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eatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvE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H兠H;Ht桫H兠H;Ht桤H兠H;Ht柚H兠H;Ht枧H兠H;Ht璐H兠I9剭H;Ht铓H媨L峜Ht鑸I媩$Ht鑩I媩$0Ht鑚I媩$HHt鑋I媩$`Ht鐻I媩$xHt=I嫾$Ht+I崪$I9卻H媫HHt鑭秕H媫0Ht棹H媫Ht桊H9?PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹HLBPH兝HHxHt秕H媫`Ht棂铛L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧Ht鑅H兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht H兠H;Ht棂H兠H;Ht梃H兠H;Ht枳H兠H;Ht杵H兠H;Ht璧H兠H;Ht瑜H兠I9剭H;Ht鑺H媨L峜Ht鑨I媩$Ht鑙I媩$0Ht鑊I媩$HHt鐺I媩$`Ht<I媩$xHt-I嫾$HtI崪$I9卻H媫HHt鑜眭H媫0Ht桀H媫Ht栲H)=PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹H.PH兝HHxHt梓祯H媫`Ht栝祯L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧Ht鐴H兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht楮H兠H;Ht栝H兠H;Ht柝H兠H;Ht枨H兠H;Ht瓒H兠H;Ht瑗H兠H;Ht钄H兠I9剭H;Ht鑪H媨L峜Ht鑘I媩$Ht鑉I媩$0Ht鐹I媩$HHt;I媩$`Ht,I媩$xHtI嫾$Ht I崪$I9卻H媫HHt鑌膈H媫0Ht柁H媫Ht栊H;PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹H.PH兝HHxHt桤牾H媫`Ht栀牾L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧Ht5H兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht桕H兠H;Ht栀H兠H;Ht枞H兠H;Ht璺H兠H;Ht瑕H兠H;Ht钑H兠H;Ht鑴H兠I9剭H;Ht鑚H媨L峜Ht鑈I媩$Ht鐸I媩$0Ht:I媩$HHt+I媩$`HtI媩$xHt I嫾$Ht棼I崪$I9卻H媫HHt鐻轷H媫0Ht栉H媫Ht枥H 9PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹H5PH兝HHxHt枳桴H媫`Ht枭桴L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧Ht%H兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht柃H兠H;Ht枭H兠H;Ht韪H兠H;Ht瑙H兠H;Ht钖H兠H;Ht鑵H兠H;Ht鑤H兠I9剭H;Ht鑊H媨L峜Ht鐷I媩$Ht9I媩$0Ht*I媩$HHtI媩$`Ht I媩$xHt椠I嫾$Ht桦I崪$I9卻H媫HHt<珲H媫0Ht杈H媫Ht璋H6PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹HD7PH兝HHxHt枨骢H媫`Ht韫骢L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧HtH兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht枋H兠H;Ht韫H兠H;Ht瑷H兠H;Ht钘H兠H;Ht鑶H兠H;Ht鑥H兠H;Ht鑔H兠I9剭H;Ht鐹H媨L峜Ht8I媩$Ht)I媩$0HtI媩$HHt I媩$`Ht椟黪I媩$xHt桧黪I嫾$Ht枸黪I崪$I9卻H媫HHt,艴H媫0Ht璁黪H媫Ht锠黪H4PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹H$8PH兝HHxHt璺漉H媫`Ht瑭漉L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧Ht黪H兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht韬鲺H兠H;Ht瑭鲺H兠H;Ht铇鲺H兠H;Ht鑷鲺H兠H;Ht鑦鲺H兠H;Ht鑕鲺H兠H;Ht鑄鲺H兠I9剭H;Ht:鲺H媨L峜Ht(鲺I媩$Ht鲺I媩$0Ht 鲺I媩$HHt棼貂I媩$`Ht桁貂I媩$xHt栎貂I嫾$Ht杷貂I崪$I9卻H媫HHt沲H媫0Ht铻貂H媫Ht钀貂H2PH兟H塙H兡[]A\A]胒DL夛氲f.怉UATUH夶SH冹H/PH兝HHxHt瑙怩H媫`Ht铏怩L媘PH媇HI9劯M夓H;I)躀冹I领O$dA冧Ht桴趱H兠I9>M呬劏I凕剟I凕tmI凕tVI凕t?I凕t(I凕tH;Ht瑾趱H兠H;Ht铏趱H兠H;Ht鑸趱H兠H;Ht鑧趱H兠H;Ht鑖趱H兠H;Ht鑅趱H兠H;Ht鐳趱H兠I9剭H;Ht*趱H媨L峜Ht趱I媩$Ht 趱I媩$0Ht楮篚I媩$HHt桦篚I媩$`Ht柢篚I媩$xHt柰篚I嫾$Ht杌篚I崪$I9卻H媫HHt 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H婾HHH兡[1繻脜襲蜨56HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁熔H兡1繹]DH.钃 H婾HHH兡[1繻脜襲蜨M6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.熔H兡1繹]DH. H婾HHH兡[1繻脜襲蜨%6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁芹H兡1繹]DH.钃 H婾HHH兡[1繻脜襲蜨=6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.芹H兡1繹]DH. H婾HHH兡[1繻脜襲蜨% 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁欺H兡1繹]DH.钃 H婾HHH兡[1繻脜襲蜨m6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.欺H兡1繹]DH. H婾HHH兡[1繻脜襲蜨 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁袍H兡1繹]DH.钃 H婾HHH兡[1繻脜襲蜨M 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.袍H兡1繹]DH. H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁嫩H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨e 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.嫩H兡1繹]DH.H婾HHH兡[1繻脜襲蜨U6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁蜜H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.蜜H兡1繹]DH.H婾HHH兡[1繻脜襲蜨U6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁论H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.论H兡1繹]DH.H婾HHH兡[1繻脜襲蜨u6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁鳞H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.鳞H兡1繹]DH.H婾HHH兡[1繻脜襲蜨 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁累H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨M6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.累H兡1繹]DH.H婾HHH兡[1繻脜襲蜨-6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁扣H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.扣H兡1繹]DH.H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁聚H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.聚H兡1繹]DH.H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁桔H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.桔H兡1繹]DH.H婾HHH兡[1繻脜襲蜨56HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁价H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.价H兡1繹]DH.H婾HHH兡[1繻脜襲蜨=5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁慧H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.慧H兡1繹]DH.H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁痕H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨貘5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.痕H兡1繹]DH.H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁观H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨%5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.观H兡1繹]DH.H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁港H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.港H兡1繹]DH.H婾HHH兡[1繻脜襲蜨E6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁粉H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨] 6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.粉H兡1繹]DH.H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁钝H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨 5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.钝H兡1繹]DH.H婾HHH兡[1繻脜襲蜨咡5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁帝H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.帝H兡1繹]DH.H婾HHH兡[1繻脜襲蜨征5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁篡H兡1繹]DH.钃H婾HHH兡[1繻脜襲蜨u5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.篡H兡1繹]DH.H婾HHH兡[1繻脜襲蜨M6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁驰H兡1繹]DH.钃髹H婾HHH兡[1繻脜襲蜨}6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.驰H兡1繹]DH.髹H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁槽H兡1繹]DH.钃鲔H婾HHH兡[1繻脜襲蜨e5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.槽H兡1繹]DH.鲔H婾HHH兡[1繻脜襲蜨整5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁臂H兡1繹]DH.钃踣H婾HHH兡[1繻脜襲蜨嶚5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.臂H兡1繹]DH.踣H婾HHH兡[1繻脜襲蜨U5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁佰H兡1繹]DH.钃糅H婾HHH兡[1繻脜襲蜨E6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.佰H兡1繹]DH.糅H婾HHH兡[1繻脜襲蜨6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃筵H婾HHH兡[1繻脜襲蜨e5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.筵H婾HHH兡[1繻脜襲蜨M6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃蜊H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.蜊H婾HHH兡[1繻脜襲蜨睇5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃褊H婾HHH兡[1繻脜襲蜨m5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.褊H婾HHH兡[1繻脜襲蜨咘5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃疔H婾HHH兡[1繻脜襲蜨55HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.疔H婾HHH兡[1繻脜襲蜨]6HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃镗H婾HHH兡[1繻脜襲蜨 5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.镗H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃钲H婾HHH兡[1繻脜襲蜨蹼5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.钲H婾HHH兡[1繻脜襲蜨濣5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁┷H兡1繹]DH.钃碹H婾HHH兡[1繻脜襲蜨濓5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.┷H兡1繹]DH.碹H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁ㄛH兡1繹]DH.钃燠H婾HHH兡[1繻脜襲蜨M5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.ㄛH兡1繹]DH.燠H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁йH兡1繹]DH.钃脎H婾HHH兡[1繻脜襲蜨蹰5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.йH兡1繹]DH.脎H婾HHH兡[1繻脜襲蜨嶗5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁H兡1繹]DH.钃贳H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.H兡1繹]DH.贳H婾HHH兡[1繻脜襲蜨=5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁ホH兡1繹]DH.钃檑H婾HHH兡[1繻脜襲蜨黻5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.ホH兡1繹]DH.檑H婾HHH兡[1繻脜襲蜨藉5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁ほH兡1繹]DH.钃枸H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.ほH兡1繹]DH.枸H婾HHH兡[1繻脜襲蜨U5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁［H兡1繹]DH.钃幺H婾HHH兡[1繻脜襲蜨曪5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.［H兡1繹]DH.幺H婾HHH兡[1繻脜襲蜨e5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁③H兡1繹]DH.钃孥H婾HHH兡[1繻脜襲蜨盱5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.③H兡1繹]DH.孥H婾HHH兡[1繻脜襲蜨甄5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁≯H兡1繹]DH.钃遨H婾HHH兡[1繻脜襲蜨e5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.≯H兡1繹]DH.遨H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁犤H兡1繹]DH.钃溘H婾HHH兡[1繻脜襲蜨嶌5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.犤H兡1繹]DH.溘H婾HHH兡[1繻脜襲蜨眈5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁熪H兡1繹]DH.钃阚H婾HHH兡[1繻脜襲蜨M5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.熪H兡1繹]DH.阚H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁炢H兡1繹]DH.钃廑H婾HHH兡[1繻脜襲蜨曠5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.炢H兡1繹]DH.廑H婾HHH兡[1繻脜襲蜨呪5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁澺H兡1繹]DH.钃巅H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.澺H兡1繹]DH.巅H婾HHH兡[1繻脜襲蜨蓬5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁溮H兡1繹]DH.钃噗H婾HHH兡[1繻脜襲蜨曢5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.溮H兡1繹]DH.噗H婾HHH兡[1繻脜襲蜨 5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁涆H兡1繹]DH.钃咣H婾HHH兡[1繻脜襲蜨嶊5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.涆H兡1繹]DH.咣H婾HHH兡[1繻脜襲蜨侦5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁氎H兡1繹]DH.钃捋H婾HHH兡[1繻脜襲蜨%5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.氎H兡1繹]DH.捋H婾HHH兡[1繻脜襲蜨E5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁欅H兡1繹]DH.钃蒇H婾HHH兡[1繻脜襲蜨碡5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.欅H兡1繹]DH.蒇H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁樭H兡1繹]DH.钃苒H婾HHH兡[1繻脜襲蜨珍5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.樭H兡1繹]DH.苒H婾HHH兡[1繻脜襲蜨]5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁椲H兡1繹]DH.钃圹H婾HHH兡[1繻脜襲蜨澿5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.椲H兡1繹]DH.圹H婾HHH兡[1繻脜襲蜨U5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁栛H兡1繹]DH.钃谯H婾HHH兡[1繻脜襲蜨彗5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.栛H兡1繹]DH.谯H婾HHH兡[1繻脜襲蜨蹊5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁曐H兡1繹]DH.钃氽H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.曐H兡1繹]DH.氽H婾HHH兡[1繻脜襲蜨召5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁斲H兡1繹]DH.钃刿H婾HHH兡[1繻脜襲蜨%5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.斲H兡1繹]DH.刿H婾HHH兡[1繻脜襲蜨%5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁撣H兡1繹]DH.钃综H婾HHH兡[1繻脜襲蜨揭5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.撣H兡1繹]DH.综H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁捽H兡1繹]DH.钃舟H婾HHH兡[1繻脜襲蜨 5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.捽H兡1繹]DH.舟H婾HHH兡[1繻脜襲蜨 5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁戂H兡1繹]DH.钃折H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.戂H兡1繹]DH.折H婾HHH兡[1繻脜襲蜨哲5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁愛H兡1繹]DH.钃咱H婾HHH兡[1繻脜襲蜨 5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.愛H兡1繹]DH.咱H婾HHH兡[1繻脜襲蜨娃5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁徾H兡1繹]DH.钃盂H婾HHH兡[1繻脜襲蜨M5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.徾H兡1繹]DH.盂H婾HHH兡[1繻脜襲蜨咆5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁庅H兡1繹]DH.钃役H婾HHH兡[1繻脜襲蜨澬5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.庅H兡1繹]DH.役H婾HHH兡[1繻脜襲蜨曉5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁嵺H兡1繹]DH.钃眼H婾HHH兡[1繻脜襲蜨-5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.嵺H兡1繹]DH.眼H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁屰H兡1繹]DH.钃雄H婾HHH兡[1繻脜襲蜨m5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.屰H兡1繹]DH.雄H婾HHH兡[1繻脜襲蜨遐5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁嬠H兡1繹]DH.钃羡H婾HHH兡[1繻脜襲蜨5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.嬠H兡1繹]DH.羡H婾HHH兡[1繻脜襲蜨55HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?璁娵H兡1繹]DH.钃污H婾HHH兡[1繻脜襲蜨澺5HH兡1繹]胒H1繦H兡[]脨USH夳H冹凓tb~@凓t凓uH?.娵H兡1繹]DH.污H婾HHH兡[1繻脜襲蜨遘5HH兡1繹]胒H1繦H兡[]脨HLH媥HxA隼tLO婦A鄲Lc蜭婫hAL壢H凌勆夞M纔JI2I鱼L呣tI 驢c聣袽H凌AI逾M 繪婫HP冴Hc襇嫎0G 婦 椕DI鱼I2牍AVAUATUH夶SL媤L媜 M9I>M夓I峖M)鬒冹HI领A冧PI9勵M呬剤I凕toI凕t[I凕tGI凕t3I凕t I凕t 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H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$瑁ＹHH$H婥(H4$H媩$H婯H婼 L婯0Ld3L=3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tuＹHH$H婥(H4$H婰$H媨H婼 L婯0La3L讪3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@钃①HH$H婥(H4$H婰$H媨H婼 L婯0Ld3L3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬鑲嵸L媆$H壝M呟tH夌H夋A親夁锜①L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$鑎≠HH$H婥H $H媩$H婼 L婯0LS3H$H婥(L3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$栌犢HH$H婥(H4$H媩$H婯H婼 L婯0L瘫3L=3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu8犢HH$H婥(H4$H婰$H媨H婼 L婯0L┇3L搂3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@杳熧HH$H婥(H4$H婰$H媨H婼 L婯0LD3Le3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬璨娰L媆$H壝M呟tH夌H夋A親夁柘熧L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$鑾炠HH$H婥H $H媩$H婼 L婯0LS3H$H婥(L湤3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$炠HH$H婥(H4$H媩$H婯H婼 L婯0L飑3L諡3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu鑘澷HH$H婥(H4$H婰$H媨H婼 L婯0Lq3Lz3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@梵溬HH$H婥(H4$H婰$H媨H婼 L婯0L專3L53H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬桠囐L媆$H壝M呟tH夌H夋A親夁溬L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$杈涃HH$H婥H $H媩$H婼 L婯0L3H$H婥(L詽3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$3涃HH$H婥(H4$H媩$H婯H婼 L婯0L寳3Le3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu铇氋HH$H婥(H4$H婰$H媨H婼 L婯0L3LR3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@#氋HH$H婥(H4$H婰$H媨H婼 L婯0L簟3L53H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬呝L媆$H壝M呟tH夌H夋A親夁/氋L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$桀樫HH$H婥H $H媩$H婼 L婯0Lc3H$H婥(L虜3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$鑓樫HH$H婥(H4$H媩$H婯H婼 L婯0LT3L叀3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu枞椯HH$H婥(H4$H婰$H媨H婼 L婯0LI3L殶3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@鑃椯HH$H婥(H4$H婰$H媨H婼 L婯0L43LE3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬鐱傎L媆$H壝M呟tH夌H夋A親夁鑏椯L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$栙HH$H婥H $H媩$H婼 L婯0L33H$H婥(L堀3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$钃曎HH$H婥(H4$H媩$H婯H婼 L婯0L43L廷3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu桫斮HH$H婥(H4$H婰$H媨H婼 L婯0L3L鷲3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@鑳斮HH$H婥(H4$H婰$H媨H婼 L婯0L瑹3Lゥ3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬鑢L媆$H壝M呟tH夌H夋A親夁鑿斮L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$鐽撡HH$H婥H $H媩$H婼 L婯0L{3H$H婥(LL3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$杳捹HH$H婥(H4$H媩$H婯H婼 L婯0L3Lm3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu(捹HH$H婥(H4$H婰$H媨H婼 L婯0Li3Lr3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@璩戀HH$H婥(H4$H婰$H媨H婼 L婯0Lt3L3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬琚|L媆$H壝M呟tH夌H夋A親夁杩戀L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$鑯愘HH$H婥H $H媩$H婼 L婯0L粬3H$H婥(L湐3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$梵徺HH$H婥(H4$H媩$H婯H婼 L婯0L湒3L蜎3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu鑈徺HH$H婥(H4$H婰$H媨H婼 L婯0L亷3L蕥3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@桡庂HH$H婥(H4$H婰$H媨H婼 L婯0L<3L3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬枰yL媆$H壝M呟tH夌H夋A親夁栾庂L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$璁嵸HH$H婥H $H媩$H婼 L婯0L嫆3H$H婥(L3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$#嵸HH$H婥(H4$H媩$H婯H婼 L婯0L鋽3L}3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu鑸屬HH$H婥(H4$H婰$H媨H婼 L婯0L闃3L鷹3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@屬HH$H婥(H4$H婰$H媨H婼 L婯0L3L53H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬wL媆$H壝M呟tH夌H夋A親夁屬L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$柁娰HH$H婥H $H媩$H婼 L婯0L33H$H婥(LL3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$鑃娰HH$H婥(H4$H媩$H婯H婼 L婯0L3L-3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu韪壻HH$H婥(H4$H婰$H媨H婼 L婯0L鶌3Lb3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@鐲壻HH$H婥(H4$H婰$H媨H婼 L婯0Ld3L禈3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬2tL媆$H壝M呟tH夌H夋A親夁鐿壻L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$堎HH$H婥H $H媩$H婼 L婯0L硡3H$H婥(L軘3H塗$H塊H墈 H吚H塂$L塁(L塋$L塖0剶H夌H夋虚f悆x卽H荄$鑳囐HH$H婥(H4$H媩$H婯H婼 L婯0L鋮3L 3H吚H $H塻H塗$H墈 H塂$L塁(L塋$L塖0t H夌H夋蠬婦$(dH3%(H兡0[胒D凗H荄$tu梃嗁HH$H婥(H4$H婰$H媨H婼 L婯0L閭3LJ3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0刵H夌H夋虚\@鑣嗁HH$H婥(H4$H婰$H媨H婼 L婯0L詶3L鍠3H吚H<$H塻H塗$H塊 H塂$L塁(L塋$L塖0匇H夌H夋虚琬鑒qL媆$H壝M呟tH夌H夋A親夁嗁L媆$H壝M呟t際夌H夋A与跮媆$H壝M呟t蜨夌H夋A与綥媆$H壝M呟t盚夌H夋A与SH夳H冹0dH%(H塂$(1繦8帉媝咑!H荄$>呝HH$H婥H $H媩$H婼 L婯0Lc3H$H婥(L軌3H塗$H塊H墈 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I兞Lc泸F蔍兡騀d束駿8騀t怿駿X隍FX怛E^泸E^騠E.喒/D>榀f.DAVH夝I壩H)鳤UI壵ATUH夣SH夳H冹H凐;~\H柳L$嘗夋铚L夑L夞H夘L夌鑻I夎L夅L,$M)郒)貺塼$H夑L夋H夁H六I柳鑠 H兡[]A\A]A^H兡[]A\A]A^擞f.DAWAVI壩AUATUSH冹HH吷L嫭$L嫟$剸M吚M壡剨I売JH夬H凓刅L9巬H壨H另?L峀 H夣L塋$H褆$H婰$H廘夁H)鱄H)螴峣H冿H巹M$ I婾M蒐c袸兟M婬 駼4因B|银駻8X躜^﨟夰H样L峀EGMc翴兝騀买FL馒駾X膨E^萬A.噛H吷t H壪牖I夐I)馡六L壥H夁H塗$($H夑L塡$8H塂$0L塋$ H媡$(L媆$ H婰$L,$L塪$H矵夀H媆$0M壺L塡$(H壜H塂$ H夁枘谟L塪$L媎$(L夞L,$L媡$ H夘L媗$8H+L$M)鏜夬L夨L夑钀谟H兡H[]A\A]A^A_脨M壛H夳H夨I灵?H)荕罤I样J,庪 H)螴峑H冿H~xI $I婾L婱M婸 蒐c罥兝駻8駼买BL馒X买^菻夰H样L婨E繫c繧兝駼买Bd馒X隍^鉬.醰匟吷tH壪肽H夁H夑H)荋)騂墊$H龙H羭$闀M$.D?I婾M峀-C?Lc蒑媂 I兞Lc泸F蔍兡騀d束駿8騀t怿駿X隍FX怛E^泸E^騠E.喒/D>榀f.DAVH夝I壩H)鳤UI壵ATUH夣SH夳H冹H凐;~\H柳L$嘗夋椟L夑L夞H夘L夌桦I夎L夅L,$M)郒)貺塼$H夑L夋H夁H六I柳枥赜H兡[]A\A]A^H兡[]A\A]A^閛嬗f.DAWAVI壩AUATUSH冹HH吷L嫭$L嫟$剸M吚M壡剨I売JH夬H凓刅L9巬H壨H另?L峀 H夣L塋$H褆$H婰$H廘夁H)鱄H)螴峣H冿H巹M$ I婾M蒐c袸兟M婬 駼4因B|银駻8X躜^﨟夰H样L峀EGMc翴兝騀买FL馒駾X膨E^萬A.噛H吷t H壪牖I夐I)馡六L壥H夁H塗$($H夑L塡$8H塂$0L塋$ 鐯 H媡$(L媆$ H婰$L,$L塪$H矵夀H媆$0M壺L塡$(H壜H塂$ H夁钄萦L塪$L媎$(L夞L,$L媡$ H夘L媗$8H+L$M)鏜夬L夨L夑鑐萦H兡H[]A\A]A^A_脨M壛H夳H夨I灵?H)荕罤I样J,庪 H)螴峑H冿H~xI $I婾L婱M婸 蒐c罥兝駻8駼买BL馒X买^菻夰H样L婨E繫c繧兝駼买Bd馒X隍^鉬.醰匟吷tH壪肽H夁H夑H)荋)騂墊$H龙H羭$闀M$.D?I婾M峀-C?Lc蒑媂 I兞Lc泸F蔍兡騀d束駿8騀t怿駿X隍FX怛E^泸E^騠E.喒/D>榀f.DAVH夝I壩H)鳤UI壵ATUH夣SH夳H冹H凐;~\H柳L$嘗夋笥L夑L夞H夘L夌 笥I夎L夅L,$M)郒)貺塼$H夑L夋H夁H六I柳钀塾H兡[]A\A]A^H兡[]A\A]A^/f.DAWAVI壩AUATUSH冹HH吷L嫭$L嫟$剸M吚M壡剨I売JH夬H凓刅L9巬H壨H另?L峀 H夣L塋$H褆$H婰$H廘夁H)鱄H)螴峣H冿H巹M$ I婾M蒐c袸兟M婬 駼4因B|银駻8X躜^﨟夰H样L峀EGMc翴兝騀买FL馒駾X膨E^萬A.噛H吷t H壪牖I夐I)馡六L壥H夁H塗$($H夑L塡$8H塂$0L塋$ 鑰H媡$(L媆$ H婰$L,$L塪$H矵夀H媆$0M壺L塡$(H壜H塂$ H夁暧L塪$L媎$(L夞L,$L媡$ H夘L媗$8H+L$M)鏜夬L夨L夑栊橛H兡H[]A\A]A^A_脨M壛H夳H夨I灵?H)荕罤I样J,庪 H)螴峑H冿H~xI $I婾L婱M婸 蒐c罥兝駻8駼买BL馒X买^菻夰H样L婨E繫c繧兝駼买Bd馒X隍^鉬.醰匟吷tH壪肽H夁H夑H)荋)騂墊$H龙H羭$闀M$.D?I婾M峀-C?Lc蒑媂 I兞Lc泸F蔍兡騀d束駿8騀t怿駿X隍FX怛E^泸E^騠E.喒/D>榀f.DAVH夝I壩H)鳤UI壵ATUH夣SH夳H冹H凐;~\H柳L$嘗夋铚苡L夑L夞H夘L夌鑻苡I夎L夅L,$M)郒)貺塼$H夑L夋H夁H六I柳栌H兡[]A\A]A^H兡[]A\A]A^檫f.DAWAVI壩AUATUSH冹HH吷L嫭$L嫟$剸M吚M壡剨I売JH夬H凓刅L9巬H壨H另?L峀 H夣L塋$H褆$H婰$H廘夁H)鱄H)螴峣H冿H巹M$ I婾M蒐c袸兟M婬 駼4因B|银駻8X躜^﨟夰H样L峀EGMc翴兝騀买FL馒駾X膨E^萬A.噛H吷t H壪牖I夐I)馡六L壥H夁H塗$($H夑L塡$8H塂$0L塋$ 枥H媡$(L媆$ H婰$L,$L塪$H矵夀H媆$0M壺L塡$(H壜H塂$ H夁鑴L塪$L媎$(L夞L,$L媡$ H夘L媗$8H+L$M)鏜夬L夨L夑鑀H兡H[]A\A]A^A_脨M壛H夳H夨I灵?H)荕罤I样J,庪 H)螴峑H冿H~xI $I婾L婱M婸 蒐c罥兝駻8駼买BL馒X买^菻夰H样L婨E繫c繧兝駼买Bd馒X隍^鉬.醰匟吷tH壪肽H夁H夑H)荋)騂墊$H龙H羭$闀M$.D?I婾M峀-C?Lc蒑媂 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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>殭櫃櫃?鹂褚MbP???殭櫃櫃?$@吞烫烫?ffffff??{瓽醶?郈.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(valid_score_updater_.size()) at %s, line %d . Check failed: tree_idx >= 0 && static_cast(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . /__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 . Check failed: hessians != nullptr && objective_function_ == nullptr at %s, line %d . Check failed: hessians == nullptr at %s, line %d . N8LightGBM4DARTEN8LightGBM4GOSSEN8LightGBM2RFEZN8LightGBM4GOSS7BaggingEiEUliiiPiS1_E_ZN8LightGBM2RF12TrainOneIterEPKfS2_EUlS2_iE_昆暘昆暘8牤<?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(num_data_)) == (tree_leaf_prediction.size()) at %s, line %d . Check failed: (static_cast(models_.size())) == (tree_leaf_prediction[0].size()) at %s, line %d . %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(train_data_->num_total_features())) == (config->monotone_constraints.size()) at %s, line %d . Check failed: (static_cast(train_data_->num_total_features())) == (config->feature_contri.size()) at %s, line %d . Cannot use ``monotone_constraints`` in %s objective, please disable it.train_data Can't be NULL 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 mappersStopped 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牂櫣?Tree=#include "gbdt.h"#include #include #include #include #include #include #include namespace LightGBM { }; , ++early_stop_round_counter; return;early_stop_round_counter = 0;if (average_output_) {LeafLeafByMap} // namespace LightGBMl2_rootroot_mean_squared_errorrmsemaemulticlassovamulticlass_ovaovrxentropycross_entropyxentlambdacross_entropy_lambdarank_xendcgxe_ndcgxe_ndcg_martxendcg_martnullcustomnaregression_l2mean_absolute_errorsoftmaxnaninfinfinityUnknown 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 #include #include 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&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { 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&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map& 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_歞~QJ勛桝犎雲筇渦<7~渦<7靠靠靠靠靠靠靠 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(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 zero/__w/1/s/src/io/multi_val_sparse_bin.hpp昆暘8N8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE殭櫃櫃??@}?multi_loglossintermediatemulti_errorauc_muvotingadvanced[boosting: ] [objective: [metric: [tree_learner: [device_type: binary_loglossmapkldivkullback_leiblerlambdarankmean_average_precisiontasktrainconvert_modelrefittestrefit_treeUnknown task type %sdevice_typeUnknown device type %stree_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 explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=%d).Although "deterministic" is set, the results ran by GPU may be non-deterministic.CUDA currently requires double precision calculations.min_data_in_leaf has been increased to 2 because this is required when path smoothing is active.Cannot use "intermediate" or "advanced" monotone constraints in parallel learning, auto set to "basic" method.Cannot use "intermediate" or "advanced" 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.Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. Will set min_data_in_leaf to 1.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_listmlistworkersnodesstoddeterministicforce_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_dpnum_gpu[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [deterministic: [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: [num_gpu: 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 . Check failed: (num_gpu) > (0) at %s, line %d . [monotone_constraints_method: [cegb_penalty_feature_coupled: [saved_feature_importance_type: [lambdarank_truncation_level: _Dataset::sparse_bin_histogram/__w/1/s/src/io/dataset.cpptargetinit_scoreDataset::InitTrainDataset::dense_bin_histogramwtnum_features: %d num_total_features: %d num_groups: %d num_data: %d feature_names: max_bin_by_feature: %s, %d, forced_bins: feature %d: %lf, NA, Using Sparse Multi-Val BinUsing Dense Multi-Val BinnonameDataset::FastFeatureBundlingBianry file %s already exists.binSaving data to binary file %sD/__w/1/s/include/LightGBM/utils/threading.hCheck failed: max_cnt_per_block >= min_cnt_per_block at %s, line %d . Find the same feature name (%s) in Dataset::AddFeaturesFrom, change its name to (%s)Dataset::ConstructHistogramsMultiValDataset::sparse_bin_histogram_mergeDataset::sparse_bin_histogram_moveDataset::PushDataToMultiValBinCheck failed: (num_used_indices) == (num_data_) at %s, line %d . /__w/1/s/include/LightGBM/feature_group.hCheck failed: is_multi_val_ at %s, line %d . Check failed: other->is_multi_val_ at %s, line %d . Dataset::GetMultiBinFromSparseFeaturesBug. There should be only one multi-val group.Dataset::GetMultiBinFromSparseFeatures: sparse rate %fDataset::GetMultiBinFromAllFeaturesCheck failed: static_cast(most_freq_bins.size()) == ncol at %s, line %d . Dataset::GetMultiBinFromAllFeatures: sparse rate %fDataset::TestMultiThreadingMethodCannot set both of `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 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 . File %s exists, cannot save binary to itCannot write binary data to %s Check failed: (num_total_features_) == (static_cast(bin_mappers->size())) at %s, line %d . There are no meaningful features, as all feature values are constant.Using sparse features with CUDA is currently not supported.Check failed: (static_cast(bin_mappers->size())) == (num_feature) at %s, line %d . Check failed: (static_cast(num_total_features_)) == (io_config.max_bin_by_feature.size()) at %s, line %d . Check failed: (*(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end()))) > (1) at %s, line %d . Cannot add features from other Dataset with a different number of rowsCheck failed: num_groups_ == static_cast(features_in_group.size()) at %s, line %d . Check failed: (static_cast(bin_mappers->size())) == (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_殭櫃櫃?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 featuresUsing too small ``bin_construct_sample_cnt`` may encounter unexpected errors and poor accuracy.Size 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(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(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 %sCheck failed: (dataset->num_total_features_) == (static_cast(feature_names_.size())) at %s, line %d . Check failed: label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_ at %s, line %d . Check failed: weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_ at %s, line %d . Check failed: group_idx_ < 0 || group_idx_ < dataset->num_total_features_ at %s, line %d . 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_牂櫳?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....weightLoading weights....initLoading initial scores...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薄*游褿薄*游仪檝杶檝桛Data 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.N8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE/__w/1/s/src/io/train_share_states.cppCheck failed: cur_num_bin == feature_group->bin_offsets_.back() at %s, line %d . 333333?殭>00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 d'爢@B枠狨蕷;*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(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector cat_threshold = {(const std::unordered_map& arr) { Check failed: (strs.size()) == (static_cast(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 游褿 游仪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@binary_errorgammagamma_deviancetweediehuberfairpoissonmulti_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_17BinaryErrorMetricEEEN8LightGBM17BinaryErrorMetricEN8LightGBM9AUCMetricEN8LightGBM22AveragePrecisionMetricEN8LightGBM9MapMetricEN8LightGBM16MulticlassMetricINS_16MultiErrorMetricEEEN8LightGBM16MultiErrorMetricEN8LightGBM16MulticlassMetricINS_25MultiSoftmaxLoglossMetricEEEN8LightGBM25MultiSoftmaxLoglossMetricEN8LightGBM11AucMuMetricEN8LightGBM10NDCGMetricEN8LightGBM16RegressionMetricINS_10RMSEMetricEEEN8LightGBM10RMSEMetricEN8LightGBM16RegressionMetricINS_8L2MetricEEEN8LightGBM8L2MetricEN8LightGBM16RegressionMetricINS_14QuantileMetricEEEN8LightGBM14QuantileMetricEN8LightGBM16RegressionMetricINS_8L1MetricEEEN8LightGBM8L1MetricEN8LightGBM16RegressionMetricINS_15HuberLossMetricEEEN8LightGBM15HuberLossMetricEN8LightGBM16RegressionMetricINS_14FairLossMetricEEEN8LightGBM14FairLossMetricEN8LightGBM16RegressionMetricINS_13PoissonMetricEEEN8LightGBM13PoissonMetricEN8LightGBM16RegressionMetricINS_10MAPEMetricEEEN8LightGBM10MAPEMetricEN8LightGBM16RegressionMetricINS_11GammaMetricEEEN8LightGBM11GammaMetricEN8LightGBM16RegressionMetricINS_19GammaDevianceMetricEEEN8LightGBM19GammaDevianceMetricEN8LightGBM16RegressionMetricINS_13TweedieMetricEEEN8LightGBM13TweedieMetricEN8LightGBM18CrossEntropyMetricEN8LightGBM24CrossEntropyLambdaMetricEN8LightGBM25KullbackLeiblerDivergenceE-仚梣=彊姟;罆& .>狏DA@噙|=?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 machine爢NSt6thread5_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_?莙莙 C牤<嶍禒髌>0y$鮧 CII@@醶?????vector::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: (tree->num_leaves()) <= (data_partition_->num_leaves()) at %s, line %d . CUDA Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_CUDA=1GPU Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_GPU=1N8LightGBM15CUDATreeLearnerEN8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_N8LightGBM26FeatureParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_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 . /__w/1/s/src/treelearner/feature_histogram.hppCheck failed: (cache_size_) >= (2) at %s, line %d . Check failed: (train_data->num_features()) > (0) at %s, line %d . Number of data points in the train set: %d, number of used features: %dCheck failed: (num_features_) == (train_data_->num_features()) at %s, line %d . DataPartition::Splitvector::_M_insert_auxleftrightSerialTreeLearner::TrainSerialTreeLearner::SplitInnerTotal Bins %dSt22_Maybe_get_result_typeILb1ESt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEESt22_Weak_result_type_implISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEESt17_Weak_result_typeISt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEEN8LightGBM15BasicConstraintEN8LightGBM11TreeLearnerEN8LightGBM17FeatureConstraintEN8LightGBM15ConstraintEntryEN8LightGBM20BasicConstraintEntryEN8LightGBM26AdvancedFeatureConstraintsEN8LightGBM19LeafConstraintsBaseEN8LightGBM23AdvancedConstraintEntryEN8LightGBM20BasicLeafConstraintsEN8LightGBM27IntermediateLeafConstraintsEN8LightGBM23AdvancedLeafConstraintsEN8LightGBM17SerialTreeLearnerEZN8LightGBM13DataPartition5SplitEiPKNS_7DatasetEiPKjibiEUliiiPiS6_E_St5_BindIFSt7_Mem_fnIMN8LightGBM16FeatureHistogramEFvddiPKNS1_17FeatureConstraintEdPNS1_9SplitInfoEEEPS2_St12_PlaceholderILi1EESC_ILi2EESC_ILi3EESC_ILi4EESC_ILi5EESC_ILi6EEEEZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb1ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1_ZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2_牤<怈N8LightGBM25VotingParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM25VotingParallelTreeLearnerINS_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 col_ptr type in PredictSparseCSCUnknown indptr type in PredictSparseCSRUnknown matrix type in LGBM_BoosterPredictSparseOutputstatic bool yamc::rwlock::ReaderPrefer::release_rlock(yamc::rwlock::ReaderPrefer::state*)yamc::shared_lock::~shared_lock() [with Mutex = yamc::alternate::basic_shared_mutex]static void yamc::rwlock::ReaderPrefer::acquire_rlock(yamc::rwlock::ReaderPrefer::state*)static void yamc::rwlock::ReaderPrefer::release_wlock(yamc::rwlock::ReaderPrefer::state*)*Z31LGBM_BoosterPredictSparseOutputEUllE_*Z25LGBM_BoosterPredictForCSCEUliE_*Z26RowFunctionFromDenseMatricPKviiiiEUliE_*Z26RowFunctionFromDenseMatricPKviiiiEUliE0_*Z26RowFunctionFromDenseMatricPKviiiiEUliE1_*Z26RowFunctionFromDenseMatricPKviiiiEUliE2_*Z30RowPairFunctionFromDenseMatricPKviiiiEUliE_*Z28RowPairFunctionFromDenseRowsPPKviiEUliE_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE0_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE1_*Z22IterateFunctionFromCSCPKviPKiS0_illiEUliE2_SsZ18RowFunctionFromCSRIiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUliE_Z18RowFunctionFromCSRIiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUliE0_Z18RowFunctionFromCSRIiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUliE1_Z18RowFunctionFromCSRIiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUliE2_Z18RowFunctionFromCSRIlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUllE_Z18RowFunctionFromCSRIlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUllE0_Z18RowFunctionFromCSRIlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUllE1_Z18RowFunctionFromCSRIlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKviPKiSA_illEUllE2_;鴎~ 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