain feature_infosUnknown early stopping type: Binary early stopping needs predictions to be of length oneMulticlass early stopping needs predictions to be of length two or larger*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_/__w/1/s/src/io/bin.cppUsing OrderedSparseBin->ConstructHistogram() insteadCheck failed: max_bin > 0 at %s, line %d . -Check failed: bin_upper_bound.size() <= static_cast<size_t>(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 . -8N8LightGBM10OrderedBinEN8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM21Dense4bitsBinIteratorEN8LightGBM13Dense4bitsBinEN8LightGBM8DenseBinIhEEN8LightGBM8DenseBinItEEN8LightGBM8DenseBinIjEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16OrderedSparseBinIhEEN8LightGBM16OrderedSparseBinItEEN8LightGBM16OrderedSparseBinIjEEN8LightGBM16DenseBinIteratorIhEEN8LightGBM16DenseBinIteratorItEEN8LightGBM16DenseBinIteratorIjEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE@p}?multi_loglossmulti_errorvoting[boosting: ] -[objective: [metric: [tree_learner: [device_type: binary_loglossndcgmapkldivkullback_leiblerlambdarankmean_average_precisiondevice_typegpuUnknown device type %stasktrainconvert_modelrefitUnknown task type %stestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallelgbrtrandom_forestUnknown parameter %s - /__w/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 trainingMulticlass 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_leavesDo not support non-ASCII characters in config.Parameter %s should be of type int, got "%s"Check failed: valid.size() == valid_data_initscores.size() at %s, line %d . - naninfinfinityUnknown token %s in data file-+stodvalidnum_iterationslearning_ratenum_leavesnum_threadsmax_depthmin_data_in_leafmin_sum_hessian_in_leafpos_bagging_fractionneg_bagging_fractionbagging_freqbagging_seedfeature_fractionfeature_fraction_bynodefeature_fraction_seedearly_stopping_roundfirst_metric_onlylambda_l1lambda_l2min_gain_to_splitdrop_ratemax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotfeature_contriforcedsplits_filenameforcedbins_filenamerefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledverbositymax_binmax_bin_by_featuremin_data_in_binbin_construct_sample_cnthistogram_pool_sizedata_random_seedoutput_modelsnapshot_freqinput_modeloutput_resultinitscore_filenamevalid_data_initscoresmax_conflict_rateis_enable_sparsesparse_thresholduse_missingzero_as_missingtwo_roundlabel_columnweight_columngroup_columnignore_columncategorical_featurenum_iteration_predictpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageis_unbalancescale_pos_weightsigmoidboost_from_averagereg_sqrtfair_cpoisson_max_delta_steptweedie_variance_powermax_positionlambdamart_normlabel_gainmetric_freqis_provide_training_metricmulti_error_top_knum_machineslocal_listen_porttime_outmachine_list_filenamegpu_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: [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_statemin_data_per_leafmin_datamin_child_samplesmin_sum_hessian_per_leafmin_sum_hessianmin_hessianmin_child_weightpos_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_lambdamin_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_filenameinput_init_scorevalid_data_init_scoresvalid_init_score_filevalid_init_scoreis_pre_partitionis_enable_bundleis_sparseuse_two_round_loadingis_save_binaryis_save_binary_filehas_headergroup_idquery_columnquery_idignore_featureblacklistcat_featurecategorical_columncat_columnis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribconvert_model_filenum_classesunbalanced_setsmetricsmetric_typesoutput_freqis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinelocal_portmachine_list_filemachine_listmlistworkersnodesParameter %s should be of type double, got "%s"Parameter %s should be "true"/"+" or "false"/"-", got "%s"/__w/1/s/src/io/config_auto.cppCheck failed: num_iterations >=0 at %s, line %d . -Check failed: learning_rate >0.0 at %s, line %d . -Check failed: num_leaves >1 at %s, line %d . -Check failed: num_leaves <=131072 at %s, line %d . -Check failed: min_data_in_leaf >=0 at %s, line %d . -Check failed: min_sum_hessian_in_leaf >=0.0 at %s, line %d . -Check failed: bagging_fraction >0.0 at %s, line %d . -Check failed: bagging_fraction <=1.0 at %s, line %d . -Check failed: pos_bagging_fraction >0.0 at %s, line %d . -Check failed: pos_bagging_fraction <=1.0 at %s, line %d . -Check failed: neg_bagging_fraction >0.0 at %s, line %d . -Check failed: neg_bagging_fraction <=1.0 at %s, line %d . -Check failed: feature_fraction >0.0 at %s, line %d . -Check failed: feature_fraction <=1.0 at %s, line %d . -Check failed: feature_fraction_bynode >0.0 at %s, line %d . -Check failed: feature_fraction_bynode <=1.0 at %s, line %d . -Check failed: lambda_l1 >=0.0 at %s, line %d . -Check failed: lambda_l2 >=0.0 at %s, line %d . -Check failed: 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 . -[cegb_penalty_feature_coupled: d~QJחA_/__w/1/s/src/io/dataset.cpptargetweightsnonameBianry file %s already exists.binSaving data to binary file %swtnum_features: %d +Check failed: (train_data->metadata().init_score()) == (nullptr) at %s, line %d . +Check failed: hessians != nullptr && objective_function_ == nullptr at %s, line %d . +Check failed: hessians == nullptr at %s, line %d . +N8LightGBM4DARTEN8LightGBM4GOSSEN8LightGBM2RFEZN8LightGBM4GOSS7BaggingEiEUliiiPiS1_E_ZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_8<?8GBDT::UpdateScoreGBDT::BoostingNo object function providedGBDT::Train.snapshot_iter_GBDT::BaggingGBDT::BoostFromAverageStart training from score %lfregression_l1quantilemapeIteration:, training : , valid_Use subset for baggingGBDT::TrainOneIter/__w/1/s/include/LightGBM/tree.hCheck failed: (max_depth_) >= (0) at %s, line %d . +/__w/1/s/src/boosting/gbdt.cppCheck failed: (leaf_pred[i]) < (models_[model_index]->num_leaves()) at %s, line %d . +Check failed: (tree_leaf_prediction.size()) > (0) at %s, line %d . +Check failed: (static_cast<size_t>(num_data_)) == (tree_leaf_prediction.size()) at %s, line %d . +Check failed: (static_cast<size_t>(models_.size())) == (tree_leaf_prediction[0].size()) at %s, line %d . +%f seconds elapsed, finished iteration %dRe-bagging, using %d data to trainDisabling boost_from_average in %s may cause the slow convergenceNumber of class for initial score errorEarly stopping at iteration %d, the best iteration round is %dOutput of best iteration round: +%sCheck failed: (static_cast<size_t>(train_data_->num_total_features())) == (config->monotone_constraints.size()) at %s, line %d . +Check failed: (static_cast<size_t>(train_data_->num_total_features())) == (config->feature_contri.size()) at %s, line %d . +Check failed: (num_tree_per_iteration_) == (objective_function_->NumModelPerIteration()) at %s, line %d . +Cannot reset training data, since new training data has different bin mapperstrain_data Can't be NULL at %s, line %d . +Stopped training because there are no more leaves that meet the split requirementsCannot add validation data, since it has different bin mappers with training data*ZN8LightGBM4GBDT7BaggingEiEUliiiPiS1_E_*ZN8LightGBM4GBDT12TrainOneIterEPKfS2_EUlS2_iE_N8LightGBM8BoostingEN8LightGBM8GBDTBaseEN8LightGBM4GBDTE?Tree=#include "gbdt.h"#include <LightGBM/metric.h>#include <ctime>#include <sstream>#include <chrono>#include <string>#include <vector>#include <utility>namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBMl2_rootroot_mean_squared_errorrmseovrmulticlassovaxentropycross_entropyxentlambdacross_entropy_lambdarank_xendcgxe_ndcgxe_ndcg_martxendcg_martnullcustomnaregression_l2mean_absolute_errormaesoftmaxmulticlass_ovananinfinfinityUnknown token %s in data filetruefalse"name":""version":""num_class":"num_tree_per_iteration":"label_index":"max_feature_idx":"objective":"", +"average_output":",""feature_names":[""],"monotone_constraints":["feature_infos":},{"min_value":"max_value":"values":[]}"values":["tree_info":["tree_index":"feature_importances":%lu#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifmonotone_constraintsWrong line at model file: %snum_classnum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesvector::_M_emplace_back_auxWrong size of feature_infosobjectivetree_sizesend of parametersversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=feature_names=monotone_constraints=feature_infos=tree_sizes=end of treesfeature_importances: +parameters:v3Model format error, expect a tree here. met %s#include <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>double (*PredictTreePtr[])(const double*) = { int early_stop_round_counter = 0;std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);for (int i = 0; i < num_iteration_for_pred_; ++i) {for (int k = 0; k < num_tree_per_iteration_; ++k) {output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { void GBDT::PredictLeafIndex(const double* features, double *output) const {int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;for (int i = 0; i < total_tree; ++i) {output[i] = (*PredictTreeLeafPtr[i])(features);double (*PredictTreeLeafByMapPtr[])(const std::unordered_map<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Unknown importance type: only support split=0 and gain=1mean_absolute_percentage_errorModel file doesn't specify the number of classesModel file doesn't specify the label indexModel file doesn't specify max_feature_idxModel file doesn't contain feature_namesWrong size of monotone_constraintsModel file doesn't contain feature_infosZN8LightGBM9ArrayArgsIiE8ArgMaxMTERKSt6vectorIiSaIiEEEUlimmE_d~QJחAu<7~u<7 Binary early stopping needs predictions to be of length oneUnknown early stopping type: %sMulticlass early stopping needs predictions to be of length two or larger*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_/__w/1/s/src/io/bin.cppMultiValSparseBin::MergeData/__w/1/s/src/io/multi_val_dense_bin.hppCheck failed: (num_data_) == (num_used_indices) at %s, line %d . +Check failed: (max_bin) > (0) at %s, line %d . +Check failed: (bin_upper_bound.size()) <= (static_cast<size_t>(max_bin)) at %s, line %d . +/__w/1/s/src/io/multi_val_sparse_bin.hppCheck 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 zero8N8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE??@p}?multi_loglossgpuintermediatemulti_errorauc_muvoting[boosting: ] +[objective: [metric: [tree_learner: [device_type: binary_loglossmapkldivkullback_leiblerlambdarankmean_average_precisiondevice_typeUnknown device type %stasktrainconvert_modelrefitUnknown task type %stestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallelboostinggbrtrandom_forest'"Unknown parameter %s + AUC-mu matrix must have zeros on diagonal. Overwriting value in position %d of auc_mu_weights with 0.auc_mu_weights must have %d elements, but found %dAUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %d of auc_mu_weights.Number of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingMulticlass objective and metrics don't matchHistogram LRU queue was enabled (histogram_pool_size=%f). +Will disable this to reduce communication costsDon't support forcedsplits in %s tree learnerAccuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leavesmin_data_in_leaf has been increased to 2 because this is required when path smoothing is active.Cannot use "intermediate" monotone constraints in parallel learning, auto set to "basic" method.Cannot use "intermediate" monotone constraints with feature fraction different from 1, auto set monotone constraints to "basic" method.Monotone penalty greater than tree depth. Monotone features won't be used.Parameter %s should be of type int, got "%s"??-+config_filetask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalidvalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationnum_iterationsn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorsshrinkage_ratelearning_rateetanum_leafnum_leavesmax_leavesmax_leaftree_typetree_learner_typenum_threadnum_threadsnthreadnthreadsn_jobsdevicerandom_statehist_pool_sizehistogram_pool_sizemin_data_per_leafmin_data_in_leafmin_datamin_child_samplesmin_sum_hessian_per_leafmin_sum_hessian_in_leafmin_sum_hessianmin_hessianmin_child_weightpos_sub_rowpos_bagging_fractionpos_subsamplepos_baggingneg_sub_rowneg_bagging_fractionneg_subsampleneg_baggingsubsample_freqbagging_freqbagging_fraction_seedbagging_seedsub_featurefeature_fractioncolsample_bytreesub_feature_bynodefeature_fraction_bynodecolsample_bynodeearly_stopping_roundsearly_stopping_roundearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphalambda_l1reg_lambdalambda_l2min_split_gainmin_gain_to_splitrate_dropdrop_ratetopkmcmonotone_constraintmonotone_constraining_methodmonotone_constraints_methodmc_methodmonotone_splits_penaltymonotone_penaltyms_penaltymc_penaltyfeature_contribfeature_contrifcfpfeature_penaltyfsforcedsplits_filenameforced_splits_filenameforced_splits_fileforced_splitsverboseverbositymodel_inputinput_modelmodel_inmodel_outputoutput_modelmodel_outsave_periodsnapshot_freqsubsample_for_binbin_construct_sample_cntdata_seeddata_random_seedis_sparseis_enable_sparseis_enable_bundleis_pre_partitiontwo_rounduse_two_round_loadinghas_headerlabel_columnweight_columngroup_columngroup_idquery_columnquery_idignore_featureignore_columnblacklistcat_featurecategorical_featurecategorical_columncat_columnis_save_binaryis_save_binary_fileis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribpredict_resultoutput_resultprediction_resultpredict_nameprediction_namepred_namename_predconvert_model_filenum_classesis_unbalanceunbalanced_setsmetricsmetric_typesoutput_freqmetric_freqis_provide_training_metricis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinenum_machineslocal_portlocal_listen_portmachine_list_filemachine_list_filenamemachine_listmlistworkersnodesstodforce_col_wiseforce_row_wisemax_depthfeature_fraction_seedextra_treesextra_seedfirst_metric_onlymax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotrefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledpath_smoothinteraction_constraintssaved_feature_importance_typemax_binmax_bin_by_featuremin_data_in_binuse_missingzero_as_missingfeature_pre_filterforcedbins_filenamestart_iteration_predictnum_iteration_predictpredict_disable_shape_checkpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageobjective_seedscale_pos_weightsigmoidboost_from_averagereg_sqrtfair_cpoisson_max_delta_steptweedie_variance_powerlambdarank_truncation_levellambdarank_normlabel_gainmulti_error_top_kauc_mu_weightstime_outgpu_platform_idgpu_device_idgpu_use_dp[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [force_col_wise: [force_row_wise: [histogram_pool_size: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [pos_bagging_fraction: [neg_bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_bynode: [feature_fraction_seed: [extra_trees: [extra_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [min_gain_to_split: [drop_rate: [max_drop: [skip_drop: [xgboost_dart_mode: [uniform_drop: [drop_seed: [top_rate: [other_rate: [min_data_per_group: [max_cat_threshold: [cat_l2: [cat_smooth: [max_cat_to_onehot: [top_k: [monotone_constraints: [monotone_penalty: [feature_contri: [forcedsplits_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [path_smooth: [interaction_constraints: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [data_random_seed: [is_enable_sparse: [enable_bundle: [use_missing: [zero_as_missing: [feature_pre_filter: [pre_partition: [two_round: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [forcedbins_filename: [objective_seed: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [lambdarank_norm: [label_gain: [eval_at: [multi_error_top_k: [auc_mu_weights: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: Parameter %s should be of type double, got "%s"Parameter %s should be "true"/"+" or "false"/"-", got "%s"/__w/1/s/src/io/config_auto.cppCheck failed: (num_iterations) >= (0) at %s, line %d . +Check failed: (learning_rate) > (0.0) at %s, line %d . +Check failed: (num_leaves) > (1) at %s, line %d . +Check failed: (num_leaves) <= (131072) at %s, line %d . +Check failed: (min_data_in_leaf) >= (0) at %s, line %d . +Check failed: (min_sum_hessian_in_leaf) >= (0.0) at %s, line %d . +Check failed: (bagging_fraction) > (0.0) at %s, line %d . +Check failed: (bagging_fraction) <= (1.0) at %s, line %d . +Check failed: (pos_bagging_fraction) > (0.0) at %s, line %d . +Check failed: (pos_bagging_fraction) <= (1.0) at %s, line %d . +Check failed: (neg_bagging_fraction) > (0.0) at %s, line %d . +Check failed: (neg_bagging_fraction) <= (1.0) at %s, line %d . +Check failed: (feature_fraction) > (0.0) at %s, line %d . +Check failed: (feature_fraction) <= (1.0) at %s, line %d . +Check failed: (feature_fraction_bynode) > (0.0) at %s, line %d . +Check failed: (feature_fraction_bynode) <= (1.0) at %s, line %d . +Check failed: (lambda_l1) >= (0.0) at %s, line %d . +Check failed: (lambda_l2) >= (0.0) at %s, line %d . +Check failed: (min_gain_to_split) >= (0.0) at %s, line %d . +Check failed: (drop_rate) >= (0.0) at %s, line %d . +Check failed: (drop_rate) <= (1.0) at %s, line %d . +Check failed: (skip_drop) >= (0.0) at %s, line %d . +Check failed: (skip_drop) <= (1.0) at %s, line %d . +Check failed: (top_rate) >= (0.0) at %s, line %d . +Check failed: (top_rate) <= (1.0) at %s, line %d . +Check failed: (other_rate) >= (0.0) at %s, line %d . +Check failed: (other_rate) <= (1.0) at %s, line %d . +Check failed: (min_data_per_group) > (0) at %s, line %d . +Check failed: (max_cat_threshold) > (0) at %s, line %d . +Check failed: (cat_l2) >= (0.0) at %s, line %d . +Check failed: (cat_smooth) >= (0.0) at %s, line %d . +Check failed: (max_cat_to_onehot) > (0) at %s, line %d . +Check failed: (top_k) > (0) at %s, line %d . +Check failed: (monotone_penalty) >= (0.0) at %s, line %d . +Check failed: (refit_decay_rate) >= (0.0) at %s, line %d . +Check failed: (refit_decay_rate) <= (1.0) at %s, line %d . +Check failed: (cegb_tradeoff) >= (0.0) at %s, line %d . +Check failed: (cegb_penalty_split) >= (0.0) at %s, line %d . +Check failed: (path_smooth) >= (0.0) at %s, line %d . +Check failed: (max_bin) > (1) at %s, line %d . +Check failed: (min_data_in_bin) > (0) at %s, line %d . +Check failed: (bin_construct_sample_cnt) > (0) at %s, line %d . +Check failed: (num_class) > (0) at %s, line %d . +Check failed: (scale_pos_weight) > (0.0) at %s, line %d . +Check failed: (sigmoid) > (0.0) at %s, line %d . +Check failed: (alpha) > (0.0) at %s, line %d . +Check failed: (fair_c) > (0.0) at %s, line %d . +Check failed: (poisson_max_delta_step) > (0.0) at %s, line %d . +Check failed: (tweedie_variance_power) >= (1.0) at %s, line %d . +Check failed: (tweedie_variance_power) < (2.0) at %s, line %d . +Check failed: (lambdarank_truncation_level) > (0) at %s, line %d . +Check failed: (metric_freq) > (0) at %s, line %d . +Check failed: (multi_error_top_k) > (0) at %s, line %d . +Check failed: (num_machines) > (0) at %s, line %d . +Check failed: (local_listen_port) > (0) at %s, line %d . +Check failed: (time_out) > (0) at %s, line %d . +[monotone_constraints_method: [cegb_penalty_feature_coupled: [saved_feature_importance_type: [lambdarank_truncation_level: _/__w/1/s/src/io/dataset.cpptargetinit_scoreDataset::sparse_bin_histogramDataset::dense_bin_histogramBianry file %s already exists.binSaving data to binary file %swtnum_features: %d num_total_features: %d num_groups: %d num_data: %d feature_names: -monotone_constraints: %s, -feature_penalty: %d, -max_bin_by_feature: %lf, +max_bin_by_feature: %s, %d, forced_bins: -feature %d: NA, vector::_M_range_checkCheck failed: num_used_indices == num_data_ at %s, line %d . -Check failed: num_data > 0 at %s, line %d . -Cannot change forced bins after constructed Dataset handle.Check failed: static_cast<size_t>(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d . -Check failed: static_cast<size_t>(num_total_features_) == io_config.feature_contri.size() 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.File %s exists, cannot save binary to itCannot write binary data to %s Cannot add features from other Dataset with a different number of rows/__w/1/s/include/LightGBM/feature_group.hCheck failed: static_cast<int>(bin_mappers->size()) == num_feature at %s, line %d . -Check failed: num_total_features_ == static_cast<int>(bin_mappers->size()) at %s, line %d . -There are no meaningful features, as all feature values are constant.Check failed: static_cast<size_t>(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 . -______LightGBM_Binary_File_Token______ -ffffff???Data file %s is emptyCannot open data file %sbin_upper_boundColumn_name:Using column %s as weightUsing column %s as labelCurrent query exceeds the range of the query file, -please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctThe output cannot be monotone with respect to categorical featuresSize of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupCould not read binary data from %sBinary file error: token has the wrong sizeBinary file error: header has the wrong sizeBinary file error: header is incorrect/__w/1/s/src/io/dataset_loader.cppCheck failed: static_cast<size_t>(dataset->num_total_features_) == config_.monotone_constraints.size() at %s, line %d . -Check failed: static_cast<size_t>(dataset->num_total_features_) == config_.feature_contri.size() at %s, line %d . -Check failed: static_cast<size_t>(dataset->num_total_features_) == config_.max_bin_by_feature.size() at %s, line %d . -Check failed: *(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())) > 1 at %s, line %d . -Binary file error: meta data has the wrong sizeBinary file error: meta data is incorrectBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %dInput file is not LightGBM binary fileCould not open %s. Will ignore.Check failed: forced_bins_json.is_array() at %s, line %d . -Check failed: feature_num < num_total_features at %s, line %d . -Feature %d is categorical. Will ignore forced bins for this feature.Check failed: static_cast<size_t>(num_col) == config_.max_bin_by_feature.size() at %s, line %d . -Check failed: dataset->num_total_features_ == static_cast<int>(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 . -Could not find label column %s in data file +feature %d: %lf, NA, Dataset::InitTrainUsing Sparse Multi-Val BinUsing Dense Multi-Val BinnonameDataset::FastFeatureBundlingDataset::PushDataToMultiValBinCheck failed: (num_used_indices) == (num_data_) at %s, line %d . +Dataset::ConstructHistogramsMultiValDataset::sparse_bin_histogram_mergeDataset::sparse_bin_histogram_moveFile %s exists, cannot save binary to itCannot write binary data to %s Dataset::GetMultiBinFromSparseFeaturesBug. There should be only one multi-val group.Dataset::GetMultiBinFromSparseFeatures: sparse rate %fDataset::GetMultiBinFromAllFeaturesDataset::GetMultiBinFromAllFeatures: sparse rate %fDataset::TestMultiThreadingMethodCannot set both `force_col_wise` and `force_row_wise` to `true` at the same timeinit for col-wise cost %f seconds, init for row-wise cost %f secondscol-wise cost %f seconds, row-wise cost %f secondsAuto-choosing col-wise multi-threading, the overhead of testing was %f seconds. +You can set `force_col_wise=true` to remove the overhead.Auto-choosing row-wise multi-threading, the overhead of testing was %f seconds. +You can set `force_row_wise=true` to remove the overhead. +And if memory is not enough, you can set `force_col_wise=true`.Check failed: (num_data) > (0) at %s, line %d . +Cannot add features from other Dataset with a different number of rows/__w/1/s/include/LightGBM/feature_group.hCheck failed: (static_cast<int>(bin_mappers->size())) == (1) at %s, line %d . +Check failed: (num_total_features_) == (static_cast<int>(bin_mappers->size())) at %s, line %d . +There are no meaningful features, as all feature values are constant.Check failed: (static_cast<int>(bin_mappers->size())) == (num_feature) at %s, line %d . +Check failed: (static_cast<size_t>(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 . +______LightGBM_Binary_File_Token______ +*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE_*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE0_333333??Data file %s is emptyCannot open data file %sbin_upper_boundColumn_name:Using column %s as weightUsing column %s as labelMaking second pass...Current query exceeds the range of the query file, +please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctThe output cannot be monotone with respect to categorical featuresSize of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupCould not read binary data from %sBinary file error: token has the wrong sizeBinary file error: header has the wrong sizeBinary file error: header is incorrect/__w/1/s/src/io/dataset_loader.cppCheck failed: (static_cast<size_t>(dataset->num_total_features_)) == (config_.max_bin_by_feature.size()) at %s, line %d . +Check failed: (*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end()))) > (1) at %s, line %d . +Binary file error: meta data has the wrong sizeBinary file error: meta data is incorrectBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %dInput file is not LightGBM binary fileCould not open %s. Will ignore.Check failed: forced_bins_json.is_array() at %s, line %d . +Check failed: (feature_num) < (num_total_features) at %s, line %d . +Feature %d is categorical. Will ignore forced bins for this feature.Size of feature_names error, should equal with total number of featuresFind whitespaces in feature_names, replace with underlinesDo not support special JSON characters in feature name.Check failed: (static_cast<size_t>(num_col)) == (config_.max_bin_by_feature.size()) at %s, line %d . +Could not find label column %s in data file or data file doesn't contain headerCould not find ignore column %s in data fileCould not find weight column %s in data fileUsing column %s as group/query idCould not find group/query column %s in data fileCould not find categorical_feature %s in data filelabel_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column number %d as labelignore_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameweight_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column number %d as weightgroup_column is not a number, if you want to use a column name, please add the prefix "name:" to the column nameUsing column number %d as group/query idcategorical_feature is not a number, if you want to use a column name, -please add the prefix "name:" to the column nameCould not recognize data format of %sUsing 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 data*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader23ExtractFeaturesFromFileEPKcPKNS_6ParserERKSt6vectorIiSaIiEEPNS_7DatasetEEUliRKS6_ISsSaISsEEE_ZN8LightGBM10TextReaderIiE9CountLineEvEUliPKcmE_ZN8LightGBM10TextReaderIiE18ReadAndFilterLinesERKSt8functionIFbiEEPSt6vectorIiSaIiEEEUliPKcmE_ZN8LightGBM10TextReaderIiE14SampleFromFileEPNS_6RandomEiPSt6vectorISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE23SampleAndFilterFromFileERKSt8functionIFbiEEPSt6vectorIiSaIiEEPNS_6RandomEiPS7_ISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE26ReadPartAndProcessParallelERKSt6vectorIiSaIiEERKSt8functionIFviRKS2_ISsSaISsEEEEEUliiE_HDFS support is not enabledrbwbhdfs://N8LightGBM17VirtualFileWriterEN8LightGBM17VirtualFileReaderEN8LightGBM9LocalFileE'%c' (%d)%.17g\\\"\b\f\n\r\t\u%04x\u2028\u2029Malformed commentUnexpected end of inputUnescaped in stringBad \u escape: Invalid escape character Expected JSON object, got Bad type for in /__w/1/s/src/io/json11.cppi != 0Parse error: expected Invalid in numberExpected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected trailing Unexpected end of input after start of commentUnexpected end of input inside multi-line commentUnexpected end of input in stringLeading 0s not permitted in numbersAt least one digit required in fractional partAt least one digit required in exponentExceeded maximum nesting depthjson11::Json json11::{anonymous}::JsonParser::expect(const string&, json11::Json)N6json119JsonValueEN6json115ValueILNS_4Json4TypeE1EdEEN6json1110JsonDoubleEN6json115ValueILNS_4Json4TypeE1EiEEN6json117JsonIntEN6json115ValueILNS_4Json4TypeE2EbEEN6json1111JsonBooleanEN6json115ValueILNS_4Json4TypeE3ESsEEN6json1110JsonStringEN6json115ValueILNS_4Json4TypeE4ESt6vectorIS1_SaIS1_EEEEN6json119JsonArrayEN6json115ValueILNS_4Json4TypeE5ESt3mapISsS1_St4lessISsESaISt4pairIKSsS1_EEEEEN6json1110JsonObjectEN6json115ValueILNS_4Json4TypeE0ENS_10NullStructEEEN6json118JsonNullESt23_Sp_counted_ptr_inplaceIN6json118JsonNullESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1111JsonBooleanESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonDoubleESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json117JsonIntESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonStringESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json119JsonArrayESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonObjectESaIS1_ELN9__gnu_cxx12_Lock_policyE2EElabel cannot be nullptrLoading query weights....queryLoading query boundaries....initLoading initial scores....weightLoading weights...Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query fileInitial score size doesn't match data sizeLength of label is not same with #dataLength of weights is not same with #dataSum of query counts is not same with #dataData partition error, data didn't match queriesCannot used query_id for parallel trainingWeights size doesn't match data sizeQuery size doesn't match data sizeInvalid initial score file. Redundant or insufficient columnsv~v*G*ǜu<7~u<7 Data file %s doesn't exist.Input format error when parsing as TSVInput format error when parsing as CSVInput format error when parsing as LibSVMData 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.Label should be the first column in a LibSVM fileData file %s doesn't contain a label column.N8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 -d'@Bʚ;*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE2_%gnum_leaves=num_cat=split_feature=split_gain=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=shrinkage=if (fval <= || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":||"threshold":""decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","internal_value":"missing_type":"Zero","missing_type":"NaN","internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":num_catleaf_valueshrinkageleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typecat_boundariesif (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { /__w/1/s/include/LightGBM/utils/common.hCheck failed: strs.size() == static_cast<size_t>(n) at %s, line %d . -Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold field G label 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 %d/__w/1/s/src/metric/dcg_calculator.cppCheck failed: eval_at->at(i) > 0 at %s, line %d . -'}&_poissonbinary_erroraucgammagamma_deviancetweediehuberfair[%s:%s]: sum-of-weights = %f%s offset term = %fmap@ndcg@multi_error@/__w/1/s/src/metric/xentropy_metric.hpplabel_ Can't be NULL at %s, line %d . -[%s:%s]: (metric) labels passed interval [0, 1] check[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) all weights must be positive[%s:%s]: (metric) at least one weight is negativeFor MAP metric, there should be query informationTotal groups: %d, total data: %dThe NDCG metric requires query information/__w/1/s/src/metric/regression_metric.hppCheck failed: label > 0 at %s, line %d . -InitInitInitN8LightGBM6MetricEN8LightGBM12BinaryMetricINS_19BinaryLoglossMetricEEEN8LightGBM19BinaryLoglossMetricEN8LightGBM12BinaryMetricINS_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_10RMSEMetricEEEN8LightGBM10RMSEMetricEN8LightGBM16RegressionMetricINS_8L2MetricEEEN8LightGBM8L2MetricEN8LightGBM16RegressionMetricINS_14QuantileMetricEEEN8LightGBM14QuantileMetricEN8LightGBM16RegressionMetricINS_8L1MetricEEEN8LightGBM8L1MetricEN8LightGBM16RegressionMetricINS_15HuberLossMetricEEEN8LightGBM15HuberLossMetricEN8LightGBM16RegressionMetricINS_14FairLossMetricEEEN8LightGBM14FairLossMetricEN8LightGBM16RegressionMetricINS_13PoissonMetricEEEN8LightGBM13PoissonMetricEN8LightGBM16RegressionMetricINS_10MAPEMetricEEEN8LightGBM10MAPEMetricEN8LightGBM16RegressionMetricINS_11GammaMetricEEEN8LightGBM11GammaMetricEN8LightGBM16RegressionMetricINS_19GammaDevianceMetricEEEN8LightGBM19GammaDevianceMetricEN8LightGBM16RegressionMetricINS_13TweedieMetricEEEN8LightGBM13TweedieMetricEN8LightGBM18CrossEntropyMetricEN8LightGBM24CrossEntropyLambdaMetricEN8LightGBM25KullbackLeiblerDivergenceE-q=;& .>DA@|=?Trying to bind port %d...0.0.0.0Binding port %d succeededBinding port %d failedSet TCP_NODELAY failedListening...Socket accept error, code: %dSocket recv error, code: %dConnected to rank %dSocket construction errorSocket send error, code: %drank=Connecting to rank %d failed, waiting for %d millisecondsFinished linking network in %f secondsSet SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at leastSet SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at leastMachine list file %s doesn't existmachine_list size is larger than the parameter num_machines, ignoring redundant entriesCannot find any ip and port. -Please check machine_list_filename or machines parameterWorld size is larger than the machine_list size, change world size to %dMachine list file doesn't contain the local machineNSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS4_iEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS5_iEEEESaISC_ELN9__gnu_cxx12_Lock_policyE2EEff?Local rank: %d, total number of machines: %dPlease initilize the network interface firstNSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS4_iEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS5_iEUlvE_vEEEESaIS9_ELN9__gnu_cxx12_Lock_policyE2EEnum_class:sigmoid: sqrtContains only one class[%s]: sum of weights is zero[%s]: sum of labels is zero[%s:%s]: havg = %f -> initscore = %f[%s:%s]: pavg=%f -> initscore=%f[%s:%s]: pavg = %f -> initscore = %fStarting 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 resultsNumber of positive: %d, number of negative: %d/__w/1/s/src/objective/xentropy_objective.hpp[%s:%s]: (objective) labels passed interval [0, 1] check[%s]: at least one weight is negative[%s]: at least one weight is non-positive[%s:%s]: min, max weights = %f, %f; ratio = %fLambdarank tasks require query informationSigmoid parameter %f should be greater than zeroCannot set is_unbalance and scale_pos_weight at the same timeCannot use sqrt transform in %s Regression, will auto disable it[%s]: at least one target label is negativeMet 'abs(label) < 1', will convert them to '1' in MAPE objective and metricObjective should contain num_class fieldUnknown objective type name: %sLabel must be in [0, %d), but found %d in label/__w/1/s/src/objective/regression_objective.hppCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . -Sigmoid param %f should be greater than zeroCheck failed: threshold >= weighted_cdf[pos - 1] at %s, line %d . -Check failed: threshold < weighted_cdf[pos] at %s, line %d . -BoostFromScoreInitBoostFromScoreInitBoostFromScoreN8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM14LambdarankNDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC1ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC1ERKNS_6ConfigESt8functionIFbfEEEUlfE_?qq C<ư>II@@z?????vector<bool>::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: tree->num_leaves() <= data_partition_->num_leaves() at %s, line %d . -GPU Tree Learner was not enabled in this build. -Please recompile with CMake option -DUSE_GPU=1N8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEE<N8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEETotal Bins %dleftrightNo further splits with positive gain, best gain: %fTrained a tree with leaves = %d and max_depth = %d/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: inner_feature_index >= 0 at %s, line %d . -Check failed: num_machines > 1 at %s, line %d . -Check failed: bag_cnt == num_data_ at %s, line %d . -Invalid categorical threshold split'Forced Split' will be ignored since the gain getting worse. Check failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d . -/__w/1/s/src/treelearner/feature_histogram.hppCheck failed: offset == num_total_bin at %s, line %d . -Check failed: cache_size_ >= 2 at %s, line %d . -cegb_penalty_feature_lazy should be the same size as feature number.cegb_penalty_feature_coupled should be the same size as feature number.Check failed: num_features_ == train_data_->num_features() at %s, line %d . -Number of data: %d, number of used features: %dSt22_Maybe_get_result_typeILb1ESt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEESt22_Weak_result_type_implISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEESt17_Weak_result_typeISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEEN8LightGBM11TreeLearnerEN8LightGBM17SerialTreeLearnerESt5_BindIFSt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEPS2_St12_PlaceholderILi1EES9_ILi2EES9_ILi3EES9_ILi4EES9_ILi5EES9_ILi6EEEE@N8LightGBM25VotingParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM25VotingParallelTreeLearnerINS_17SerialTreeLearnerEEE;$F@F09HR9P9(:ȧ,:(E:xZ: o::;:;:y::8; @;p;إ;`;<0<8`< <<pllP:l0MlO0mp`m0vmІmPnnnno@+@o3po6oOoPppd qhF`qFqFqFrGrG sHHsH@tHt8I`uIvIv(J@wxJwJxJx8K0yKyKPzxLpzLzMzN{8O|xO`OPphPPRRHT U`xUU`(VxV0Vp(WWЭX PX@pX XXXдXYYYеYY Z``ZPZлZ`H[[[[\H\0\@\0 ]X]]]]^@^^^0_0_pX_`_`X```08aaaHbbPbcP(cpd0de@h@0i`i+Plp/l@3l7@m:n =nC@oFoIoLHpOpRpUtp` u0kumvvhwzp`{|`|@~FFFG(G@G@`G`xGG(L0LM@XMM`N`NpN NPQ@XQQQ Q@S@hSSSS TPTT T`WPWcpc0 d`d epe@ ePf`ff% g+xg2g8 h;ph=iP?0jAjDjGj Hk KkpNk@QmYmpZHn[Hq\qP^q_@r@crcr h shHshpspsts0{Ht|t|xu}hv`wwPx@xxPxhyyHzzP{x||p}h}}h~P -~Hph !ȀP&@*(.X 28؁0990:p0<=?@@pAE HHIJKL@MO0PPQ@PRpPSPTІPUpV@pWp`Z`[\0]p_`0`pa8bdf(gXhjp~PȌH8 x@@@@h@phP0АHp@ (` 8p؍(Џ` @@##Б`$$@p%*В0 ;p;`<PBhCȔ`CQP`R0TЖpU0V@\\(Pax0eikH no@ P`ؘ 8Ph @șp؛`H@## $($X@%x%/000(P1H5`FS8SXVmp qȠz{X`ȡXp X`0h @0ئ0P pp@P`p@H00``00ج(X` ЮpPP0 0 /GxP\Ȳp\xyX@`Pйh0PpPp @8`PгhPP`x0 %p&)-pPE@I0XpȺ x(@ p(p5`7p7JS0`hؼn(`tн{`|}h@(x`@@0@00`H`x HЩh pp@`xP@pP @ p@@$`$$&@.08@H8H@XX ex pe @ -`  Ph `ЛОH 0@ 0(@@ P`p(@Xp00ph(@Xp 0@0 h`x 0@ H`x@`p0Xp  0@@XP P@(P@`P(@@Xp0@P0H`x  0 P p  8 P!h0!P!!!"0"p"8"X"x0### $p$H$ %`')(P+X,..3`445p55(6`708809@0;0=0?0A@0C0EE@G0G`HKL0M8NpN R`UH\bPgP0lpv w8 wP@wh`wwwwwx x@x(`xh z{~h0H`@P 8Ph pp`H`8Фh 8x0x8P`H`@0(p@@0Xx`P8x h@0 hP @0Pp00@ p')H`,358P9P;@KhpMPNNQXW\Plxn@os8uzPP`@Г`p`H`0P PX   - -0h   H    @ 0p @ x!-(=L0[oh0(0@@ 0p@PP@p `0p8pxP X@08p pP`0Ph@H0( `p` p 0`0  ` P!P!`!!` -@" p" ""P#pp#@#$p$`$" %$x% (%`*%p0'`3 (p7+N ,U-`V1g(2pyP3yh336Ъ6в77@889``998&&&@''p`(0( (@ 0) -)`) *)`*0,*1+p7+9p,:,;,@-AX-L-RH.Y.\/`]@/@^/ _/e0fx0m0s1 z12203(4 44(5Кh55`6`7 x7`79h:P:::`0;;<@X<Я>@p>>P>>(???`0@p@ @ 8AAAB0XB`C@E@@H.XIKIiJЏXJ@J@K@K(LLL!:":0"<"<0#(=#x=$=%0>p%B%B,@C0-Cp2(D2hDp3D8D;(E;E=E0@(F@PFAF`BGC@GNGPGQHRH0TI0VKXXKXN0ZO[@O `HM0``M@`xM``M`M`M`M`M aN0a0Nb`N bxNcN cHQ0c`Q@cxQPcVdZg@[Pl[ [0x\Ј\P\@(]`]]]^8^P^Аh^^^^^ ^0^@__`8ahaaaaP(bbbbb chcpc@efgpg@gOOOOP@(PP@P`XPppPPPPPP Q0Q@0QPQ`QpQQQR R8RPRhRR R0RPR`RRS(S@SXSpSSSSS S0T@TP0TpHT`TxTTTT T@T`U U8UPUhUU U@U`UUUV(V@V XV@pV`VVVW WP0WHW`WxWW@WWXP HX pXP -X -XP X Yp HYYY`8Z%Z`,([p,\0/H\/X_2_2 `4p`6c80d@8Pd8pd8d8d@AepA eLpeNeP`fT h@X`h[hP^h0ai0cXi0eigii(jkhj@njpj8kk0kk lhllpmHm0mm(n`nxnpn(ooPo(0pEpEq0HrHr`JprMr@Rt`\0uguru zp0zp@zqPzq`z0qpzHqz`qzxqzqzqzq{Ps}s s@t@tHv`vvPv wpw`wx`xpx y@8y xyyz@`zzz8{{p{{P|| -}X}@}~@ ~ %@~*X~2~5~0DGI0`L`N`P8SUX@0Z@`H`frv0x ؅`( xh`@@P `Pp@@Ȉ@ @P  0#Њ&8'@-؋1@178989X9x9p@@E8J NؒQ(ThY\p_HdPhȔ`koh@su{`0PЗ0 PpP`0pPP`Лp p@`0О Pp0 p (0x`Ȥp#h).p4h`:P@FL0@SpVY`p]0aHpfkq8wЫ y@z X 8Ы0H`x 0@P؍`p 8PhЬȎ 0(@@PX`ppЏЭ0H`x 0@Pؐ`p `P0h`ءHp н0@`PP@0Ю p!`'-3P:ȱ;=H>x@hAAA`BB0@C@I0POPUеP[ `ap`gpms`yPБ@0 0к@ Pp`p`P@ 0 0п@ P#p`)p/5`;AGPMSY@_fl0 r0x@~ Јp P` 8p0X0@@ P8X -` P0  +0p+X.14P6X8:<00?xpACEP0HpJT0WZ[[0]`b(z({0@@ Pp x @`P`x00 8 p px&P'h(,4x5@8``YpYYY0YH ZhPZpZZZZ[00\`\ck0t}Pp00 pШ`0 HйpX08ph p X0@`(ppp0 ` -`0@A8M"pOOOOO0OHO`OxOPP P0PpPP(P@PXPpPPPPQQPQQXR R0R@RPR`RR(R@RXRpRRRSS S0S@S0PSH`S`pSxSSSSSSS S8TPTh@TPT`TTTTU@U8UXUxV0VpVVVW8PWXWxWW0X`XXX8YX`YxYZ0Z`Z aX@ax`a b@bpccd(`e ff8gh`h ii@jp kkk@l0lPnPq rrpsyP0XpП8Php8`@@ `@(0H  (``8@`p0 80(`!`"()x+0.( 0/x 5 P8 -;x -; -< @>h P? `@ AX B C EH  F 0G H8IJK(LxNpOOPR8SUW8XZ[0]_`0bce0pghj0kmo0prs(u`wx(pz{}(Љ8 @ p(p`Xp X @ p 0p!!h"P"#X##`$ h$@$ %.p%P2% 3@&A&H&`KP'R'pU(oX(`s(0t()y)@)8* **P(-h--`-p8.0//pX+p+++P+p++,, 8,X,x,,, ,pX.0..//00X0Pp0001X10x112@ X2V2Ї3Вh3НP43Р34004P4p45Ч850X556`607h777008p808 8(9`h999(:h::::;;;0<` <@(=h==="@>(>+>,8?-?3?4 @ Fp@pGBLXC`XCZDgXDhDw`EP~E0HJxKPK(L@NXN 606`77;@h;`<@@AXA0pAABhB0BC xD`#0E#HE#(F0F8F;HGBG LHV`H^I iXI0vI@wJw8JxJzK}xLLMhMMNВOЗPOOPPPз(RкR@RSPPS`SSPT @T0T@TxUPU0VWPXxbP P@Q Q8QpQ0Q@RPT0UV`xVp VpWphW!W!W"X&XP20Y2HYP7YP8YP?ZAXZDZGZI[LH[[[Pi[lH\Pp\p\ ]0x]P]^H^^^(_p_ _`h``p` a@paa Hb`bbc0&hc/c>dzRx $0?FJ w?;*3$"D*2\*2t+*+*؂Ђ Ȃ&Ad<؂TlLP*AAG ] +please add the prefix "name:" to the column nameCould not recognize data format of %sCheck failed: (dataset->num_total_features_) == (static_cast<int>(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 . +Using 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 data*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader20LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE_*ZN8LightGBM13DatasetLoader22SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPSt6vectorIiSaIiEEEUliE0_*ZN8LightGBM13DatasetLoader23ExtractFeaturesFromFileEPKcPKNS_6ParserERKSt6vectorIiSaIiEEPNS_7DatasetEEUliRKS6_ISsSaISsEEE_ZN8LightGBM10TextReaderIiE9CountLineEvEUliPKcmE_ZN8LightGBM10TextReaderIiE18ReadAndFilterLinesERKSt8functionIFbiEEPSt6vectorIiSaIiEEEUliPKcmE_ZN8LightGBM10TextReaderIiE14SampleFromFileEPNS_6RandomEiPSt6vectorISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE23SampleAndFilterFromFileERKSt8functionIFbiEEPSt6vectorIiSaIiEEPNS_6RandomEiPS7_ISsSaISsEEEUliPKcmE_ZN8LightGBM10TextReaderIiE26ReadPartAndProcessParallelERKSt6vectorIiSaIiEERKSt8functionIFviRKS2_ISsSaISsEEEEEUliiE_rbwbhdfs://N8LightGBM17VirtualFileWriterEN8LightGBM17VirtualFileReaderEN8LightGBM9LocalFileE'%c' (%d)%.17g\\\"\b\f\n\r\t\u%04x\u2028\u2029Malformed commentUnexpected end of inputUnescaped in stringBad \u escape: Invalid escape character ]Expected JSON object, got Bad type for in /__w/1/s/src/io/json11.cppParse error: expected Invalid in numberExpected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected trailing Unexpected end of input after start of commentUnexpected end of input inside multi-line commentUnexpected end of input in stringCheck failed: (i) != (0) at %s, line %d . +Leading 0s not permitted in numbersAt least one digit required in fractional partAt least one digit required in exponentExceeded maximum nesting depthN6json119JsonValueEN6json115ValueILNS_4Json4TypeE1EdEEN6json1110JsonDoubleEN6json115ValueILNS_4Json4TypeE1EiEEN6json117JsonIntEN6json115ValueILNS_4Json4TypeE2EbEEN6json1111JsonBooleanEN6json115ValueILNS_4Json4TypeE3ESsEEN6json1110JsonStringEN6json115ValueILNS_4Json4TypeE4ESt6vectorIS1_SaIS1_EEEEN6json119JsonArrayEN6json115ValueILNS_4Json4TypeE5ESt3mapISsS1_St4lessISsESaISt4pairIKSsS1_EEEEEN6json1110JsonObjectEN6json115ValueILNS_4Json4TypeE0ENS_10NullStructEEEN6json118JsonNullESt23_Sp_counted_ptr_inplaceIN6json118JsonNullESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1111JsonBooleanESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonDoubleESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json117JsonIntESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonStringESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json119JsonArrayESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN6json1110JsonObjectESaIS1_ELN9__gnu_cxx12_Lock_policyE2EELoading query weights...label cannot be nullptr.queryLoading query boundaries....initLoading initial scores....weightLoading weights...Using weights in data file, ignoring the additional weights fileUsing query id in data file, ignoring the additional query filePlease note that `init_score` is not saved in binary file. +If you need it, please set it again after loading Dataset.Data partition error, data didn't match queriesCannot used query_id for parallel trainingWeights size doesn't match data sizeQuery size doesn't match data sizeInitial score size doesn't match data sizeLength of weights is not same with #dataLength of label is not same with #dataSum of query counts is not same with #dataInvalid initial score file. Redundant or insufficient columns*G*Ǚv~vData file %s doesn't exist. /__w/1/s/src/io/parser.cppData file %s couldn't be read.Check failed: (max_col_idx) > (0) at %s, line %d . +Input format error when parsing as TSVInput format error when parsing as CSVInput format error when parsing as LibSVMData file %s should have at least one line.Data file %s only has one line.Unknown format of training data.Label should be the first column in a LibSVM fileData file %s doesn't contain a label column.N8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 +d'@Bʚ;*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE2_%gnum_leaves=num_cat=split_feature=split_gain=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=shrinkage=if (fval <= || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":||"threshold":""decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","internal_value":"missing_type":"Zero","missing_type":"NaN","internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":num_catleaf_valueshrinkageleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typecat_boundaries/__w/1/s/include/LightGBM/utils/common.hCheck failed: (num_chars) >= (0) at %s, line %d . +if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { Check failed: (strs.size()) == (static_cast<size_t>(n)) at %s, line %d . +Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold field G label should be int type (met %f) for ranking task, +for the gain of label, please set the label_gain parameterLabel should be non-negative (met %f) for ranking taskLabel %zu is not less than the number of label mappings (%zu)/__w/1/s/src/metric/dcg_calculator.cppCheck failed: (ref_eval_at[i]) > (0) at %s, line %d . +'}&_] outside [auc[%s:%s]: sum-of-weights = %f%s offset term = %fmap@ndcg@poissonbinary_errorgammagamma_deviancetweediehuberfairmulti_error@[%s]: does not tolerate element [#%i = /__w/1/s/src/metric/xentropy_metric.hpplabel_ Can't be NULL at %s, line %d . +[%s:%s]: (metric) labels passed interval [0, 1] check[%s:%s]: (metric) weights not allowed to be negative[%s:%s]: sum-of-weights = %f is non-positive[%s:%s]: (metric) all weights must be positive[%s:%s]: (metric) at least one weight is negativeFor MAP metric, there should be query informationTotal groups: %d, total data: %dThe NDCG metric requires query information/__w/1/s/src/metric/regression_metric.hppCheck failed: (label) > (0) at %s, line %d . +InitInitInitN8LightGBM6MetricEN8LightGBM12BinaryMetricINS_19BinaryLoglossMetricEEEN8LightGBM19BinaryLoglossMetricEN8LightGBM12BinaryMetricINS_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM11AucMuMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_10RMSEMetricEEEN8LightGBM10RMSEMetricEN8LightGBM16RegressionMetricINS_8L2MetricEEEN8LightGBM8L2MetricEN8LightGBM16RegressionMetricINS_14QuantileMetricEEEN8LightGBM14QuantileMetricEN8LightGBM16RegressionMetricINS_8L1MetricEEEN8LightGBM8L1MetricEN8LightGBM16RegressionMetricINS_15HuberLossMetricEEEN8LightGBM15HuberLossMetricEN8LightGBM16RegressionMetricINS_14FairLossMetricEEEN8LightGBM14FairLossMetricEN8LightGBM16RegressionMetricINS_13PoissonMetricEEEN8LightGBM13PoissonMetricEN8LightGBM16RegressionMetricINS_10MAPEMetricEEEN8LightGBM10MAPEMetricEN8LightGBM16RegressionMetricINS_11GammaMetricEEEN8LightGBM11GammaMetricEN8LightGBM16RegressionMetricINS_19GammaDevianceMetricEEEN8LightGBM19GammaDevianceMetricEN8LightGBM16RegressionMetricINS_13TweedieMetricEEEN8LightGBM13TweedieMetricEN8LightGBM18CrossEntropyMetricEN8LightGBM24CrossEntropyLambdaMetricEN8LightGBM25KullbackLeiblerDivergenceE-q=;& .>DA@|=?Trying to bind port %d...0.0.0.0Binding port %d succeededBinding port %d failedSet TCP_NODELAY failedListening...Socket accept error, code: %dSocket recv error, code: %dConnected to rank %dSocket construction errorSocket send error, code: %drank=Connecting to rank %d failed, waiting for %d millisecondsFinished linking network in %f secondsSet SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at leastSet SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at leastMachine list file %s doesn't existmachine_list size is larger than the parameter num_machines, ignoring redundant entriesCannot find any ip and port. +Please check machine_list_filename or machines parameterWorld size is larger than the machine_list size, change world size to %dMachine list file doesn't contain the local machineNSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS4_iEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt7_Mem_fnIMN8LightGBM7LinkersEFviEEPS5_iEEEESaISC_ELN9__gnu_cxx12_Lock_policyE2EEff?Local rank: %d, total number of machines: %dPlease initilize the network interface firstNSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS4_iEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN8LightGBM7Linkers8SendRecvEiPciiS5_iEUlvE_vEEEESaIS9_ELN9__gnu_cxx12_Lock_policyE2EEnum_class:sigmoid: sqrt[%s]: sum of weights is zeroContains only one class[%s]: sum of labels is zeroRanking tasks require query information[%s:%s]: havg = %f -> initscore = %f[%s:%s]: pavg=%f -> initscore=%f[%s:%s]: pavg = %f -> initscore = %f/__w/1/s/src/objective/xentropy_objective.hpp[%s:%s]: (objective) labels passed interval [0, 1] check[%s]: at least one weight is negative[%s]: at least one weight is non-positive[%s:%s]: min, max weights = %f, %f; ratio = %fNumber of positive: %d, number of negative: %dSigmoid parameter %f should be greater than zeroCannot set is_unbalance and scale_pos_weight at the same timeCannot use sqrt transform in %s Regression, will auto disable it[%s]: at least one target label is negativeMet 'abs(label) < 1', will convert them to '1' in MAPE objective and metricObjective should contain num_class fieldUnknown objective type name: %sLabel must be in [0, %d), but found %d in label/__w/1/s/src/objective/regression_objective.hppCheck failed: alpha_ > 0 && alpha_ < 1 at %s, line %d . +Sigmoid param %f should be greater than zeroCheck failed: (threshold) >= (weighted_cdf[pos - 1]) at %s, line %d . +Check failed: (threshold) < (weighted_cdf[pos]) at %s, line %d . +BoostFromScoreInitBoostFromScoreInitBoostFromScoreN8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM16RankingObjectiveEN8LightGBM14LambdarankNDCGEN8LightGBM10RankXENDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC1ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC1ERKNS_6ConfigESt8functionIFbfEEEUlfE_ZN8LightGBM9ArrayArgsIfE8ArgMaxMTERKSt6vectorIfSaIfEEEUlimmE_ZN8LightGBM9ArrayArgsIdE8ArgMaxMTERKSt6vectorIdSaIdEEEUlimmE_?qq C<ư>0y$k CII@@z?????vector<bool>::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: (tree->num_leaves()) <= (data_partition_->num_leaves()) at %s, line %d . +GPU Tree Learner was not enabled in this build. +Please recompile with CMake option -DUSE_GPU=1N8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_N8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEESerialTreeLearner::ConstructHistogramsSerialTreeLearner::BeforeFindBestSplit/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: (num_machines) > (1) at %s, line %d . +Check failed: (bag_cnt) == (num_data_) at %s, line %d . +share_state_ Can't be NULL at %s, line %d . +Get historical Histogram for leaf %d failed, will skip the ``RecomputeBestSplitForLeaf``Check failed: (data_partition_->num_leaves()) >= (tree->num_leaves()) at %s, line %d . +SerialTreeLearner::BeforeTrainInvalid categorical threshold split'Forced Split' will be ignored since the gain getting worse.cegb_penalty_feature_lazy should be the same size as feature number.cegb_penalty_feature_coupled should be the same size as feature number.No further splits with positive gain, best gain: %fTrained a tree with leaves = %d and max_depth = %dDetected that num_threads changed during training (from %d to %d), it may cause unexpected errors.SerialTreeLearner::FindBestSplitsFromHistogramsCheck failed: (best_split_info.left_count) > (0) at %s, line %d . +Check failed: (best_split_info.right_count) > (0) at %s, line %d . +Check failed: (num_features_) == (train_data_->num_features()) at %s, line %d . +/__w/1/s/src/treelearner/feature_histogram.hppCheck failed: (cache_size_) >= (2) at %s, line %d . +Number of data points in the train set: %d, number of used features: %dCheck failed: (train_data->num_features()) > (0) at %s, line %d . +DataPartition::Splitvector<bool>::_M_insert_auxleftrightSerialTreeLearner::TrainSerialTreeLearner::SplitInnerTotal Bins %dSt22_Maybe_get_result_typeILb1ESt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEEEESt22_Weak_result_type_implISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEEEESt17_Weak_result_typeISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEEEEN8LightGBM11TreeLearnerEN8LightGBM19LeafConstraintsBaseEN8LightGBM20BasicLeafConstraintsEN8LightGBM27IntermediateLeafConstraintsEN8LightGBM17SerialTreeLearnerEZN8LightGBM13DataPartition5SplitEiPKNS_7DatasetEiPKjibiEUliiiPiS6_E_St5_BindIFSt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiRKNS1_15ConstraintEntryEdPNS1_9SplitInfoEEEPS2_St12_PlaceholderILi1EESC_ILi2EESC_ILi3EESC_ILi4EESC_ILi5EESC_ILi6EEEEZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiRKNS_15ConstraintEntryEdPNS_9SplitInfoEE2_<@N8LightGBM25VotingParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM25VotingParallelTreeLearnerINS_17SerialTreeLearnerEEEunknown exceptionField not found/__w/1/s/src/c_api.cpppm_ != nullptrUsed indices of subsets->rwcount & writer_maskInput data type error or field not foundUnknown indptr type in LGBM_BoosterFreePredictSparseUnknown data type in LGBM_BoosterFreePredictSparseUnknown data type in RowFunctionFromDenseMatricCheck failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d . +Unknown data type in CSC matrix/__w/1/s/include/LightGBM/utils/yamc/yamc_shared_lock.hpp/__w/1/s/include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp0 < (s->rwcount & reader_mask)Unknown data type in RowFunctionFromCSR(s->rwcount & reader_mask) < reader_maskCannot change forced bins after constructed Dataset handle.Cannot change data_random_seed after constructed Dataset handle.Cannot change max_bin after constructed Dataset handle.Cannot change max_bin_by_feature after constructed Dataset handle.Cannot change bin_construct_sample_cnt after constructed Dataset handle.Cannot change min_data_in_bin after constructed Dataset handle.Cannot change use_missing after constructed Dataset handle.Cannot change zero_as_missing after constructed Dataset handle.Cannot change categorical_feature after constructed Dataset handle.Cannot change feature_pre_filter after constructed Dataset handle.Cannot change is_enable_sparse after constructed Dataset handle.Cannot change pre_partition after constructed Dataset handle.Cannot change enable_bundle after constructed Dataset handle.Cannot change header after constructed Dataset handle.Cannot change two_round after constructed Dataset handle.Cannot change label_column after constructed Dataset handle.Cannot change weight_column after constructed Dataset handle.Cannot change group_column after constructed Dataset handle.Cannot change ignore_column after constructed Dataset handle.Reducing `min_data_in_leaf` with `feature_pre_filter=true` may cause unexpected behaviour for features that were pre-filtered by the larger `min_data_in_leaf`. +You need to set `feature_pre_filter=false` to dynamically change the `min_data_in_leaf`.Check failed: (num_used_row_indices) > (0) at %s, line %d . +used_row_indices should be sorted in SubsetThe number of columns should be greater than zero.The number of columns should be smaller than INT32_MAX.Check failed: (inner_data.first) < (num_col) at %s, line %d . +Using self-defined objective functionContinued train from model is not supported for c_api, +please use continued train with input scoreDo not support feature parallel in c apiOnly find one worker, will switch to serial tree learnerCannot change num_class during trainingCannot change boosting during trainingCannot change metric during trainingUnknown data type in PredictSparseUnknown indptr type in PredictSparseCSRUnknown col_ptr type in PredictSparseCSCUnknown matrix type in LGBM_BoosterPredictSparseOutputstatic bool yamc::rwlock::ReaderPrefer::release_rlock(yamc::rwlock::ReaderPrefer::state*)yamc::shared_lock<Mutex>::~shared_lock() [with Mutex = yamc::alternate::basic_shared_mutex<yamc::rwlock::ReaderPrefer>]static void yamc::rwlock::ReaderPrefer::acquire_rlock(yamc::rwlock::ReaderPrefer::state*)static void 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