vendor/arm64-darwin/libxgboost.dylib in xgb-0.7.3 vs vendor/arm64-darwin/libxgboost.dylib in xgb-0.8.0

- old
+ new

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Tl@I@ -Ti?T__q T*_ qCT }* ?@@T q} + @B ¬df@NN„NN`?b !T?aT  +} - +  I@l@ iaT__qk + +  I@l@ iaT__q T*_ qCT }* ?@@T q} ++ @B ¬df@ N!N"N#N`?b !T?aT  +} + +  I@l@ iaT__q T*_ qCT }* ?@@T q} ++ @B ¬df@NNNN`?b !T?aT  +} + +  I@l@ iaT__q T*_ qCT }* ?@@T q} ++ @B ¬df@ n!n"n#n`?b !T?aT  +} + +  I@l@ iaT_O{qTq T qaTcHBR$ReRchBR$RRc(BR$RR{BOAM #P!0bRP#P!0 bRF#=0{BOA_#60+ +40_qk T*_ qT }* ?@@T q} + Jk!Td<C A@d4n(&M6c< m6maѣMc<C4nc(m&M6bm< m6m!ѢMb="4nB(M&M6aM< m6m!Ma=4n!(-&6` -< 6-< M6maM +} +  J!k! Tl@I@ "Ti?T__qk T*_ qT }* ?@@T q} + Jk!Td<C A@4n(&M6c< m6maѣMc<c4nc(m&M6bm< m6m!ѢMb=B4nB(M&M6aM< m6m!Ma=!4n!(-&6` -< 6-< M6maM +} +  J!k! Tl@I@ )Ti?T__q T*_ qCT }* ?@@T q} + @B ¬df@NN„NN`?b !T?aT  +} - +  I@l@ iaT__q+ T*_ q T ~* ?@@T q} + +  I@l@ iaT__q T*_ qCT }* ?@@T q} ++ @B ¬df@ N!N"N#N`?b !T?aT  +} + +  I@l@ iaT__q T*_ qCT }* ?@@T q} ++ @B ¬df@NNNN`?b !T?aT  +} + +  I@l@ iaT__q T*_ qCT }* ?@@T q} ++ @B ¬df@ n!n"n#n`?b !T?aT  +} + +  I@l@ iaT__q+ T*_ q T ~* ?@@T q} @+@ Jk! 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R +`Rp`4 RO +h=<UA@==hlRyH9#"RcR^@9 .@?qAb@!`R@m +!"R?ұ96@ + +i"@) kaT`R84 R +h=<UA@==hlRyH9#RcRY^@9 .@?qAb?!`R?5 +!(R?9h6@ + +h +@h +i@ kT`R4 R +h=<UA@==hlRyH9#"RcR^@9 .@?qAb?!`R? +!hBR?`9h6@ +)h979_9h +@9h@hZ8H4ZA +8 +Z8h4Z: +{YOXWW_VgUoT_! R" + #    97Z8h5 +@e +Z8h4Z84Z + +O{(R_9 Ry@)_97{COB_@D +{COB__9h6@; + +og_WO{C@qT^9h7aT(R8 +`R>4 R +h=<UA@==hlRyH9#"RcR_^@9 .@?qAb>!`R>; +!|R>9h6@ +uh@Tl +h@aT`R4 R +h=<UA@==hlRyH9#RcR$^@9 .@?qAb>!`R> +!BR>e9(7h9h7 @xBi@_qT)Rkk mij8 ։RB ɚ86,@@%Tu +% RB ɚ(87 @ +h96i@xBd +H!<RHH! BRS>' _@9B ɚ(?6@6i"@)h kT`R4 Rl +h=<UA@==hlRyH9#"RcR^@9 .@?qAb#>!`R> +!`R>9h6@: +5 +h +@h +Z85! R +`RO`4 R. +h=<UA@==hlRyH9#"RcRp^@9 .@?qAb=!`R=L +!"R=96@ + +i"@) kaT`R4 R +h=<UA@==hlRyH9#RcR8^@9 .@?qAb=!`R= +!(R=y9h6@ + +h +@h +i@ kT`Rݮ4 R +h=<UA@==hlRyH9#"RcR^@9 .@?qAbs=!`Ro= +!hBRi=?9h6@ +)h979_9h +@9h@hZ8H4Z +8 +Z8h4Z +{YOXWW_VgUoT_! R + "&&    96@C +Z8h4Z + +O{C @?q(TjJ Lii8k `@{AO¨<@{AO¨@{AO¨k@{AO¨@{AO¨@{AO¨@{AO¨d@{AO¨$R +!!tCBB + +V +O{CqhT*i) ++ih8J @{AO¨( TI?TIT+9L8 k+8 AT%( TI?iT)T+9L8 k+8 AT#( TI?IT T+@9L@8 +8 aT{AO¨_@T l}) J M . k @@d!k!aT!T@T l}) J M . k @@ l k!aTATbT 5k| @=<d!N@<)AaT@T7J ) k| @=< l N@<)AaT@T#7J ) kzI* @B @$&¬ N!N„"N#N@?B TT}Tl}) J M . k @@ k!aTTJ ) R? +!$!tCBBS +A + +O{CqhT*i) ++ih8J @{AO¨( TI?TIT+@9L@8 k1+8 AT%( TI?iT)T+@9L@8 k1+8 AT#( TI?IT T+@9L@8 +8 aT{AO¨_@T l}) J M . k @@d!.k!aT!T@T l}) J M . k @@ l .k!aTATbT 5k| @=<d!n@<)AaT@T7J ) k| @=< l n@<)AaT@T#7J ) kzI* @B @$&¬ N!N„"N#N@?B TT}Tl}) J M . k @@ k!aTTJ ) Rw +!$T!tCBB +y + +O{CqT*i)1 ++ih8J @{AO¨i~(TJ _T) ?)T* @,E@ k E_AT+Ci~(TJ _T) ?iT* @,E@ k E_ATi~(TJ _ T) ?T* @,E@ E_aT{AO¨_B +K|i~( I ,M  @¬dN!dNBdNcdN? AT_ TB +K|i~( I ,M  @¬lNlNlNlN? AT_ ATB +K|i~( I ,M  @¬NN„NㄣN? AT_ TR +!$!tCBB + + +O{CqT*i)I ++ih8J @{AO¨i~(TJ _T) ?)T* @,E@ k1 E_AT+Ci~(TJ _T) ?iT* @,E@ k1 E_ATi~(TJ _ T) ?T* @,E@ E_aT{AO¨_B +K|i~( I ,M  @¬dn!dnBdncdn? AT_ TB +K|i~( I ,M  @¬lnlnlnln? AT_ ATB +K|i~( I ,M  @¬NN„NㄣN? AT_ TR! +!$!tCBB5 +# +p +O{CqT*i)a ++ih8J @{AO¨i}(!TJ _T) ?)T*  @,@ 닱 _AT+Ci}(!TJ _IT) ?T*  @,@ 닱 _ATi}(!TJ _ T) ?T*  @,@ _aT{AO¨_C +K}i}( I ,M  @¬4N4N4N4Nnnnn? !!T_ TC +K}i}( I ,M  @¬4N14NR4Ns4Nnnnn? !!T_ ATC +K}i}( I ,M  @¬NN„NN? !T_ TRn +!$K!tCBB +p + +O{CqT*i)y ++ih8J @{AO¨i}(!TJ _T) ?)T*  @,@ 1 _AT+Ci}(!TJ _IT) ?T*  @,@ 1 _ATi}(!TJ _ T) ?T*  @,@ _aT{AO¨_C +K}i}( I ,M  @¬4n4n4n4nnnnn? !!T_ TC +K}i}( I ,M  @¬4n14nR4ns4nnnnn? !!T_ ATC +K}i}( I ,M  @¬NN„NN? !T_ TR +!$!tCBB + + + +O{qhT*i) ++ih8J @#B!T RB#B! R`8#!{BOA_փi~(TJ _IT) ?T*@!E@ ! L E_ATi~(TJ _ T) ?T*@!E@  L E_ATi~(TJ _T) ?iT*@!E@(!E_aTB +K|i~( I ,M  @¬nnnnnnnn? A!T_ TB +K|i~( I ,M  @¬n1nRnsnnnnn? A!T_ TB +K|i~( I ,M  @¬$N!%NB&Nc'N? AT_ TR +!$!tCBB + +#P!E +N!O{qhT*i) ++ih8J @#A!T RA#A! 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AT_ TR +!$!tCBB + +2 +O{Cq@TqT qATi~(TJ _IT) ?T* @,E@ + E_aT*#i~(TJ _T) ?IT* @,E@ J E_aTi~(TJ _ T) ?T* @,E@ * E_aT{AO¨_B +K|i~( I ,M  @¬ N!N"N#N? AT_ TB +K|i~( I ,M  @¬ n!n"n#n? AT_ aTB +K|i~( I ,M  @¬NNNN? AT_ TR@ +!$!tCBBT +B + +O{Cq@TqT qATi}(!TJ _IT) ?T*  @,@ _aT*#i}(!TJ _T) ?IT*  @,@ _aTi}(!TJ _ T) ?T*  @,@ _aT{AO¨_C +K}i}( I ,M  @¬ N!N"N#N? !T_ TC +K}i}( I ,M  @¬ n!n"n#n? !T_ aTC +K}i}( I ,M  @¬NNNN? !T_ TR +!$z!tCBB + + +O{Cq@TqT qATi}(!TJ _IT) ?T*  @,@ _aT*#i}(!TJ _T) ?IT*  @,@ _aTi}(!TJ _ T) ?T*  @,@ _aT{AO¨_C +K}i}( I ,M  @¬ N!N"N#N? !T_ TC +K}i}( I ,M  @¬ n!n"n#n? !T_ aTC +K}i}( I ,M  @¬NNNN? !T_ TR +!$!tCBB + +I +O{(R_9Ry@)_97{COB_@ +{COB__9h6@ +' +og_WO{C@qT^9-7-T(R8C +`R£4 R +H=<UA@==hlRyH9#"RcR^@9 .@?qAbX2!`RT2 +!|RN2$9h6@o +uh@T +h@aT`R4 Rf +H=<UA@==hlRyH9#RcR^@9 .@?qAb2!`R2 +!BR297xB&C) kT +i"@)h kT`RK4 R* +H=<UA@==hlRyH9#"RcRl^@9 .@?qAb1!`R1H +!`R19h6@ + +h +@h +Z85! 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T@)@( @@ @9 +4#84 N`@ !)$u  TH'C*T~@_ Tii8qTS@RN@N!@ )T@(T@hH@ R" NN}@(R +)?|6 +RN + @)@i @N +`@xh +@h +@(TG@7@H#8 N`@ !)$u X"YT7@TG@H'C*T~@_ Tii8qTS@RN@N!@ )T@(T@hH@ R" NN"@hR +)?|6 +RN + @)@i @N +`@xh +@h +@hu +? qT +@t" +h@a +@) +$ +  +} +?qTr +@t" +h@L +@ + + +h +{QOPWO_NgMoL#Km_] +Z +ccog_WO{ CCC{kAT*a  |c_`T'@))C?TT @BR[ @h'@(C*#T +"T#@#A!;\R#a!RyA!$<Ry@ ]@9* 1@_qaya!X<BRy#Pc@^9h6@ + + +T54s}7R @R} T" !@ C#aT/#@(#A!;\R#a!RyA!=Ry@ ]@9* 1@_qazya!X<BRvy#c@:H_9h6@@ + + @~ iij@"q#TK,_ "1KTK, ¬df@NN„NN`?b !T T ҋ ˌ}J ) ,@M@ LkaT BT~ -h'@ @TC}Ө %` h -a#@˿Tt @s@TT^ @{IOHWG_FgEoD_Қt5еv֚739cZT @ A C#T#@#]R#RR@ ]@9* 1@_qaBR#V@x_9h6@\ `@`#=   #0@h^9h6`@ @S@TT^ @|eC_ W -O { H%@@@2(R)q T##tRHUr#o=Cq T* )E#+ih8J @T2 T#@Ba!8bR?<!R'A!;R##@ ]@9* 1@_qa!3BR#@#_9h6@@2x4 S@2#9 ՁRg#@( T2^? aRO#@(!\I)%@)@?T{LOKWJ_IC_@2=S@2#" ՁR,#@@2) @2b ( y"[u=#@# P7WO{H@Ѷ=(R7@@ 3ѥBR'R]Ȃ]T`=h -@=C]T@ 3{GOFWE__WO { -6@}|@*@J JCkUJ} _ T@K1@BAM@yA=y<⃂<Ⴡ<+@j +h'@ @TC}Ө ` +` h +a#@˿TZ +t @s@TT^? + @9 +{IOHWG_FgEoD_ҚT;u +V=wZ<9cZT @ A C#T# @#\R#RxRx@ ]@9* 1@_qaxBRx#lb@x_9h6@ + + + `@`#Sb   #Fb@h^9h6`@ + + @S@TT^ + @ + +(bC_ W +O { HA@`  +(R)q T##tRHUr#o=Cq T*)>+ih8J @t  + T##@wA!>Rtqa!RDxA!|R@x#@ ]@9* 1@_qa8xa!X<BR4xa#@#_9h6@R +P +`  +4 S` #9 ՁR +#@( +. + t  +? aR +#@(! + +\I)A)@?T{LOKWJ_IC_` x +S` #" ՁRg +#@` b) ` B c +  +" + +M +Sa#@# D +MaWO{H@Ѷ=(R7@`@ +ѥBR'R +]Ȃ]T`=h +@=C]T`@ + +{GOFWE_a_W O +{ 6@ +|@*@J JCkUJ} _T@BAK!@A=+ =@==/@j R% -#;##ю FT +C;C#ю FT @@ !T@} iiJ -i)@bT+@ ?qT3t"?qT+t"h@"{JOIWH_G__WO{H@(R'@V2bRr%R&R!&4V2@@bT`=h -@ =g `4@{GOFWE_D_Q_WO{H@(R'@V2bRr%R4V2@@bT`=h -@ =#`4@{GOFWE_D_ WO{H@Ѷ=(R7@@ 3ѥBR'R]Ȃ]T`=h -@=C]T@ 3{GOFWE_ԭC_WO{H@ѿ(R7@@ 3"R'R#@肈#\  T@ 3h{HOGWF_EC_@(肈 +i)@bT/@ + ?qTo +t" +?qTg +t" +h@#B +# + +# + +^ +{KOJWI_H_V +S + +`_WO{H@(R'@v bRr%R&R] +b +4v @@bT`=h +@ =hI +`4@{GOFWE_D_h`_WO{H@(R'@V bRr%R + +4V @@bT`=h +@ =$ +`4@{GOFWE_D_$`WO{H@Ѷ=(R7@@@ +ѥBR'R +]Ȃ]T`=h +@=C]T@@ + +{GOFWE__C_WO{H@ѿ(R7@@@ +"R'R +#@肈#\  T@@ + +{HOGWF_EC_@(肈 6 붃#TT`=h -@=C#@  T\_WO{H@(R'@V2Rr%R&R4V2@@bT`=h -@ =]`4@{GOFWE_D_G{9)q T )U#+ih8J @@)(@+%Aj} +@=C#@  T\__WO{H@(R'@V Rr%R&RS +X +4V @@bT`=h +@ =^? +`4@{GOFWE_D_^_{9-q T)>+ih8J @@)(@+%Aj} +@)) yi@b{_@)(@+%Aj} +@)) yi{_@)(@+%Aj} +@)) ii<!^{_@)(@+%Aj} +@)) yi|!^{_@)(@+%Aj} +@)) yi!^{_@)(@+%Aj} +@)) yi"{_@)(@+%Aj} +@)) ii<!~{_@)(@+%Aj} +@)) yi|!~{_@)(@+%Aj} +@)) yi!~{_@)(@+%Aj} -+@)) yi#{_6#mO{(  T(N N @@h@? a T - @_ )T!Th@ T> ++@)) yi#{_r +#mO{(  T(N N @@h@? a T + @_ )T!Th@ Tz> G [k @ JDa -)Tth&G?h2`@i@ #6i@ o i*FKk6) +)Tyh&G?h2`@i@ #6i@ o i*FKk6) (! -" i@ BTj@J B@- TJ1C_` "TC@!(#?!T bT hh@ i@K} ik )T( @((i@ j(  !-{COB#Am_C_WO{ G@ [) -@HD!T&G?2Cb7R@ )D? T@J}D ˌDA ITC )D T#T iTC j +@HD!Ty&G?2Cb7R@ )D? T@J}D ˌDA ITyC )D T#T iTC j * TbT @T ҋD @)D?-|ӿT~ |ӏzӐ{  " #$!"#$ !1T T @@ -1612,22 +5151,21 @@ J~J}w ?T (}x@!@ !ET(@@) @@ ! T@)@(@@ ! T@@ @a@ !ETh@ @i`@@ !T!@b@@ "Th@ T@i T@@@a@ !Th@@i3 R`@@ ! TT_@ !Ti@ @j x"B T[h"@ !$T&ThB_ !T!aT-`@@ !DT@i@' !T@@R`@@ !DTR`@@ !T`@@ !T _@  T`@!@ !T"!_ !T T@+@* R`@@ !Tx"cTT*@ @+ - @ #!@ T8!_ T?T @T@@ !T@@ 5'#!5y7ˉ T#  `@@ !Td#6 )#+ih8J @֠@a@ !ETh@ ` + @ #!@ T8!_ T?T @T@@ !T@@ 5'#!5y7ˉ T#  `@@ !Td#6)?+ih8J @֠@a@ !ETh@ ` @b@@ "Th@  T@ia"bB{EODWC_BgAoƨt"wBxb~@a@ !eT@@ @@ !e T=@n=<N'@ m Ti@ha@  Ti@h&[b @a@`@@ !Th@ "%Ti @ih ',  Th@@i` @a@ !T`=@n`=@ "Ti@h' ' "Ti@h&i@i"@' !T@i+i@i"' !Ti @i NhbT @ %T@)! @ ET @ l @ AT @k! ! Tk kaaj !_T{EODWC_BgAoƨ_!@@@@ "T@ !TI@ H !%T(@I@)H @@ !eT@)@ (@@HRa@ T!'(Ra@ T)@ (@@' !TI@)HHR Na@ T Ra@ T_(Ra@ eTI@j@Ji@@!@ !T)@J@*I @@ !ET @*@ -) __ __WO{( C?T(R -J#Lii8k `ր_a@ !e Th@ ` +) __ __WO{( C?T(RJ?Lii8k `ր_a@ !e Th@ ` @b@_ "%Th@  T_i(Rb @a@`@@ !eTh@ " Ti @ih 'Ya"bB"n(Ru"vBwbd_a@ !T@_@@ !T=@n=<N'@ Ti@ha@ Ti@h&$ ETh@_i` @@ -1643,535 +5181,957 @@ (@_T)@ ,@l  y<@y<_+m#mWO{/@k@@ m@ @oo @C@-c8$$Nd n  @ # " @ @("A)A_T T @C @@ #T@A@! @C @!(# ! " @ @(!A@B @!("AJA)A_TTA@‘@-B8$ND n!B @!" # A@B @!("JA)A_T TA_AcTD|B?BT0BTA n l""" l T_TA_ k kATl|A- L ? B1MTkDm~|L + N#ND LD$Ne%N$ LaTT{WOVWU#Tm+Sm   n l!a lT @h DT) o oooo "A8"!!(L(AN)A T"_@ T 8 @!"JL*"@@ !dTAN"@(" "T@N"@@ !T!"8! !"IL)"@(" "ET -o oo$@!   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Tl@@)TmAn -@o@@@ii@@5  @M.M@ dT @!b@}ӣik!"!(#i+(({DOCWB#AmC_6C#mWO{H@ 4Q)(R#)@U еbRrR%R]3#b4oU е 3#V@4#A)* +@o@@@ii@@5  @M.M@ dT @!b@}ӣik!"!(#i+(({DOCWB#AmC_)?C#mWO{H@ 4Q)(R#)@=bRrR%RP?3#U?4o= 3#I?@4#A)* kT } *)! Tl@@)TmAn -@o@@@ii@@5  @M.M@ dT @!b@}ӣik!"!(#i+(({DOCWB#AmC_CWO{H@4Q)(R#)@@ CscSBR'R"@k@* +@o@@@ii@@5  @M.M@ dT @!b@}ӣik!"!(#i+(({DOCWB#AmC_>CWO{H@4Q)(R#)@<CscSBR'R?@k@* kT }o *)!_kTl@@TmAn -@o@@@ii@@5  @M.M@( dT @""c@}ӤikB#B($i+!( @ {DOCWBC_֊CWO{H@ -4Q)(R#)@@ CscS"R'R'C)k ? kT@o H k ) +@o@@@ii@@5  @M.M@( dT @""c@}ӤikB#B($i+!( <>{DOCWBC_}>CWO{H@ +4Q)(R#)@<CscS"R'R>'C)k ? kT@o H k ) ? k')"T? kT *l} *k! To@@ TpAq -@`@@@hl1@@1B0@N1M@( dT @""@.}jnB#B($j.!( @ {DOCWBC_.C#mWO{H@ 4Q)(R#)@U RrR%R&RU3#Z4oU 3#N@4#A)* +@`@@@hl1@@1B0@N1M@( dT @""@.}jnB#B($j.!( <>{DOCWBC_!>C#mWO{H@ 4Q)(R#)@=RrR%R&RH>3#M>4o= 3#A>@4#A)* kT } *)! Tl@@)TmAn -@o@@@ii@@5  @M.M@ dT @!b@}ӣik!"!(#i+(({DOCWB#AmC_5@_OAWèOAWè_O¨_`@]^_ֶѨr8_\9_OBWA_Ĩ_t6@_O{ C( %@@MRi ) 9(@(Q@S9AR 5=<h Q0@ =<<=9`b#H 9+Ѵa"#^T@@@@?+@T@@@@?979(7 ^) )%@)@?T{IOH_@U9(6@Q97973@F9h6969h6@:#R_O{CR=  !B B`BSA{a !0 GA{__{RH {_H (__{R -o E=A@9{_O{C)@ (?T`"{AO¨_?T{AO¨_ @@<`4{AO¨_@ @_O{C EA@@@?{AO¨_O{C EA@@@?{AO¨_WO{ @HR_9h 53==(R#yK9 @ WR@V @9(@qXK3ɂ*@qU47hT -KT`47KT@'aBc@S@TT87шr8(6^eшr8h6]`@\{FOEWD_C_EO{C@cHR_9(R#yh 53==K9`@@ @?RBc )R)`@t@ iqT9@@?_9h6@&( AcA@?{EOD__9h6@( AcA@.CWO{A8h4#@4t"@ -qBT<U@#RX(R9@4 @TT87Ѩr8(6^Ѩr8h6]@ t"@ -qTa !h8Ra !9BRa !h:RUt"@`@y`@h@@?{DOCWBC_@#F97# mo g_WO{=7`@xa=Oha`BN@@gqkTC;@`@Syb@B"@aab7@ I*%B))} -I)~ii@b ((i)g@?@;@`@7ySC3!  T@@?@Cks BRgjT?@7@?@w@;@aD-@ @R?;@@ )@C ?#E#!)R)R+ IGA||C!6G $@))C#BW$@))C#`@x_g o -[BRҴK@sh" h@ @?t'@sh" h@ @?h/@sh" h@ @?\{ROQWP_OgNoM#Lm_@t" @ @?A'@t" @ @?5/@t" @ @?)!@@ @$@RWO{@ -T= -@ =<@ <~hak@ DlU}  H TJ JDJ} K1KU_ 1 T|cR= -@* =<@* <~*t.@T`<l^, <=`<l_, <)}?l  TtV@i*h -ATT{BOAWè,T87Ѩr8(6^!Ѩr8h6]i*h -{BOAWè_O{CR  !B BdB.{a !! GA{_WO{H@4Q;)(R#:)@@ ѤѥѦBR'R5]kȂ]kT`@h@C]kT@ {GOFWE_U_WO{H@4Q(R)3@V bRrR%R&R4V \;@kbT`@h@i`4\{GOFWE_D__WO{H@4Q(R)3@V bRrR%R4V \;@kbT`@h@%`4\{GOFWE_D_͝WO{H@4Q;)(R#:)@@ ѤѥѦBR'Ru]kȂ]kT`@h@C]kT@ V{GOFWE_֕C_WO{H@4Q9)(RCC@@ ѥѦ"R'R;[y)k肈  kT@ *{HOGWF_EC_֩C\( k肈 +@o@@@ii@@5  @M.M@ dT @!b@}ӣik!"!(#i+(({DOCWB#AmC_=OAWè=5@_O¨_`@=OBWA_Ĩ_=]=\9_֠^=6@_ֶѨr8_OAWè__^=t=]=O{ CA@R))9(@(Q@S9 R=H=<<@ =<<=9`b#t<5+Ѵa"^T@@@@?+@T@@@@?979(7H^)A)@?T{IOH_@D=9(6@@==97979@5=9h6969h6@)=9R_O{CR,= !lCBBB=0=}9{! -;q@A{__={R=!{_!(__<{R<o=F=A@9{_O{C)@H0?T`"{AO¨_?T{AO¨_ @@@>`4{AO¨__O{C=FA@@@?{AO¨_O{C=FA@@@?{AO¨<_WO{ @fHR_9?==(R#yK9 @ WR@V @9(@qXK3ɂ*@qU=47hT +KT{=`47KT@'aBc!@S@TT87шr8(6^T<шr8h6]O<@K<{FOEWD_C_֩O{C@cHR_9(R#y?==K9`@@ @?R1<c )R)`@t@ iqT9@@?_9h6@<-AcA@L{EOD__9h6@<-AcA@;d8CWO{@#@T @TT87Ѩr8(6^;Ѩr8h6];@ ;`"@qTӤ!RФͤ!BR!R4`"@`@h@@?{DOCWBC_#8!# mo g_WO{=7o8`@xa=Oha`BN@@gqkTCC9;@`@b@B"@aab@b7@ I*%C))} +I)~ii((i)v@g@?@?8;@SC3!  T@@?@}ks BRgjT?@7@?@w@x@|9;@aD-@ @R?;@a@@ -@C ?#E#!)R)R7;1@A||C6  9$@))C#B<9$@))C#99`@'_g o +[BR5 @sh" h@ @?}:'@sh" h@ @?q:/@sh" h@ @?e:{ROQWP_OgNoM#Lm_@t" @ @?J:'@t" @ @?>:/@t" @ @?2: 7!@@ @$@RO{CR: !pCBB::6{!X-;9u@A{_WO{H@4Q;)(R#:)@ѤѥѦBR'R:]kȂ]kT`@h@C]kT:{GOFWE_ֻ_WO{H@4Q(R)3@bbRrR%R&Rf:k:4b\;@kbT`@h@R:`4\{GOFWE_D_w_WO{H@4Q(R)3@bbRrR%R":':4b\;@kbT`@h@f:`4\{GOFWE_D_3WO{H@4Q;)(R#:)@ѤѥѦBR'R:]kȂ]kT`@h@C*]kT9{GOFWE_C_WO{H@4Q9)(RCC@ѥѦ"R'R9[y)k肈  kT9{HOGWF_EC_֩C\( k肈 6  -k[9)TkT`@h@C\  kT\N_WO{H@4Q(R)3@V RrR%R&R4V \;@kbT`@h@b`4\{GOFWE_D_ -WO{H@4Q;)(R#:)@@ ѤѥѦBR'R]kȂ]kT`@b -= =C]kT@ {GOFWE_Ҝ_WO{H@4Q(R)3@V bRrR%R&RX]4V \;@kbT`@b -==D`4\{GOFWE_D_֎_WO{H@4Q(R)3@V bRrR%R4V \;@kbT`@b -==`4\{GOFWE_D_JWO{H@4Q;)(R#:)@@ ѤѥѦBR'R]kȂ]kT`@b -= =CQ]kT@ {GOFWE_C_WO{H@4Q9)(RCC@@ ѥѦ"R'R[y)k肈  kT@ {HOGWF_EC_֩C\( k肈 +k[9)TkT`@h@C\  kT\_WO{H@4Q(R)3@bRrR%R&R_9d94b\;@kbT`@h@K9`4\{GOFWE_D_pWO{H@4Q;)(R#:)@ѤѥѦBR'R=9]kȂ]kT`@b += =CZ]kT9{GOFWE_8_WO{H@4Q(R)3@bbRrR%R&R884b\;@kbT`@b +==8`4\{GOFWE_D__WO{H@4Q(R)3@bbRrR%R884b\;@kbT`@b +==8`4\{GOFWE_D_ְWO{H@4Q;)(R#:)@ѤѥѦBR'R}8]kȂ]kT`@b += =C]kT^8{GOFWE_xC_WO{H@4Q9)(RCC@ѥѦ"R'RC8[y)k肈  kT28{HOGWF_EC_֩C\( k肈 6  k[9)TkT`@b -= =C\  kT\˛_WO{H@4Q(R)3@V RrR%R&RQV4V \;@kbT`@b -===`4\{GOFWE_D_ևg_WO{H@h4Q)(R#)@@ CscSBR'R.@k@@kbTxf@z^Av@(@I@% @*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)@ {FOEWD_CgB_og_WO{H@4Q)(R#)@5 bRrR%R&R3#45 @ @kbTyj@{bAw@H@i@%)@*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)3#`4@{FOEWD_CgBoA_og_WO{H@4Q)(R#)@5 bRrR%Rl3#q45 @ @kbTyj@{bAw@AH@i@%)@*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)3#F`4@{FOEWD_CgBoA_g_WO{H@h4Q)(R#)@ CscSBR'R6@k@@kbTxf@z^Av@(@I@% @*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i) {FOEWD_CgB_og_WO{H@4Q)(R#)@ CscS"R'R[C)k  kT {GOFWE_DgCoB_@@( k += =CL\  kT\1_WO{H@4Q(R)3@bRrR%R&R774b\;@kbT`@b +==7`4\{GOFWE_D_g_WO{H@h4Q)(R#)@CscSBR'R7@k@@kbTxf@z^Av@y8(@I@% @*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)7{FOEWD_CgB_og_WO{H@4Q)(R#)@bbRrR%R&RN73#S74b@ @kbTyj@{bAw@)8H@i@%)@*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)3#(7`4@{FOEWD_CgBoA_og_WO{H@4Q)(R#)@bbRrR%R63#64b@ @kbTyj@{bAw@7H@i@%)@*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)3#6`4@{FOEWD_CgBoA_g_WO{H@h4Q)(R#)@CscSBR'R6@k@@kbTxf@z^Av@7(@I@% @*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)6{FOEWD_CgB_og_WO{H@4Q)(R#)@CscS"R'Rv6[C)k  kTe6{GOFWE_DgCoB_@@( k 6  -k[)bTkT@  kTyj@{bAw@H@i@%)@*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)og_WO{H@4Q)(R#)@5 RrR%R&Rq3#v45 @ @kbTyj@{bAw@FH@i@%)@*M(@@  T}!ih!"@ |}iiA(ai)"@ii (`i)3#K`4@{FOEWD_CgBoA_CWO{H@4Q)(R#)@ CscSBR'R=@k@) kbTo?kTj.@l -@J@k@@l1K@jM,AM@( dTm@@}cil""B(#bi,!( A {DOCWBC_C#mWO{H@4Q)(R#)@5 bRrR%R&R3#4o5  3#4#A)) kbTj.@l -@J@k@@l1K@jM,@M@ dTm@@}bil!!("ai,((@{DOCWB#AmC_C#mWO{H@4Q)(R#)@5 bRrR%R3#4o5  3#4#A)) kbTj.@l -@J@k@@l1K@jM,@M@ dTm@@}bil!!("ai,((@{DOCWB#AmC_CWO{H@4Q)(R#)@ CscSBR'R`@k@) kbTo?kTj.@l -@J@k@@l1K@jM,AM@( dTm@@}cil""B(#bi,!( A 4{DOCWBC_CWO{H@4Q)(R#)@ CscS"R'R'C)k ? 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Z1_ TH}eB|eAfj68bhHRu[g@ T\T^9B|e(A Ri[ 7fH[k78bt*@_`T@<K_<b_}?_Ka  Tt*@u&h -_T{EODWC_BgAoƨ{eTSaH8h6@^seu&h -{EODWC_BgAoƨ_d dd 9[eDbu@b_WO{@v@@TttG0 ׂ"ѿT^`>e[1Te4he@Rn`@u.e{COBWA_Ĩ_;O{CR.e  ՁTX BTXDe2eb{oI c UXA{_CoO{Re!ReCCRqf#Re#RP#,d{TOSoRC_#"daO{Rd!RgeCCRGfӑG0  RБ͑RPX;d{BOA_O;N;da!@@ @$@R_WO{8@@@uѿ!Ti@4yw4@@*?DWAT@?kTg Th@yw4 -՚h@ywtRd@Xh@#a"@#""@ %T'ѿ  *! ) !1Iu@ѿTi@(ywAT@kT@@ TT ՚( Ti@(yw( @ +)C)} *_ j1? Z1_ TH}i^B|c^A`_j68bhHRu[_@ T\T^9B|E^(A Ri[ 7@_H[k78bt*@_`T@<K_<b_}?_Ka  Tt*@u&h +_T{EODWC_BgAoƨ^TSaH8h6@^^u&h +{EODWC_BgAoƨ_\\\ 9]^ZuZZ_WO{@v@@T4LP ׂ"ѿT^`][1Tk^4^@R`@u]{COBWA_Ĩ_6O{CR]  Ձ\X \X]]Z{S a\ ;]XA{_CoO{R]!R,^CCR!_#RX/#Rd#:.]{TOSoRC_#0.{]YO{Rx]!R^CCR^h!Lp  RebR6ΥY]{BOA_ťĥM]Y!@@ @$@R_WO{8@@@uѿ!Ti@4yw4@@*?DWAT@?kTg Th@yw4 +՚h@ywtR ]@Xh@#a"@#""@ %T'ѿ  *! ) !1Iu@ѿTi@(ywAT@kT@@ TT ՚( Ti@(yw( @ ՚i@(yw( - A(y7@@ѿ aT cT ՚(i@( h@hR{COBWA_Ĩ_daWO{(aTTR?Tbu@T{BOAWè("Th@#a"@!)TgX 80.&T)R(!Ț00?CT{BOAWè_b0?T{BOAWèO{C(}ӈ}c`@h@cti@?y(TH A @gX 80. &HT) `@@cP?cT, Ԛl@y) + A(y7@@ѿ aT cT ՚(i@( h@hR{COBWA_Ĩ_\"YWO{(aTTR?Tx[u@T{BOAWè("Th@#a"@!)TgX 80.&T)R(!Ț00?CT{BOAWè_P[0?T{BOAWèO{C(}ӈ}u\`@h@j\ti@?y(TH A @gX 80. &HT) `@@R\P?cT, Ԛl@y) @  T' @*K@cTl Ԛ Tl@yk N @N@@k@T m@k}ӭik@l@ikjy+ @@ -10335,13 +16966,13 @@  {AO¨_<{ @j(@gX 80. &TI)_hT ʚ) @yi,@ TJ+@kT!@+@ T cTl ʚ  T +@kT!@+@ Tk - T{B_#xQ@@!c R{B_WO{CY |97h97h97h^9H7t6@`"@``&c`{BOAWè_`f@bh96`Z@bh9H6`N@bh^96`B@bt6@h:@T Ta 8i6^b`6@t:b`"@_WO{@@T%G0 h + T{B_#@@[ R{B_WO{#b |97h97h97h^9H7t6@`"@``&[`{BOAWè_`f@[h96`Z@[h9H6`N@[h^96`B@[t6@h:@T Ta 8i6^~[`6@t:z[`"@_WO{@@TTLP h @@T!w -^`b@1TWc@4b@R`@@b{COBWA_Ĩ_I9 qhT*D Պ+ih8J @ M0 N_qHT*D ՋLii8k ` UTX!RRRT`M0 ե CUTX!R$RRJ գTTXRDRRC TTXRdRR< cSTXRRR5 RTXRRR. գPTXRRR' ՃOTXRRR _qHT*D ՋLii8k ` cOTX!RR%R M0 ^ NTX!R$R%R CNTXRDR%R գMTXRdR%R MTXRR%R cLTXRR%R CJTXRR%R #ITXRR%R_qHT*JD ՋLii8k ` ITX!RRERM0  ՃHTX!R$RER GTXRDRER CGTXRdRER գFTXRRER FTXRRER CTXRRER BTXRRER_qT*D ՋLii8k ` Mp @M0  ATX!RReRy CATX!R$ReRr գ@TXRDReRk @TXRdReRd c?TXRReR] >TXRReRV_qk +^``[@1T[@4[@R`@@R[{COBWA_Ĩ_ֿqhT*gJ Պ+ih8J @T TN_qHT*cJP ՋLii8k ` |\X!RRR`T ՟ C|\X!R$RR գ{\XRDRR~ {\XRdRRw cz\XRRRp y\XRRRi գw\XRRRb Ճv\XRRR[_qHT*ZJP ՋLii8k ` cv\X!RR%RHT X u\X!R$R%R> Cu\XRDR%R7 գt\XRdR%R0 t\XRR%R) cs\XRR%R" Cq\XRR%R #p\XRR%R_qHT*JRJP ՋLii8k ` p\X!RRERT  Ճo\X!R$RER n\XRDRER Cn\XRdRER գm\XRRER m\XRRER j\XRRER i\XRRER_qT*IJP ՋLii8k ` TP @T  j\X!RReR Cj\X!R$ReR գi\XRDReR i\XRdReR ch\XRReR g\XRReR_qT*jBJP ՋLii8k ` {TP ՕxT Ւ ca\X!RRRx `\X!R$RRq #`\XRDRRj Ճ_\XRdRRc ^\XRRR\ C^\XRRRU_qT**;JP ՋLii8k ` uT0 Y@qT V ]\X!RRR< C]\X!R$RR5 գ\\XRDRR. \\XRdRR' c[\XRRR  Z\XRRR_qk T*_ qT }* ?@@T q} + Jk!Td<C A@d4N(&M6c< m6maѣMc<C4Nc(m&M6bm< m6m!ѢMb="4NB(M&M6aM< m6m!Ma=4N!(-&6` -< 6-< M6maM +} +  J!k! Tl@I@ *Ti?T__qk T*_ qT }* ?@@T q} + Jk!Td<C A@d4n(&M6c< m6maѣMc<C4nc(m&M6bm< m6m!ѢMb="4nB(M&M6aM< m6m!Ma=4n!(-&6` -< 6-< M6maM +} @@ -10353,1915 +16984,1809 @@ + Jk!Td<C A@4n(&M6c< m6maѣMc<c4nc(m&M6bm< m6m!ѢMb=B4nB(M&M6aM< m6m!Ma=!4n!(-&6` -< 6-< M6maM +} +  J!k! 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N4dmlc2io15SingleFileSplitEN4dmlc10InputSplitEN4dmlc2io18ThreadedInputSplitEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEENSt3__110__function6__funcIZN4dmlc2io18ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUlPPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEENSt3__110__function6__baseIFbPPN4dmlc2io14InputSplitBase5ChunkEEEEZN4dmlc2io18ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_NSt3__110__function6__funcIZN4dmlc2io18ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUlvE_NS_9allocatorIS7_EEFvvEEEZN4dmlc2io18ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUlvE_N4dmlc2io16CachedInputSplitENSt3__110__function6__funcIZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_NSt3__110__function6__funcIZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE_NS_9allocatorIS5_EEFvvEEEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE_NSt3__110__function6__funcIZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_NSt3__110__function6__funcIPFvvENS_9allocatorIS3_EES2_EEPFvvEFvvEN4dmlc2io12LineSplitterEN4dmlc2io16RecordIOSplitterEN4dmlc2io23IndexedRecordIOSplitterEIvvIIIIIIIIIIIIIIIIIIvIIIIIIIIIIIIIIIIIIIIIIIIIII*/ *fffffffffffffffffffffffffffffffffffffffffff fff).38=j[ !%(,"&*.26:!III[/#-N4dmlc2io14InputSplitBaseENSt3__16__nodeIcEENSt3__110shared_ptrINS_13__empty_stateIcEEE27__shared_ptr_default_deleteIS2_S2_EENSt3__113__empty_stateIcEENSt3__120__l_anchor_multilineIcEENSt3__120__r_anchor_multilineIcEENSt3__115__word_boundaryIcNS_12regex_traitsIcEEEENSt3__111__lookaheadIcNS_12regex_traitsIcEEEENSt3__123__match_any_but_newlineIcEENSt3__120__match_char_collateIcNS_12regex_traitsIcEEEENSt3__118__back_ref_collateIcNS_12regex_traitsIcEEEENSt3__128__begin_marked_subexpressionIcEENSt3__126__end_marked_subexpressionIcEENSt3__16__loopIcEENSt3__117__repeat_one_loopIcEENSt3__121__empty_non_own_stateIcEEN4dmlc2io10FileSystemEN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamE/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccInvalid pointer argument: configouturiXGDMatrixCreateFromURIsilentdata_split_modeDMatrix/Booster has not been initialized or has already been disposed.static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structurefmapfnameftypeUSE_RMMBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPCLANG_VERSIONDEBUGUSE_FEDERATEDjson_strCheck failed: ec == std::errc()Unknown global parameters: { }Check failed: e) %02d:%02d:%02d/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hArgument `` is required for ``.Invalid type for: ``, expecting one of the: {`}, got: `/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset IteratorAdapterCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.cache_prefixXGDMatrixCreateFromCallbacknthreadnextresetmissingArgument `missing` is required.`, XGDeviceQuantileDMatrixCreateFromCallback1.7.0XGQuantileDMatrixCreateFromCallbackCheck failed: _ref/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Slicing DMatrix columns is not supported for Proxy DMatrix.Proxy DMatrix cannot return data batch.c_interface_strCheck failed: p_mCurrent DMatrix type does not support set data.Check failed: c_interfaceindptrindicesdataXGDMatrixCreateFromCSREx2.0.0XGDMatrixCreateFromCSRc_json_config/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.hget<Array const>(array).size() == 1Column: typestrptr % alignment == 0Input pointer misalignment.n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.streamversionMissing `version' field for array interfaceMissing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4shapeMissing `shape' field for array interfaceMissing `data' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.`typestr' should be of format <endian><type><size of type in bytes>.Check failed: sizeof(long double) == 16Half type is not supported.- is not supported.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html -Or open an issue.i < DCheck failed: m == 1 || n == 1stridesj_strides.size() == j_shape.size()stride and shape don't match.size == 0Empty data with non-zero shape.Empty data passed in.maskj_shape.size() == 1type_length == 1mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.strides.size() == 1get<Integer>(strides.at(0)) == type_lengthMemory should be contiguous.Only dimensional array is valid.Check failed: mask_it == array.cend() || IsA<Null>(mask_it->second)Masked array is not yet supported.+sCheck failed: std::string(schema->format) == "+s"Check failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falseColumn data type not supported by XGBoostCheck failed: rb_Cannot import non-existent record batchCheck failed: !schema_->columns.empty()Cannot import record batch without a schemanbatchXGDMatrixCreateFromArrowCallbackCannot reset RecordBatchesIterAdapterbinary saving only supported by SimpleDMatrixfieldout_featureslenCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupout_lenout_dptrCheck failed: pp_mout_indptrout_indicesout_datap_m->Info().num_col_ <= std::numeric_limits<unsigned>::max()Check failed: page.page/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptrn_threads >= 1;unknown;unknown;0;0;;dmatsjson_parametersout_strdtrainCheck failed: dtrgradhessevnamesout_resultlearnergradient_boostergblineardartgbtreegbtree_model_paramnum_parallel_treeUnknown booster:Booster has not been initialized or has already been disposed.DMatrix has not been initialized or has already been disposed.typeXGBoosterPredictFromDMatrixiteration_beginiteration_endntree_limitCheck failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.trainingstrict_shapeout_dimout_shapechunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast<bst_ulong>(1), std::multiplies<>{}) == chunksize * rowsget<Integer const>(config["cache_id"]) == 0Cache ID is not supported yetInplacePredictImplCheck failed: p_predtCheck failed: proxyInvalid input type for inplace predict.array_interfacejsonubjrstr.size() >= 3str[0] == '{'wbufwrite position exceed fixed buffer size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hjson_configformatXGBoosterSaveModelToBufferdeprecatedUnknown format: `read can not have position excceed buffer lengthXGBoosterGetModelRaw` is deprecated, please use `XGBoosterSaveModelToBuffer` instead.Check failed: p_outtextout_modelscustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.fqfeature_map.Size() == n_features/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast<int>(names_.size())iintfloatcunknown feature type, use i for indicator and q for quantitysuccesskeyfeaturesfeature_namefeature_typeUnknown field for Booster feature info:importance_typeXGBoosterFeatureScorefeature_mapfeature_namesout_n_featuresfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()out_scoresname_str/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.hWorld size is less than 1.Rank is less than 0. is greater than world_size - 1: ./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support.Unknown communicator type.XGBOOST_COMMUNICATORrabitfederatedUnknown communicator type xgboost_communicator==1=0/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hFailed to initialize RabitUnknown data typeUnknown allreduce operation/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/socket.ccFailed to get addr info for: 0.0.0.0::127.0.0.1::1Check failed: !this->IsClosed()str.size() < std::numeric_limits<std::int32_t>::max()this->SendAll(&len, sizeof(len)) == sizeof(len)Failed to send string length.bytes == str.size()Failed to send string.send/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.hFailed to call ``: this->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to recv string length.bytes == lenFailed to recv string.recvstatic_cast<std::int32_t>(socket.Domain()) == static_cast<std::int32_t>(addr.Domain())socketsystem::CloseSocket(handle_)NaN0E0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachable/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccentries_per_column.size() == info.num_col_/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast<size_t>(n_columns)column_sizes_tloc[i].size() == static_cast<size_t>(n_columns)/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/threading_utils.hSortedSketchContainerinvalid init parameterCheck failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps))/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.ccCheck failed: outpointer_ <= buffer_.size()file://://rbOpening failed: Not implemented/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc\\\\"\b\f\n\r\t\u%04xNumberArrayU8ArrayObject of type can not be indexed by string. can not be indexed by Integer.Unknown construct, around character position: , " "\0 "Unknown escapenullExpecting null value "null"ch != -1cursor_.Pos(): raw_str_.size():Expecting digit` is not supported for typed array.f64 is not supported.High precision number is not supported./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hbeg <= size_Expecting: "", got: "EOF"\0"/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/common.hPesudoHuberParamhuber_slopeThe delta term in Pseudo-Huber loss.Required parameter of No conversion could be performed*p == ')'Invalid NAN literalkey SketchContainerImpl/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cccolumns_size_.size() != 0n_threads_ >= 1group_ptr.size() >= 2group_ptr.back() == n_samplescount <= total_entriesmingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0float32float64bool8int32int8int16int64/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hUnknown data table type.base_rowid < group_ptr.back()Row: is not found in any group.worker_segments.size() >= 1AllReducen_columns == sketches_.size()Number of columns differs across workersnum_cuts.size() == 0feature_ptr.front() == 0rank_size == totalCheck failed: reduced[i].dataCheck failed: worker_feature.data()PushRowPagesketches_.size() == n_columnsweights.size() == info.num_row_hessian.size() == info.num_row_group_ptr.back() == hessian.size()cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max()cut_size > cuts->cut_ptrs_.HostVector().back()Check failed: a.dataCheck failed: a.data && reduced[fidx].data check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=Check failed: nbig < nquantile: too many large chunk] rmin=, rmax=, wmin=, v=/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than max_cat + 1 >= n_categoriesMaximum cateogry should not be lesser than the total number of categories.HostSketchContainerPushColPagesketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()aft_loss_distributionnormalextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time modeloptional, default=Enum ( exisit!Enums: ), /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor (): ========/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccInvaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again.fi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: validInput data contains `inf` or `nan`All feature_types must be one of {int, float, i, q, c}.base_marginCheck failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.labelthis->labels.Size() % this->num_row_ == 0Incorrect size for labels.Label contains NaN, infinity or a value too large.qidCheck failed: non_dec`qid` must be sorted in non-decreasing order along with data.weightWeights must be positive values.label_lower_boundlabel_upper_boundfeature_weightsFeature weight must be greater than 0.Unknown key for MetaInfo: Check failed: key matrix with entries loaded from transpose.offset.Size() == num_columns + 1self_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()key >= builder_base_row_offset/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hnum_rownum_colnum_nonzerolabelsgroup_ptrweightslabels_lower_boundlabels_upper_boundfeature_typesfield_cnt == kNumFieldWrong number of fieldsBinary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.major >= 1minor >= 6Check failed: fi->Read(&num_field)MetaInfo: invalid formatnum_field >= expectedMetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.)MetaInfo: the given binary file contains extra fields which will be ignored.MetaInfo: Invalid format for Check failed: strm->Read(&name)name == expected_name Expected field: , got: Check failed: strm->Read(&type_val)Check failed: type == expected_typeExpected field of type: got field type: Check failed: strm->Read(&is_scalar)Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: strm->Read(field)Check failed: !is_scalar to be a tensor; got a scalarCheck failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field) to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.this->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/validation.hCheck failed: valid_query_groupInvalid group structure.<Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info.size == this->num_col_Length of must be equal to number of columns.Check failed: infoUnknown feature info name: Unknown feature info: this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0shape[i] == r.Shape(i)group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.group_ptr_.back() == num_row_Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.weights_.Size() == num_row_Size of weights must equal to number of rows.labels.Shape(0) == num_row_Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.Check failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.hcache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..rautoLoad part of data partscsvlibsvmNo format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format=No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csvEncountered parser error: -/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()Check failed: parser_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.hCheck failed: array.valid.Size() == 0Meta info like label or weight can not have missing value./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: columns_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: p_fmat->SingleColBlock()Check failed: !sorted_sketchrow_ptr.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalrawRaw GHistIndex binary data format.Check failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hcount_ == n_batches_count_ >= 1Check failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.Check failed: proxy_Check failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.fi->Tell() == offsetCheck failed: fmt->Read(page.get(), fi.get())Read a page in seconds./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type format.front() == '.'thread constructor failed/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.hUnknown type: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./any.hCheck failed: type_ != nullptrThe any container is empty requested=Check failed: *(type_->ptype_info) == typeid(T)The stored type mismatch stored=Check failed: !cache_info_->written MB written in Check failed: page_this->count_ >= 1source_->Iter() == this->count_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccCheck failed: ref_Check failed: p_cutsref_->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.n_features == num_cols()Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.n_features >= 1Data must has at least 1 column.accumulated_rows == Info().num_row_Check failed: p_sketchrbegin == Info().num_row_proxy->Info().labels.Size() == 0Check failed: ellpack_`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.Iterative DMatrix must have at least 1 batch.d == batch_param_.gpu_idCheck failed: proxy_handleInvalid proxy handle.Check failed: typed/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.hCheck failed: !std::isnan(sparse_thresh)/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hInconsistent max_bin between Quantile DMatrix and Booster:Check failed: !param.regen && param.hess.empty()Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.page.row_ptr.size() == h_offset.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptrSlicing DMatrix is not supported for Quantile DMatrix./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same<AdapterT, CSCAdapter>::value)Expecting CSCAdapter/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hparam.gpu_id >= 0param.max_bin >= 2Check failed: r != BatchParam{}Batch parameter is not initialized.Generating new Gradient Index.param.gpu_id == -1batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid input file formattmagic == kMagicinvalid format, magic number mismatchoffset_vec.back() == info_.num_nonzero_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnCheck failed: data_Column is empty.row.page/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_DMatrix-rinfo_.num_col_ != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hMake cache:.col.pagethis->Info().num_col_ != 0.sorted.col.pagethis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size().gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_Check failed: kv.secondCouldn't remove external memory cache file ; you may want to remove it manuallySlicing DMatrix is not supported for external memory.Can not obtain a single CSR page for external memory DMatrixiter_.Next() == 1Must have at least 1 batch.Raw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage filen_bytes == (page->data).Size() * sizeof(Entry)Check failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cclayer_begin == 0Linear booster does not support prediction range.GBLinearTrainParamLinear booster, implement generalized linear model.updatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.get<String>(in["name"]) == "gblinear"modelgpu_coord_descentcoord_descentLoading configuration on a CPU only machine. Changing updater to `coord_descent`./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(&param_, sizeof(param_)) == sizeof(param_)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster.DoBoostPredictBatchPredictBatchInternalbase_margin.Size() == nsize * ngroup/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hInplace predict is not supported by current booster.gblinear does not support prediction of leaf index { "bias": [ ], "weight": [ ] }bias: +Or open an issue.i < DCheck failed: m == 1 || n == 1stridesj_strides.size() == j_shape.size()stride and shape don't match.size == 0Empty data with non-zero shape.Empty data passed in.maskj_shape.size() == 1type_length == 1mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.strides.size() == 1get<Integer>(strides.at(0)) == type_lengthMemory should be contiguous.Only dimensional array is valid.Check failed: mask_it == array.cend() || IsA<Null>(mask_it->second)Masked array is not yet supported.XGDMatrixCreateFromCSCExXGDMatrixCreateFromCSCfloat32float64bool8int32int8int16int64Unknown data table type.+sCheck failed: std::string(schema->format) == "+s"Check failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falseColumn data type not supported by XGBoostCheck failed: rb_Cannot import non-existent record batchCheck failed: !schema_->columns.empty()Cannot import record batch without a schemanbatchXGDMatrixCreateFromArrowCallbackCannot reset RecordBatchesIterAdapterbinary saving only supported by SimpleDMatrixfieldout_featureslenCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupout_lenout_dptrCheck failed: pp_mout_indptrout_indicesout_datap_m->Info().num_col_ <= std::numeric_limits<unsigned>::max()Check failed: page.page/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.hn_threads >= 1;unknown;unknown;0;0;;The quantile cut hasn't been generated yet. Unless this is a `QuantileDMatrix`, quantile cut is generated during training./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/context.hUnknown device.cpucudai < data.size()end <= data.size()n_numeric == n_features - n_categoriesindptr.back() == vals.size() + mins.size() - n_categories<dmatsjson_parametersout_strdtrainCheck failed: dtrgradhessevnamesout_resultlearnergradient_boostergblineardartgbtree_model_paramnum_parallel_treeUnknown booster:Booster has not been initialized or has already been disposed.DMatrix has not been initialized or has already been disposed.typeXGBoosterPredictFromDMatrixiteration_beginiteration_endntree_limitCheck failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.trainingstrict_shapeout_dimout_shapechunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast<bst_ulong>(1), std::multiplies<>{}) == chunksize * rowsInplacePredictImplCheck failed: p_predtp_predt->Size() >= n_samplesCheck failed: proxyInvalid input type for inplace predict.array_interfacejsonubjrstr.size() >= 3str[0] == '{'wSaving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.bufcurr_ptr_ + size <= buffer_size_json_configformatXGBoosterSaveModelToBufferdeprecatedUnknown format: `read can not have position excceed buffer length/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hXGBoosterGetModelRaw1.6.0Check failed: p_outtextout_modelscustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.fqfeature_map.Size() == n_features/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast<int>(names_.size())iintfloatcunknown feature type, use i for indicator and q for quantitysuccesskeyfeaturesfeature_namefeature_typeUnknown field for Booster feature info:importance_typeXGBoosterFeatureScorefeature_mapfeature_namesout_n_featuresfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()out_scoresname_str/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.hWorld size is less than 1.Rank is less than 0. is greater than world_size - 1: ./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support.Unknown communicator type.XGBOOST_COMMUNICATORrabitfederatedin-memoryin-memory-ncclUnknown communicator type xgboost_communicator==1=0/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hFailed to initialize RabitUnknown data typeUnknown allreduce operationFloating point types do not support bitwise operations.IN_MEMORY_WORLD_SIZEIN_MEMORY_RANKin_memory_world_sizein_memory_rank/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/in_memory_communicator.hFederated world size must be set.Federated rank must be set.Unknown data type.rank/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/in_memory_handler.ccCheck failed: world_size_ < world_sizeIn memory handler already initialized.unique_lock::unlock: not lockedCheck failed: world_size_ > 0In memory handler already shutdown. rank : waiting for current sequence number: handling request: all requests received: waiting for all clients: sending reply: all replies sentAllgatherInvalid data typeInvalid reduce operationAllreduceBroadcast/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/socket.ccFailed to get addr info for: 0.0.0.0::127.0.0.1::1Check failed: !this->IsClosed()str.size() < std::numeric_limits<std::int32_t>::max()this->SendAll(&len, sizeof(len)) == sizeof(len)Failed to send string length.bytes == str.size()Failed to send string.send/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.hFailed to call ``: this->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to recv string length.bytes == lenFailed to recv string.recvstatic_cast<std::int32_t>(socket.Domain()) == static_cast<std::int32_t>(addr.Domain())socketsystem::CloseSocket(handle_)NaN0E0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hbad_malloc: Failed to allocate bytes./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/../common/ref_resource_view.hmem_->Size() >= nreinterpret_cast<std::uintptr_t>(ptr) % std::alignment_of_v<T> == 0w_n_bytes == n_bytesw_n_bytes == remaining\\\\"\b\f\n\r\t\u%04x` is deprecated since, use `` instead.The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead. + + E.g. tree_method = "hist", device = "cuda" +/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/error_msg.ccYou have manually specified the `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.device E.g. device=cpu/cuda/cuda:0Empty dataset at worker: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: , while the input data is on: . +Potential solutions: +- Use a data structure that matches the device ordinal in the booster. +- Set the device for booster before call to inplace_predict. + +This warning will only be shown once. +/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachable/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccCheck failed: !offsetsCheck failed: offsetsentries_per_column.size() == info.num_col_/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast<size_t>(n_columns)column_sizes_tloc[i].size() == static_cast<size_t>(n_columns)/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/threading_utils.hSortedSketchContainerinvalid init parameterCheck failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps))/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.ccCheck failed: outfd >= 0Failed to open:. offset <= std::numeric_limits<off_t>::max()File size has exceeded the limit on the current system.ptr != ((void *)-1)Failed to map: pointer_ <= buffer_.size()file://://rbOpening failed: munmap(handle_->base_ptr, handle_->base_size) != -1Faled to call munmap: close(handle_->fd) != -1Faled to close: Not implemented/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.ccCheck failed: ret.ec == std::errc()NumberArrayU8ArrayObject of type can not be indexed by string. can not be indexed by Integer.Unknown construct, around character position: , " "\0 Unknown escapenullExpecting null value "null"ch != -1cursor_.Pos(): raw_str_.size():Expecting digit` is not supported for typed array.f64 is not supported.High precision number is not supported./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hbeg <= size_Expecting: "", got: "EOF"\0"/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/common.hPesudoHuberParamhuber_slopeThe delta term in Pseudo-Huber loss.Required parameter of No conversion could be performed*p == ')'Invalid NAN literalkey /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.ccfeature_ptr.front() == 0rank_size == totalSketchContainerImplcolumns_size_.size() != 0n_threads_ >= 1h_weights.size() == batch.Size()Invalid size of sample weight.group_ptr.size() >= 2info.weights_.Size() == n_groupsgroup_ptr.back() == n_samples the number of rows from the data.Size of weight must equal to the number of query groups when ranking group is used.Invalid query group structure. The number of rows obtained from group doesn't equal to count <= total_entriesmingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hbase_rowid < group_ptr.back()Row: is not found in any group.worker_segments.size() >= 1AllReducen_columns == sketches_.size()Number of columns differs across workersnum_cuts.size() == 0Check failed: reduced[i].dataCheck failed: worker_feature.data()PushRowPagesketches_.size() == n_columnsweights.size() == info.num_row_hessian.size() == info.num_row_group_ptr.back() == hessian.size()p_cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max()cut_size > p_cuts->cut_ptrs_.HostVector().back()Check failed: a.dataCheck failed: a.data && reduced[fidx].data check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=Check failed: nbig < nquantile: too many large chunk] rmin=, rmax=, wmin=, v=/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than max_cat + 1 >= n_categoriesMaximum cateogry should not be lesser than the total number of categories.HostSketchContainerPushColPagesketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=QuantileLossParamquantile_alphaList of quantiles for quantile loss./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0Check failed: ctx_/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/algorithm.hCheck failed: ctx->IsCPU()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/ranking_utils.ccrank.size() == predt.size()LambdaRankParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/ranking_utils.hUnreachable.%u[-]?@g_rank.size() == sorted_idx.size()Check failed: label_is_integerWhen using relevance degree as target, label must be either 0 or positive integer.Check failed: label_is_validRelevance degress must be lesser than or equal to when the exponential NDCG gain function is used. Set `ndcg_exp_gain` to false to use custom DCG gain.lambdarank_pair_methodMethod for constructing pairs.lambdarank_num_pair_per_sampleNumber of pairs for each sample in the list.lambdarank_unbiasedUnbiased lambda mart. Use extended IPW to debias click positionlambdarank_bias_normLp regularization for unbiased lambdarank.ndcg_exp_gainWhen set to true, the label gain is 2^rel - 1, otherwise it's rel.optional, default=trueCheck failed: is_binary can only be used with binary labels./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/stats.cct_v.Shape(1) != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/stats.hCheck failed: alpha >= 0 && alpha <= 1k >= 0aft_loss_distributionnormalextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time modellimit >= 1Invalid thread limit for OpenMP./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor (): ========/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccInvaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again.fi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)Context/Users/runner/work/ml-builds/ml-builds/xgboost/src/context.ccBoth `device` and `gpu_id` are specified. Use `device` instead.Check failed: opt_id.has_value()Invalid value for `gpu_id`. Got:new_d.ordinal == this->device_.ordinalthis->device_.ordinal == kCpuIdthis->device_.ordinal > kCpuIdInvalid argument for `device`. Expected to be one of the following: +- cpu +- cuda +- cuda:<device ordinal> # e.g. cuda:0 +- gpu +- gpu:<device ordinal> # e.g. gpu:0 +gpu(:[0-9]+)?|cuda(:[0-9]+)?|cpugpuopt_id.value() <= std::numeric_limits<bst_d_ordinal_t>::max()Ordinal value too large.CGot: `Random number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number.Device ordinal.Number of threads to use.n_jobsfail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not.Alias /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: validAll feature_types must be one of {int, float, i, q, c}.base_marginCheck failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.labelthis->labels.Size() % this->num_row_ == 0Incorrect size for labels.Label contains NaN, infinity or a value too large.qidCheck failed: non_dec`qid` must be sorted in non-decreasing order along with data.weightWeights must be positive values.label_lower_boundlabel_upper_boundfeature_weightsFeature weight must be greater than 0.Unknown key for MetaInfo: Check failed: keythis->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0transpose.offset.Size() == num_columns + 1self_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()key >= builder_base_row_offsetInput data contains `inf` or a value too large, while `missing` is not set to `inf`/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hnum_rownum_colnum_nonzerolabelsgroup_ptrweightslabels_lower_boundlabels_upper_boundfeature_typesfield_cnt == kNumFieldWrong number of fieldsBinary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.major >= 1minor >= 6Check failed: fi->Read(&num_field)MetaInfo: invalid formatnum_field >= expectedMetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.)MetaInfo: the given binary file contains extra fields which will be ignored.MetaInfo: Invalid format for Check failed: strm->Read(&name)name == expected_name Expected field: , got: Check failed: strm->Read(&type_val)Check failed: type == expected_typeExpected field of type: got field type: Check failed: strm->Read(&is_scalar)Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: strm->Read(field)Check failed: !is_scalar to be a tensor; got a scalarCheck failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field) to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hthis->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/validation.hCheck failed: valid_query_groupInvalid group structure.Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info.size == this->num_col_Length of must be equal to number of columns.Check failed: infoUnknown feature info name: Unknown feature info: group_ptr_.size() == weights_.Size() + 1group_ptr_.back() == num_row_the actual number of rows given by data.weights_.Size() == num_row_Size of weights must equal to number of rows.labels.Shape(0) == num_row_Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.Invalid device ordinal. Data is associated with a different device ordinal than the booster. The device ordinal of the data is: ; the device ordinal of the Booster is: /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.hXGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workerscache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..rLoad part of data partsautoColumn-wise data split is not support for external memory.Splitting data by column matrix with entries loaded from /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: parser_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.harray.valid.Capacity() == 0Meta info like label or weight can not have missing value.Check failed: t_out.CContiguous()shape[i] == r.Shape(i)/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.ccname_args_cache.size() <= 2Only one `#` is allowed in file path for cachefile specificationURI parameter `format` is required for loading text data: filename?format=csvname_args.size() == 2Check failed: std::getline(is, kv.first, '=')Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg ?/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: columns_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.hCheck failed: storage.Resource()->Type() == ResourceHandler::kMalloc[Internal Error]: Cannot grow the vector when external memory is used.m_size >= storage.size()Check failed: resource/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/io.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/ref_resource_view.hnum_nonzeros_.size() == n_featuresCheck failed: p_fmat->SingleColBlock()Check failed: !sorted_sketchrow_ptr.size() == 0n_bytes >= this->data.size()Check failed: this->data.empty()Check failed: resource->Type() == common::ResourceHandler::kMallocCheck failed: malloc_resource/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalrawRaw GHistIndex binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_format.ccCheck failed: fiCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hcache_info_->offset.size() == n_batches_ + 1count_ == n_batches_count_ >= 1Multiple threads attempting to use Sparse DMatrix.Check failed: proxy_Check failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.Check failed: !(*ring_)[count_].valid()Check failed: curr_exce_Check failed: fmt->Read(page.get(), fi.get())/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type format.front() == '.'thread constructor failed/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.hUnknown type: Check failed: !cache_info_->writtenwbab MB written in seconds.Check failed: page_this->count_ >= 1source_->Iter() == this->count_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccCheck failed: refCheck failed: p_cutsref->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.n_features == num_cols()Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.n_features >= 1Data must has at least 1 column.accumulated_rows == Info().num_row_Check failed: p_sketchh_ft.size() == n_featuresrbegin == Info().num_row_this->ghist_->Features() == Info().num_col_proxy->Info().labels.Size() == 0Check failed: !detail::RegenGHist(param, batch_)`QuantileDMatrix` not initialized.`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.Iterative DMatrix must have at least 1 batch.Check failed: proxy_handleInvalid proxy handle.Check failed: typedh_ft.size() == n_ft/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.hCheck failed: !std::isnan(sparse_thresh)/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hparam.max_bin == batch_.max_binCheck failed: !param.regen && param.hess.empty()Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.Inconsistent `max_bin`. `max_bin` should be the same across different QuantileDMatrix, and consistent with the Booster being trained.page.row_ptr.size() == h_offset.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptrSlicing DMatrix is not supported for Quantile DMatrix.Slicing DMatrix columns is not supported for Quantile DMatrix.Not implemented./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device.Check failed: p_fmatFailed to fallback./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same<AdapterT, CSCAdapter>::value || std::is_same<AdapterT, CSCArrayAdapter>::value)Expecting CSCAdapter/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/error_msg.hSample size too large for the current updater. Maximum number of samples:. Consider using a different updater or tree_method.batch_param_.max_bin == param.max_binCheck failed: !detail::RegenGHist(batch_param_, param)Generating new Ellpack page.param.max_bin >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/batch_utils.hCheck failed: r.Initialized()Batch parameter is not initialized.Inconsistent sparse threshold.Generating new Gradient Index.batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid input file formattmagic == kMagicinvalid format, magic number mismatchoffset_vec.back() == info_.num_nonzero_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnCheck failed: data_Column is empty.row.page/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_DMatrix-rinfo_.num_col_ != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hMake cache:.col.pagethis->Info().num_col_ != 0.sorted.col.pagethis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size().gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_Check failed: kv.secondCouldn't remove external memory cache file ; you may want to remove it manuallySlicing DMatrix is not supported for external memory.Slicing DMatrix columns is not supported for external memory.Can not obtain a single CSR page for external memory DMatrixCheck failed: iter_.Next()Must have at least 1 batch.Raw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage fileCheck failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cclayer_begin == 0Linear booster does not support prediction range.GBLinearTrainParamLinear booster, implement generalized linear model.updatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.get<String>(in["name"]) == "gblinear"modelgpu_coord_descentdevice="cuda", updater="coord_descent"coord_descentLoading configuration on a CPU only machine. Changing updater to `coord_descent`./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(&param_, sizeof(param_)) == sizeof(param_)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by the current booster.DoBoostPredictBatchPredictBatchInternalbase_margin.Size() == nsize * ngroup/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hInplace predict is not supported by the current booster.gblinear does not support prediction of leaf index { "bias": [ ], "weight": [ ] }bias: weight: -Check failed: !model_.weight.empty()Model is not initializedCheck failed: trees.empty()gblinear doesn't support number of trees for feature importance.importance_type == "weight"gblinear only has `weight` defined for feature importance.gblinear_train_paramCheck failed: this->updater_DeprecatedGBLinearModelParamboosted_roundsallocator<T>::allocate(size_t n) 'n' exceeds maximum supported size/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from to : -Stack trace: - [bt] (_Z%zudeprecated_num_featuredeprecated_num_output_group is not presentedInvalid Parameter format for value should be smaller equal to int (non-negative)', valid values are: , (/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type cpu_predictorGBTree/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccDANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.Check failed: this->configured_Check failed: tparam_.GetInitialised()Using updaters: Tree method is automatically selected to be 'approx' for distributed training.Tree method is automatically set to 'approx' since external-memory data matrix is used.Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'.Using tree method: grow_histmakergrow_colmaker,pruneTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.grow_quantile_histmakergrow_gpu_histUnknown tree_method () detectedCheck failed: !updaters_.empty()ups.size() == updaters_.size()Internal Error: mismatched updater sequence. -Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree()Updater: `` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.CommitModelget<String>(in["name"]) == "gbtree"gbtree_train_paramLoading from a raw memory buffer on CPU only machine. Changing predictor to auto.predictor +Check failed: !model_.weight.empty()Model is not initializedCheck failed: trees.empty()gblinear doesn't support number of trees for feature importance.importance_type == "weight"gblinear only has `weight` defined for feature importance.gblinear_train_paramCheck failed: this->updater_DeprecatedGBLinearModelParamboosted_rounds/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from to :: + [bt] (_Zdeprecated_num_featuredeprecated_num_output_groupInvalid Parameter format for expect value should be smaller equal to int (non-negative)', valid values are: , Check failed: (/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type cpu_predictor/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccUsing tree method: Check failed: !updaters_.empty()model_.param.num_parallel_tree == trees.size()model_.param.num_parallel_tree == 1Boosting random forest is not supported for current objective.Check failed: !trees.front()->IsMultiTarget()Update tree leaftrees.size() == model_.param.num_parallel_treeCheck failed: !updaters_.front()->CanModifyTree()Updater: `` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsMismatching size between number of rows from input data and size of gradient vector.n_out % gpair->Size() == 0gpair->Size() == n_outCommitModelget<String>(in["name"]) == "gbtree"gbtree_train_paramtree_train_param Loading from a raw memory buffer (like pickle in Python, RDS in R) on a CPU-only machine. Consider using `save_model/load_model` instead. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html - for more details about differences between saving model and serializing.tree_method Changing `tree_method` to `hist`.Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.specified_updaterdefaultprocess_typeCheck failed: configured_Check failed: p_gbtreethis->model_.learner_model_param->num_feature != 0layer_trees != 0layer_end > layer_beginstep >= 1this->model_.trees_to_update.size() == this->model_.trees.size()Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees.Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.GBTreeModelParamGBTreeTrainParamDartTrainParamTree booster, gradient boosted trees.Tree booster, dart.BoostNewTreesngroup != 0Current objective doesn't support external memory.in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsout_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin <= tree_endXGBoost version not compiled with OneAPI support.model_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0Unsupported data type for inplace predict.Check failed: successCurrent Predictor: gpu_predictortree_begin == 0Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.gaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: idx <= total_n_treesInvalid tree index. in_it < tree_endnum_treesNumber of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.size_leaf_vectorReserved option for vector tree.updater_seqTree updater sequence.updateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.oneapi_predictorPredictor algorithm typeapproxexactChoice of tree construction method.Alias sample_typeDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.learning_rateLearning rate(step size) of update.trueget<String>(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == p_out_preds->predictions.Size()dart_train_paramdrop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast<int32_t>(trees.size())fi->Read(&param, sizeof(param)) == sizeof(param)GBTree: invalid model filefi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast<int>(trees.size())Check failed: ctx_treestree_infoGlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: n_classes <= 1 || n_targets <= 1Multi-class multi-output is not yet supported. n_classes:, n_targets:Check failed: common::AsConst(base_score_).Data()->HostCanRead()Check failed: base_score_.Size() == 1Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead()LearnerModelParamLegacyLearnerTrainParamGenericParametergpu_id:%02d:%02d:%02d) XGBoost version not compiled with GPU support.Invalid thread limit for OpenMP. vs. eval_metricnum_roundCONFIG-offset:LearnerCheck failed: IsA<Object>(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.learner_model_paramobjectiveboosterattributesnum_featurenum_classnum_targetboost_from_averagebase_scoreCannot find argument 'map::at: key not foundCheck failed: !this->need_configuration_Call Configure before saving model.Check failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: ret.ec == std::errc{}Check failed: ret.ec == std::errc()ConfigureCheck failed: matrix.firstCheck failed: !matrix.second.ref.expired()num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max())Unfortunately, XGBoost does not support data matrices with features or greatermparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface?0multi:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7learner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: this->obj_Check failed: n_targets == t || 1 == tInconsistent labels.Check failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: ptr_paramInvalid parameter "" contains whitespace. -Parameters: { ", } are not used. -UpdateOneIterPredictRawGetGradientCheck failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)Check failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterEvalOneItermultiple_predictions <= 1Perform one kind of prediction at a time.Unsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.Read(&header[0], 4) == 4UInvalid model formatfi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)SAVED_PARAM_count_poisson_max_delta_stepmulti:metricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibility/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hfixed_size->Read(&buffer[0], size) == size/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hdata_.Size() == detail::CalcSize(shape_);this->learner_model_param_.num_feature != 0begin_layer >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scorelearner_train_paramgeneric_param - If you are loading a serialized model (like pickle in Python, RDS in R) generated by - older XGBoost, please export the model by calling `Booster.save_model` from that version - first, then load it back in current version. See: + for more details about differences between saving model and serializing.tree_method Changing `tree_method` to `hist`.grow_gpu_histgrow_quantile_histmakerChanging updater from `grow_gpu_hist` to `grow_quantile_histmaker`.specified_updaterdefaultprocess_typeCheck failed: p_gbtreeCheck failed: this->model_.learner_model_param->Initialized()step >= 1end != beginEmpty slice is not allowed.this->model_.trees_to_update.size() == this->model_.trees.size()Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees.out_model.iteration_indptr.front() == 0out_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees.Check failed: gpu_predictor_Check failed: cpu_predictor_GBTreeModelParamGBTreeTrainParamDartTrainParamTree booster, gradient boosted trees.Tree booster, dart.The `exact` tree method is not supported on GPU.grow_colmaker,pruneUnknown tree_method: `unreachableUnknown device type:grow_histmakergrow_gpu_approxCheck failed: tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAutoOnly the hist tree method is supported for building multi-target trees with vector leaf.Check failed: ctx_->IsCPU()GPU is not yet supported for vector leaf.BoostNewTreesn_groups != 0Current objective doesn't support external memory.in_gpair->Size() % n_groups == 0Umust have exactly ngroup * nrow gpairs/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/error_msg.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.hiteration_indptr.size() == 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.hCheck failed: !model.iteration_indptr.empty()end <= model.BoostedRounds()Out of range for tree layers.tree_begin <= tree_endUnknown data type for inplace prediction:tree_begin == 0Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.gaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: idx <= total_n_treesInvalid tree index.out_l == n_layersnum_treesNumber of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.updater_seqTree updater sequence.updateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.approxexactChoice of tree construction method.sample_typeDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.GBTreeget<String>(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()Check failed: !this->model_.learner_model_param->IsVectorLeaf()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()Unsupported data type for inplace predict.predts.predictions.Size() == p_out_preds->predictions.Size()Check failed: successdart_train_paramdrop trees, weight = Check failed: tree_param_.GetInitialised()/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast<int32_t>(trees.size())fi->Read(&param, sizeof(param)) == sizeof(param)GBTree: invalid model filefi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treeslayer_trees != 0model.trees.size() == model.param.num_treesmodel.tree_info.size() == model.param.num_treesmodel.iteration_indptr.back() == model.param.num_treesparam.num_trees == static_cast<int>(trees.size())treestree_infoiteration_indptrtrees_json.size() == param.num_treestree_info_json.size() == param.num_treesCheck failed: !iteration_indptr.empty()iteration_indptr.back() == param.num_treesGlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccmulti-target-multi-class is not yet supported. Output classes:, output targets:Check failed: std::as_const(base_score_).Data()->HostCanRead()base_score_.Size() == 1Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead()LearnerModelParamLegacyLearnerTrainParam] %zueval_metricCONFIG-offset:Learner/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/cache.hCheck failed: mcontainer_.size() < max_size_Check failed: it != container_.cend()remained.size() + expired.size() == container_.size()queue_.size() == container_.size() vs. unordered_map::at: key not foundCheck failed: IsA<Object>(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.learner_model_paramobjectiveboosterattributesnum_featurenum_classnum_targetboost_from_averagebase_scoreCannot find argument 'map::at: key not foundCheck failed: lock_.try_lock()Check failed: that.lock_.try_lock()Check failed: !this->need_configuration_Call Configure before saving model.Check failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: ret.ec == std::errc{}ConfigureCheck failed: matrix.first.ptrCheck failed: !matrix.second.ref.expired()mparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface?/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/error_msg.hn_features <= max_n_featuresUnfortunately, XGBoost does not support data matrices with features or greatermulti:softprobnum_output_groupmulti:softmaxcount:poisson0.7Check failed: tparam_.GetInitialised()learner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: this->obj_Check failed: n_targets == t || 1 == tInconsistent labels.Check failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: ptr_paramgpu_idInvalid parameter "" contains whitespace. +Parameters: { "", } are not used. +UpdateOneIterPredictRawGetGradientbase_score.Size() == 1Check failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/aggregator.hCheck failed: std::equal(data.cbegin(), data.cend(), sync.cbegin())Different model parameter across workers.learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterEvalOneItermultiple_predictions <= 1Perform one kind of prediction at a time.Unsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.Read(&header[0], 4) == 4UInvalid model formatfi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)count_poisson_max_delta_stepmulti:metricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityfixed_size->Read(&buffer[0], size) == sizedata_.Size() == detail::CalcSize(shape_)Check failed: !this->learner_model_param_.IsVectorLeaf()Please use JSON/UBJ format for model serialization with multi-output models.;this->learner_model_param_.num_feature != 0begin >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scoreInvalid version string in configlearner_train_paramget<String const>(objective_fn["name"]) == tparam_.objectivegeneric_paramIf you are loading a serialized model (like pickle in Python, RDS in R) or +configuration generated by an older version of XGBoost, please export the model by calling +`Booster.save_model` from that version first, then load it back in current version. See: - https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html + https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html - for more details about differences between saving model and serializing. -ModelConfigInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0Global bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.Number of target for multi-target regression.Whether we should calculate the base score from training data. but value='/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hpos <= value.length()'Out of range value/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h for Parameter exceed bound [ should be greater equal to optional, default=, requiredenum_back_map_.count(value) != 0UValue not found in enum declareddsplitcolrowData split mode for distributed training.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableGradient booster used for training.reg:squarederrorObjective function used for obtaining gradient.) -falseRandom number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number.nthreadNumber of threads to use.n_jobsThe primary GPU device ordinal.fail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not. has not been registered in LinearTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater Learning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaCoordinateParamUpdate linear model according to coordinate descent algorithm.top_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.linear_train_paramcoordinate_paramCoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeatureUpdate linear model according to shotgun coordinate descent algorithm./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. -Supported options are: {cyclic, shuffle}WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.ccauc <= 1.0faucReceiver Operating Characteristic Area Under the Curve.aucprArea under PR curve for both classification and rank./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.hlabels.Size() != 0labels.Size() == predts.size()info.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()auc <= 1Total AUC across groups: , valid groups: n_classes != 0auc <= local_areaDataset is empty, or contains only positive or negative samples.info.group_ptr_.size() >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples.labels.Shape(1) == 1AUC doesn't support multi-target model.PR-AUC supports only binary relevance for learning to rank.rmseRooted mean square error.rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.loglossNegative loglikelihood for logistic regression.mpheMean Pseudo-huber error.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.errorBinary classification error.tweedie-nlogliktweedie-nloglik@rho for tweedie regression./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cupreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0pseudo_huber_paraminfo.labels.Shape(0) == info.num_row_slope != 0.0slope for pseudo huber cannot be 0.%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)tweedie-nloglik@/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU buildermerrorMulticlass classification error.mloglossMulticlass negative loglikelihood./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cupreds.Size() == 0Check failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchnclass >= 1Umlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class)MultiClassEvaluation: label must be in [0, num_class), num_class= but found in labelamsAMS metric for higgs.preprecision@k for rank.ndcgndcg@k for rank.mapmap@k for rank.cox-nloglikNegative log partial likelihood of Cox proportional hazards model./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !collective::IsDistributed()metric AMS do not support distributed evaluationbest-ams-ratio=%u[-]?label size predict size not matchgptr.size() != 0Umust specify group when constructing rank filegptr.back() == preds.Size()EvalRank: group structure must match number of predictionCheck failed: tparam_Cox metric does not support distributed evaluationaft-nloglikNegative log likelihood of Accelerated Failure Time model.interval-regression-accuracyaft_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Check failed: metric_AFT metric must be configured first, with distribution type and scale/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.ccbegin_pos <= sorted_pos.size()nptr.size() > 0n_unique == n_leafCheck failed: !position.empty()h_node_ptr.back() <= info.num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.hk + 1 < h_node_ptr.size()Check failed: h_row_set.empty()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hCheck failed: alpha >= 0 && alpha <= 1k >= 0survival:aftAFT loss function/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distribution/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective functionbinary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cuinfo.labels.Size() != 0Ulabel set cannot be emptylabels are not correctly providedpreds.size=, label.size=SoftmaxMultiClassParamSoftmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Number of output class in the multi-class classification.softmax_multiclass_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. +for more details about differences between saving model and serializing. +ModelConfigInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/io.hGlobal bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.Number of output targets. Can be set automatically if not specified.Whether we should calculate the base score from training data. but value='/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hpos <= value.length()Some trailing characters could not be parsed: '' for Parameter exceed bound [ should be greater equal to optional, default=, required has already been registered in enum_back_map_.count(value) != 0Udisable_default_eval_metricFlag to disable default metric. Set to >0 to disablegbtreeGradient booster used for training.reg:squarederrorObjective function used for obtaining gradient.multi_strategyone_output_per_treemulti_output_treeStrategy used for training multi-target models. `multi_output_tree` means building one single tree for all targets.falseEnum ( exisit!Enums: ), LinearTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater learning_rateLearning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaCoordinateParamUpdate linear model according to coordinate descent algorithm.top_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.linear_train_paramcoordinate_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_coordinate.ccSave config for CPU updater.CoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeatureUpdate linear model according to shotgun coordinate descent algorithm./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. +Supported options are: {cyclic, shuffle}WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.ccauc <= 1.0faucReceiver Operating Characteristic Area Under the Curve.aucprArea under PR curve for both classification and rank./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/algorithm.hlabels.Size() != 0labels.Size() == predts.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../collective/aggregator.hinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()auc <= 1Total AUC across groups: , valid groups: n_classes != 0auc <= 1.0Dataset is empty, or contains only positive or negative samples.info.group_ptr_.size() >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples.labels.Shape(1) == 1AUC doesn't support multi-target model.PR-AUC supports only binary relevance for learning to rank.rmseRooted mean square error.rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.loglossNegative loglikelihood for logistic regression.mpheMean Pseudo-huber error.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.errorBinary classification error.tweedie-nlogliktweedie-nloglik@rho for tweedie regression.quantileQuantile regression error./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cupreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0pseudo_huber_paraminfo.labels.Shape(0) == info.num_row_slope != 0.0slope for pseudo huber cannot be 0.%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)tweedie-nloglik@quantile_loss_paramname == "quantile"/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/quantile_loss_utils.hCheck failed: GetInitialised()Check failed: !quantile_alpha.Get().empty()quantile alpha must be in the range [0.0, 1.0].Check failed: !alpha_.Empty()dat[1] > 0n_targets != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function merrorMulticlass classification error.mloglossMulticlass negative loglikelihood./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cupreds.Size() == 0Check failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchnclass >= 1Umlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class)MultiClassEvaluation: label must be in [0, num_class), num_class= but found in labelamsAMS metric for higgs.cox-nloglikNegative log partial likelihood of Cox proportional hazards model.preprecision@k for rank.mapmap@k for ranking.ndcgndcg@k for ranking./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !collective::IsDistributed()metric AMS do not support distributed evaluationbest-ams-ratio=Cox metric does not support distributed evaluationlambdarank_paramCheck failed: p_cache->Param() == param_/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/ranking_utils.hCheck failed: param_.GetInitialised()the size of label.Groups() == info.weights_.Size()score <= 1.0 + kRtEpsInvalid output score, might be caused by invalid query group weight.weight.weights.size() == p_cache->Groups()aft-nloglikNegative log likelihood of Accelerated Failure Time model.interval-regression-accuracyaft_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Check failed: metric_AFT metric must be configured first, with distribution type and scale/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.ccbegin_pos <= sorted_pos.size()nptr.size() > 0n_unique == n_leafCheck failed: !position.empty()h_node_ptr.back() <= info.num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/algorithm.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../collective/aggregator.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.hk + 1 < h_node_ptr.size()Check failed: h_row_set.empty()y_idx <= info.labels.Shape(1)/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hsurvival:aftAFT loss function/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distribution/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective functionbinary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cuinfo.labels.Size() != 0Ulabel set cannot be emptylabels are not correctly providedpreds.size=, label.size=/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/init_estimation.hInvalid shape of labels.info.weights_.Size() == info.num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.cch_n_rel.size() == gptr.back()h_n_rel.size() == label.Size()LambdaRank with NDCG loss as objectiveLambdaRank with RankNet loss as objectiveLambdaRank with MAP loss as objective./Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/ranking_utils.hrank:ndcgrank:pairwiseti+tj-info.labels.Size() == predt.Size()iter == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.hg_rank.size() == g_label.Size()info.labels.Shape(1) <= 1multi-output for LTR is not yet supported.Check failed: param_.ndcg_exp_gainNDCG gain can not be set for the pairwise objective.NDCG gain can not be set for the MAP objective.SoftmaxMultiClassParamSoftmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Number of output class in the multi-class classification.softmax_multiclass_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class).Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.ccUnknown objective function: `` -Check failed: base_scoreLambdaRankParamPairwise rank objective.LambdaRank with NDCG as objective.LambdaRank with MAP as objective.rank:pairwiserank:ndcgrank:mapnum_pairsampleNumber of pair generated for each instance.fix_list_weightNormalize the weight of each list by this value, if equals 0, no effect will happenlambda_rank_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuCheck failed: gptr.size() != 0 && gptr.back() == info.labels.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: Computing gradients on CPU.RegLossParamRegression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.reg:linearreg:pseudohubererrorRegression Pseudo Huber error.PoissonRegressionParamPoisson regression for count data.survival:coxCox regression for censored survival data (negative labels are considered censored).reg:gammaGamma regression for severity data.TweedieRegressionParamreg:tweedieTweedie regression for insurance data.reg:absoluteerrorMean absoluate error.reg:squaredlogerrorreg:logisticbinary:logisticbinary:logitrawscale_pos_weightScale the weight of positive examples by this factorreg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuinfo.labels.Size() == preds.Size()Invalid shape of labels.info.weights_.Size() == info.num_row_label must be greater than -1 for rmsle so that log(label + 1) can be valid.label must be in [0,1] for logistic regression/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror./Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/common.hMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.poisson_regression_paramPoissonRegression: label must be nonnegativeCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!GammaRegression: label must be positive.tweedie_variance_powerTweedie variance power. Must be between in range [1, 2).tweedie_regression_paramTweedieRegression: label must be nonnegativeStringView{get<String const>(in["name"])} == StringView{"reg:absoluteerror"}Sum of weights is close to 0.0, skipping base score estimation.t_v.Shape(1) != 0Make predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccout_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so far/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.hInplace predict accepts only DMatrixProxy as input.m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.ncolumns != 0base_margin.Shape(1) == ngroup/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()[Internal error]: DMatrix: has expired.unordered_map::at: key not foundUnknown predictor type model.learner_model_param->num_output_group != 0Invalid shape of base_margin. Expected: ()margin.Shape(0) == n_samplesmargin.Shape(1) == n_groups/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: -newsize != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:TreeParamTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccUnknown Model Builder:{,}Dump text representation of treeDump json representation of treeGraphvizParamdotDump graphviz representation of treeCheck failed: node.IsLeaf()this->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodessize != 0categories_segmentscategories_sizescategories_nodescategoriesunique_path[i].pweight == 0Unique path must have zero weightj_end - j_begin != 0std::numeric_limits<bst_cat_t>::min() != max_cat[] DMLC_LOG_STACK_TRACE_DEPTH/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits<int>::max()number of nodes in the tree exceed 2^31fi->Read(&param, sizeof(TreeParam)) == sizeof(TreeParam)param.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast<int>(deleted_nodes_.size()) == param.num_deletedparam.num_nodes == static_cast<int>(nodes_.size())param.num_nodes == static_cast<int>(stats_.size())param.deprecated_num_roots == 1Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.loss_changessplit_indicesstatic_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodestree_paramn_nodes != 0loss_changes.size() == n_nodessum_hessiansum_hessian.size() == n_nodesbase_weightsbase_weights.size() == n_nodesleft_childrenlefts.size() == n_nodesright_childrenrights.size() == n_nodesparentsparents.size() == n_nodesindices.size() == n_nodessplit_conditionsconds.size() == n_nodesdefault_leftdefault_left.size() == n_nodessplit_typen_nodes == split_categories_segments.size()__', Possible Arguments: +Check failed: base_scoreRegression with quantile loss.reg:quantileerror/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/quantile_obj.cuget<String const>(in["name"]) == Name()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/quantile_loss_utils.hparam_.quantile_alpha.Get().size() == alpha_.Size()n_alphas != 0n_targets >= n_alphaspreds.Size() == info.num_row_ * n_targetsh_weights.size() == h_labels.Size()alpha.size() == alpha_.Size()The objective is not yet configured.info.labels.Shape(1) == 1Multi-target is not yet supported by the quantile loss.Check failed: !alpha.empty()RegLossParamRegression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.reg:linearreg:pseudohubererrorRegression Pseudo Huber error.PoissonRegressionParamPoisson regression for count data.survival:coxCox regression for censored survival data (negative labels are considered censored).reg:gammaGamma regression for severity data.TweedieRegressionParamreg:tweedieTweedie regression for insurance data.reg:absoluteerrorMean absoluate error.reg:squaredlogerrorreg:logisticbinary:logisticbinary:logitrawscale_pos_weightScale the weight of positive examples by this factorreg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuinfo.labels.Size() == preds.Size()label must be greater than -1 for rmsle so that log(label + 1) can be valid.label must be in [0,1] for logistic regression/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror.Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression.poisson_regression_paramPoissonRegression: label must be nonnegativeCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!GammaRegression: label must be positive.tweedie_variance_powerTweedie variance power. Must be between in range [1, 2).tweedie_regression_paramTweedieRegression: label must be nonnegativeStringView{get<String const>(in["name"])} == StringView{"reg:absoluteerror"}base_margin->Size() == 1Sum of weights is close to 0.0, skipping base score estimation.Make predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccCheck failed: !model.learner_model_param->IsVectorLeaf()Predict DMatrix with column splitout_preds->size() == n_samples * n_groupsCheck failed: xgboost::collective::IsDistributed()column-split prediction is only supported for distributed trainingout_preds->size() == p_fmat->Info().num_row_ * model_.learner_model_param->num_output_group/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/multi_target_tree_model.hCheck failed: IsLeaf(nidx)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hCheck failed: !p_m->Info().IsColumnSplit()Inplace predict support for column-wise data split is not yet implemented.m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.predict instancePredict instance with column splitPredict leaf with column splitout_preds->size() == p_fmat->Info().num_row_ * (tree_end_ - tree_begin_)Predict contributionCheck failed: !p_fmat->Info().IsColumnSplit()Predict contribution support for column-wise data split is not yet implemented.ngroup != 0ncolumns != 0base_margin.Shape(1) == ngroupPredict interaction contributionPredict interaction contribution support for column-wise data split is not yet implemented./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_treeshap.ccunique_path[i].pweight == 0Unique path must have zero weight/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccUnknown predictor type model.learner_model_param->num_output_group != 0Invalid shape of base_margin. Expected: ()margin.Shape(0) == n_samplesmargin.Shape(1) == n_groups/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: +newsize != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/fit_stump.ccn_targets == gpair.Shape(1)Check failed: h_sum.CContiguous()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.ccnodes_to_build.size() == valid_candidates.size()HistMakerTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/param.ccCheck failed: *local_tree == ref_treedebug_synchronizeCheck if all distributed tree are identical after tree construction.max_cached_hist_nodeMaximum number of nodes in CPU histogram cache. Only for internal usage./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/multi_target_tree_model.ccparam_->size_leaf_vector > 1Check failed: this->IsLeaf(nidx)Collapsing a split node to leaf weight.Size() == this->NumTarget()weights_.size() >= next_nidx * weight.Size()loss_changessplit_indicessplit_conditionsparentsleft_childrenright_childrennidx < left_.size()nidx < right_.size()nidx < parent_.size()nidx < split_index_.size()parent_.size() >= 1parent_.size() == left_.size()left_.size() == right_.size()split_idx < this->param_->num_featureparent_[nidx] != InvalidNodeId()p_weight.Size() == base_weight.Size()l_weight.Size() == left_weight.Size()r_weight.Size() == right_weight.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:TreeParamTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccUnknown Model Builder:{,}Dump text representation of treeDump json representation of treeGraphvizParamdotDump graphviz representation of treeCheck failed: !IsMultiTarget()Check failed: node.IsLeaf()this->split_types_.size() == this->Size()this->GetSplitCategoriesPtr().size() == this->Size()size != 0split_typecategories_segmentscategories_sizescategories_nodescategoriessum_hessianbase_weightsdefault_leftj_end - j_begin > 0std::numeric_limits<bst_cat_t>::min() != max_catallocator<T>::allocate(size_t n) 'n' exceeds maximum supported size[Stack trace: +DMLC_LOG_STACK_TRACE_DEPTHparam_.num_nodes < std::numeric_limits<int>::max()number of nodes in the tree exceed 2^31Check failed: IsMultiTarget()split_index < this->param_.num_featureCheck failed: this->p_mt_tree_param_.size_leaf_vector > 1fi->Read(&param_, sizeof(TreeParam)) == sizeof(TreeParam)param_.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast<int>(deleted_nodes_.size()) == param_.num_deletedparam_.num_nodes == static_cast<int>(nodes_.size())param_.num_nodes == static_cast<int>(stats_.size())param_.deprecated_num_roots == 1Please use JSON/UBJSON for saving models with multi-target trees.Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.tree_paramstatic_cast<bst_node_t>(deleted_nodes_.size()) == param_.num_deletedthis->split_categories_segments_.size() == param_.num_nodes__', Possible Arguments: ---------------- - : num_nodesget<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)mean_values->size() > 0UNumber of features used in tree construction.num_deletedSize of leaf vector, reserved for vector tree expect Invalid Input: '/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h has already been registered in min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.max_binif using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)max_cat_to_onehotMaximum number of categories to use one-hot encoding based split.max_cat_thresholdMaximum number of categories considered for split. Used only by partition-basedsplits.min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methoduniformgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsparse_thresholdpercentage threshold for treating a feature as sparsealphagammaetaOut of range value for , value='Some trailing characters could not be parsed: '1{nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}pos != std::string::nposidx < names_.size()FeatureMap feature index exceed bound{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{cond}{left}{right}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}Unknown feature map type.Check failed: is_categorical in feature map is numerical but tree node is categorical.Check failed: is_numerical in feature map is categorical but tree node is numerical.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} + : n_nodes != 0loss_changes.size() == n_nodessum_hessian.size() == n_nodesbase_weights.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesdefault_left.size() == n_nodesnum_nodesget<String>(out["tree_param"]["num_nodes"]) == std::to_string(param_.num_nodes)this->split_types_.size() == param_.num_nodesmean_values->size() > 0UNumber of features used in tree construction.num_deletedsize_leaf_vectorSize of leaf vector, reserved for vector tree is not presentedInvalid Input: '/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.hValue not found in enum declaredLearning rate(step size) of update.min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.max_binif using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)max_cat_to_onehotMaximum number of categories to use one-hot encoding based split.max_cat_thresholdMaximum number of categories considered for split. Used only by partition-basedsplits.min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightmax_delta_stepMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methoduniformgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsparse_thresholdpercentage threshold for treating a feature as sparsealphagammaetaOut of range value for , value='Out of range value/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h) +10 has not been registered in {nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}pos != std::string::nposidx < names_.size()FeatureMap feature index exceed bound{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{cond}{left}{right}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}Unknown feature map type.Check failed: is_categorical in feature map is numerical but tree node is categorical.Check failed: is_numerical in feature map is categorical but tree node is numerical.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} {left} {right}{stat}{nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}] "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}, "gain": {loss_chg}, "cover": {sum_hess}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties} { "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {newline}{indent}{nodes}{newline}yes_color#0000FFEdge color when meets the node condition.#FF0000Edge color when doesn't meet the node condition.rankdirPassed to graphiz via graph_attr.condition_node_paramsConditional node configurationleaf_node_paramsLeaf node configurationgraph_attrsAny other extra attributes for graphviz `graph_attr`.Failed to parse graphviz parameters: With error: " graph [ {key}="{value}" ] {key}{value}The following parameters for graphviz are not recognized: -/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.hch == '{'Error at, Expect '{' but get ' Line , around ^``ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'ch == '\"', Expect '"' but get 'unknown string escape \, Expect '"' but reach end of line {nid} [ label="{fname}:{cond}" {params}] +/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/json.hch == '{'Error at, Expect '{' but get ' Line , around ^``ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'ch == '\"', Expect '"' but get 'unknown string escape \, Expect '"' but reach end of line {nid} [ label="{fname}:{cond}" {params}] {nid} -> {child} [label="{branch}" color="{color}"] noyes, missing{child}{color} {nid} [ label="{fname}{<}{cond}" {params}] {<} {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}{parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} -{nodes}}{rankdir}{graph_attrs}/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater Tree constructor that uses approximate histogram construction for each node.GlobalApproxUpdatertrain_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccCheck failed: param.sampling_method != TrainParam::kGradientBasedGradient based sampling is not supported for approx tree method.UpdatePositionEvaluateSplitsn_total_bins == page.cut.TotalBins()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0U/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hbegin < endn_batches_ >= 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../data/../common/hist_util.hrow_ptr_[nid] == kMaxn_nodes > 0id != kMaxnodes == targeted_hists.size()i < first_dimension_.size()nthreads >= 1nid < nodes_tid < nthreads_i < ranges_.size()end > begin/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hbase_rowid == gmat.base_rowidupper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max())/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == true(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()n_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endBuildHistogram/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hCheck failed: !features.empty()partition > 0cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max())cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max())LeafPartitionCheck failed: tree[node.node_id].IsLeaf()ptr_offset <= row_set.Data()->size()UpdatePredictionCacheout_preds.Size() == data->Info().num_row_out_preds.Size() > 0UCheck failed: p_last_treeout_preds.DeviceIdx() == GenericParameter::kCpuIdpart.Size() == n_nodesColMakerTrainParamgrow_colmakerGrow tree with parallelization over columns.opt_dense_colEXP Param: speed optimization for dense column.default_directionlearnrightDefault direction choice when encountering a missing valuecolmaker_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training.Check failed: this->ctx_Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0param_.max_depth > 0exact tree method doesn't support unlimited depth.fmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.ridx < position_.size()ridx exceed bound ridx= pos=prunePruner that prune the tree according to statistics.TreePrunerPrunerUpdate/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cctree pruning end, extra nodes, pruned nodes, max_depth=Check failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccrow_ptr.size() >= 2ExpandTreeUpdateTreeInitSamplingGrow tree using quantized histogram.Quantile::Builder/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/hist_util.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hexponent <= modrefreshRefresher that refreshes the weight and statistics according to data./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits<unsigned>::max()Syncher that synchronize the tree in all distributed nodes.rabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_reduce_bufferDMLC_WORKER_CONNECT_RETRYrabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delay/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccConnecting to (failed): [] +{nodes}}{rankdir}{graph_attrs}/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater Tree constructor that uses approximate histogram construction for each node.GlobalApproxUpdaterhist_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccCheck failed: hist_param_.GetInitialised()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/../tree/hist/../split_evaluator.hp.monotone_constraints.size() <= n_featuresThe size of monotone constraint should be less or equal to the number of features./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/sampler.hCheck failed: out.Contiguous()param.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.exponent <= modUpdatePositionEvaluateSplitsn_total_bins == page.cut.TotalBins()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0U/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hn_targets >= 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/io.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../data/../common/ref_resource_view.hgpair.Shape(1) == n_targetsp_fmat->Info().num_row_ == gpair.Shape(0)target_builders_.size() == n_targetsCheck failed: dummy_sub.empty()begin < endCheck failed: p_nodes_to_buildCheck failed: p_nodes_to_subCheck failed: !this->hist_.HasExceeded()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/../tree/hist/hist_cache.hcurrent_size_ == new_sizeCheck failed: gpair.Contiguous()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.hnodes == targeted_hists.size()i < first_dimension_.size()row_ptr_[nid] == kMaxnid < nodes_tid < nthreads_id != kMaxi < ranges_.size()Check failed: !nodes_to_build.empty()end > beginnidx != kRoot/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hbase_rowid == gmat.base_rowidupper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max())/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == truegidx > -1p_tree->LeftChild(nidx) + 1 == p_tree->RightChild(nidx)n_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endBuildHistogramnodes_to_build.size() >= nodes_to_sub.size()nodes_to_sub.size() + nodes_to_build.size() == valid_candidates.size() * 2gpair.Shape(1) == p_tree->NumTargets()t_gpair.Shape(0) == p_fmat->Info().num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hCheck failed: !features.empty()partition > 0cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max())cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max())LeafPartitionCheck failed: tree.IsLeaf(node.node_id)ptr_offset <= row_set.Data()->size()UpdatePredictionCacheout_preds.Size() == data->Info().num_row_out_preds.Size() > 0UCheck failed: p_last_treeout_preds.Shape(1) == n_targetsout_preds.DeviceIdx() == Context::kCpuIdpart.Size() == n_nodesColMakerTrainParamgrow_colmakerGrow tree with parallelization over columns.opt_dense_colEXP Param: speed optimization for dense column.default_directionlearnrightDefault direction choice when encountering a missing valuecolmaker_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/split_evaluator.hCheck failed: this->ctx_Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0param_.max_depth > 0exact tree method doesn't support unlimited depth.fmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.ridx < position_.size()ridx exceed bound ridx= pos=prunePruner that prune the tree according to statistics.TreePrunerPrunerUpdate/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cctree pruning end, extra nodes, pruned nodes, max_depth=Check failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1Grow tree using quantized histogram./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccCheck failed: param->monotone_constraints.empty()monotone constraintMultiTargetHistBuilderHistUpdaterUpdateTreep_tree->LeftChild(candidate.nid) > candidate.nidroot_sum_tloc.Shape(1) == gpair.Shape(1)Check failed: root_sum.CContiguous()Check failed: p_tree->IsMultiTarget()left_child > candidate.nidright_child > candidate.nidstats_.Shape(1) == n_targetsrow_ptr.size() >= 2out_preds.Size() == data->Info().num_row_ * p_last_tree_->NumTargets()refreshRefresher that refreshes the weight and statistics according to data./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits<unsigned>::max()Syncher that synchronize the tree in all distributed nodes.rabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_reduce_bufferDMLC_WORKER_CONNECT_RETRYrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delay/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccConnecting to (failed): [] -Retry connecting to IP(retry time: ): [tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic)tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic)sync::Invalid tracker message, init failureReConnectLink failure 3tracker.Send(xgboost::StringView{task_id}) == task_id.size()NULLtask connected to the trackerReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14ReConnectLink failure 15ReConnectLink: bad socketcannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkworker256MBDMLC_NUM_ATTEMPT%[^=]=%smapred_tip_idmapreduce_task_idhadoop_mode is set but cannot find mapred_task_idmapred_task_id%drabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc workercan only call Init oncestartgethostname(&buf[0], HOST_NAME_MAX)shutdownFailed to shutdown due to%sprint%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}bind(handle_, handle, sizeof(std::remove_reference_t<decltype(addr.Handle())>))getsockname(handle_, reinterpret_cast<sockaddr *>(&res_addr), &addrlen)listen(handle_, backlog)getsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast<char *>(&error), &len)acceptfcntlsetsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast<char *>(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast<char *>(&tcp_no_delay), sizeof(tcp_no_delay))too large type_nbytes=%lu, buffer_size=%lu/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutFailed to poll.ReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkAllgatherRing failedAllreduce failedBroadcast failedunknown enum_opunknown data_typeDataType does not support bitwise or operationFailed to initialize RABIT.Failed to shutdown RABIT worker.the rabit has not been initializedLibSVMParserParamLibFMParserParamCSVParserParamlibfm/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: TryLoadCache()failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hCheck failed: fi->Read(&label)Bad RowBlock formatCheck failed: fi->Read(&weight)Check failed: fi->Read(&qid)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))MB read, MB/secfinish reading at %g MB/seclabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at File formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.hchunk.size != 0Uqid:Check failed: out->label.size() + 1 == out->offset.size()Check failed: base <= 10 && base >= 2sign == true/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"Check failed: out->field.size() == out->index.size()/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hparam_.format == "csv"Check failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsDelimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()hdfs://viewfs:///Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfss3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol stdinCheck failed: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type .split.part/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.hname_cache.size() == 1Uonly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1InputSplit do not support write/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)unique_lock::unlock: not lockedCheck failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Check failed: out_data_ != NULLCalling Value at beginning or end?Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroyNo thread/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedCheck failed: this->InitCachedIter()Failed to initialize CachedIterResetPartition is not supported in CachedInputSplit/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != end/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0invalid record io format(reinterpret_cast<size_t>(begin) & 3UL) == 0U(reinterpret_cast<size_t>(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->endInvalid RecordIO Format(reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U(reinterpret_cast<size_t>(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagic/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index files/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: files_[i].size % align_bytes == 0file do not align by bytesCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is neededfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[]=file offset not calculated correctlyC/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectoryCouldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: stdoutwbCheck failed: allow_null LocalFileSystem::Open "": LocalFileSystem.ListDirectory ..Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteCheck failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET)*TTTTTTTTTTTTTTTTTTTTT@)(af,. -v4Ut.ld@V5$a($$ <$"T$&$ +L$\-$/$;$<$>$>$H$J$$Ml$P$S$|VD$xY$\$a$a$ i8$pP$drh$s$Xw$|$$؆$$(8$$P$l$H$|$$($,$ $Я$x$$T$$$$$$l$P$($d($@H$$$XL$\$ l$$$$<$$d@$d$t$<$p$$$$|$<$l $dD$$4$` P$ $,$$$$t#$#$T%$X($*($t,H$-p$.$P/$,1$4$50$7P$;$|@\$Ap$D$H0$Kl$N$hUh$$X$Zh$]$T`h$a$c$\g<$Pip$k$l$rt$t$u$v$ x$z(${H$~$|$ $x$t $ԍ$̐<$,$D$4$$П $ $Hp$$xL%@%d%8%ܽ%|% $%D%0%L%4%d%%x%$(%X%,t%t %` %@\ -%X -%T %p % %0 % x %l  %h%4%%%%%%$)`%+%00\%(2%5%D64%<=,%@L%D%H%J %L%N%Q%S%T8%XXH%Yl%\%gX%h%l%0p%Tt%x4%|%|%% %$ % %h8!%LL!%!%,"%8"%ܩ"%"%"%#%t4#%HL#%D,$%ػP$%x$%$%$%p$%\%%H@%%4h%% %%$%%%%pP&%&%&%<'%('%'% '%'%(%`<(%Xd(%T(%$(%H(%(%$)%&<)%)d)%))%t,)%7)%H;*%<,*%>T*%?|*%\A*%B*%XK*%pM+%OD+%\Pl+%R+%]+%qh-%r-% s-%s-%ht-%u.%Pv@.%yX.%tz.%X{.%4}.%~.% $/%L/%`/%t/%/%@/%H/%/%ē/%/%x 0%<0%l0%40%<0%D0% 0%0%x,1%\1%1%̱1%1%1%1%<2%L2%d|2%`2%\2%$2%2% 3%<3%l3%H3%3%3%3%3%\(4%X4%4%4%t4%84%4%`5%D5%Pt5%l5%45%5%@ 6%<6%Pl6%L6%H6%6%6%|6%,7%\7%p7%7%T7%@7%7%@8% -L8% `8% t8% 8%8%X8% 9%<9%X9% t9%9% 9%@$:%& :%'D:%0)d:%p*:%+:%D,:%L2T;%5;%A;%B;%C;%G,<%K`<%Nx<%Q<%T<%$X<%X[<%(b=%TeD=%l=%m=%p=%u=%8x>%}>%~>%,>%$?%lT?%8?% @%T$@%h<@%P@%@%Ш@%A%A%ĭ,A%|xA%XA%A%A%8A%A%$B%B%PB%`hB%B%,B%B%B%<C%$C%0C%@pC%@C%`C%C%C% D%($D%$<D%D%xD%D%D%tD% E%p'E%-`F%/|F%2F%3F%l9F%;G%B`G%CG%DEG%FG%XJTH% LhH%LxH%tNH%QH%V0I%\I%`I%`J%bLJ%c`J%dtJ%eJ%fJ%PzJ%J%pXK%؅K%K%K%D0L%tL%L%ĚM%<M%PM%dM%xM%M%M%M%M%$N%ttN%N%0O%DO%PO%`O%pO%TO%xO%O%P%(P%pP%|P%DQ% Q%Q%tQ%Q% R%x R%4R%HR%\R%xR%R%XR% S%hXS%S%4(T%dT%xT%T%T%$T%T%,U%<$U%|U%U% -U% V%V%DW%\%,X%(pX%|)X%+X%H/X%$0X%0(Y%1<Y%3PY%3dY%4xY%6Y%9Y%:Y%,>Z%?(Z%?dZ%AxZ%$BZ%CZ%<DZ%XEZ%HZ%I[%O[%VH\%$Y\\%\\%`\%gt]%|j]%`k]%l ^%m ^%n4^%oH^%p\^%qp^%|H_%p_%T_%$_%4_%D_%(`%D$`%T8`%l`%Ċ`%,a%pa%Мa%<,b%@b%|b%b%b%Ԥb%b%b%c%İ`c%tc%IJc%0c% c%d% ,d%(@d%Td%@hd%\|d%d%d%Pe%Hde%e%e%4|f%f%f%g%(g%<g%Pg%dg%xg%Ph%h%hh%8h%Hh%Xi%<i%X,i%h@i%ti%i%i%pj%TDj%Llj% -j% Dk%Xk%Hk%k%k%k%@l%p l%%(m%%8m%;\m%<lm% =|m%TCm%Em%Khn%Mn%Pn%Rn%U0o%Yto%\o%x_o%_o%dp%fp%iPp%kxp%np%op%8rp%hwq%{q%p|q%h~q%~q%DDr%r%̎r% Ts%s%H t%Pt%t%t%p|v%<,w%w%x%hx%4x%Hx%tly%y%Xdz%Xxz%0z%z%z%hz%z%8z%z%{%p{%h{%{%\{%4|%|%|%p T}%P}%$~%p~%~%8",% #H%|0%X2%$H(%XO؂%<Xă%b%|k%oԅ%y4%|P%~p%,%(H%H%h%|%к%L %D%d%%%D%P%%%܍%%p%%<%h%h%<%( %t - % d%4 %$ ܑ%H" %$$ %& H%& X%' %* %, %T- %4. H%0 \%0 %1 %2 %4 %5 D%= X%4? %hA %`B %HC %E 4%lF T%G t%xG %H %pU %V %8W %Z %l\ %4^ $%c 4%n p%x % %t % L%h %L Ę% %$ (% d% % ș%, % % @% h% % % ̚% % ܛ%` % $% P% Ȝ%$ % 8% -P% +Retry connecting to IP(retry time: ): [tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic)tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic)sync::Invalid tracker message, init failureReConnectLink failure 3tracker.Send(xgboost::StringView{task_id}) == task_id.size()NULLtask connected to the trackerReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14ReConnectLink failure 15ReConnectLink: bad socketcannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkworker256MBDMLC_NUM_ATTEMPT%[^=]=%smapred_tip_idmapreduce_task_idhadoop_mode is set but cannot find mapred_task_idmapred_task_id%drabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc workercan only call Init oncestartgethostname(&buf[0], HOST_NAME_MAX)shutdownFailed to shutdown due to%sprint%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}bind(handle_, handle, sizeof(std::remove_reference_t<decltype(addr.Handle())>))getsockname(handle_, reinterpret_cast<sockaddr *>(&res_addr), &addrlen)listen(handle_, backlog)getsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast<char *>(&error), &len)acceptfcntlsetsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast<char *>(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast<char *>(&tcp_no_delay), sizeof(tcp_no_delay))too large type_nbytes=%lu, buffer_size=%lu/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutFailed to poll.ReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkAllgatherRing failedAllreduce failedBroadcast failedunknown enum_opunknown data_typeDataType does not support bitwise AND operationDataType does not support bitwise OR operationDataType does not support bitwise XOR operationFailed to initialize RABIT.Failed to shutdown RABIT worker.the rabit has not been initializedLibSVMParserParamLibFMParserParamCSVParserParamlibsvmlibfmcsv.split.part/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.hname_cache.size() == 1Uonly one `#` is allowed in file path for cachefile specificationname_args.size() == 1U/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: TryLoadCache()failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Check failed: out_data_ != NULLCalling Value at beginning or end?Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroyNo thread/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hCheck failed: fi->Read(&label)Bad RowBlock formatCheck failed: fi->Read(&weight)Check failed: fi->Read(&qid)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))MB read, MB/secfinish reading at %g MB/seclabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at File formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.hchunk.size != 0Uqid:Check failed: out->label.size() + 1 == out->offset.size()Check failed: base <= 10 && base >= 2sign == true/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"Check failed: out->field.size() == out->index.size()/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hparam_.format == "csv"Check failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsDelimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() == 0 || out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()hdfs://viewfs:///Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfss3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol stdinCheck failed: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1InputSplit do not support write/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedCheck failed: this->InitCachedIter()Failed to initialize CachedIterResetPartition is not supported in CachedInputSplit/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != end/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0invalid record io format(reinterpret_cast<size_t>(begin) & 3UL) == 0U(reinterpret_cast<size_t>(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->endInvalid RecordIO Format(reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U(reinterpret_cast<size_t>(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagic/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index files/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: files_[i].size % align_bytes == 0file do not align by bytesCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is neededfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[]=file offset not calculated correctly/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectoryCouldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: stdoutCheck failed: allow_null LocalFileSystem::Open "": LocalFileSystem.ListDirectory ..Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteCheck failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET)*TTTTTTTTTTTTTTTTTTTTR5L=w(>|P +<Sh/@A!Ub*m/wL=l2/A3/@CX3/G3/J4/L4/P4/U5/XW5/Y6/e 7/ f7/h(7/h<7/rd8/w8/z8/h}89/L9/09/,:/\X:/ȉd:/:/:/p:/Ԝ(;/d@;/T\;/8h</l</</D</Թ</̻</8=/=/=/=/ >/`D>/T>/x>/`?/ ?/h?/?/?/P@/t@/@/@/\A/A/B/8B/C/XC/|C/ +D/LD/@E/E/E/ E/E/,F/PF/tF/F/ F/!F/"G/%G/&G/)H/,-4I/H1J/45J/;xK/h=K/8?K/CXL/EpL/4JL/O4M/SM/Y8N/[dN/dePO/e`O/DgO/HjO/lO/dnO/poP/|p@P/@qPP/spP/vP/wP/yP/}@Q/R/\R/R/@$S/ГS/ T/l U/h4U/U/<TV/V/\W/W/|W/0X/TX/X/xX/Y/4Y/$Y/ Z/DZ/hZ/Z/ [/\/x\/ ]/@]/`^/^/|_/(`/`/\a/$a/8b/b/d/@d/<dd/d/d/d/#g/h%g/x(g/*g/*h/h,,h/D.Lh/ 0lh/0h/d1h/2h/@i/Bi/LCi/pQpj/Vk/Xxk/[ l/^l/t`m/Dcm/gTn/jn/no/Lup/wp/{q/؂(r/s/s/Xt/ \t/4tt/t/@u/u/pv/ <w/$w/<Dx/DDy/|y/ty/z/z/hz/$\{/{/|/H|/|/}/,~/ ~/4/l/\</\/xȀ/؀//8/8/d//l//, Ą/x/ "(/#T/`+/-//p/1Ȉ/1܈/4d/L6/7/<4/X>p/>/P?/AĊ/D܊/L/P/<S4/U\/V/(W/4XȌ/@Y/LZ/X[4/\l/]/@e̍/l/@s/yD/ l/h///Ď/؏/|//ȓ /,L/x/0/4/,@/$/`/,/@/LT/h/Ķ|////$/d/,/|@/T/h/t/l /L//H/\X///|H/|/// +/ / ,/< </ h/,//d//8/4p//ȗ//H D/`%d/(/8)/p*Ԙ/H,/-//@/<5p/47/8/9/;/<8/=H/t?p/@A/XB/D/E/H8/I`/hJ/L/N؛/8P/Q(/xSP/Tx/W/LXȜ/Z/[/|`@/dbh/4c/e/g/h/i0/kX/m/o/pО/ls/t /<vH/Pxp/hz/{/P}//p8/ `/t/$/xؠ/(/l/Ԣ//T/@/d///// /L/t//p/l/Ĥ/ؤ/p/x/@/,4/d/D/ĥ/إ///$/T//L/lȦ/ܦ/T/\/D/\t//$/@̧///4/d////ب// $/T/H///l ة/0 +/ / @/p/0//ت/T//|,/\/l/#/T$ث/&/`($/)T/t,/p-/l./4/Ȭ/ 3/4/ 6D/:t/;/</|=/hAԭ/B/hD4/Fd/G/PJ/K̮/PM/Q,/dSH/8Tl/$]/`^/`</`H/aX/ah/$c/c/xjL/l/0p/\|ȱ/(}/`~/,</̅p/h///p/Ќ/$/ЎD/d/|/H/d/ij/̠ܳ/ /ȪH/`p///|/Ȼ/<//x/L/@//x/ /8/ L/ //(/(/<//p/0ȸ//P//</4 /`/xx//D"й/"ܹ/#/T((/)4/*@/X//X0/x1/I/Kغ/O/dR@/Td/<U/V/`ܻ/tb/b/d/ph@/jl/lr4/x/|zȽ/}/~/h@/T/D/ԓx/</p/Ԡ////(//D/@///0`/@t/P/4/X//P/l//x//Dd/t/ /4/p//////D/0/T +x//H/P/4/////,/@/"/<$/'/(,4/L2/5/6/08P/@9d/P:x/4;/X</h=/>/A$/TC8/F/LK/|QP/T/U/LW/\X/lY/PZ$/t[8/\L/b/c/h/4j4/8r/zh/t|/// / /4/xH/\/p//X/Ď/ /H/\/p//Ԕ///t/@/,/@//x/X///P/`/p/T,/x@/T/,/h/|///////P/d|/h/D////T/h/|/////8/L/d//// / +/ ,/ @/0T/|//x(/</x/ /'T/t*/X+/,/-/.//(/0</5P/?(/`Cd/DDx/E/$F/4G/H/4I/DJ/LL/`Nl/xR/LS/0U/(VD/l[/^/_0/$cp/d/g/Dk/ l /lH/m\/op/o/p/t/v/x/\|D/8}X/~/////|/T/ $/4//h//4/H/D/</p/ /t///Ы$/Tt/0///|//0/d//H/h/x////x/T//,,/@/(/4 /l D/ /p / / </ / /t /@ / /$ \/d& p/( /, / 3 X/4 t/7 /9 /> /? </lB d/D /hL X/R /xT /tW /X 4/H^ d/(g /d{ l/ / /ܡ /P $/< l/d / /ت / / / 8/ij L/ /0 /` @/t h/\ x/L /( / / / / (/ 8/@ L/ |/ / / / / /H /p +P/ +`/ +p/ -x% -% -%< -0%( -D% -% -%  -% -%h -D%T -X%@' -%X* -ȟ%* -؟%t, -%. -,%2 -x%7 -%,: -Р%; -%? -h% G -%8J -L%N -%O -Ԣ%R -$%S -4%T -l%U -%W -%X -%pY -%` -t%|e -%pi -d%n -%|n 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