vendor/libxgboost.dylib in xgb-0.5.3 vs vendor/libxgboost.dylib in xgb-0.6.0
- old
+ new
@@ -1,1000 +1,1065 @@
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HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtHh H\ LT [A^]ÐHt UHHP(]f. D UHAVSIHpIFPH9t
HtHP(HP I~@IF H9t
HtHP(HP I I& HtH H L[A^] f. @ UHSPH_HtjXHCHtH[]HHPHH[] D UHH?H9t
HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtH H L [A^]ÐHt UHHP(]f. D UHAVSIHpIFPH9t
HtHP(HP I~@IF H9t
HtHP(HP I I& HtHT HH L[A^]< f. @ UHSPH_HtjXHCHtH[]HHPHH[]! D UHH?H9t
HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtHh H\ LT [A^]ÐHt UHHP(]f. D UHAVSIHpIFPH9t
HtHP(HP I~@IF H9t
HtHP(HP I I& HtH H L[A^] f. @ UHSPH_HtjXHCHtH[]HHPHH[] D UHH?H9t
HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtH H L [A^]ÐHt UHHP(]f. D UHAVSIHpIFPH9t
HtHP(HP I~@IF H9t
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HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtHh H\ LT [A^]ÐHt UHHP(]f. D UHAVSIHpIFPH9t
HtHP(HP I~@IF H9t
HtHP(HP I I& HtH H L[A^] f. @ UHSPH_HtjXHCHtH[]HHPHH[] D UHAWAVATSIIH^ H9t IHHH蔻I9uI?LMf [A\A^A_]$ f D UHAWAVATSIIH^ H9t IHHHDI9uI?LMf [A\A^A_] f D UHAWAVSPIIH^H9tHHI I9uI>LM~ H[A^A_]f. UHSPHt H{g H_ H[]f. UHAWAVSPH? HHL~Mt2IH^ L9tHHHdI9uI~LM~ H[A^A_]fUHH?H9t
HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtHh H\ LT [A^]ÐHt UHHP(]f. D UHAVSIHpIFPH9t
HtHP(HP I~@IF H9t
HtHP(HP I I& HtH H L[A^] f. @ UHSPH_HtjXHCHtH[]HHPHH[] D UHAWAVATSIIH^H9t IHPH蔸I9uI?LMf[A\A^A_]$ f D UHAWAVATSIIH^H9t IHPHDI9uI?LMf[A\A^A_] f D UHAWAVSPIIH^H9tHHI I9uI>LM~ H[A^A_]f. UHSPHt H{g H_ H[]f. UHAWAVATSIIH^ H9t IHHHtI9uI?LMf [A\A^A_] f D UHAWAVATSIIH^ H9t IHHH$I9uI?LMf [A\A^A_] f D UHAWAVSPIIH^H9tHH) I9uI>LM~s H[A^A_]f. UHSPHt H{G H? H[]f. UHAWAVATSIIH^H9t IHPHTI9uI?LMf[A\A^A_]
f D UHAWAVATSIIH^H9t IHPHI9uI?LMf[A\A^A_]
f D UHAWAVSPIIH^H9tHH
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HtHP(HP ]f. D Ht UHHP(]f. D UHAVSIH@H9t
HtHP(HP I I& HtH
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HtHP(HP I~@IF H9t
HtHP(HP I I& HtHt Hh
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HtHP(HP ]f. D Ht UHHP(]f. D UHH?H9t
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h h& h5 hC ~hP th_ jhn `h| Vh Lh Bh 8h .h $h h h h h& h: hI h] hp h h h h h h h h zh ph fh \h) Rh8 HhH >hW 4hp *h h5 hB hY hu h h h h h h h h h h h) h< hK vhZ lhi bhz Xh Nh Dh :h 0h &h h h Q A 0C 0E 0C 0E ? CN4dmlc9parameter16FieldAccessEntryE N4dmlc9parameter10FieldEntryIiEE N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIxEExEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIxEExEE N4dmlc9parameter10FieldEntryIyEE N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIyEEyEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIyEEyEE N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEE NSt3__118basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE N7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE N7xgboost4data12DMatrixProxyE NSt3__120__shared_ptr_pointerIPN7xgboost17BatchIteratorImplINS1_10SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE NSt3__114default_deleteIN7xgboost17BatchIteratorImplINS1_10SparsePageEEEEE NSt3__120__shared_ptr_pointerIPN7xgboost17BatchIteratorImplINS1_7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE NSt3__114default_deleteIN7xgboost17BatchIteratorImplINS1_7CSCPageEEEEE NSt3__120__shared_ptr_pointerIPN7xgboost17BatchIteratorImplINS1_13SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE NSt3__114default_deleteIN7xgboost17BatchIteratorImplINS1_13SortedCSCPageEEEEE NSt3__120__shared_ptr_pointerIPN7xgboost17BatchIteratorImplINS1_11EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE NSt3__114default_deleteIN7xgboost17BatchIteratorImplINS1_11EllpackPageEEEEE N7xgboost4data10CSRAdapterE N7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEE N4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEE N7xgboost4data15CSRArrayAdapterE N7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEE N4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEE N7xgboost4data10CSCAdapterE N7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEE N4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEE N7xgboost4data12DenseAdapterE N7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEE N4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEE N7xgboost4data16DataTableAdapterE N7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEE N4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEE N7xgboost4data12ArrayAdapterE N7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEE N4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEE NSt3__119basic_istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE N5rabit5utils19MemoryFixSizeBufferE N4dmlc10SeekStreamE N5rabit5utils18MemoryBufferStreamE N4dmlc10ParamErrorE NSt3__120__shared_ptr_pointerIPN7xgboost7DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE NSt3__114default_deleteIN7xgboost7DMatrixEEE NSt3__120__shared_ptr_pointerIPN7xgboost4data12DMatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE NSt3__114default_deleteIN7xgboost4data12DMatrixProxyEEE NSt3__120__shared_ptr_pointerIPN7xgboost4data12ArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE NSt3__114default_deleteIN7xgboost4data12ArrayAdapterEEE NSt3__110shared_ptrIN7xgboost4data12ArrayAdapterEEE NSt3__120__shared_ptr_pointerIPN7xgboost4data15CSRArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE NSt3__114default_deleteIN7xgboost4data15CSRArrayAdapterEEE NSt3__110shared_ptrIN7xgboost4data15CSRArrayAdapterEEE gffffff Q ~jt*Ǻ"<8b->Nc{-1+x#c^- Knu7X%Y^_GzK`eq]p J&gչ o"4%QttS`؈$C(ӦfHRl ۩#j+vv=IJ+10Q=ѬV Zbu)j}և]:1VӾa' miK$!Q㢢P@9j>`KUq݉ [qB1h?(SuYQ!"GMk9X 3D۸N <C.{$~ۃ6/l##ՂF! @ j e _ vH @0 ļ 4&k 7y W4 Ngm =`X @x P MJ- yCx(,*42q0 /'|!1錠9Y>R$ n`F"x| +MraB ydiWC *f J\)c 7]`xxֲ K}Yq\Ck
3T 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 @ NSt3__119basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE N4dmlc5ErrorE N7xgboost6common11DenseColumnIhEE N7xgboost6common6ColumnIhEE N7xgboost6common12SparseColumnIhEE N7xgboost6common11DenseColumnItEE N7xgboost6common6ColumnItEE N7xgboost6common12SparseColumnItEE N7xgboost6common11DenseColumnIjEE N7xgboost6common6ColumnIjEE N7xgboost6common12SparseColumnIjEE N7xgboost6common16PeekableInStreamE N7xgboost6common15FixedSizeStreamE NSt3__114basic_ifstreamIcNS_11char_traitsIcEEEE NSt3__113basic_filebufIcNS_11char_traitsIcEEEE ! N7xgboost10JsonStringE N7xgboost9JsonArrayE N7xgboost10JsonObjectE N7xgboost10JsonNumberE N7xgboost11JsonIntegerE N7xgboost8JsonNullE N7xgboost11JsonBooleanE N7xgboost10JsonReaderE N7xgboost10JsonWriterE A ? _''7 A ? Q D 75 /
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3T 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 NSt3__119basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE N4dmlc5ErrorE N7xgboost6common16PeekableInStreamE N7xgboost6common15FixedSizeStreamE NSt3__114basic_ifstreamIcNS_11char_traitsIcEEEE NSt3__113basic_filebufIcNS_11char_traitsIcEEEE ! N7xgboost10JsonStringE N7xgboost9JsonArrayE N7xgboost10JsonObjectE N7xgboost10JsonNumberE N7xgboost11JsonIntegerE N7xgboost8JsonNullE N7xgboost11JsonBooleanE N7xgboost10JsonReaderE N7xgboost10JsonWriterE Q D ? ?'7 A A _'75 /
- NSt3__120__shared_ptr_emplaceIN7xgboost16HostDeviceVectorIjEENS_9allocatorIS3_EEEE Q K Q O Y@v? L N4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEE Q B .A N7xgboost5ValueE A ? ! K K K K S S S S S S S S O O O OQ N Q L 2 N7xgboost7DMatrixE N7xgboost4data11FileAdapterE N4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEE N4dmlc7istreamE N4dmlc7istream5InBufE N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEE NSt3__117bad_function_callE NSt3__120__shared_ptr_pointerIPN7xgboost10SparsePageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE NSt3__114default_deleteIN7xgboost10SparsePageEEE N7xgboost4data24ExternalMemoryPrefetcherINS_10SparsePageEEE N4dmlc8DataIterIN7xgboost10SparsePageEEE N4dmlc12ThreadedIterIN7xgboost10SparsePageEEE N4dmlc12ScopedThreadE NSt3__110__function6__funcIZN7xgboost4data24ExternalMemoryPrefetcherINS2_10SparsePageEEC1ERKNS3_9CacheInfoEEUlPPS5_E_NS_9allocatorISC_EEFbSB_EEE NSt3__110__function6__baseIFbPPN7xgboost10SparsePageEEEE ZN7xgboost4data24ExternalMemoryPrefetcherINS_10SparsePageEEC1ERKNS0_9CacheInfoEEUlPPS2_E_ NSt3__110__function6__funcIZN7xgboost4data24ExternalMemoryPrefetcherINS2_10SparsePageEEC1ERKNS3_9CacheInfoEEUlvE_NS_9allocatorISA_EEFvvEEE NSt3__110__function6__baseIFvvEEE ZN7xgboost4data24ExternalMemoryPrefetcherINS_10SparsePageEEC1ERKNS0_9CacheInfoEEUlvE_ N7xgboost4data13SimpleDMatrixE N7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEE N7xgboost17BatchIteratorImplINS_10SparsePageEEE N7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEE N7xgboost17BatchIteratorImplINS_7CSCPageEEE N7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEE N7xgboost17BatchIteratorImplINS_13SortedCSCPageEEE N7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEE N7xgboost17BatchIteratorImplINS_11EllpackPageEEE eAN7xgboost4data17SparsePageDMatrixE N7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_10SparsePageEEES3_EE N7xgboost4data24ExternalMemoryPrefetcherINS_7CSCPageEEE N4dmlc8DataIterIN7xgboost7CSCPageEEE N4dmlc12ThreadedIterIN7xgboost7CSCPageEEE NSt3__110__function6__funcIZN7xgboost4data24ExternalMemoryPrefetcherINS2_7CSCPageEEC1ERKNS3_9CacheInfoEEUlPPS5_E_NS_9allocatorISC_EEFbSB_EEE NSt3__110__function6__baseIFbPPN7xgboost7CSCPageEEEE ZN7xgboost4data24ExternalMemoryPrefetcherINS_7CSCPageEEC1ERKNS0_9CacheInfoEEUlPPS2_E_ NSt3__110__function6__funcIZN7xgboost4data24ExternalMemoryPrefetcherINS2_7CSCPageEEC1ERKNS3_9CacheInfoEEUlvE_NS_9allocatorISA_EEFvvEEE ZN7xgboost4data24ExternalMemoryPrefetcherINS_7CSCPageEEC1ERKNS0_9CacheInfoEEUlvE_ N7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_7CSCPageEEES3_EE N7xgboost4data24ExternalMemoryPrefetcherINS_13SortedCSCPageEEE N4dmlc8DataIterIN7xgboost13SortedCSCPageEEE N4dmlc12ThreadedIterIN7xgboost13SortedCSCPageEEE NSt3__110__function6__funcIZN7xgboost4data24ExternalMemoryPrefetcherINS2_13SortedCSCPageEEC1ERKNS3_9CacheInfoEEUlPPS5_E_NS_9allocatorISC_EEFbSB_EEE NSt3__110__function6__baseIFbPPN7xgboost13SortedCSCPageEEEE ZN7xgboost4data24ExternalMemoryPrefetcherINS_13SortedCSCPageEEC1ERKNS0_9CacheInfoEEUlPPS2_E_ NSt3__110__function6__funcIZN7xgboost4data24ExternalMemoryPrefetcherINS2_13SortedCSCPageEEC1ERKNS3_9CacheInfoEEUlvE_NS_9allocatorISA_EEFvvEEE ZN7xgboost4data24ExternalMemoryPrefetcherINS_13SortedCSCPageEEC1ERKNS0_9CacheInfoEEUlvE_ N7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_13SortedCSCPageEEES3_EE N7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_11EllpackPageEEES3_EE ! NSt3__110__function6__funcIN7xgboost4data3$_0ENS_9allocatorIS4_EEFPNS3_16SparsePageFormatINS2_10SparsePageEEEvEEE NSt3__110__function6__baseIFPN7xgboost4data16SparsePageFormatINS2_10SparsePageEEEvEEE N7xgboost4data19SparsePageRawFormatINS_10SparsePageEEE N7xgboost4data16SparsePageFormatINS_10SparsePageEEE N7xgboost4data3$_0E NSt3__110__function6__funcIN7xgboost4data3$_1ENS_9allocatorIS4_EEFPNS3_16SparsePageFormatINS2_7CSCPageEEEvEEE NSt3__110__function6__baseIFPN7xgboost4data16SparsePageFormatINS2_7CSCPageEEEvEEE N7xgboost4data19SparsePageRawFormatINS_7CSCPageEEE N7xgboost4data16SparsePageFormatINS_7CSCPageEEE N7xgboost4data3$_1E NSt3__110__function6__funcIN7xgboost4data3$_2ENS_9allocatorIS4_EEFPNS3_16SparsePageFormatINS2_13SortedCSCPageEEEvEEE NSt3__110__function6__baseIFPN7xgboost4data16SparsePageFormatINS2_13SortedCSCPageEEEvEEE N7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE N7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEE N7xgboost4data3$_2E NSt3__120__shared_ptr_emplaceIN7xgboost10SparsePageENS_9allocatorIS2_EEEE A : A 3 N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEE NSt3__110__function6__funcIN7xgboost3gbm3$_0ENS_9allocatorIS4_EEFPNS2_15GradientBoosterEPKNS2_17LearnerModelParamEEEE NSt3__110__function6__baseIFPN7xgboost15GradientBoosterEPKNS2_17LearnerModelParamEEEE N7xgboost3gbm8GBLinearE N7xgboost15GradientBoosterE N7xgboost5ModelE N7xgboost3gbm3$_0E ! N7xgboost3gbm13GBLinearModelE Q @ A 4 1 q q o ! 1 # 1 & 1 + 1 - Q F 1 % A ; ?N7xgboost3gbm6GBTreeE N4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEE N4dmlc9parameter10FieldEntryIN7xgboost13PredictorTypeEEE N4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEE NSt3__110__function6__funcIN7xgboost3gbm3$_6ENS_9allocatorIS4_EEFPNS2_15GradientBoosterEPKNS2_17LearnerModelParamEEEE N7xgboost3gbm3$_6E NSt3__110__function6__funcIN7xgboost3gbm3$_7ENS_9allocatorIS4_EEFPNS2_15GradientBoosterEPKNS2_17LearnerModelParamEEEE N7xgboost3gbm4DartE N7xgboost3gbm3$_7E N7xgboost3gbm11GBTreeModelE A 0 A = a S ! a ^ q m 1 ) 1 / p ! 1 ' N7xgboost7LearnerE N7xgboost12ConfigurableE N4dmlc12SerializableE N7xgboost11LearnerImplE N7xgboost9LearnerIOE N7xgboost20LearnerConfigurationE NSt3__115basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE N4dmlc6StreamE N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEE N4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEE N4dmlc9parameter10FieldEntryIN7xgboost13DataSplitModeEEE N4dmlc9parameter10FieldEntryIbEE N4dmlc9parameter10FieldEntryINSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE N4dmlc9parameter10FieldEntryIxEE N4dmlc9parameter10FieldEntryImEE ! s A > > NSt3__110__function6__funcIN7xgboost6linear3$_0ENS_9allocatorIS4_EEFPNS2_13LinearUpdaterEvEEE NSt3__110__function6__baseIFPN7xgboost13LinearUpdaterEvEEE N7xgboost6linear17CoordinateUpdaterE N7xgboost13LinearUpdaterE N7xgboost6linear21CyclicFeatureSelectorE N7xgboost6linear15FeatureSelectorE N7xgboost6linear22ShuffleFeatureSelectorE N7xgboost6linear22ThriftyFeatureSelectorE N7xgboost6linear21GreedyFeatureSelectorE N7xgboost6linear21RandomFeatureSelectorE N7xgboost6linear3$_0E NSt3__110__function6__funcIN7xgboost6linear3$_0ENS_9allocatorIS4_EEFPNS2_13LinearUpdaterEvEEE N7xgboost6linear14ShotgunUpdaterE N7xgboost6linear3$_0E ? 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@@ -5167,35 +4711,40 @@
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- 4 v -L 2` YQ|B " C C X aW o o @ UK ( i D D 2 + ! B BD ! 5 50u e x false /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.cc Check failed: ec == std::errc() : Unknown global parameters: { , } Check failed: e Check failed: 0 XGBoost distributed mode detected, will split data among workers DMatrix/Booster has not been intialized or has already been disposed. Check failed: p_m Check failed: m Current DMatrix type does not support set data. nthread static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0U slice does not support group structure binary saving only supported by SimpleDMatrix Check failed: type >= 1 && type <= 4 XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead. group Booster has not been intialized or has already been disposed. DMatrix has not been intialized or has already been disposed. iteration_begin iteration_end strict_shape json str.size() > 2 str[0] == '{' r w Check failed: p_out text feature_name feature_type Unknown field for Booster feature info: [ ] : %02d:%02d:%02d
+ 4 v -L 2` YQ|B " C C X aW o o @ UK ( i D D 2 + ! B BD ! 5 50u e x false /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.cc Check failed: ec == std::errc() : Unknown global parameters: { , } Check failed: e Check failed: 0 XGBoost distributed mode detected, will split data among workers missing cache_prefix nthread DMatrix/Booster has not been initialized or has already been disposed. Check failed: p_m Check failed: m Current DMatrix type does not support set data. static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0U slice does not support group structure binary saving only supported by SimpleDMatrix Check failed: type >= 1 && type <= 4 XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead. group Booster has not been initialized or has already been disposed. DMatrix has not been initialized or has already been disposed. iteration_begin iteration_end ntree_limit Check failed: iteration_end == 0 Only one of the `ntree_limit` or `iteration_range` can be specified. `ntree_limit` is deprecated, use `iteration_range` instead. strict_shape json str.size() > 2 str[0] == '{' r w Check failed: p_out text feature_name feature_type Unknown field for Booster feature info: importance_type feature_map feature_names features.size() <= scores.size() scores.size() % features.size() == 0ul features.size() == scores.size() [ ] : %02d:%02d:%02d
Stack trace:
- [bt] ( ) allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size _Z DMLC_LOG_STACK_TRACE_DEPTH %zu /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h Check failed: at_first_ Cannot reset IteratorAdapter Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columns Number of columns between batches changed from to /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.h XGBoost version not compiled with GPU support. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.h Slicing DMatrix is not supported for Proxy DMatrix. Not implemented. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h get<Array const>(jinterface).size() == 1 Column: typestr n_bits == num_rows Shape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting. Check failed: array.find("mask") == array.cend() Masked array is not yet supported. stream version Missing `version' field for array interface Missing `typestr' field for array interface typestr.size() == 3 typestr.front() != '>' shape Missing `shape' field for array interface Missing `data' field for array interface Only version <= 3 of `__cuda_array_interface__' are supported. `typestr' should be of format <endian><type><size of type in bytes>. ( vs. Big endian is not supported. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.h Invalid cast, from map::at: key not found is not supported. Bit field Boolean Integer Unsigned integer Floating point Complex floating point Timedelta Object String Unicode Other Invalid type code: in `typestr' of input array.
-Please verify the `__cuda_array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html
-Or open an issue. j_shape.size() == 2 Only 1-D and 2-D arrays are supported. shape.first * shape.second == 0 Empty data with non-zero shape. Empty data passed in. j_shape.size() == 1 type_length == 1 mask 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 strides.size() == 1 get<Integer>(strides.at(0)) == type_length Memory should be contigious. j_strides.size() <= 2 Check failed: valid Invalid strides in array. Only dimensional array is valid. Check failed: num_rows == 1 || num_cols == 1 Array should be a vector instead of matrix. missing /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.h Invalid missing value: learner gradient_booster gblinear dart gbtree gbtree_train_param num_parallel_tree Unknown booster: chunksize == groups Unknown prediction type: std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rows write position exceed fixed buffer size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h read can not have position excceed buffer length /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h fid == static_cast<int>(names_.size()) i q int float unknown feature type, use i for indicator and q for quantity __ Cannot find argument ' ', Possible Arguments:
+ [bt] ( ) allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size _Z DMLC_LOG_STACK_TRACE_DEPTH %zu /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h Check failed: at_first_ Cannot reset IteratorAdapter Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columns Number of columns between batches changed from to /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.h XGBoost version not compiled with GPU support. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.h Slicing DMatrix is not supported for Proxy DMatrix. Not implemented. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h get<Array const>(jinterface).size() == 1 Column: typestr n_bits == num_rows Shape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting. Check failed: array.find("mask") == array.cend() Masked array is not yet supported. stream version Missing `version' field for array interface Missing `typestr' field for array interface Check failed: typestr.size() == 3 || typestr.size() == 4 shape Missing `shape' field for array interface Missing `data' field for array interface Only version <= 3 of `__cuda_array_interface__/__array_interface__' are supported. `typestr' should be of format <endian><type><size of type in bytes>. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.h Invalid cast, from - is not supported. Bit field Boolean Integer Unsigned integer Floating point Complex floating point Timedelta Object String Unicode Other Invalid 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. j_shape.size() == 2 Only 1-D and 2-D arrays are supported. shape.first * shape.second == 0 Empty data with non-zero shape. Empty data passed in. j_shape.size() == 1 type_length == 1 mask 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 strides.size() == 1 get<Integer>(strides.at(0)) == type_length ( vs. Memory should be contigious. j_strides.size() <= 2 Check failed: valid Invalid strides in array. strides: ( , ), shape: ( ) Only dimensional array is valid. Check failed: num_rows == 1 || num_cols == 1 Array should be a vector instead of matrix. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.h Invalid missing value: learner gradient_booster gblinear dart gbtree gbtree_train_param num_parallel_tree Unknown booster: chunksize == groups Unknown prediction type: std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rows write position exceed fixed buffer size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h read can not have position excceed buffer length /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h fid == static_cast<int>(names_.size()) i q int float c unknown feature type, use i for indicator and q for quantity custom_feature_names.size() == n_features Incorrect number of feature names. feature_names.size() == n_features feature_types.size() == n_features Incorrect number of feature types. f feature_map.Size() == n_features idx < names_.size() FeatureMap feature index exceed bound map::at: key not found __ Cannot find argument ' ', Possible Arguments:
----------------
- : get<Integer const>(config["cache_id"]) == 0 Cache ID is not supported yet Check failed: p_predt NaN 0E0 ;unknown;unknown;0;0;; /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.cc sibling.size() == size parent.size() == size cut.Values().size() > 0U curent_bin_size == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.h bins_type_size == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.h sizeof(BinIdxType) == bins_type_size_ /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.h Check failed: impl_ != nullptr batch_size < offset_vec.size() ibegin + inst.size() == iend Check failed: false /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.cc Size() == other.Size() Size() == other.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc pointer_ <= buffer_.size() Check failed: out file:// :// Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.h Not implemented rb /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc Check failed: ret.ec == std::errc() \ \\ \" \b \f \n \r \t \u%04x Number Array Object of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first. Unknown construct , around character position: , " " \0
" Unknown escape null Expecting null value "null" ch != -1 cursor_.Pos(): raw_str_.size(): Expecting digit beg <= size_ Expecting: " ", got: " EOF" \0" HostSketchContainer /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cc columns_size_.size() != 0 count <= total_entries PushRowPage sketches_.size() == info.num_col_ worker_segments.size() >= 1 AllReduce num_cuts.size() == 0 n_columns == sketches_.size() Number of columns differs across workers Check failed: reduced[i].data Check failed: a.data Check failed: a.data && reduced[fid].data cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max() cut_size > cuts->cut_ptrs_.HostVector().back() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.h invalid init parameter Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)) base_rowid < group_ptr.back() Row: is not found in any group. mingap= , maxgap= , wgap= Check failed: size <= sa.size + sb.size bug in combine src.size == 0 this->size == 0 Check failed: worker_feature.data() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.cc features.size() > 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.h array.size() == weights.size() aft_loss_distribution normal extreme Choice of distribution for the noise term in Accelerated Failure Time model aft_loss_distribution_scale Scaling factor used to scale the distribution in Accelerated Failure Time model Invalid Parameter format for expect but value=' optional, default= , required Invalid Input: ' ', valid values are: ' Required parameter of is not presented value for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default= /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h enum_back_map_.count(value) != 0U Value not found in enum declared key has already been registered in Enum ( exisit! )
- Enums: ), Out of range value for , value=' pos <= value.length() Some trailing characters could not be parsed: ' Out of range value No conversion could be performed /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h *p == ')' Invalid NAN literal statistic count elapsed /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc Timer for did not get stopped properly. s, calls @ us ======== Monitor: ======== From rank: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.cc Invaid 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.cc Input data contains `inf` or `nan` num_row num_col num_nonzero labels group_ptr weights base_margin labels_lower_bound labels_upper_bound feature_names feature_types field_cnt == kNumField Wrong number of fields categorical All feature_types must be one of {int, float, i, q, categorical}. major == 1 Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again. Check failed: fi->Read(&num_field) MetaInfo: invalid format num_field >= expected MetaInfo: 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. this->base_margin_.Size() % this->num_row_ == 0 Incorrect size of base margin vector. label Unknown data type weight Weights must be positive values. qid Check failed: non_dec `qid` must be sorted in non-decreasing order along with data. label_lower_bound label_upper_bound feature_weights Feature weight must be greater than 0. Unknown key for MetaInfo: 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. Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_ Number of columns must be consistent across batches. that.group_ptr_.size() != 0 group_ptr_.size() == weights_.Size() + 1 Size 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_.Size() == 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_weigths.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_ == 0 Size of base margin must be a multiple of number of rows. cache_file.find('#') == std::string::npos Only one `#` is allowed in file path for cache file specification. .r - XGBOOST_TEST_NPART Load part of data parts auto matrix with entries loaded from csv libsvm No 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=csv Encountered parser error:
- .group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from self_offset.size() == other_offset.size() self_data.size(): other_data.size(): beg <= data.size() ptr < offset.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h float32 float64 bool8 int32 int8 int16 int64 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h Unknown data table type. Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == device Data is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: MetaInfo: Invalid format. Check failed: strm->Read(&name) name == expected_name Expected field: , got: Check failed: strm->Read(&type_val) Check failed: type == expected_type Expected field of type: got field type: Check failed: strm->Read(&is_scalar) Check failed: is_scalar Expected field to be a scalar; got a vector Check failed: strm->Read(field) Check failed: !is_scalar to be a vector; got a scalar Check failed: strm->Read(&shape.first) Check failed: strm->Read(&shape.second) shape.second == 1 Number of columns is expected to be 1. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.h Couldn't remove external memory cache file ; you may want to remove it manually .row.page page->Size() == 0 Check failed: qids.empty() || qids.size() == info.num_row_ SparsePageSource Finished writing to cache_shards.size() != 0U raw Cache file exists already; Is there another DMatrix with the same cache prefix? It can be caused by previously used DMatrix that hasn't been collected by language environment garbage collector. Otherwise please remove it manually. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h name_shards.size() == format_shards.size() thread constructor failed SparsePageWriter Finished writing to Unknown format type info.name_shards.size() != 0U Check failed: finfo->Read(&tmagic) tmagic == kMagic invalid format, magic number mismatch Check failed: fi->Read(&format) Invalid page format /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.h Check failed: !producer_sig_processed_.load(std::memory_order_acquire) unique_lock::unlock: not locked Check failed: producer_sig_.load(std::memory_order_acquire) == kProduce Make sure you call BeforeFirst not inconcurrent with Next! Check failed: produce_end_.load(std::memory_order_acquire) Check failed: out_data_ != NULL Calling Value at beginning or end? Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroy Check failed: cell != __null || produce_end_.load(std::memory_order_acquire) Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy No thread Check failed: mutex_.try_lock() Multiple threads attempting to use prefetcher key >= builder_base_row_offset /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required .ellpack.page /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page_source.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPageSource is required /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.cc Check failed: (std::is_same<AdapterT, CSCAdapter>::value) Expecting CSCAdapter Check failed: param != BatchParam{} Batch parameter is not initialized. param.gpu_id >= 0 param.max_bin >= 2 Check failed: in_stream->Read(&tmagic) invalid input file format /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h Check failed: page_ != nullptr /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h Check failed: source_ != nullptr .col.page Writing to in MB/s, written CSCPageSource: Finished writing to Check failed: *out_page == nullptr Check failed: qrecycle_.Pop(out_page) .sorted.col.page SortedCSCPageSource: Finished writing to /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.h Slicing DMatrix is not supported for external memory. Raw binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.cc page->offset.Size() != 0U Invalid SparsePage file fi->Read(dmlc::BeginPtr(data_vec), (page->data).Size() * sizeof(Entry)) == (page->data).Size() * sizeof(Entry) fid + 1 < disk_offset_.size() disk_offset_[fid] > curr_offset fi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i], size_to_read * sizeof(Entry)) == size_to_read * sizeof(Entry) Check failed: page.offset.Size() != 0 && offset_vec[0] == 0 offset_vec.back() == page.data.Size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.cc offset + n_rows + 1 <= in_offset.size() h_offset.size() > 0 n_entries == in_offset.at(offset + n_rows) - in_offset.at(offset) out->Size() != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cc layer_begin == 0 Linear booster does not support prediction range. layer_end == 0 GBLinearTrainParam Linear booster, implement generalized linear model. updater shotgun Update algorithm for linear model. One of shotgun/coord_descent tolerance Stop if largest weight update is smaller than this number. max_row_perbatch Maximum rows per batch. string /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h get<String>(in["name"]) == "gblinear" model gpu_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.h fi->Read(¶m_, sizeof(param_)) == sizeof(param_) /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.h Slice is not supported by current booster. DoBoost PredictBatch PredictBatchInternal base_margin.size() == nsize * ngroup Inplace predict is not supported by current booster. gblinear does not support prediction of leaf index { "bias": [ , ], "weight": [ ] } bias:
+ : get<Integer const>(config["cache_id"]) == 0 Cache ID is not supported yet Check failed: p_predt NaN 0E0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.cc sibling.size() == size parent.size() == size Check failed: false ;unknown;unknown;0;0;; /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.cc Size() == other.Size() Size() == other.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc pointer_ <= buffer_.size() Check failed: out file:// :// Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.h Not implemented rb /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc Check failed: ret.ec == std::errc() \ \\ \" \b \f \n \r \t \u%04x Number Array Object of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first. Unknown construct , around character position: , " " \0
" Unknown escape null Expecting null value "null" ch != -1 cursor_.Pos(): raw_str_.size(): Expecting digit beg <= size_ Expecting: " ", got: " EOF" \0" HostSketchContainer /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cc columns_size_.size() != 0 n_threads_ >= 1 count <= total_entries PushRowPage sketches_.size() == n_columns weights.size() == info.num_row_ worker_segments.size() >= 1 AllReduce num_cuts.size() == 0 n_columns == sketches_.size() Number of columns differs across workers cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max() cut_size > cuts->cut_ptrs_.HostVector().back() group_ptr.size() >= 2 group_ptr.back() == n_samples hessian.size() == info.num_row_ group_ptr.back() == hessian.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.h invalid init parameter Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)) mingap= , maxgap= , wgap= Check failed: size <= sa.size + sb.size bug in combine src.size == 0 this->size == 0 Check failed: reduced[i].data Check failed: worker_feature.data() Check failed: a.data Check failed: a.data && reduced[fidx].data /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.cc features.size() > 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.h array.size() == weights.size() aft_loss_distribution normal extreme Choice of distribution for the noise term in Accelerated Failure Time model aft_loss_distribution_scale Scaling factor used to scale the distribution in Accelerated Failure Time model Invalid Parameter format for expect but value=' optional, default= , required Invalid Input: ' ', valid values are: ' Required parameter of is not presented value for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default= /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h enum_back_map_.count(value) != 0U Value not found in enum declared key has already been registered in Enum ( exisit! )
+ Enums: ), Out of range value for , value=' pos <= value.length() Some trailing characters could not be parsed: ' Out of range value No conversion could be performed /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h *p == ')' Invalid NAN literal /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc Timer for did not get stopped properly. s, calls @ us ======== Monitor ( ): ======== /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.cc Invaid 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.cc Input data contains `inf` or `nan` num_row num_col num_nonzero labels group_ptr weights base_margin labels_lower_bound labels_upper_bound feature_types field_cnt == kNumField Wrong number of fields All feature_types must be one of {int, float, i, q, c}. major == 1 Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again. Check failed: fi->Read(&num_field) MetaInfo: invalid format num_field >= expected MetaInfo: 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. this->base_margin_.Size() % this->num_row_ == 0 Incorrect size of base margin vector. label Unknown data type Label contains NaN, infinity or a value too large. weight Weights must be positive values. qid Check failed: non_dec `qid` must be sorted in non-decreasing order along with data. label_lower_bound label_upper_bound feature_weights Feature weight must be greater than 0. Unknown key for MetaInfo: 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. Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_ Number of columns must be consistent across batches. that.group_ptr_.size() != 0 group_ptr_.size() == weights_.Size() + 1 Size 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_.Size() == 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_weigths.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_ == 0 Size of base margin must be a multiple of number of rows. matrix with entries loaded from cache_file.find('#') == std::string::npos Only one `#` is allowed in file path for cache file specification. .r XGBOOST_TEST_NPART Load part of data parts auto csv libsvm No 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=csv Encountered parser error:
+ .group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from transpose.offset.Size() == num_columns + 1 self_offset.size() == other_offset.size() self_data.size(): other_data.size(): beg <= data.size() ptr < offset.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h float32 float64 bool8 int32 int8 int16 int64 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h Unknown data table type. Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == device Data 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/file_iterator.h Check failed: !type_.empty() Check failed: parser_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/iterative_device_dmatrix.h Slicing DMatrix is not supported for Device DMatrix. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.h Check failed: page_ != nullptr MetaInfo: Invalid format. Check failed: strm->Read(&name) name == expected_name Expected field: , got: Check failed: strm->Read(&type_val) Check failed: type == expected_type Expected field of type: got field type: Check failed: strm->Read(&is_scalar) Check failed: is_scalar Expected field to be a scalar; got a vector Check failed: strm->Read(field) Check failed: !is_scalar to be a vector; got a scalar Check failed: strm->Read(&shape.first) Check failed: strm->Read(&shape.second) shape.second == 1 Number of columns is expected to be 1. key >= builder_base_row_offset /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.cc cut.Values().size() > 0U curent_bin_size == common::kUint32BinsTypeSize n_threads >= 1 row_ptr.size() == 0 /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.h Check failed: impl_ != nullptr /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.h batch_size < offset_vec.size() ibegin + inst.size() == iend raw Raw GHistIndex binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h Check failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cc cuts_.Values().size() != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h Check failed: !at_end_ n_prefetch_batches > 0 total batches: fetch_it < cache_info_->offset.size() std::count_if(ring_->cbegin(), ring_->cend(), [](auto const &f) { return f.valid(); }) == n_prefetch_batches Sparse DMatrix assumes forward iteration. fi->Tell() == offset Check failed: fmt->Read(page.get(), fi.get()) /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h Unknown format type format.front() == '.' thread constructor failed Check failed: !cache_info_->written Check failed: page_ this->count_ == this->n_batches_ this->count_ >= 1 source_->Iter() == this->count_ Check failed: lock_.try_lock() Multiple threads attempting to use Sparse DMatrix. count_ == n_batches_ count_ >= 1 Check failed: proxy_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h Unknown type: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./any.h Check failed: type_ != nullptr The any container is empty requested= Check failed: *(type_->ptype_info) == typeid(T) The stored type mismatch stored= /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.cc Check failed: on_host Not implemented on device. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.cc Check failed: (std::is_same<AdapterT, CSCAdapter>::value) Expecting CSCAdapter Check failed: param != BatchParam{} Batch parameter is not initialized. param.gpu_id >= 0 param.max_bin >= 2 param.gpu_id == -1 batch_param_.hess.data() == param.hess.data() Check failed: in_stream->Read(&tmagic) invalid input file format tmagic == kMagic invalid format, magic number mismatch /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h DMatrix -r /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc info_.num_col_ != 0 .row.page Check failed: sparse_page_source_ .col.page this->Info().num_col_ != 0 .sorted.col.page .gradient_index.page cuts.Values().size() != 0 Check failed: ghist_index_source_ Check failed: proxy_handle Invalid proxy handle. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.h Make cache: Check failed: kv.second Couldn't remove external memory cache file ; you may want to remove it manually Slicing DMatrix is not supported for external memory. iter_.Next() == 1 Must have at least 1 batch. this->page_->Size() == n_features_ this->page_->data.Size() == csr->data.Size() Raw binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.cc page->offset.Size() != 0U Invalid SparsePage file n_bytes == (page->data).Size() * sizeof(Entry) Check failed: page.offset.Size() != 0 && offset_vec[0] == 0 offset_vec.back() == page.data.Size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cc layer_begin == 0 Linear booster does not support prediction range. layer_end == 0 GBLinearTrainParam Linear booster, implement generalized linear model. updater shotgun Update algorithm for linear model. One of shotgun/coord_descent tolerance Stop if largest weight update is smaller than this number. max_row_perbatch Maximum rows per batch. string /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h get<String>(in["name"]) == "gblinear" model gpu_coord_descent coord_descent Loading 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.h fi->Read(¶m_, sizeof(param_)) == sizeof(param_) /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.h Slice is not supported by current booster. DoBoost PredictBatch PredictBatchInternal base_margin.size() == nsize * ngroup Inplace predict is not supported by current booster. gblinear does not support prediction of leaf index { "bias": [ ], "weight": [ ] } bias:
weight:
- gblinear_train_param Check failed: this->updater_ DeprecatedGBLinearModelParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc Unknown gbm type cpu_predictor GBTree /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.cc DANGER 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_histmaker,prune grow_colmaker,prune Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker. grow_quantile_histmaker grow_gpu_hist Unknown tree_method ( ) detected BoostNewTrees ngroup != 0 in_gpair->Size() % ngroup == 0U must have exactly ngroup * nrow gpairs 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 rounds gpair->Size() == p_fmat->Info().num_row_ Mismatching size between number of rows from input data and size of gradient vector. CommitModel get<String>(in["name"]) == "gbtree" Loading from a raw memory buffer on CPU only machine. Changing predictor to auto. predictor tree_method Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist. specified_updater default process_type Check failed: configured_ Check failed: p_gbtree layer_end > layer_begin step >= 1 Check failed: this->model_.trees_to_update.empty() out_preds->version == 0 Check failed: cpu_predictor_ Data is on CUDA device, but XGBoost is not compiled with CUDA support. GBTreeModelParam GBTreeTrainParam DartTrainParam Tree booster, gradient boosted trees. Tree booster, dart. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h tree_begin <= tree_end XGBoost version not compiled with OneAPI support. tparam_.num_parallel_tree != 0 model_.learner_model_param->num_output_group != 0 Unsupported data type for inplace predict. Check failed: success tree_begin == 0 Predict 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. in_it < tree_end num_trees Number of features used for training and prediction. size_leaf_vector Reserved option for vector tree. Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. updater_seq Tree updater sequence. update Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model. gpu_predictor oneapi_predictor Predictor algorithm type approx exact Choice of tree construction method. has not been registered in Alias sample_type uniform Different types of sampling algorithm. normalize_type forest Different types of normalization algorithm. rate_drop Fraction of trees to drop during the dropout. Whether at least one tree should always be dropped during the dropout. skip_drop Probability of skipping the dropout during a boosting iteration. learning_rate Learning rate(step size) of update. eta true 1 boolean get<String>(in["name"]) == "dart" weight_drop Check failed: p_dart Check failed: p_dart->weight_drop_.empty() Check failed: predictor i >= p_out_preds->version p_out_preds->predictions.Size() == predts.predictions.Size() predts.predictions.Size() == out_preds->predictions.Size() dart_train_param drop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.cc param.num_trees == static_cast<int32_t>(trees.size()) fi->Read(¶m, sizeof(param)) == sizeof(param) GBTree: invalid model file fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_trees param.num_trees == static_cast<int>(trees.size()) gbtree_model_param trees tree_info GlobalConfiguration verbosity Flag to print out detailed breakdown of runtime. use_rmm Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost LearnerModelParamLegacy LearnerTrainParam GenericParameter gpu_id eval_metric num_round CONFIG-offset: Learner /Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.cc Check failed: IsA<Object>(in) learner_model_param objective booster attributes num_feature num_class base_score Check failed: !this->need_configuration_ Call Configure before saving model. Configure /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/generic_parameters.h
-n_gpus: n_gpus Check failed: matrix.first Check 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 greater mparam_.num_feature != 0 0 feature is supplied. Are you using raw Booster interface? multi:softprob num_output_group multi:softmax max_delta_step count:poisson 0.7 _param Invalid parameter " " contains whitespace.
+ Check failed: !model_.weight.empty() Model is not initialized importance_type == "weight" gblinear only has `weight` defined for feature importance. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.h vec->size() == shape[0] * shape[1] gblinear_train_param Check failed: this->updater_ DeprecatedGBLinearModelParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc Unknown gbm type cpu_predictor GBTree /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.cc DANGER 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_histmaker,prune grow_colmaker,prune Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker. grow_quantile_histmaker grow_gpu_hist Unknown tree_method ( ) detected BoostNewTrees ngroup != 0 in_gpair->Size() % ngroup == 0U must have exactly ngroup * nrow gpairs 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 rounds gpair->Size() == p_fmat->Info().num_row_ Mismatching size between number of rows from input data and size of gradient vector. CommitModel get<String>(in["name"]) == "gbtree" Loading from a raw memory buffer on CPU only machine. Changing predictor to auto. predictor tree_method Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist. specified_updater default process_type Check failed: configured_ Check failed: p_gbtree this->model_.learner_model_param->num_feature != 0 layer_trees != 0 layer_end > layer_begin step >= 1 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_preds->version == 0 tree_end <= model_.trees.size() Invalid number of trees. Check failed: cpu_predictor_ Data is on CUDA device, but XGBoost is not compiled with CUDA support. GBTreeModelParam GBTreeTrainParam DartTrainParam Tree booster, gradient boosted trees. Tree booster, dart. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h tree_begin <= tree_end XGBoost version not compiled with OneAPI support. tparam_.num_parallel_tree != 0 model_.learner_model_param->num_output_group != 0 Unsupported data type for inplace predict. Check failed: success Current Predictor: gpu_predictor tree_begin == 0 Predict 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. gain total_gain cover total_cover Unknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: in_it < tree_end num_trees Number of features used for training and prediction. size_leaf_vector Reserved option for vector tree. Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. updater_seq Tree updater sequence. update Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model. oneapi_predictor Predictor algorithm type approx exact Choice of tree construction method. has not been registered in Alias sample_type uniform Different types of sampling algorithm. normalize_type forest Different types of normalization algorithm. rate_drop Fraction of trees to drop during the dropout. Whether at least one tree should always be dropped during the dropout. skip_drop Probability of skipping the dropout during a boosting iteration. learning_rate Learning rate(step size) of update. eta true 1 boolean get<String>(in["name"]) == "dart" weight_drop Check failed: p_dart Check failed: p_dart->weight_drop_.empty() Check failed: predictor i >= p_out_preds->version p_out_preds->predictions.Size() == predts.predictions.Size() predts.predictions.Size() == out_preds->predictions.Size() dart_train_param drop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.cc param.num_trees == static_cast<int32_t>(trees.size()) fi->Read(¶m, sizeof(param)) == sizeof(param) GBTree: invalid model file fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_trees param.num_trees == static_cast<int>(trees.size()) gbtree_model_param trees tree_info GlobalConfiguration verbosity Flag to print out detailed breakdown of runtime. use_rmm Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost LearnerModelParamLegacy LearnerTrainParam GenericParameter gpu_id eval_metric num_round CONFIG-offset: Learner /Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.cc Check failed: IsA<Object>(in) learner_model_param objective booster attributes num_feature num_class base_score Check failed: !this->need_configuration_ Call Configure before saving model. Configure Check failed: matrix.first Check 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 greater mparam_.num_feature != 0 0 feature is supplied. Are you using raw Booster interface? multi:softprob num_output_group multi:softmax max_delta_step count:poisson 0.7 _param Invalid parameter " " contains whitespace.
Parameters: { ", } might not be used.
- This may not be accurate due to some parameters are only used in language bindings but
- passed down to XGBoost core. Or some parameters are not used but slip through this
- verification. Please open an issue if you find above cases.
+ This could be a false alarm, with some parameters getting used by language bindings but
+ then being mistakenly passed down to XGBoost core, or some parameter actually being used
+ but getting flagged wrongly here. Please open an issue if you find any such cases.
- UpdateOneIter PredictRaw GetGradient Check failed: tparam_.dsplit != DataSplitMode::kAuto Precondition violated; dsplit cannot be 'auto' in distributed mode Column-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_ != nullptr Predict must happen after Load or configuration BoostOneIter EvalOneIter binary:logistic error logloss 1.3.0 binary:logitraw auc 1.4.0 merror Starting in XGBoost , the default evaluation metric used with the objective ' ' was changed from ' ' to ' '. Explicitly set eval_metric if you'd like to restore the old behavior. multiple_predictions <= 1 Perform one kind of prediction at a time. Unsupported prediction type: bs64 header != "bs64" Base64 format is no longer supported in brick. binf fp.Read(&header[0], 4) == 4U fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_) BoostLearner: wrong model format Check failed: fi->Read(&tparam_.objective) Check failed: fi->Read(&tparam_.booster) SAVED_PARAM_ count_poisson_max_delta_step multi: metrics Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility ; begin_layer >= 0 learner_train_param generic_param Model Config fp.Read(&header[0], header.size()) == serialisation_header_.size() Check failed: header == serialisation_header_
+ UpdateOneIter PredictRaw GetGradient Check failed: tparam_.dsplit != DataSplitMode::kAuto Precondition violated; dsplit cannot be 'auto' in distributed mode Column-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_ != nullptr Predict must happen after Load or configuration BoostOneIter EvalOneIter binary:logistic error logloss 1.3.0 binary:logitraw auc 1.4.0 merror Starting in XGBoost , the default evaluation metric used with the objective ' ' was changed from ' ' to ' '. Explicitly set eval_metric if you'd like to restore the old behavior. multiple_predictions <= 1 Perform one kind of prediction at a time. Unsupported prediction type: bs64 header != "bs64" Base64 format is no longer supported in brick. binf fp.Read(&header[0], 4) == 4U fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_) BoostLearner: wrong model format Check failed: fi->Read(&tparam_.objective) Check failed: fi->Read(&tparam_.booster) SAVED_PARAM_ count_poisson_max_delta_step multi: metrics Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility ; this->learner_model_param_.num_feature != 0 begin_layer >= 0 out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_feature out_impl->learner_model_param_.num_feature != 0 best_iteration best_score learner_train_param generic_param Model Config fp.Read(&header[0], header.size()) == serialisation_header_.size() Check failed: header == serialisation_header_
If you are loading a serialized model (like pickle in Python) generated by older
XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. There's a simple script for helping
the process. See:
@@ -5563,1002 +5111,1333 @@
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for reference to the script, and more details about differences between saving model and
serializing.
- fp.Read(&sz, sizeof(sz)) == sizeof(sz) sz > 0 Global 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. int (non-negative) dsplit col row Data split mode for distributed training. disable_default_eval_metric Flag to disable default metric. Set to >0 to disable Gradient booster used for training. reg:squarederror Objective function used for obtaining gradient. Random number seed during training. random_state seed_per_iteration Seed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode. Number of threads to use. n_jobs The primary GPU device ordinal. fail_on_invalid_gpu_id Fail with error when gpu_id is invalid. gpu_page_size GPU page size when running in external memory mode. validate_parameters Enable checking whether parameters are used or not.
- Deprecated. Single process multi-GPU training is no longer supported.
- Please switch to distributed training with one process per GPU.
- This can be done using Dask or Spark. See documentation for details. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.cc Unknown linear updater LinearTrainParam Learning rate of each update. reg_lambda L2 regularization on weights. reg_alpha L1 regularization on weights. feature_selector cyclic shuffle thrifty greedy random Feature selection or ordering method. lambda alpha CoordinateParam coord_descent Update linear model according to coordinate descent algorithm. top_k The number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features. linear_train_param coordinate_param CoordinateUpdater /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.h unknown coordinate selector: UpdateFeature Update linear model according to shotgun coordinate descent algorithm. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.cc Unsupported feature selector for shotgun updater.
- Supported options are: {cyclic, shuffle} WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cc Check failed: !labels.empty() labels.size() == predts.size() n_classes != 0 auc <= 1.0f info.group_ptr_.size() >= 2 Receiver Operating Characteristic Area Under the Curve. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.h Invalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.h info.weights_.Size() == info.group_ptr_.size() - 1 info.group_ptr_.back() == info.labels_.Size() auc <= 1 Total AUC across groups: , valid groups: Dataset contains only positive or negative samples. rmse Rooted mean square error. rmsle Rooted mean square log error. mae Mean absolute error. mape Mean absolute percentage error. mphe Mean Pseudo Huber error. Negative loglikelihood for logistic regression. poisson-nloglik Negative loglikelihood for poisson regression. gamma-deviance Residual deviance for gamma regression. gamma-nloglik Negative log-likelihood for gamma regression. Binary classification error. tweedie-nloglik tweedie-nloglik@rho for tweedie regression. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cu preds.Size() == info.labels_.Size() label and prediction size not match, hint: use merror or mlogloss for multi-class classification %f sscanf(param, "%f", &threshold_) == 1 unable to parse the threshold value for the error metric Check failed: param != nullptr tweedie-nloglik must be in format tweedie-nloglik@rho Check failed: rho_ < 2 && rho_ >= 1 tweedie variance power must be in interval [1, 2) tweedie-nloglik@ /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.cc Unknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builder Multiclass classification error. mlogloss Multiclass negative loglikelihood. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cu preds.Size() == 0 Check failed: preds.Size() % info.labels_.Size() == 0 label and prediction size not match nclass >= 1U mlogloss and merror are only used for multi-class classification, use logloss for binary classification Check 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 label ams AMS metric for higgs. aucpr Area under PR curve for both classification and rank. pre precision@k for rank. ndcg ndcg@k for rank. map map@k for rank. cox-nloglik Negative log partial likelihood of Cox proportioanl hazards model. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cc AMS must be in format ams@k ams@ Check failed: !distributed metric AMS do not support distributed evaluation best-ams-ratio= info.labels_.Size() != 0U label set cannot be empty label size predict size not match gptr.back() == info.labels_.Size() EvalAucPR: group structure must match number of prediction dat[1] > 0.0f AUC-PR: the dataset only contains pos or neg samples dat[0] <= dat[1] AUC-PR: AUC > 1.0 Check failed: !auc_error AUC-PR: error in calculation %u[-]? gptr.size() != 0U must specify group when constructing rank file gptr.back() == preds.Size() EvalRank: group structure must match number of prediction Cox metric does not support distributed evaluation aft-nloglik Negative log likelihood of Accelerated Failure Time model. interval-regression-accuracy aft_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cu Unknown probability distribution preds.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 survival:aft AFT loss function /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cu info.labels_lower_bound_.Size() == ndata info.labels_upper_bound_.Size() == ndata info.weights_.Size() == ndata Number of weights should be equal to number of data points. Unrecognized distribution /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.h Not part of device code. WITH_CUDA: binary:hinge Hinge loss. Expects labels to be in [0,1f] /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cu labels are not correctly provided preds.size= , label.size= SoftmaxMultiClassParam Softmax 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.cu Check failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size()) SoftmaxMultiClassObj: label size and pred size does not match.
+ fp.Read(&sz, sizeof(sz)) == sizeof(sz) sz > 0 Global 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. int (non-negative) dsplit col row Data split mode for distributed training. disable_default_eval_metric Flag to disable default metric. Set to >0 to disable Gradient booster used for training. reg:squarederror Objective function used for obtaining gradient. Random number seed during training. random_state seed_per_iteration Seed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode. Number of threads to use. n_jobs The primary GPU device ordinal. fail_on_invalid_gpu_id Fail with error when gpu_id is invalid. validate_parameters Enable checking whether parameters are used or not. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.cc Unknown linear updater LinearTrainParam Learning rate of each update. reg_lambda L2 regularization on weights. reg_alpha L1 regularization on weights. feature_selector cyclic shuffle thrifty greedy random Feature selection or ordering method. lambda alpha CoordinateParam Update linear model according to coordinate descent algorithm. top_k The number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features. linear_train_param coordinate_param CoordinateUpdater /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.h unknown coordinate selector: UpdateFeature Update linear model according to shotgun coordinate descent algorithm. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.cc Unsupported feature selector for shotgun updater.
+ Supported options are: {cyclic, shuffle} WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cc Check failed: !labels.empty() labels.size() == predts.size() n_classes != 0 auc <= 1.0f info.group_ptr_.size() >= 2 Receiver Operating Characteristic Area Under the Curve. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.h Invalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.h info.weights_.Size() == info.group_ptr_.size() - 1 info.group_ptr_.back() == info.labels_.Size() auc <= 1 Total AUC across groups: , valid groups: Dataset contains only positive or negative samples. rmse Rooted mean square error. rmsle Rooted mean square log error. mae Mean absolute error. mape Mean absolute percentage error. mphe Mean Pseudo Huber error. Negative loglikelihood for logistic regression. poisson-nloglik Negative loglikelihood for poisson regression. gamma-deviance Residual deviance for gamma regression. gamma-nloglik Negative log-likelihood for gamma regression. Binary classification error. tweedie-nloglik tweedie-nloglik@rho for tweedie regression. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cu preds.Size() == info.labels_.Size() label and prediction size not match, hint: use merror or mlogloss for multi-class classification %f sscanf(param, "%f", &threshold_) == 1 unable to parse the threshold value for the error metric Check failed: param != nullptr tweedie-nloglik must be in format tweedie-nloglik@rho Check failed: rho_ < 2 && rho_ >= 1 tweedie variance power must be in interval [1, 2) tweedie-nloglik@ /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.cc Unknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builder Multiclass classification error. mlogloss Multiclass negative loglikelihood. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cu preds.Size() == 0 Check failed: preds.Size() % info.labels_.Size() == 0 label and prediction size not match nclass >= 1U mlogloss and merror are only used for multi-class classification, use logloss for binary classification Check 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 label ams AMS metric for higgs. aucpr Area under PR curve for both classification and rank. pre precision@k for rank. ndcg ndcg@k for rank. map map@k for rank. cox-nloglik Negative log partial likelihood of Cox proportional hazards model. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cc AMS must be in format ams@k ams@ Check failed: !distributed metric AMS do not support distributed evaluation best-ams-ratio= info.labels_.Size() != 0U label set cannot be empty label size predict size not match gptr.back() == info.labels_.Size() EvalAucPR: group structure must match number of prediction dat[1] > 0.0f AUC-PR: the dataset only contains pos or neg samples dat[0] <= dat[1] AUC-PR: AUC > 1.0 Check failed: !auc_error AUC-PR: error in calculation %u[-]? gptr.size() != 0U must specify group when constructing rank file gptr.back() == preds.Size() EvalRank: group structure must match number of prediction Cox metric does not support distributed evaluation aft-nloglik Negative log likelihood of Accelerated Failure Time model. interval-regression-accuracy aft_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cu Unknown probability distribution preds.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 survival:aft AFT loss function /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cu info.labels_lower_bound_.Size() == ndata info.labels_upper_bound_.Size() == ndata info.weights_.Size() == ndata Number of weights should be equal to number of data points. Unrecognized distribution /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.h Not part of device code. WITH_CUDA: binary:hinge Hinge loss. Expects labels to be in [0,1f] /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cu labels are not correctly provided preds.size= , label.size= SoftmaxMultiClassParam Softmax 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.cu Check 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.cc Unknown objective function: ` `
- LambdaRankParam Pairwise rank objective. LambdaRank with NDCG as objective. LambdaRank with MAP as objective. rank:pairwise rank:ndcg rank:map num_pairsample Number of pair generated for each instance. fix_list_weight Normalize the weight of each list by this value, if equals 0, no effect will happen lambda_rank_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cu Check failed: gptr.size() != 0 && gptr.back() == info.labels_.Size() group structure not consistent with #rows group ponter size: labels size: group pointer back: Computing gradients on CPU. RegLossParam Regression with squared error. Regression with root mean squared logarithmic error. Logistic regression for probability regression task. Regression Pseudo Huber error. Logistic regression for binary classification task. Logistic regression for classification, output score before logistic transformation. reg:linear PoissonRegressionParam Possion regression for count data. survival:cox Cox regression for censored survival data (negative labels are considered censored). reg:gamma Gamma regression for severity data. TweedieRegressionParam reg:tweedie Tweedie regression for insurance data. reg:squaredlogerror reg:logistic reg:pseudohubererror scale_pos_weight Scale the weight of positive examples by this factor reg_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cu Loss: 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.h Check failed: base_score > 0.0f && base_score < 1.0f base_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_param PoissonRegression: label must be nonnegative Check failed: last_abs_y <= abs_y CoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed! GammaRegression: label must be positive. tweedie_variance_power Tweedie variance power. Must be between in range [1, 2). tweedie_regression_param TweedieRegression: label must be nonnegative Make predictions using CPU. /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.cc model.learner_model_param->num_output_group != 0 out_preds->Size() == n Ignoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group model.param.size_leaf_vector == 0 size_leaf_vector is enforced to 0 so far m->NumColumns() == model.learner_model_param->num_feature Number of columns in data must equal to trained model. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h Check failed: type_ != nullptr The any container is empty requested= Check failed: *(type_->ptype_info) == typeid(T) The stored type mismatch stored= ncolumns != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.cc Check failed: container_.find(m) != container_.cend() Check failed: container_.at(m).ref.lock() [Internal error]: DMatrix: has expired. Unknown predictor type unordered_map::at: key not found /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.cc Failed to parse feature interaction constraint:
+ LambdaRankParam Pairwise rank objective. LambdaRank with NDCG as objective. LambdaRank with MAP as objective. rank:pairwise rank:ndcg rank:map num_pairsample Number of pair generated for each instance. fix_list_weight Normalize the weight of each list by this value, if equals 0, no effect will happen lambda_rank_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cu Check failed: gptr.size() != 0 && gptr.back() == info.labels_.Size() group structure not consistent with #rows group ponter size: labels size: group pointer back: Computing gradients on CPU. RegLossParam Regression with squared error. Regression with root mean squared logarithmic error. Logistic regression for probability regression task. Regression Pseudo Huber error. Logistic regression for binary classification task. Logistic regression for classification, output score before logistic transformation. reg:linear PoissonRegressionParam Poisson regression for count data. survival:cox Cox regression for censored survival data (negative labels are considered censored). reg:gamma Gamma regression for severity data. TweedieRegressionParam reg:tweedie Tweedie regression for insurance data. reg:squaredlogerror reg:logistic reg:pseudohubererror scale_pos_weight Scale the weight of positive examples by this factor reg_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cu Loss: 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.h Check failed: base_score > 0.0f && base_score < 1.0f base_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_param PoissonRegression: label must be nonnegative Check failed: last_abs_y <= abs_y CoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed! GammaRegression: label must be positive. tweedie_variance_power Tweedie variance power. Must be between in range [1, 2). tweedie_regression_param TweedieRegression: label must be nonnegative Make predictions using CPU. /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.cc model.learner_model_param->num_output_group != 0 out_preds->Size() == n Ignoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group model.param.size_leaf_vector == 0 size_leaf_vector is enforced to 0 so far m->NumColumns() == model.learner_model_param->num_feature Number of columns in data must equal to trained model. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h ncolumns != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.cc Check failed: container_.find(m) != container_.cend() Check failed: container_.at(m).ref.lock() [Internal error]: DMatrix: has expired. Unknown predictor type unordered_map::at: key not found /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.cc Failed to parse feature interaction constraint:
With error:
- newsize != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.cc std::floor(d) == d Found floating point number in interaction constraints Unknown value type for interaction constraint: TreeParam TrainParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc Unknown Model Builder: Dump text representation of tree Dump json representation of tree GraphvizParam dot Dump graphviz representation of tree Check failed: node.IsLeaf() fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam) param.num_nodes != 0 fi->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_deleted param.num_nodes == static_cast<int>(nodes_.size()) param.num_nodes == static_cast<int>(stats_.size()) param.deprecated_num_roots == 1 categories_segments categories_sizes categories_nodes categories j_end - j_begin != 0 std::numeric_limits<bst_cat_t>::min() != max_cat this->split_types_.size() == param.num_nodes this->GetSplitCategoriesPtr().size() == param.num_nodes tree_param n_nodes != 0 loss_changes loss_changes.size() == n_nodes sum_hessian sum_hessian.size() == n_nodes base_weights base_weights.size() == n_nodes left_children lefts.size() == n_nodes right_children rights.size() == n_nodes parents parents.size() == n_nodes split_indices indices.size() == n_nodes split_conditions conds.size() == n_nodes default_left default_left.size() == n_nodes split_type n_nodes == split_categories_segments_.size() static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deleted this->split_categories_segments_.size() == param.num_nodes num_nodes get<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes) this->node_mean_values_.size() > 0U unique_path[i].pweight == 0 Unique path must have zero weight /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h param.num_nodes < std::numeric_limits<int>::max() number of nodes in the tree exceed 2^31 Number of features used in tree construction. num_deleted Size of leaf vector, reserved for vector tree min_split_loss Minimum loss reduction required to make a further partition. max_depth Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policy max_leaves Maximum number of leaves; 0 indicates no limit. max_bin if using histogram-based algorithm, maximum number of bins per feature grow_policy depthwise lossguide Tree 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) min_child_weight Minimum sum of instance weight(hessian) needed in a child. L2 regularization on leaf weight L1 regularization on leaf weight default_direction learn right Default direction choice when encountering a missing value Maximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraint subsample Row subsample ratio of training instance. sampling_method gradient_based Sampling 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_bynode Subsample ratio of columns, resample on each node (split). colsample_bylevel Subsample ratio of columns, resample on each level. colsample_bytree Subsample ratio of columns, resample on each tree construction. sketch_eps EXP Param: Sketch accuracy of approximate algorithm. sketch_ratio EXP Param: Sketch accuracy related parameter of approximate algorithm. cache_opt EXP Param: Cache aware optimization. refresh_leaf Whether the refresh updater needs to update leaf values. monotone_constraints Constraint of variable monotonicity interaction_constraints Constraints 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 information split_evaluator elastic_net,monotonic The criteria to use for ranking splits sparse_threshold percentage threshold for treating a feature as sparse enable_feature_grouping if >0, enable feature grouping to ameliorate work imbalance among worker threads max_conflict_rate when grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary. max_search_group when grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined. gamma double {nid}:[{fname}] yes={yes},no={no} {nid} {fname} {yes} pos != std::string::npos idx < names_.size() FeatureMap feature index exceed bound {tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing} {tabs} {cond} {left} {right} {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. {tabs}{nid}:leaf={leaf}{stats} ,cover={cover} {leaf} {stats} {cover} {parent}{stat}
+ newsize != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.cc std::floor(d) == d Found floating point number in interaction constraints Unknown value type for interaction constraint: TreeParam TrainParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc Unknown Model Builder: { } Dump text representation of tree Dump json representation of tree GraphvizParam dot Dump graphviz representation of tree Check failed: node.IsLeaf() fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam) param.num_nodes != 0 fi->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_deleted param.num_nodes == static_cast<int>(nodes_.size()) param.num_nodes == static_cast<int>(stats_.size()) param.deprecated_num_roots == 1 categories_segments categories_sizes categories_nodes categories j_end - j_begin != 0 std::numeric_limits<bst_cat_t>::min() != max_cat this->split_types_.size() == param.num_nodes this->GetSplitCategoriesPtr().size() == param.num_nodes tree_param n_nodes != 0 loss_changes loss_changes.size() == n_nodes sum_hessian sum_hessian.size() == n_nodes base_weights base_weights.size() == n_nodes left_children lefts.size() == n_nodes right_children rights.size() == n_nodes parents parents.size() == n_nodes split_indices indices.size() == n_nodes split_conditions conds.size() == n_nodes default_left default_left.size() == n_nodes split_type n_nodes == split_categories_segments_.size() static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deleted this->split_categories_segments_.size() == param.num_nodes num_nodes get<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes) mean_values->size() > 0U unique_path[i].pweight == 0 Unique path must have zero weight /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h param.num_nodes < std::numeric_limits<int>::max() number of nodes in the tree exceed 2^31 Number of features used in tree construction. num_deleted Size of leaf vector, reserved for vector tree min_split_loss Minimum loss reduction required to make a further partition. max_depth Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policy max_leaves Maximum number of leaves; 0 indicates no limit. max_bin if using histogram-based algorithm, maximum number of bins per feature grow_policy depthwise lossguide Tree 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) min_child_weight Minimum sum of instance weight(hessian) needed in a child. L2 regularization on leaf weight L1 regularization on leaf weight default_direction learn right Default direction choice when encountering a missing value Maximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraint subsample Row subsample ratio of training instance. sampling_method gradient_based Sampling 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_bynode Subsample ratio of columns, resample on each node (split). colsample_bylevel Subsample ratio of columns, resample on each level. colsample_bytree Subsample ratio of columns, resample on each tree construction. sketch_eps EXP Param: Sketch accuracy of approximate algorithm. sketch_ratio EXP Param: Sketch accuracy related parameter of approximate algorithm. cache_opt EXP Param: Cache aware optimization. refresh_leaf Whether the refresh updater needs to update leaf values. monotone_constraints Constraint of variable monotonicity interaction_constraints Constraints 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 information split_evaluator elastic_net,monotonic The criteria to use for ranking splits sparse_threshold percentage threshold for treating a feature as sparse max_conflict_rate when grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary. max_search_group when grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined. gamma double {nid}:[{fname}] yes={yes},no={no} {nid} {fname} {yes} pos != std::string::npos {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": {left}, "no": {right}, "missing": {missing} , "gain": {loss_chg}, "cover": {sum_hess} {{properties} {stat}, "children": [{left}, {right}
+{right} {parent} {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 #0000FF Edge color when meets the node condition. #FF0000 Edge color when doesn't meet the node condition. rankdir Passed to graphiz via graph_attr. condition_node_params Conditional node configuration leaf_node_params Leaf node configuration graph_attrs Any other extra attributes for graphviz `graph_attr`. Failed to parse graphviz parameters:
" 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.h ch == '{' 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}"]
- yes no , missing {child} {color} {nid} [ label="leaf={leaf-value}" {params}]
+ /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.h ch == '{' 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}"]
+ no yes , missing {child} {color} {branch} {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.cc Unknown tree updater ColMakerTrainParam grow_colmaker Grow tree with parallelization over columns. opt_dense_col EXP Param: speed optimization for dense column. train_param colmaker_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.cc Updater `grow_colmaker` or `exact` tree method doesn't support distributed training. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.h Max leaves and max depth cannot both be unconstrained. this->max_depth <= 31 max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes. Check failed: param_.cache_opt Support for `cache_opt' is removed in 1.0.0 fmat.Info().num_row_ == position_.size() param_.sampling_method == TrainParam::kUniform Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist. ridx < position_.size() ridx exceed bound ridx= pos= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h grow_local_histmaker Tree constructor that uses approximate histogram construction. grow_histmaker Tree constructor that uses approximate global of histogram construction. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h fi.Read(&this->size, sizeof(this->size)) == sizeof(this->size) fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) check quantile stats, nbig= , n= srcsize= , maxsize= , range= , chunk= Check failed: nbig < n quantile: too many large chunk ] rmin= , rmax= , wmin= , v= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.cc Check failed: param_.max_depth > 0 max_depth must be larger than 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.h node2workindex_[nid] == static_cast<int>(wid) summary_array_.size() == sketchs_.size() offset == -2 this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1 fid * 2 + 1 < fminmax_.size() FeatHelper fid exceed query bound ret > 0U Check failed: tree[nid].IsLeaf() sketch->temp.size < max_size invalid maximum size max_size= , stemp.size INFO: rmax= , sum_total= , naxt_goal= , size= sketch->temp.size <= max_size Finalize: invalid maximum size, max_size= , stemp.size= fid * 2 < fminmax_.size() fv= , hist.size= hist[ ]= , hist.last= istart != hist.size this->qexpand_.size() == 1U this->wspace_.rptr.back() == this->wspace_.cut.size() prune Pruner that prune the tree according to statistics. TreePruner PrunerUpdate /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cc tree pruning end, extra nodes, pruned nodes, max_depth= Check failed: !tree[pid].IsLeaf() Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf() Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf() nid >= 1 Quantile::Builder Update SubtractionTrick UpdatePredictionCache /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc out_preds.size() > 0U Check failed: (param_.max_depth > 0 || param_.max_leaves > 0) max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity. max_depth cannot be 0 (unlimited) when grow_policy is depthwise. row_indices.size() + unused_rows_.size() == info.num_row_ min_nbins_per_feature > 0U EvaluateSplits ApplySplit (*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild() upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) InitNewNode BuildLocalHistograms SplitSiblings BuildNodeStats ExpandWithLossGuide CPUHistMakerTrainParam GmatInitialization grow_fast_histmaker (Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram. Grow tree using quantized histogram. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.h elem_of_each_node_.size() == 0U Check failed: e.begin != nullptr n_left + n_right == e.Size() begin + n_left <= e.end begin + n_left + n_right == e.end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/column_matrix.h gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val gmat_bin_size == kUint32BinsTypeSize bins_type_size_ == kUint32BinsTypeSize cpu_hist_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h Attempted to load internal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html single_precision_histogram Use single precision to build histograms. SyncHistograms SyncHistogramsAllreduce /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h begin < end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.h end > begin nid < nodes_ i < first_dimension_.size() i < ranges_.size() AddHistRows row_ptr_[nid] == kMax Check failed: (*p_last_tree_)[nid].IsLeaf() id != kMax cut_ptr[fid] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) cut_ptr[fid + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) nodes == targeted_hists.size() tid < nthreads_ grow_fast_histmaker is deprecated, use grow_quantile_histmaker instead. QuantileHistMaker refresh Refresher that refreshes the weight and statistics according to data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.cc batch.Size() < std::numeric_limits<unsigned>::max() Syncher that synchronize the tree in all distributed nodes. NULL worker rabit_reduce_buffer 256MB DMLC_TASK_ID DMLC_ROLE DMLC_NUM_ATTEMPT DMLC_TRACKER_URI DMLC_TRACKER_PORT DMLC_WORKER_CONNECT_RETRY %[^=]=%s mapred_tip_id mapreduce_task_id hadoop_mode is set but cannot find mapred_task_id rabit_task_id rabit_hadoop_mode mapred_task_id %d rabit_num_trial mapred_map_tasks mapreduce_job_maps hadoop_mode is set but cannot find mapred_map_tasks rabit_world_size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.cc Rabit Module currently only work with dmlc worker can only call Init once start Failed to shutdown due to %s print rabit_tracker_uri rabit_tracker_port rabit_tree_reduce_minsize rabit_reduce_ring_mincount rabit_reduce_ring_mincount should be greater than 0 rabit_bootstrap_cache rabit_debug rabit_timeout rabit_timeout_sec rabit_timeout_sec should be non negative second rabit_enable_tcp_no_delay Connect to (failed): [ ]
- Connect Retry connect to ip(retry time ): [ ReConnectLink failure 1 ReConnectLink failure 2 sync::Invalid tracker message, init failure ReConnectLink failure 3 task connected to the tracker ReConnectLink failure 4 must keep rank to same if the node already have one tracker got overwhelemed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specified ReConnectLink failure 5 ReConnectLink failure 6 ReConnectLink failure 7 ReConnectLink failure 8 ReConnectLink failure 9 ReConnectLink failure 10 ReConnectLink failure 12 ReConnectLink failure 13 ReConnectLink failure, link rank inconsistent Override a link that is active ReConnectLink failure 14 ReConnectLink failure 15 ReConnectLink: bad socket tcp no delay is not implemented on non unix platforms cannot find parent in the link cannot find prev ring in the link cannot find next ring in the link failed in ReconnectLink buffer size inconsistent must assign buffer_size Allreduce: size check Allreduce: boundary error Broadcast: root should be smaller than world size need to assume rank structure write ptr boundary check [%d] read_ptr boundary check fail to get host name Socket::Close double close the socket or close without create error during send SendStr %lu%c invalid format for %s invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB} Create cannot obtain address of %s Does not support IPv6 Socket %s Error:%s SendAll RecvAll 0.0.0.0 TryBindHost GetSockError error during send RecvStr Accept SetNonBlock-1 SetNonBlock-2 SetKeepAlive too large type_nbytes=%lu, buffer_size=%lu /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.h Poll timeout Poll ReadToRingBuffer: buffer not allocated ReadToRingBuffer: max_size_read check Allreduce: boundary check AllgatherRing failed Allreduce failed Broadcast failed unknown enum_op unknown data_type Failed to initialize RABIT. Failed to shutdown RABIT worker. Read pickle string not implemented DataType does not support bitwise or operation the rabit has not been initialized cannot initialize reduce handle twice must intialize handle to call AllReduce LibSVMParserParam LibFMParserParam CSVParserParam libfm format File format indexing_mode If >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_column Column index (0-based) that will put into label. delimiter Delimiter used in the csv file. weight_column Column index that will put into instance weights. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h Check failed: TryLoadCache() failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.h Check failed: fi->Read(&label) Bad RowBlock format Check 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/sec finish reading at %g MB/sec label.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.h finish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc Unknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h param_.format == "libsvm" /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.h chunk.size != 0U qid: Check failed: out->label.size() + 1 == out->offset.size() Check failed: base <= 10 && base >= 2 sign == true /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h cannot call ParseNext /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h param_.format == "libfm" Check failed: out->field.size() == out->index.size() /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.h param_.format == "csv" Check failed: param_.label_column != param_.weight_column || param_.label_column < 0 Must have distinct columns for labels and instance weights Delimiter ' ' 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.cc Please compile with DMLC_USE_HDFS=1 to use hdfs s3:// http:// https:// Please compile with DMLC_USE_S3=1 to use S3 azure:// Please compile with DMLC_USE_AZURE=1 to use Azure unknown filesystem protocol stdin Check failed: part < nsplit invalid input parameter for InputSplit::Create indexed_recordio need to pass index file to use IndexedRecordIO recordio unknown input split type .split .part /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.h name_cache.size() == 1U only one `#` is allowed in file path for cachefile specification Check 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.h Check failed: fp_ != NULL SingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1 InputSplit do not support write /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.h Check failed: nread == sizeof(size) has invalid cache file format Check failed: fi_->Read(p->begin, size) == size BeforeFirst is not supported Check failed: this->InitCachedIter() Failed to initialize CachedIter ResetPartition is not supported in CachedInputSplit /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.cc Check failed: begin != end /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.cc Check failed: fi->Read(&lrec, sizeof(lrec)) != 0 invalid record io format (reinterpret_cast<size_t>(begin) & 3UL) == 0U (reinterpret_cast<size_t>(end) & 3UL) == 0U Check failed: p >= pbegin + 2 Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end Invalid RecordIO Format (reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U (reinterpret_cast<size_t>(chunk->end) & 3UL) == 0U Check failed: chunk->begin <= chunk->end Check failed: cflag == 1U Check failed: p[0] == RecordIOWriter::kMagic /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.cc expanded_list.size() == 1ul IndexedRecordIOSplitter does not support multiple index files /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.cc Check failed: files_[i].size % align_bytes == 0 file do not align by bytes Check 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 needed files_.size() != 0U Cannot find any files that matches the URI pattern curr= ,begin= ,end= ,fileptr= ,fileoffset= offset[ file offset not calculated correctly C /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.cc Check failed: !IsSymlink(info.path.name) Symlink not supported in TemporaryDirectory Couldn'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.h lstat(path.c_str(), &sb) == 0 dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.cc LocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory .. stdout wb Check failed: allow_null LocalFileSystem::Open " ": Check failed: std::fwrite(ptr, 1, size, fp_) == size FileStream.Write incomplete Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET) L P XQ aQ ! 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+{nodes}} {rankdir} {graph_attrs} /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.cc Unknown tree updater ColMakerTrainParam grow_colmaker Grow tree with parallelization over columns. opt_dense_col EXP Param: speed optimization for dense column. train_param colmaker_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.cc Updater `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/param.h Max leaves and max depth cannot both be unconstrained. this->max_depth <= 31 max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes. Check failed: param_.cache_opt Support for `cache_opt' is removed in 1.0.0 fmat.Info().num_row_ == position_.size() param_.sampling_method == TrainParam::kUniform Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist. ridx < position_.size() ridx exceed bound ridx= pos= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h grow_local_histmaker Tree constructor that uses approximate histogram construction. grow_histmaker Tree constructor that uses approximate global of histogram construction. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h fi.Read(&this->size, sizeof(this->size)) == sizeof(this->size) fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) check quantile stats, nbig= , n= srcsize= , maxsize= , range= , chunk= Check failed: nbig < n quantile: too many large chunk ] rmin= , rmax= , wmin= , v= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.cc Check failed: param_.max_depth > 0 max_depth must be larger than 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.h node2workindex_[nid] == static_cast<int>(wid) summary_array_.size() == sketchs_.size() offset == -2 this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1 fid * 2 + 1 < fminmax_.size() FeatHelper fid exceed query bound ret > 0U Check failed: tree[nid].IsLeaf() sketch->temp.size < max_size invalid maximum size max_size= , stemp.size INFO: rmax= , sum_total= , naxt_goal= , size= sketch->temp.size <= max_size Finalize: invalid maximum size, max_size= , stemp.size= fid * 2 < fminmax_.size() fv= , hist.size= hist[ ]= , hist.last= istart != hist.size this->qexpand_.size() == 1U this->wspace_.rptr.back() == this->wspace_.cut.size() prune Pruner that prune the tree according to statistics. TreePruner PrunerUpdate /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cc tree pruning end, extra nodes, pruned nodes, max_depth= Check failed: !tree[pid].IsLeaf() Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf() Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf() nid >= 1 Quantile::Builder Update UpdatePredictionCache /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc out_preds.Size() > 0U out_preds.DeviceIdx() == GenericParameter::kCpuId Check failed: (param_.max_depth > 0 || param_.max_leaves > 0) max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity. max_depth cannot be 0 (unlimited) when grow_policy is depthwise. InitSampling row_indices.size() == info.num_row_ min_nbins_per_feature > 0U (*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild() upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) SplitSiblings nodes_for_subtraction_trick_.size() == nodes_for_explicit_hist_build_.size() CPUHistMakerTrainParam GmatInitialization grow_fast_histmaker (Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram. Grow tree using quantized histogram. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.h elem_of_each_node_.size() == 0U Check failed: e.begin != nullptr n_left + n_right == e.Size() begin + n_left <= e.end begin + n_left + n_right == e.end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/../common/column_matrix.h gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val gmat_bin_size == kUint32BinsTypeSize bins_type_size_ == kUint32BinsTypeSize cpu_hist_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h Attempted to load internal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html single_precision_histogram Use single precision to build histograms. ExpandTree EvaluateSplits /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/hist_util.h id != kMax ApplySplit /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h begin < end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.h any_missing == true sizeof(BinIdxType) == bins_type_size_ any_missing == any_missing_ i < first_dimension_.size() i < ranges_.size() access element that is not in the set nodes == targeted_hists.size() row_ptr_[nid] == kMax nid < nodes_ tid < nthreads_ end > begin /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.h Check failed: !features.empty() cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) Check failed: (*p_last_tree_)[nid].IsLeaf() /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h max_bin_per_feat >= 2 exponent <= mod grow_fast_histmaker is deprecated, use grow_quantile_histmaker instead. QuantileHistMaker refresh Refresher that refreshes the weight and statistics according to data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.cc batch.Size() < std::numeric_limits<unsigned>::max() Syncher that synchronize the tree in all distributed nodes. NULL worker rabit_reduce_buffer 256MB DMLC_TASK_ID DMLC_ROLE DMLC_NUM_ATTEMPT DMLC_TRACKER_URI DMLC_TRACKER_PORT DMLC_WORKER_CONNECT_RETRY %[^=]=%s mapred_tip_id mapreduce_task_id hadoop_mode is set but cannot find mapred_task_id rabit_task_id rabit_hadoop_mode mapred_task_id %d rabit_num_trial mapred_map_tasks mapreduce_job_maps hadoop_mode is set but cannot find mapred_map_tasks rabit_world_size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.cc Rabit Module currently only work with dmlc worker can only call Init once start Failed to shutdown due to %s print rabit_tracker_uri rabit_tracker_port rabit_tree_reduce_minsize rabit_reduce_ring_mincount rabit_reduce_ring_mincount should be greater than 0 rabit_bootstrap_cache rabit_debug rabit_timeout rabit_timeout_sec rabit_timeout_sec should be non negative second rabit_enable_tcp_no_delay Connect to (failed): [ ]
+ Connect Retry connect to ip(retry time ): [ ReConnectLink failure 1 ReConnectLink failure 2 sync::Invalid tracker message, init failure ReConnectLink failure 3 task connected to the tracker ReConnectLink failure 4 must keep rank to same if the node already have one tracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specified ReConnectLink failure 5 ReConnectLink failure 6 ReConnectLink failure 7 ReConnectLink failure 8 ReConnectLink failure 9 ReConnectLink failure 10 ReConnectLink failure 12 ReConnectLink failure 13 ReConnectLink failure, link rank inconsistent Override a link that is active ReConnectLink failure 14 ReConnectLink failure 15 ReConnectLink: bad socket tcp no delay is not implemented on non unix platforms cannot find parent in the link cannot find prev ring in the link cannot find next ring in the link failed in ReconnectLink buffer size inconsistent must assign buffer_size Allreduce: size check Allreduce: boundary error Broadcast: root should be smaller than world size need to assume rank structure write ptr boundary check [%d] read_ptr boundary check fail to get host name Socket::Close double close the socket or close without create error during send SendStr %lu%c invalid format for %s invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB} Create cannot obtain address of %s Does not support IPv6 Socket %s Error:%s SendAll RecvAll 0.0.0.0 TryBindHost GetSockError error during send RecvStr Accept SetNonBlock-1 SetNonBlock-2 SetKeepAlive too large type_nbytes=%lu, buffer_size=%lu /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.h Poll timeout Poll ReadToRingBuffer: buffer not allocated ReadToRingBuffer: max_size_read check Allreduce: boundary check AllgatherRing failed Allreduce failed Broadcast failed unknown enum_op unknown data_type Failed to initialize RABIT. Failed to shutdown RABIT worker. Read pickle string not implemented DataType does not support bitwise or operation the rabit has not been initialized cannot initialize reduce handle twice must initialize handle to call AllReduce LibSVMParserParam LibFMParserParam CSVParserParam libfm format File format indexing_mode If >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_column Column index (0-based) that will put into label. delimiter Delimiter used in the csv file. weight_column Column index that will put into instance weights. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h Check failed: TryLoadCache() failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.h Check failed: fi->Read(&label) Bad RowBlock format Check 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/sec finish reading at %g MB/sec label.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.h finish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc Unknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h param_.format == "libsvm" /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.h chunk.size != 0U qid: Check failed: out->label.size() + 1 == out->offset.size() Check failed: base <= 10 && base >= 2 sign == true /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h cannot call ParseNext /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h param_.format == "libfm" Check failed: out->field.size() == out->index.size() /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.h param_.format == "csv" Check failed: param_.label_column != param_.weight_column || param_.label_column < 0 Must have distinct columns for labels and instance weights Delimiter ' ' 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.cc Please compile with DMLC_USE_HDFS=1 to use hdfs s3:// http:// https:// Please compile with DMLC_USE_S3=1 to use S3 azure:// Please compile with DMLC_USE_AZURE=1 to use Azure unknown filesystem protocol stdin Check failed: part < nsplit invalid input parameter for InputSplit::Create indexed_recordio need to pass index file to use IndexedRecordIO recordio unknown input split type .split .part /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.h name_cache.size() == 1U only one `#` is allowed in file path for cachefile specification Check 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.h Check failed: fp_ != NULL SingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1 InputSplit do not support write /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.h Check failed: !producer_sig_processed_.load(std::memory_order_acquire) unique_lock::unlock: not locked Check failed: producer_sig_.load(std::memory_order_acquire) == kProduce Make sure you call BeforeFirst not inconcurrent with Next! Check failed: produce_end_.load(std::memory_order_acquire) Check failed: out_data_ != NULL Calling Value at beginning or end? Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroy Check failed: cell != __null || produce_end_.load(std::memory_order_acquire) Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy No thread /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.h Check failed: nread == sizeof(size) has invalid cache file format Check failed: fi_->Read(p->begin, size) == size BeforeFirst is not supported Check failed: this->InitCachedIter() Failed to initialize CachedIter ResetPartition is not supported in CachedInputSplit /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.cc Check failed: begin != end /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.cc Check failed: fi->Read(&lrec, sizeof(lrec)) != 0 invalid record io format (reinterpret_cast<size_t>(begin) & 3UL) == 0U (reinterpret_cast<size_t>(end) & 3UL) == 0U Check failed: p >= pbegin + 2 Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end Invalid RecordIO Format (reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U (reinterpret_cast<size_t>(chunk->end) & 3UL) == 0U Check failed: chunk->begin <= chunk->end Check failed: cflag == 1U Check failed: p[0] == RecordIOWriter::kMagic /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.cc expanded_list.size() == 1ul IndexedRecordIOSplitter does not support multiple index files /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.cc Check failed: files_[i].size % align_bytes == 0 file do not align by bytes Check 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 needed files_.size() != 0U Cannot find any files that matches the URI pattern curr= ,begin= ,end= ,fileptr= ,fileoffset= offset[ file offset not calculated correctly C /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.cc Check failed: !IsSymlink(info.path.name) Symlink not supported in TemporaryDirectory Couldn'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.h lstat(path.c_str(), &sb) == 0 dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.cc LocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory .. stdout wb Check failed: allow_null LocalFileSystem::Open " ": Check failed: std::fwrite(ptr, 1, size, fp_) == size FileStream.Write incomplete Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET) L P XQ aQ Q! 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o4DeviceCanWriteEv o DeviceSpanEv lHostVectorEv m DeviceSpanEv lHostVectorEv m Device mConstHostSpanEv m PointerEv mCanReadEv o PointerEv mCanReadEv o FillEj nCopyE n RK nSt16initializer_listIjE n S1_ nNSt3__16vectorIjNS2_9allocatorIjEEEE n ExtendERKS1_ oResizeEmj o HostCanWriteEv oDeviceAccessEv o C pD raSEOS3_ r1 s8HostSpanEv u4 v6 w 2E p1E q mS2_i qSt16initializer_listIS2_Ei qRKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi qOS3_ r mS2_i qSt16initializer_listIS2_Ei qRKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi qOS3_ r 2Ev r1Ev r 5EmptyEv r4SizeEv r9 r1 s8HostSpanEv u DeviceIdxEv sSetDeviceEi x 0 s3DevicePointerEv u1HostPointerEv u DeviceSpanEv sHostVectorEv u 5Const t0 t8ConstDevicePointerEv u3 u6ConstHostPointerEv u1Host u2 w4DeviceCanWriteEv w DeviceSpanEv tHostVectorEv u DeviceSpanEv uHostVectorEv u Device uConstHostSpanEv u PointerEv uCanReadEv w PointerEv vCanReadEv w FillES2_ vCopyE v RK vSt16initializer_listIS2_E w S3_ wNSt3__16vectorIS2_NS4_9allocatorIS2_EEEE w Ѐ ExtendERKS3_ wResizeEmS2_ x HostCanWriteEv wDeviceAccessEv x Ј C xD zaSEOS1_ z1 {8HostSpanEv }4 ~6 2E x1E y mmi ySt16initializer_listImEi zRKNSt3__16vectorImNS2_9allocatorImEEEEi zOS1_ z mmi ySt16initializer_listImEi zRKNSt3__16vectorImNS2_9allocatorImEEEEi zOS1_ z 2Ev z1Ev z 5EmptyEv z4SizeEv z9 {1 {8HostSpanEv } DeviceIdxEv {SetDeviceEi 0 {3DevicePointerEv }1HostPointerEv } DeviceSpanEv {HostVectorEv } 5Const |0 |8ConstDevicePointerEv }3 }6ConstHostPointerEv ~1Host ~2 4DeviceCanWriteEv DeviceSpanEv |HostVectorEv ~К DeviceSpanEv }HostVectorEv ~ Device }ConstHostSpanEv ~ PointerEv }CanReadEv Л М PointerEv ~CanReadEv FillEm ~CopyE ~ RK ~St16initializer_listImE S1_ NSt3__16vectorImNS2_9allocatorImEEEE ExtendERKS1_ ResizeEmm Щ Ъ HostCanWriteEv DeviceAccessEv 4ReadEPvm ̀8PeekReadEPvm Ҁ C 4 8PeekReadEPvm 2EPNS0_16PeekableInStreamE 1EPNS0_16PeekableInStreamE ReadEPvm SeekEm TakeEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE Json SparsePage ExtendPathEPNS_11PathElementEjffi UnwindPathEPNS_11PathElementEjj Writer ӃObject ߇String Number Reader Ԛ 4SaveENS_4JsonE 5VisitEPKNS_ 9JsonArrayE 1 8JsonNullE Ч 0Json 1Json ObjectE ߄NumberE StringE IntegerE BooleanE DummyJsonObjectEv هLoadFeatureTypeERKNSt3__16vectorINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS5_IS7_EEEEPNS1_INS_11FeatureTypeENS5_ISC_EEEE GradientBooster6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterEPKNS_17LearnerModelParamE C ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE 2EO 1EO S0_ NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE ˊ S0_ ŊNSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE ъ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json ؋SparsePage12GetTransposeEi ObjecteqERKNS_5ValueE StringeqERKNS_5ValueE NumbereqERKNS_5ValueE Reader5ErrorENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i JsonArray GPUMetric15CreateGPUMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE predictor Predictor TreeParam11__MANAGER__Ev օ C ixE ƐaSERKNS_5ValueE 4SaveEPNS_10JsonWriterE 2EOS0_ 1EOS0_ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i ixE aSERKNS_5ValueE ǒ4SaveEPNS_10JsonWriterE ͒ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json EllpackPage ObjFunction6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE TreeUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ȏLearnerImpl14kRandSeedMagicE Integer Boolean ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json EllpackPage4SizeEv IntegereqERKNS_5ValueE BooleaneqERKNS_5ValueE JsonNull ParseStrERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE MetaInfo ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i JsonNulleqERKNS_5ValueE MetaInfo ixE aSERKNS_5ValueE Ț4SaveEPNS_10JsonWriterE Κ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i 5ParseEv 1 4LoadEv 9ParseNullEv С 0 1Parse 2ParseBooleanEv 3kMaxNumLengthE SkipSpacesEv ParseArrayEv StringEv ObjectEv NumberEv Json ݜdata tree ܅ 4 aSERKS0_ LoadE ȝDumpES0_PNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NS_10StringViewE PNS_10JsonReaderE C ̟1 9AllReduceEPNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_IiNS9_IiEEEE 8MakeCutsEPNS0_13HistogramCutsE 2ENSt3__16vectorImNS2_9allocatorImEEEEib 1ENSt3__16vectorImNS2_9allocatorImEEEEib 4CalcColumnSizeERKNS_10SparsePageEjm 1 6GatherSketchInfoERKNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_ImNS9_ImEEEESG_PNS3_INS6_5EntryENS9_ISH_EEEE LoadBalanceERKNS_10SparsePageEjm PushRowPageERKNS_10SparsePageERKNS_8MetaInfoE 2 1 ޑ6engine 5c_api9All op7ReducerINS0_ ץ4SerializeReducerFuncImplIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEEEvPKvPviRKN3MPI8DatatypeE 2SerializeReduceClosureIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEE6InvokeEPv 3 5BitORE SumE M mEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE axE inE mEEvPKvPviRKN3MPI8DatatypeE yEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE ĔcEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE 5StartERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ԫ4StopERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ګ 21CollectFromOtherRanksEv 15PrintStatisticsERKNSt3__13mapINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4pairImmEENS2_4lessIS9_EENS7_INSA_IKS9_SB_EEEEEE 5PrintEv Ы Version DMatrix Learner RegTree Ћ 4 6StringERKNSt3__15tupleIJiiiEEE ¯8kInvalidE LoadE S خ RKNS_4JsonE ̮PN4dmlc6StreamE Ү a elfEv veEPN meERKNSt3__15tupleIJiiiEEE ȯ S_4JsonE 4dmlc6StreamE 7xgboost S_16ParserFactoryRegI 4data19SparsePageFormatRegINS1_ 1 9MetricRegEE3GetEv 1 Ұ7CSCPageEEEE3GetEv 0SparsePageEEEE3GetEv 3SortedCSCPageEEEE3GetEv 1EllpackPageEEEE3GetEv 6CreateI D ٿ4LoadERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbbS9_m PvS2_FvS2_EFiS2_EEEPS0_T_T0_PT1_PT2_fii ڲNS_4data1 2DenseAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 0CS 6DataTableAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ۶1FileAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 5 RAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ϶CAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ն CSRArrayAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ոIteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPS0_PT_fiRKNSt3__112basic_stringIcNSD_11char_traitsIcEENSD_9allocatorIcEEEEm ۸ 4Push 7PushCSCERKS0_ INS_4data ERKS0_ 1 ù21DataTableAdapterBatchEEEyRKT_fi պ 7DenseAdapterBatchEEEyRKT_fi 5CS 6FileAdapterBatchEEEyRKT_fi ۺ RAdapterBatchEEEyRKT_fi ɺCAdapterBatchEEEyRKT_fi Ϻ 5ClearEv 1 7SetInfoEPKc 6ExtendERKS0_b 9kNumFieldE 1 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 7GetInfoEPKcPyNS_8DataTypeEPPKv 8ValidateEi 0SaveBinaryEPN4dmlc6StreamE 4GetFeatureInfoEPKcPNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEE 0LoadBinaryEPN4dmlc6StreamE 4SetFeatureInfoEPKcPS2_y PKvNS_8DataTypeEm ݾRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE DMatrix14GetThreadLocalEv ӿLearner19AllowLazyCheckPointEv RegTree 2Ev 1Ev 0Ev Ѐ Ў C D 12SetBaseRowIdEm 2E 1E v PNS_7DMatrixERKNS_10BatchParamE v PNS_7DMatrixERKNS_10BatchParamE 2Ev 1Ev 1 49__dmlc_registry_file_tag_sparse_page_raw_format__Ev 8DataPool 7 3SimpleDMatrix EllpackPageSourceC SparsePageDMatrix 2EPNS_7DMatrixERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKNS_10BatchParamE 1EPNS_7DMatrixERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKNS_10BatchParamE C 4InfoEv 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 1 22GetSortedColumnBatchesEv 2 1 INS0_1 EPN4dmlc6StreamE 2DenseAdapterEEEPT_fi 0CS 5 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi INS0_1 EPN4dmlc6StreamE 2DenseAdapterEEEPT_fi 0CS 5 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi RAdapterEEEPT_fi CAdapterEEEPT_fi RAdapterEEEPT_fi CAdapterEEEPT_fi CSRArrayAdapterEEEPT_fi IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi CSRArrayAdapterEEEPT_fi IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi ! ! ! ! data tree17QuantileHistMaker7BuilderI 1 8DataPool5SliceENSt3__110shared_ptrINS_10SparsePageEEEmmm 3SimpleDMatrix4InfoEv 7SparsePageDMatrix4InfoEv М! ! 3GetRowBatchesEv 6GetColumnBatchesEv 7GetEllpackBatchesERKNS_10BatchParamE 5SaveToLocalFileERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ! ! ! ! ! ! ! ! 4InfoEv 1 22GetSortedColumnBatchesEv ! ! 3GetRowBatchesEv 6GetColumnBatchesEv 7GetEllpackBatchesERKNS_10BatchParamE ! ! ! Ё" $ % 14SplitWritePageEv 8FinalizeEv % % gbm obj 2FeatureInteractionConstraintHost 3 1 2 6GBTree 5__dmlc_registry_file_tag_gblinear__Ev 3__dmlc_registry_file_tag_gbtree__Ev & 6 8GBLinearTrainParam11__MANAGER__Ev 3GBLinearModel9LoadModelERKNS_4JsonE 7GPUDartPredictIncENS_6common4SpanIfLm18446744073709551615EEES3_fmjj 4DartTrainParam11__MANAGER__Ev 1GBTreeModel LinearCheckLayerEjj GBTree & Ѕ& 1 6GBTree 3GBLinearModel9SaveModelEPNS_4JsonE 1GBTreeModel ( ( 8DeprecatedGBLinearModelParam11__MANAGER__Ev 4GPUDartInplacePredictIncENS_6common4SpanIfLm18446744073709551615EEES3_fmfjj ( 8GradientBoosterRegEE3GetEv 6LinearUpdaterRegEE3GetEv 2 4 0TreeGenRegEE3GetEv ( ( ( 9 1 2 7DoBoostEPNS_7DMatrixEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_20PredictionCacheEntryE ConfigureERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE LoadModelERKNS_4JsonE ( 7ConfigureUpdatersEv 1 3BoostNewTreesEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixEiPNSt3__16vectorINSA_10unique_ptrINS_7RegTreeENSA_14default_deleteISD_EEEENSA_9allocatorISG_EEEE 0LoadConfigERKNS_4JsonE 2PredictBatchEPNS_7DMatrixEPNS_20PredictionCacheEntryEbjj ( InitUpdaterERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE CommitModelEONSt3__16vectorINS3_INS2_10unique_ptrINS_7RegTreeENS2_14default_deleteIS5_EEEENS2_9allocatorIS8_EEEENS9_ISB_EEEEPNS_7DMatrixEPNS_20PredictionCacheEntryE ( 2ConfigureWithKnownDataERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEEPNS_7DMatrixE 6PerformTreeMethodHeuristicEPNS_7DMatrixE ( ( ( ) У) ) hn S I VN 8_N 16_N7xgboost7LearnerD 7xgboost K7xgboost3gbm6GBTree10SaveConfigEPNS_4JsonE 3gbm6GBTree10LoadConfigERKNS_4JsonE 7LearnerD ) 1 9SaveModelEPNS_4JsonE 5SliceEiiiPNS_15GradientBoosterEPb 0SaveConfigEPNS_4JsonE 2GetPredictorEPKNS_16HostDeviceVectorIfEEPNS_7DMatrixE ) ) ) ) ) ) * Ѝ* * ModelParam11__MANAGER__Ev TrainParam11__MANAGER__Ev * В* * 4SaveEPN4dmlc6StreamE 9SaveModelEPNS_4JsonE - 4LoadEPN4dmlc6StreamE 9LoadModelERKNS_4JsonE - - - GlobalConfiguration11__MANAGER__Ev PredictionContainer - Ѓ. D 6CreateERKNSt3__16vectorINS1_10shared_ptrINS_7DMatrixEEENS1_9allocatorIS5_EEEE 2Ev 1Ev 0Ev . . 1Ev 0Ev . 1Ev 0Ev Ѕ. . . . ModelParamC TrainParam11__MANAGER__Ev 2ERKNS_23LearnerModelParamLegacyEf 1ERKNS_23LearnerModelParamLegacyEf . . 3LearnerModelParamLegacy11__MANAGER__Ev 6ParseInteractionConstraintERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_6vectorINS9_IjNS4_IjEEEENS4_ISB_EEEE Ѕ0LearnerConfiguration11kEvalMetricE І. . 1 6kCpuIdE 1__MANAGER__Ev 4ConfigureGpuIdEb 2kDefaultSeedE . . . Є2 4 LinearUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ConsoleLogger Tr GraphvizParam11__MANAGER__Ev 4 1 4 6LinearTrainParam11__MANAGER__Ev 5CoordinateParam11__MANAGER__Ev 4 4 5__dmlc_registry_file_tag_updater_coordinate__Ev 2__dmlc_registry_file_tag_updater_shotgun__Ev 4 6 6CustomLogMessage3LogERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 2 0 8TemporaryDirectory15RecursiveDeleteERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6 D 9 1 C 2Ev 1Ev 6 ShouldLogENS0_12LogVerbosityE ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE 6 6 ackerLoggerD eeGenerator 2Ev 1Ev 6 6 6 6DefaultVerbosityEv 5GlobalVerbosityEv 6 6 2E 1E NS0_12LogVerbosityE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE 6 NS0_12LogVerbosityE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE 6 6 6 9BinaryAUCERKNSt3__16vectorIfNS1_9allocatorIfEEEES7_S7_ 1 4 3 2 6 3 5GroupRankingAUCENS_6common4SpanIKfLm18446744073709551615EEES4_f 0RankingAUCERKNSt3__16vectorIfNS1_9allocatorIfEEEERKNS_8MetaInfoE 2GPUBinaryAUCENS_6common4SpanIKfLm18446744073709551615EEERKNS_8MetaInfoEiPNSt3__110shared_ptrINS0_14DeviceAUCCacheEEE 9GPUMultiClassAUCOVRENS_6common4SpanIKfLm18446744073709551615EEERKNS_8MetaInfoEiPNSt3__110shared_ptrINS0_14DeviceAUCCacheEEE MultiClassOVRERKNSt3__16vectorIfNS1_9allocatorIfEEEERKNS_8MetaInfoE GPURankingAUCENS_6common4SpanIKfLm18446744073709551615EEERKNS_8MetaInfoEiPNSt3__110shared_ptrINS0_14DeviceAUCCacheEEE 7 7 7 7 7 7 5__ 4__dmlc_registry_file_tag_multiclass_metric__Ev 2__dmlc_registry_file_tag_survival_metric__Ev dmlc_registry_file_tag_elementwise_metric__Ev make_MetricReg_IntervalRegressionAccuracy__E 7 7 9 9 Ѐ: MetricGPURegEE3GetEv PredictorRegEE3GetEv : : 8__dmlc_registry_file_tag_rank_metric__Ev 2__make_MetricReg_ 5__make_MetricReg_PossionNegLoglik__E 1__make_MetricReg_ 3__make_MetricReg_TweedieNLogLik__E : < 3 41__dmlc_registry_file_tag_ 22 1 9ListEntry7CmpPredERKS1_S3_ 4__dmlc_registry_file_tag_aft_obj__Ev 6__dmlc_registry_file_tag_hinge_obj__Ev 5__dmlc_registry_file_tag_rank_obj__Ev > ? multiclass_obj__Ev regression_obj__Ev м? SoftmaxMultiClassParam11__MANAGER__Ev PoissonRegressionParam11__MANAGER__Ev TweedieRegressionParam11__MANAGER__Ev ? ObjFunctionRegEE3GetEv TreeUpdaterRegEE3GetEv Ю@ @ @ 5LambdaRankParam11__MANAGER__Ev 2RegLossParam11__MANAGER__Ev @ ГA B B B B 40__dmlc_registry_file_tag_cpu_predictor__Ev ݁9PredValueERKNS_6common4SpanIKNS_5EntryELm18446744073709551615EEERKNSt3__16vectorINS8_10unique_ptrINS_7RegTreeENS8_14default_deleteISB_EEEENS8_9allocatorISE_EEEERKNS9_IiNSF_IiEEEEiPNSB_4FVecEjj F F I 19ClearExpiredEntriesEv 5 9ContainerEv I CacheENSt3__110shared_ptrINS_7DMatrixEEEi EntryEPNS_7DMatrixE I I I 9ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE ߄6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE оI I 9 5ResetEv ConfigureERKNS_4tree10TrainParamEj SplitImplEijii ą I I I J J J 1 4 Ώ22CPUHistMakerTrainParam11__MANAGER__Ev 39__dmlc_registry_file_tag_updater_sync__Ev 0TrainParam11__MANAGER__Ev 8ColMakerTrainParam11__MANAGER__Ev ґ7QuantileHistMaker ֔ J J 6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_10FeatureMapEb 5MatchERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS1_3mapIS7_S7_NS1_4lessIS7_EENS5_INS1_4pairIS8_S7_EEEEEE J J 9 5EqualERKS0_ 1 4SaveEPN4dmlc6StreamE 2 8TreeShapERKNS0_4FVecEPfjjPNS_11PathElementEffiijf DumpModelERKNS_10FeatureMapEbNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE SaveModelEPNS_4JsonE J J 2GetNumLeavesEv ċ6GetNumSplitNodesEv ʋ J J 1 4LoadEPN4dmlc6StreamE 20LoadCategoricalSplitERKNS_4JsonE 9LoadModelERKNS_4JsonE 5kRootE 0ExpandNodeEijfbfffffffi 7 8FillNodeMeanValuesEv J ExpandCategoricalEijNS_6common4SpanIjLm18446744073709551615EEEbfffffff FillNodeMeanValueEi K K K K 0SaveCategoricalSplitEPNS_4JsonE 8CalculateContributionsApproxERKNS0_4FVecEPf 2CalculateContributionsERKNS0_4FVecEPfij K K L L L L L L L L M O R R 3__dmlc_registry_file_tag_updater_colmaker__Ev ̑4__dmlc_registry_file_tag_updater_histmaker__Ev ؑ0__dmlc_registry_file_tag_updater_prune__Ev Д8__dmlc_registry_file_tag_updater_quantile_hist__Ev 2__dmlc_registry_file_tag_updater_refresh__Ev R R U 5ReducerSafeImplIN7xgboost 2InvokeLambdaEPv ʔ6ReducerAlignImplIN7xgboost6detail20GradientPairInternalIfEEXadL_ZNS4_6ReduceERS4_RKS4_EEEEvPKvPviRKN3MPI8DatatypeE 4tree9GradStatsEXadL_ZNS3_6ReduceERS3_RKS3_EEEEvPKvPviRKN3MPI8DatatypeE 6detail20GradientPairInternalIdEEXadL_ZNS4_6ReduceERS4_RKS4_EEEEvPKvPviRKN3MPI8DatatypeE V ХV X ЗX X Y 7BuilderI 9ConfigureERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE 6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKNSt3__16vectorIPNS_7RegTreeENSA_9allocatorISD_EEEE 21UpdatePredictionCacheEPKNS_7DMatrixEPNS_16HostDeviceVectorIfEE 31UpdatePredictionCacheMulticlassEPKNS_7DMatrixEPNS_16HostDeviceVectorIfEEii fE dE C 6UpdateERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixEPNS_7RegTreeE 8InitDataERKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE 1 9 2 2ERKNS0_10TrainParamENSt3__110unique_ptrINS_11TreeUpdaterENS7_14default_deleteIS9_EEEENS_32FeatureInteractionConstraintHostEPKNS_7DMatrixE 1ERKNS0_10TrainParamENSt3__110unique_ptrINS_11TreeUpdaterENS7_14default_deleteIS9_EEEENS_32FeatureInteractionConstraintHostEPKNS_7DMatrixE Y Y Y Y 9 6S 1 Į2InitSamplingERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEERKNS_7DMatrixEPNS5_ImNS9_ImEEEE 4 0ApplySplitENSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSA_12ColumnMatrixERKNSA_14HistCollectionIfEEPNS_7RegTreeE 7AddSplitsToRowSetERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEEPNS_7RegTreeE ó3SplitSiblingsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEEPS9_SC_PNS_7RegTreeE ճ5 ExpandWith SetHistSynchronizerEPNS0_16HistSynchronizerIfEE FindSplitConditionsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_7RegTreeERKNS_6common16GHistIndexMatrixEPNS5_IiNS7_IiEEEE LossGuideERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEE DepthWiseERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEE Z Z BuildHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS_6common16RowSetCollection4ElemERKNSE_16GHistIndexMatrixERKNSE_21GHistIndexBlockMatrixENSE_4SpanIS8_Lm18446744073709551615EEE LossGuideENS3_11ExpandEntryES4_ Z ubtractionTrickENS_6common4SpanINS_6detail20GradientPairInternalIfEELm18446744073709551615EEES9_S9_ etHistRowsAdderEPNS0_13HistRowsAdderIfEE Z 1UpdatePredictionCacheEPKNS_7DMatrixEPNS_16HostDeviceVectorIfEEii 6SplitContainsMissingValuesENS0_9GradStatsERKNS1_9NodeEntryE ϳ0BuildLocalHistogramsERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSD_9allocatorISH_EEEE ۳2EvaluateAndApplySplitsERKNS_6common16GHistIndexMatrixERKNS4_12ColumnMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSF_9allocatorISH_EEEE 4BuildHistogramsLossGuideENS3_11ExpandEntryERKNS_6common16GHistIndexMatrixERKNS5_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSE_9allocatorISI_EEEE Z Z Z ExpandEntryC InitNewNodeEiRKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE ɳ 2Eiiifj 1Eiiifj Z Z fE11ExpandEntry7IsValidERKNS0_10TrainParamEi dE11ExpandEntry7IsValidERKNS0_10TrainParamEi Z Z Z EvaluateSplitsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSC_14HistCollectionIfEERKNS_7RegTreeE BuildNodeStatsERKNS_6common16GHistIndexMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSC_9allocatorISG_EEEE Ё[ [ [ [ и[ [ [ [ [ [ [ AddSplitsToTreeERKNS_6common16GHistIndexMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSC_9allocatorISE_EEEESI_ PartitionKernelI [ C 6UpdateERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixEPNS_7RegTreeE 8InitDataERKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE 1 9 2 2ERKNS0_10TrainParamENSt3__110unique_ptrINS_11TreeUpdaterENS7_14default_deleteIS9_EEEENS_32FeatureInteractionConstraintHostEPKNS_7DMatrixE 1ERKNS0_10TrainParamENSt3__110unique_ptrINS_11TreeUpdaterENS7_14default_deleteIS9_EEEENS_32FeatureInteractionConstraintHostEPKNS_7DMatrixE \ Ф\ \ \ 9 6S 1 2InitSamplingERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEERKNS_7DMatrixEPNS5_ImNS9_ImEEEE 4 0ApplySplitENSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSA_12ColumnMatrixERKNSA_14HistCollectionIdEEPNS_7RegTreeE 7AddSplitsToRowSetERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEEPNS_7RegTreeE 3SplitSiblingsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEEPS9_SC_PNS_7RegTreeE 5 ExpandWith SetHistSynchronizerEPNS0_16HistSynchronizerIdEE FindSplitConditionsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_7RegTreeERKNS_6common16GHistIndexMatrixEPNS5_IiNS7_IiEEEE LossGuideERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEE DepthWiseERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEE \ \ BuildHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS_6common16RowSetCollection4ElemERKNSE_16GHistIndexMatrixERKNSE_21GHistIndexBlockMatrixENSE_4SpanINS7_IdEELm18446744073709551615EEE LossGuideENS3_11ExpandEntryES4_ \ ubtractionTrickENS_6common4SpanINS_6detail20GradientPairInternalIdEELm18446744073709551615EEES9_S9_ etHistRowsAdderEPNS0_13HistRowsAdderIdEE \ 1UpdatePredictionCacheEPKNS_7DMatrixEPNS_16HostDeviceVectorIfEEii 6SplitContainsMissingValuesENS0_9GradStatsERKNS1_9NodeEntryE 0BuildLocalHistogramsERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSD_9allocatorISH_EEEE 2EvaluateAndApplySplitsERKNS_6common16GHistIndexMatrixERKNS4_12ColumnMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSF_9allocatorISH_EEEE 4BuildHistogramsLossGuideENS3_11ExpandEntryERKNS_6common16GHistIndexMatrixERKNS5_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSE_9allocatorISI_EEEE \ Љ] ] ExpandEntryC InitNewNodeEiRKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE 2Eiiifj 1Eiiifj ] Њ] ] ] ] EvaluateSplitsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSC_14HistCollectionIdEERKNS_7RegTreeE BuildNodeStatsERKNS_6common16GHistIndexMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSC_9allocatorISG_EEEE ] ] ] ] ] ] ] ] ] ] ^ AddSplitsToTreeERKNS_6common16GHistIndexMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSC_9allocatorISE_EEEESI_ PartitionKernelI ^ hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE tEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE jEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE ^ ^ ^ hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE tEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE jEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE ^ ^ ^ ^ ^ ^ ^ _ _ a b c d 1 4InitEiPPc 8FinalizeEv 9 3AllreduceBase 0Allreduce_EPvmmPFvPKvS1_iRKN3MPI8DatatypeEENS0_3mpi8DataTypeENSA_6OpTypeEPFvS1_ES1_ 2ReduceHandle C 4InitEiPPc 1 8S 20TryReduceScatterRingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE 2Ev 1Ev d d d 4ReConnectLinksEPKc 2Tr 6TryAll d hutdownEv etParamEPKcS3_ e e ackerPrintERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE y e e AllreduceEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE BroadcastEPvmi e reduce gatherRingEPvmmmm RingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE TreeEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE e e f f f reduceEPvmNS_6engine3mpi8DataTypeENS3_6OpTypeEPFvS1_ES1_ gatherEPvmmmmi f f I Finalize Get TrackerPrint Broadcast All L CheckPoint VersionNumber nit sDistributed f f R WorldSize ProcessorName ingPrevRank ank f f f f f f f gather reduce f f oadCheckPoint inkTag f f f f f f f f f cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE mEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE Ѐg g g g g g g g Лg g g g g cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE mEEvPKvPviRKN3MPI8DatatypeE g Ьg g g g g g g GetEngineEv AllgatherEPvmmmm g g пg C D 8TypeSizeERKN3MPI8DatatypeE 4InitEPFvPKvPviRKN3MPI8DatatypeEEm 9AllreduceEPvmmPFvS2_ES2_ 2Ev 1Ev g g 2Ev 1Ev g g g g g 7 6LibFMParserParam11__MANAGER__Ev 4CSVParserParam11__MANAGER__Ev 8CreateLibSVMParserI 5CreateCSVParserI LibSVMParserParam11__MANAGER__Ev CreateLibFMParserI g g g RowBlockIterI ThreadedIterINS_2io14InputSplitBase5ChunkEE14NotImplementedEv j y fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g g g g g ParserI Stream6CreateEPKcS2_b j y fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g h h h h j y fEEE3GetEv iEEE3GetEv xEEE3GetEv аh h h fEEE3GetEv iEEE3GetEv xEEE3GetEv h h h jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj yfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj h h jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj yfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj h h j y fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj iEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj xEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj h fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj iEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj xEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj h h h h h 1 23IndexedRecordIOSplitter 0FileSystem 2LineSplitter1 6RecordIOSplitter1 4InputSplitBase 5LocalFileSystem
11GetInstanceERKNS0_3URIE 22ListDirectoryRecursiveERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE u InputSplit6CreateEPKc SeekStream13CreateForReadEPKcb jjS2_ S2_jjS2_bimb u u u u w 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE w w w 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE w w w 1 9 4ResetPartitionEjj 3ReadIndexFileEPNS0_10FileSystemERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE 1 w w w w w ReadChunkEPvPm Next w ChunkEPNS_10InputSplit4BlobE BatchEPNS_10InputSplit4BlobEm w NextBatchExEPNS0_14InputSplitBase5ChunkEm ۀBeforeFirstEv w w w 4 1 D 8StripEndENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEc 9ReadChunkEPvPm 5Chunk InitEPNS0_10FileSystemEPKcmb ReadEPvm x 7InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb 4ResetPartitionEjj 1BeforeFirstEv 3ConvertToURIsERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 6ExtractNextChunkEPNS_10InputSplit4BlobEPNS1_5ChunkE x x x 2Ev 1Ev ބ0Ev x x x x x x x 4LoadEPS1_m 6AppendEPS1_m x x x { { 1 4OpenERKNS0_3URIEPKcb 1 3ListDirectoryERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE GetPathInfoERKNS0_3URIE OpenForReadERKNS0_3URIEb а| | | | N ZN PF F 4dmlc χSt3__1 7xgboost 5rabit ؟ 9parameter1 8DataIterIN 1 5ErrorE 7istream 6 4data 2io 6FieldAccessEntryE 0FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI 4FieldEntryBaseINS0_10FieldEntryI iEE ډyEE N ǫbEE xEE mEE fEE dEE jEE xEExEE yEEyEE dEEdEE iEEiEE mEEmEE fEEfEE jEEjEE xEExEE yEEyEE dEEdEE bEEbEE mEEmEE fEEfEE jEEjEE iEEiEE NSt3__1 1 ȋ2 6__
-8 4 9basic_ ܞ0 3 ȧ7 5 1__ 6__ 2__match_charIcEE ޑ basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ 4 6 1 9 8JsonNullE Ϫ5 7 ì3 20LearnerConfigurationE data Ítree 1 6detail19SingleBatchDataIterINS0_ 2 ح 5 2 0CS 6 1FileAdapterE 3SimpleDMatrixE 7SparsePageDMatrixE 9SparsePageRawFormatINS_ վ IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE CSRArrayAdapterE D ӏArrayAdapterE ʞ MatrixProxyE enseAdapterE 0__ 3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE 6__end_marked_subexpressionIcEE 1__empty_non_own_stateIcEE ے shared_ptr_ match_char_collateIcNS_12regex_traitsIcEEEE בbracket_expressionIcNS_12regex_traitsIcEEEE pointerIPN emplaceIN7xgboost1 7xgboost S_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoENS_14default_deleteIS5_EENS_9allocatorIS5_EEEE 7BatchIteratorImplINS1_ 0SparsePageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ʭ 1 7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE ՙ 0SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 3SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 1EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE default_deleteIN ʗbasic_ifstreamIcNS_11char_traitsIcEEEE 7xgboost S_13__empty_stateIcEEEE 1 Ƙ7DMatrixEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoEEE 7BatchIteratorImplINS1_ 0SparsePageEEE ѭ 1 7CSCPageEEEEE ۙ 0SparsePageEEEEE ϙ3SortedCSCPageEEEEE 1EllpackPageEEEEE RAdapterE CAdapterE 1 2 5CS 7 RAdapterBatchEEE ߚCAdapterBatchEEE 7xgboost S 4data 1 Ǯ7CSCPageEEE 1 2 5CS ɛ7 ѝ6FileAdapterBatchEEE RAdapterBatchEEE CAdapterBatchEEE 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE Ğ DenseAdapterBatchEEE ˝ArrayAdapterBatchEEE О DenseAdapterBatchEEE ArrayAdapterBatchEEE ֞ DataTableAdapterE SparsePageFormatINS_ istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ҟostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 5 6engine utils1 c_api1 9MemoryFixSizeBufferE 8MemoryBufferStreamE 0 Š2 SeekStreamE ParamErrorE InputSplitE 2 ܡ5CSRArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE DMatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ܢArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE 2 5CSRArrayAdapterEEE Ĥ DMatrixProxyEEE ArrayAdapterEEE shared_ptrIN7xgboost4data1 __ 2 5CSRArrayAdapterEEE ʤ0CSRAdapterEEE ArrayAdapterEEE DenseAdapterEEE 8kFloatPow5InvSplitE 5kFloatPow5SplitE Ђ 舀 common ̥linear metric MetricE 1 ߥ6ColumnI ͦ 1DenseColumnI 2SparseColumnI 6PeekableInStreamE 5FixedSizeStreamE hEE ƦtEE jEE hEE tEE jEE hEE tEE jEE ݊ ዀ basic_filebufIcNS_11char_traitsIcEEEE __empty_stateIcEE ، 0Json ר1 7 5GradientBoosterE 2ConfigurableE 3 StringE ObjectE NumberE ReaderE ݪWriterE JsonArrayE ߩLearnerIOE predictor12CPUPredictorE PredictorE ؍ 퍀 Json LearnerImplE ObjFunctionE TreeUpdaterE IntegerE ȪBooleanE ֪ ǎ ߎ 6HostDeviceVectorIjEENS_9allocatorIS3_EEEE 0SparsePageENS_9allocatorIS2_EEEE 7xgboost ߫St3__1 6common27ProbabilityDistributionTypeEEE 1 ValueE ModelE ؔ DMatrixE LearnerE RegTreeE 攀 E 5InBufE Е bad_function_callE í__ Ж 4ExternalMemoryPrefetcherINS_ 3S 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 嗀 0SparsePageEEE 3SortedCSCPageEEE ThreadedIterIN S RowBlockIterI 7xgboost S 1 ʯ7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE ʘ copedThreadE erializableE function6__ l_anchorIcEE ѐr_anchorIcEE ؐback_refIcEE funcI baseIF ZN PF 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS2_ ѱtree1 1 7CSCPageEEC1ERKNS3_9CacheInfoEEUl 0SparsePageEEC1ERKNS3_9CacheInfoEEUl Ҳ3SortedCSCPageEEC1ERKNS3_9CacheInfoEEUl PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE b vvEEE PN7xgboost PPN N7xgboost4tree17QuantileHistMaker7BuilderI 7xgboost 4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI 1 Ѵ7CSCPageEEEE ƻ 0SparsePageEEEE 3SortedCSCPageEEEE 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS_ ̵tree1 1 7CSCPageEEC1ERKNS0_9CacheInfoEEUl ͻ 0SparsePageEEC1ERKNS0_9CacheInfoEEUl Ͷ3SortedCSCPageEEC1ERKNS0_9CacheInfoEEUl PPS2_E_ vE_ ؚ ߜ impleBatchIteratorImplINS_ parseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_ 1 7CSCPageEEE Ĺ 0SparsePageEEE 3SortedCSCPageEEE ҹ1EllpackPageEEE ԝ BatchIteratorImplINS_ GraphvizGeneratorE 1 7CSCPageEEE ˹ 0SparsePageEEE 3SortedCSCPageEEE ٹ1EllpackPageEEE Ϡ 1 7CSCPageEEES3_EE 0SparsePageEEES3_EE ۺ3SortedCSCPageEEES3_EE 1EllpackPageEEES3_EE ¢ 碀 PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ Ӥ ݧ Ȩ PPS5_E_NS_9allocatorISC_EEFbSB_EEE ڼvE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ ɪ נּ 4data16SparsePageFormatINS2_ 1 6MetricEPKcEEE 9PredictorEPKNS2_16GenericParameterEEEE 1 7CSCPageEEEvEEE 0SparsePageEEEvEEE ξ3SortedCSCPageEEEvEEE ӯ 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1 7CSCPageEEE 0SparsePageEEE ڿ3SortedCSCPageEEE ఀ 貀 Դ 絀 ݷ 5GradientBoosterEPKNS2_17LearnerModelParamEEEE 3 1 gbm obj 8GBLinearE 1 6GBTreeE 4DartE ⹀ 3GBLinearModelE 1GBTreeModelE к ༀ 5TreeProcessTypeEEE 3 0TreeMethodEEE PredictorTypeEEE DataSplitModeEEE 뽀 À À À À basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ À StreamE ParserI Ā Ā ŀ ŀ ŀ ƀ ƀ ǀ 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS2_9allocatorIiEEEEEE ǀ 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE 6vectorIiNS3_9allocatorIiEEEEEES7_EE Ȁ ɀ ɀ LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE ˀ 1 2 7CoordinateUpdaterE 5FeatureSelectorE 4ShotgunUpdaterE ˀ LinearUpdaterE T JsonGeneratorE ̀ 1 2 CyclicFeatureSelectorE GreedyFeatureSelectorE RandomFeatureSelectorE ̀ ̀ ShuffleFeatureSelectorE ThriftyFeatureSelectorE ̀ ̀ ̀ ̀ π Ѐ 7Eval 1 9EvalAucPRE 8Eval 2 A MAPE CoxE UCE MSE Ѐ р 3Eval 4Eval 6EvalMultiLogLossE EWiseBaseINS0_ PrecisionE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE 1EvalRow 2EvalRowRMSLEEEE 0EvalRowMAEEEE 4EvalRowLogLossEEE 7EvalGammaDevianceEEE 6EvalGammaNLogLikEEE 8EvalTweedieNLogLikEEE RMSEEEE M Ҁ Ӏ Հ APEEEE PHEEEE ր ׀ ڀ ۀ ܀ ހ ߀ M RankConfigE atchErrorE ClassBaseINS0_1 4EvalMatchErrorEEE 6EvalMultiLogLossEEE RankE NDCGE 0AFTNLogLikDispatcherE 1EvalEWiseSurvivalBaseINS0_ 14EvalAFTNLogLikINS_6common 30EvalIntervalRegressionAccuracyEEE 1 20LogisticDistributionEEEEE 8NormalDistributionEEEEE 9ExtremeDistributionEEEEE ObjFunctionEvEEE TreeUpdaterEvEEE 6AFTObjE 8HingeObjE 20SoftmaxMultiClassObjE 1 3 0RegLossObjINS0_ 7 5GammaRegressionE LambdaRankObjINS0_2 CoxRegressionE 8PairwiseLambdaWeightComputerEEE 4NDCGLambdaWeightComputerEEE 3MAPLambdaWeightComputerEEE 1 22LogisticClassificationEEE 6 5SquaredLogErrorEEE 8LogisticRegressionEEE 1LogisticRawEEE LinearSquareLossEEE PseudoHuberErrorEEE PoissonRegressionE TweedieRegressionE Ӊ 䍁 琁 extGeneratorE reeGeneratorE Γ ˖ 8ColMaker 1 9 2 E 7BuilderE 1 0TreePrunerE 7QuantileHistMaker 6HistSynchronizerI 3 8BatchHistRowsAdderI CQHistMakerE TreeSyncherE 㘁 HistMakerE BaseMakerE 1CQHistMaker10CreateHistERKNS_6vectorINS2_6detail20GradientPairInternalIfEENS_9allocatorIS8_EEEEPNS2_7DMatrixERKNS5_IjNS9_IjEEEERKNS2_7RegTreeEEUlvE_NS9_ISN_EEFvvEEE 3TreeRefresher6UpdateEPNS2_16HostDeviceVectorINS2_6detail20GradientPairInternalIfEEEEPNS2_7DMatrixERKNS_6vectorIPNS2_7RegTreeENS_9allocatorISF_EEEEEUlvE0_NSG_ISL_EEFvvEEE 1CQHistMaker10CreateHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS2_9allocatorIS6_EEEEPNS_7DMatrixERKNS3_IjNS7_IjEEEERKNS_7RegTreeEEUlvE_ 3TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKNSt3__16vectorIPNS_7RegTreeENSA_9allocatorISD_EEEEEUlvE0_ 3GlobalProposalHistMakerE 7DistributedHistSynchronizerI 4DistributedHistRowsAdderI 1BatchHistSynchronizerI E 7BuilderI fEE dEE េ fEE dEE fEE dEE fEE dEE 堁 HistRowsAdderI TreeRefresherE fEE dEE fEE dEE fEE dEE ޡ ڢ ӣ bN7xgboost4tree17QuantileHistMaker7BuilderI vvENS_9allocatorIS3_EES2_EE fE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE dE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE fE11ExpandEntryES7_EEE dE11ExpandEntryES7_EEE 更 bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E ͥ bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E Ц ԧ ѭ ± 13AllreduceBaseE 7IEngineE 1ReadWrapperE 2WriteWrapperE ײ 1 9CSVParserI 1 2 4T 0ParserImplI DiskRowIterI LibFMParserI j y fEE iEE xEE ѳ j y fEE iEE xEE ﳁ _ t3__16vectorINS_4data17RowBlockContainerI 8RowBlockI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE _ t3__16vectorINS_4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE j y fEEEE iEEEE xEEEE 崁 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEEE ʉ j y fEEEEE iEEEEE xEEEEE 4data1 2io1 щ 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIjfEEE_ vE_ 嶁 BasicRowIterI LibSVMParserI j y fEE iEE xEE Ҹ fEE iEE xEE fEE iEE xEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIyfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE fEEEEE iEEEEE xEEEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIyfEEE_ vE_ ּ fEE iEE xEE ɾ PPNS3_17RowBlockContainerIjiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ؿ PPNS0_17RowBlockContainerIjiEEE_ vE_ Á Á Á Á ā ā PPNS3_17RowBlockContainerIyiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ā Ł PPNS0_17RowBlockContainerIyiEEE_ vE_ Ɓ ǁ ǁ ȁ ȁ ȁ Ɂ Ɂ Ɂ PPNS3_17RowBlockContainerIjxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ʁ ˁ PPNS0_17RowBlockContainerIjxEEE_ vE_ ˁ ́ ́ ́ ́ ρ PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ρ Ё PPNS0_17RowBlockContainerIyxEEE_ vE_ с с ҁ ҁ jfEE yfEE Ӂ extParserBaseI hreadedParserI j y fEE iEE xEE Ӂ j y ˂ fEE iEE xEE Ӂ j y fEE iEE xEE Ӂ jfEE yfEE ԁ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ԁ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ԁ jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE Ձ jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE ׁ jfEC1EPNS0_10ParserImplIjfEEEUl yfEC1EPNS0_10ParserImplIyfEEEUl PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ ׁ ف ځ fEE ĂiEE xEE ̆ځ fEE iEE xEE ӆځ fEE iEE xEE چځ ہ ہ ܁ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ܁ ށ PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ ށ ߁ jfEE yfEE j y Ӆ fEE ̅iEE xEE fEE iEE xEE ņ 1 23IndexedRecordIOSplitterE 5 ڇ8ThreadedInputSplitE 6 Ί2LineSplitterE 4InputSplitBaseE 0FileS SingleFileSplitE LocalFileSystemE 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE ÉvE_NS_9allocatorIS7_EEFvvEEE 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl 6CachedInputSplit1 Ռ PPNS2_5ChunkEE_ vE_ NJ CachedInputSplitE RecordIOSplitterE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE ΌvE_NS_9allocatorIS5_EEFvvEEE ڍ 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ ӍvE_ end_stateIcEE lookaheadIcNS_12regex_traitsIcEEEE alternateIcEE Ԓmatch_anyIcEE nodeIcEE loopIcEE owns_one_stateIcEE back_ref_icaseIcNS_12regex_traitsIcEEEE has_one_stateIcEE ʐword_boundaryIcNS_12regex_traitsIcEEEE ߐ match_char_icaseIcNS_12regex_traitsIcEEEE Бback_ref_collateIcNS_12regex_traitsIcEEEE owns_two_statesIcEE ƒrepeat_one_loopIcEE ͒ ystemE treamE N ZN F PF 4dmlc ד7xgboost St3__1 5rabit 5ErrorE 9parameter1 8DataIterIN 6 1 7istream 4data 2io 6FieldAccessEntryE 4FieldEntryBaseINS0_10FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI 0FieldEntryI ޖ iEEiEE xEExEE jEEjEE yEEyEE ΗfEEfEE dEEdEE bEEbEE NSt3__1 mEEmEE iEEiEE זxEExEE jEEjEE yEEyEE fEEfEE dEEdEE mEEmEE iEE xEE jEE ǗyEE ܗfEE dEE bEE N mEE о 辏 ȿ ࿏ 5 4 6 ϯ1 9 8JsonNullE ӷ7 3 20LearnerConfigurationE ValueE ModelE 1 2 ڟ6__ Ӝ
-9basic_ 5 8 ՛0 4basic_ifstreamIcNS_11char_traitsIcEEEE 3 7 1__ 6__ 2__match_charIcEE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE Ï basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE Λ__ ݝŏ basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ ɏ 7xgboost S 4data ՜1 7CSCPageEEE 1 2 6FileAdapterBatchEEE 5CS 7 ɏ data tree 1 ѝ6detail19SingleBatchDataIterINS0_ 2 5 2 0CS 6 Ū1FileAdapterE 3SimpleDMatrixE 7SparsePageDMatrixE 9SparsePageRawFormatINS_ IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE CSRArrayAdapterE ɏ D ArrayAdapterE MatrixProxyE ӟenseAdapterE ˏ 0__ 3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE 6__end_marked_subexpressionIcEE 1__empty_non_own_stateIcEE shared_ptr_ ءmatch_char_collateIcNS_12regex_traitsIcEEEE bracket_expressionIcNS_12regex_traitsIcEEEE pointerIPN emplaceIN7xgboost1 7xgboost ӢS_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ֝ 1 7DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoENS_14default_deleteIS5_EENS_9allocatorIS5_EEEE 7BatchIteratorImplINS1_ ٤0SparsePageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 0SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 3SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 1EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE ˏ ̏ ̏ ͏ RAdapterBatchEEE CAdapterBatchEEE Ώ 1 2 5CS ʧ7 RAdapterBatchEEE CAdapterBatchEEE Ώ RAdapterE CAdapterE Ώ 0CSRArrayAdapterBatchEEE ٨1DataTableAdapterBatchEEE Ϗ 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE Ϗ Ϗ Џ Џ Џ DenseAdapterBatchEEE ArrayAdapterBatchEEE я DenseAdapterBatchEEE ArrayAdapterBatchEEE я я ҏ ҏ DataTableAdapterE SparsePageFormatINS_ ҏ ӏ ӏ ӏ Տ StreamE ParserI ֏ 0 «2 ݻ SeekStreamE ParamErrorE InputSplitE ֏ 5 6engine utils1 c_api1 9MemoryFixSizeBufferE Ӭ8MemoryBufferStreamE ڬ֏ E 5InBufE ُ ڏ ڏ ۏ 2 ٭5CSRArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE DMatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ٮArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE shared_ptrIN7xgboost4data1 __ 2ArrayAdapterEEE 5CSRArrayAdapterEEE ȯ ݏ ݏ common linear MetricE metric 6ColumnI 1 hEE tEE jEE ̱ݏ 1DenseColumnI 2SparseColumnI 6PeekableInStreamE 5FixedSizeStreamE hEE tEE jEE ӱޏ hEE tEE űjEE ڱޏ ޏ ߏ ߏ ߏ 7xgboost 5rabit6engine13AllreduceBaseE 4dmlc2io 6common1 Բ1 9 8JsonNullE ̷7 4 3gbm 5FixedSizeStreamE 6PeekableInStreamE basic_filebufIcNS_11char_traitsIcEEEE __empty_stateIcEE 0Json 1Json ж ObjectE StringE NumberE ¶ReaderE WriterE JsonArrayE ݴPredictorE 0Json 1 7 2ConfigurableE 5GradientBoosterE 3 StringE ObjectE NumberE ɶReaderE WriterE JsonArrayE LearnerIOE predictor12CPUPredictorE PredictorE IntegerE BooleanE ڷ Json LearnerImplE ObjFunctionE TreeUpdaterE IntegerE ŷBooleanE 6HostDeviceVectorIjEENS_9allocatorIS3_EEEE ٸ0SparsePageENS_9allocatorIS2_EEEE 7xgboost St3__1 6common27ProbabilityDistributionTypeEEE 1 DMatrixE LearnerE RegTreeE DMatrixE LearnerE RegTreeE bad_function_callE __ 0SparsePageEEE 3SortedCSCPageEEE 4ExternalMemoryPrefetcherINS_ 3S 1 7CSCPageEEE 0SparsePageEEE ֻ3SortedCSCPageEEE ThreadedIterIN S RowBlockIterI 7xgboost S 1 7CSCPageEEE 0SparsePageEEE ټ3SortedCSCPageEEE copedThreadE erializableE function6__ ʽl_anchorIcEE r_anchorIcEE back_refIcEE baseIF ߽funcI b vvEEE PN7xgboost PPN N7xgboost4tree17QuantileHistMaker7BuilderI 7xgboost 4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI 1 7CSCPageEEEE 0SparsePageEEEE 3SortedCSCPageEEEE ZN ϿPF 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS2_ tree1 1 7CSCPageEEC1ERKNS3_9CacheInfoEEUl 0SparsePageEEC1ERKNS3_9CacheInfoEEUl 3SortedCSCPageEEC1ERKNS3_9CacheInfoEEUl PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS_ tree1 1 7CSCPageEEC1ERKNS0_9CacheInfoEEUl 0SparsePageEEC1ERKNS0_9CacheInfoEEUl 3SortedCSCPageEEC1ERKNS0_9CacheInfoEEUl PPS2_E_ vE_ data1 tree17QuantileHistMaker 3SimpleDMatrixE 7SparsePageDMatrixE BatchIteratorImplINS_ GraphvizGeneratorE 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1EllpackPageEEE impleBatchIteratorImplINS_ parseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_ 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1EllpackPageEEE 1 7CSCPageEEES3_EE 0SparsePageEEES3_EE 3SortedCSCPageEEES3_EE 1EllpackPageEEES3_EE PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ 聐 Ȃ PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE ȃ PPS2_E_ vE_ Ȅ 4data16SparsePageFormatINS2_ 1 6MetricEPKcEEE 9PredictorEPKNS2_16GenericParameterEEEE 1 7CSCPageEEEvEEE 0SparsePageEEEvEEE 3SortedCSCPageEEEvEEE 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE ؇ 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 臐 舐 ȉ ؉ ؊ ȋ 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE 6vectorIiNS3_9allocatorIiEEEEEES7_EE 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS2_9allocatorIiEEEEEE 5GradientBoosterEPKNS2_17LearnerModelParamEEEE 3 1 Ȓ ؒ gbm obj 8GBLinearE 1 6GBTreeE 4DartE 1 6GBTreeE 3GBLinearModelE 1GBTreeModelE 3GBLinearModelE 1GBTreeModelE 蓐 5TreeProcessTypeEEE 3 0TreeMethodEEE PredictorTypeEEE DataSplitModeEEE 蝐 LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE ஐ LinearUpdaterE T JsonGeneratorE ȯ 1 2 7CoordinateUpdaterE 5FeatureSelectorE 4ShotgunUpdaterE ௐ 1 2 CyclicFeatureSelectorE GreedyFeatureSelectorE RandomFeatureSelectorE ShuffleFeatureSelectorE ThriftyFeatureSelectorE ȱ ز 7Eval 1 9EvalAucPRE 8Eval 2 A MAPE CoxE UCE MSE 3Eval 4Eval 6EvalMultiLogLossE EWiseBaseINS0_ PrecisionE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE 1EvalRow 2EvalRowRMSLEEEE 0EvalRowMAEEEE 4EvalRowLogLossEEE 7EvalGammaDevianceEEE 6EvalGammaNLogLikEEE 8EvalTweedieNLogLikEEE RMSEEEE M ػ APEEEE PHEEEE Đ Ɛ ǐ ɐ M RankConfigE ClassBaseINS0_1 atchErrorE 4EvalMatchErrorEEE 6EvalMultiLogLossEEE ː ː ͐ ͐ ϐ ѐ Ґ RankE NDCGE Ӑ Ӑ Ր א ِ 0AFTNLogLikDispatcherE 1EvalEWiseSurvivalBaseINS0_ ې 14EvalAFTNLogLikINS_6common 30EvalIntervalRegressionAccuracyEEE 1 20LogisticDistributionEEEEE 8NormalDistributionEEEEE 9ExtremeDistributionEEEEE ې ܐ ݐ ߐ ObjFunctionEvEEE TreeUpdaterEvEEE 6AFTObjE 8HingeObjE 20SoftmaxMultiClassObjE 1 3 0RegLossObjINS0_ 7 5GammaRegressionE LambdaRankObjINS0_2 CoxRegressionE 8PairwiseLambdaWeightComputerEEE 4NDCGLambdaWeightComputerEEE 3MAPLambdaWeightComputerEEE 1 22LogisticClassificationEEE 6 5SquaredLogErrorEEE 8LogisticRegressionEEE 1LogisticRawEEE LinearSquareLossEEE PseudoHuberErrorEEE PoissonRegressionE TweedieRegressionE Ȃ Ѓ ȇ reeGeneratorE extGeneratorE ؈ 舑 Ћ Ѝ Ў ؏ 8ColMaker 9 1 2 E 7BuilderE BaseMakerE HistMakerE ؒ 1 0TreePrunerE 7QuantileHistMaker 6HistSynchronizerI 3 8BatchHistRowsAdderI CQHistMakerE TreeSyncherE 1CQHistMaker10CreateHistERKNS_6vectorINS2_6detail20GradientPairInternalIfEENS_9allocatorIS8_EEEEPNS2_7DMatrixERKNS5_IjNS9_IjEEEERKNS2_7RegTreeEEUlvE_NS9_ISN_EEFvvEEE 3TreeRefresher6UpdateEPNS2_16HostDeviceVectorINS2_6detail20GradientPairInternalIfEEEEPNS2_7DMatrixERKNS_6vectorIPNS2_7RegTreeENS_9allocatorISF_EEEEEUlvE0_NSG_ISL_EEFvvEEE 1CQHistMaker10CreateHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS2_9allocatorIS6_EEEEPNS_7DMatrixERKNS3_IjNS7_IjEEEERKNS_7RegTreeEEUlvE_ 3TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKNSt3__16vectorIPNS_7RegTreeENSA_9allocatorISD_EEEEEUlvE0_ 3GlobalProposalHistMakerE 7DistributedHistSynchronizerI 4DistributedHistRowsAdderI 1BatchHistSynchronizerI 7BuilderI E fEE dEE Ț E 7BuilderI ؛ fEE dEE fEE dEE fEE dEE Ȝ HistRowsAdderI TreeRefresherE fEE dEE fEE dEE fEE dEE ؝ fEE dEE ؞ 螑 fE11ExpandEntryES7_EEE dE11ExpandEntryES7_EEE bN7xgboost4tree17QuantileHistMaker7BuilderI vvENS_9allocatorIS3_EES2_EE fE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE dE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E С bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E ࡑ У ࣑ Ч ب Ȫ 7IEngineE 13AllreduceBaseE 1ReadWrapperE 2WriteWrapperE 謑 _ t3__16vectorINS_4data17RowBlockContainerI ݊ 8RowBlockI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE х j y fEE iEE xEE 1 9CSVParserI 1 2 0ParserImplI 4T ۉ DiskRowIterI LibFMParserI j y fEE iEE xEE ߅ j y fEEEE iEEEE xEEEE _ t3__16vectorINS_4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEEE j y fEEEEE iEEEEE xEEEEE 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl łxE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE 4data1 2io1 ٖ 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIjfEEE_ vE_ BasicRowIterI LibSVMParserI j y fEE iEE xEE fEEEE iEEEE xEEEE в fEE iEE xEE fEE iEE xEE fEEEE iEEEE xEEEE ȳ fEEEE iEEEE xEEEE ȇس fEEEEE iEEEEE xEEEEE χȴ fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl և PPNS3_17RowBlockContainerIyfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ش fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIyfEEE_ vE_ ص fEE iEE ʅxEE PPNS3_17RowBlockContainerIjiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIjiEEE_ ڃvE_ ໑ ؼ 輑 ؽ PPNS3_17RowBlockContainerIyiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE 轑 PPNS0_17RowBlockContainerIyiEEE_ vE_ Å 辑 п PPNS3_17RowBlockContainerIjxEEE_NS_9allocatorISA_EEFbS9_EEE ܆vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIjxEEE_ vE_ Ñ Ñ đ đ ő ő ő ő Ƒ PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE Ƒ PPNS0_17RowBlockContainerIyxEEE_ vE_ Ǒ Ǒ ȑ ȑ j y fEE iEE xEE ɑ j y ̏ fEE ԉiEE xEE ɑ extParserBaseI hreadedParserI Ċ j y fEE iEE xEE ɑ jfEE yfEE ʑ jfEE ֊yfEE ̑ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ̑ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ̑ jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE ͑ jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ͑ jfEC1EPNS0_10ParserImplIjfEEEUl ǎyfEC1EPNS0_10ParserImplIyfEEEUl PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ Α Α ϑ fEE ŏiEE xEE ͓ϑ fEE iEE xEE ԓϑ fEE iEE xEE ۓБ Б ґ ґ ӑ ӑ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ԑ PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ ԑ Ց Ց jfEE yfEE Ց ֑ j y ے fEE ԒiEE xEE Ɠב fEE iEE xEE ؑ ّ ّ ّ ّ ۑ ۑ ܑ ܑ ޑ ޑ ޑ ޑ 1 23IndexedRecordIOSplitterE 5 8ThreadedInputSplitE 6 ֗2LineSplitterE 4InputSplitBaseE ̜0FileS SingleFileSplitE LocalFileSystemE 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE ҖvE_NS_9allocatorIS7_EEFvvEEE ȗ 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl 6CachedInputSplit1 ݙ PPNS2_5ChunkEE_ vE_ ϗ CachedInputSplitE RecordIOSplitterE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE ֙vE_NS_9allocatorIS5_EEFvvEEE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ ۚvE_ 1 23IndexedRecordIOSplitterE 2LineSplitterE 6RecordIOSplitterE 4InputSplitBaseE Ŝ0FileSystemE 5LocalFileSystemE nodeIcEE loopIcEE end_stateIcEE ϝlookaheadIcNS_12regex_traitsIcEEEE alternateIcEE match_anyIcEE 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+6 1 (9 ʍ8 4 lsERNSt3__113basic_ostreamIcNS0_11char_traitsIcEEEENS_10StringViewE 7 ŭ5Entry8CmpValueERKS0_S2_ 3 2 detail1 common linear metric Metric6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE 6 9ToCharsUnsignedImplEPcS1_y 7FromCharFloatImplEPKciPf 4RyuPowLogUtils1 Ҋ ToCharsFloatImplEfPc ItoaUnsignedImplEPcjy 1 21InitilizeHistByZeroesI 8 7Monitor 5Index1 2G 4AllVisibleGPUsEv 3 5 6PeekableInStream 8LoadSequentialFileENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEb ف9HostSketchContainer 1AddCutPointERKNS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerEiPNS0_13HistogramCutsE lobalRandomEv HistBuilderI fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEEmm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEEmm IncrementHistI HistogramCutsC (AddCategoriesERKNSt3__13setIiNS1_4lessIiEENS1_9allocatorIiEEEEPNS0_13HistogramCutsE ColumnSampler9ColSampleENSt3__110shared_ptrINS_16HostDeviceVectorIjEEEEf fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm CopyHistI AFTParam11__MANAGER__Ev Prefetch15kNoPrefetchSizeE fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm SubtractionHistI FixedSizeStream fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_S7_mm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_S7_mm fE dE $ 9BuildHistILb !16SubtractionTrickENS0_4SpanINS_6detail20GradientPairInternalIfEELm18446744073709551615EEES7_S7_ ( 1EEEvRKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS0_16RowSetCollection4ElemERKNS_16GHistIndexMatrixENS0_4SpanIS8_Lm18446744073709551615EEE $0EEEvRKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS0_16RowSetCollection4ElemERKNS_16GHistIndexMatrixENS0_4SpanIS8_Lm18446744073709551615EEE $ 9BuildHistILb %16SubtractionTrickENS0_4SpanINS_6detail20GradientPairInternalIdEELm18446744073709551615EEES7_S7_ ( 1EEEvRKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS0_16RowSetCollection4ElemERKNS_16GHistIndexMatrixENS0_4SpanINS7_IdEELm18446744073709551615EEE (0EEEvRKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS0_16RowSetCollection4ElemERKNS_16GHistIndexMatrixENS0_4SpanINS7_IdEELm18446744073709551615EEE (
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mii ?St16initializer_listIiEi ?RKNSt3__16vectorIiNS2_9allocatorIiEEEEi ?OS1_ @ mii ?St16initializer_listIiEi ?RKNSt3__16vectorIiNS2_9allocatorIiEEEEi ?OS1_ @ 2Ev @1Ev @ 5EmptyEv @4SizeEv @9 @1 A8HostSpanEv C DeviceIdxEv ASetDeviceEi E 0 A3DevicePointerEv C1HostPointerEv C DeviceSpanEv AHostVectorEv C 5Const B0 B8ConstDevicePointerEv C3 C6ConstHostPointerEv C1Host C2 E4DeviceCanWriteEv E DeviceSpanEv BHostVectorEv C DeviceSpanEv BHostVectorEv C Device CConstHostSpanEv C PointerEv CCanReadEv E PointerEv DCanReadEv E FillEi DCopyE D RK DSt16initializer_listIiE E S1_ ENSt3__16vectorIiNS2_9allocatorIiEEEE E ExtendERKS1_ EResizeEmi F HostCanWriteEv EDeviceAccessEv E C FD HaSEOS1_ H1 I8HostSpanEv K4 L6 M 2E F1E G mhi GSt16initializer_listIhEi GRKNSt3__16vectorIhNS2_9allocatorIhEEEEi HOS1_ H mhi GSt16initializer_listIhEi GRKNSt3__16vectorIhNS2_9allocatorIhEEEEi HOS1_ HЃ І 2Ev H1Ev H 5EmptyEv H4SizeEv H9 H1 I8HostSpanEv K DeviceIdxEv ISetDeviceEi N 0 I3DevicePointerEv K1HostPointerEv K DeviceSpanEv IHostVectorEv K 5Const J0 J8ConstDevicePointerEv K3 K6ConstHostPointerEv L1Host L2 M4DeviceCanWriteEv N DeviceSpanEv JHostVectorEv L DeviceSpanEv KHostVectorEv K Device KConstHostSpanEv K PointerEv KCanReadEv M Џ А PointerEv LCanReadEv M FillEh LCopyE L RK LSt16initializer_listIhE M S1_ MNSt3__16vectorIhNS2_9allocatorIhEEEE M ExtendERKS1_ MResizeEmh N HostCanWriteEv MDeviceAccessEv N Н C ND PaSEOS2_ P1 Q8HostSpanEv T4 T6 U 2E N1E O mS1_i OSt16initializer_listIS1_Ei PRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi POS2_ P mS1_i PSt16initializer_listIS1_Ei PRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi POS2_ P 2Ev P1Ev P 5EmptyEv Q4SizeEv Q9 Q1 R8HostSpanEv T DeviceIdxEv QSetDeviceEi V 0 Q3DevicePointerEv S1HostPointerEv S DeviceSpanEv RHostVectorEv T 5Const R0 S8ConstDevicePointerEv S3 S6ConstHostPointerEv T1Host T2 U4DeviceCanWriteEv V DeviceSpanEv SHostVectorEv T DeviceSpanEv SHostVectorEv T Device SConstHostSpanEv T PointerEv SCanReadEv V Ы PointerEv TCanReadEv U FillES1_ TCopyE T RK USt16initializer_listIS1_E U S2_ UNSt3__16vectorIS1_NS3_9allocatorIS1_EEEE U ExtendERKS2_ UResizeEmS1_ V HostCanWriteEv VDeviceAccessEv V й C VD XaSEOS2_ Y1 Y8HostSpanEv \4 \6 ] 2E V1E W mS1_i WSt16initializer_listIS1_Ei XRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi XOS2_ X mS1_i XSt16initializer_listIS1_Ei XRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi XOS2_ X 2Ev X1Ev X 5EmptyEv Y4SizeEv Y9 Y1 Z8HostSpanEv \ DeviceIdxEv YSetDeviceEi ^ 0 Z3DevicePointerEv [1HostPointerEv \ DeviceSpanEv ZHostVectorEv \ 5Const [0 [8ConstDevicePointerEv [3 [6ConstHostPointerEv \1Host \2 ^4DeviceCanWriteEv ^ DeviceSpanEv [HostVectorEv \ DeviceSpanEv [HostVectorEv \ Device \ConstHostSpanEv \ PointerEv \CanReadEv ^ PointerEv \CanReadEv ^ FillES1_ ]CopyE ] RK ]St16initializer_listIS1_E ] S2_ ]NSt3__16vectorIS1_NS3_9allocatorIS1_EEEE ] ExtendERKS2_ ^ResizeEmS1_ ^ HostCanWriteEv ^DeviceAccessEv ^ C _D `aSEOS1_ a1 a8HostSpanEv d4 e6 f 2E _1E ` myi _St16initializer_listIyEi `RKNSt3__16vectorIyNS2_9allocatorIyEEEEi `OS1_ a myi `St16initializer_listIyEi `RKNSt3__16vectorIyNS2_9allocatorIyEEEEi `OS1_ a 2Ev a1Ev a 5EmptyEv a4SizeEv a9 a1 b8HostSpanEv d DeviceIdxEv aSetDeviceEi f 0 b3DevicePointerEv c1HostPointerEv d DeviceSpanEv bHostVectorEv d 5Const c0 c8ConstDevicePointerEv d3 d6ConstHostPointerEv d1Host d2 f4DeviceCanWriteEv f DeviceSpanEv cHostVectorEv d DeviceSpanEv cHostVectorEv d Device dConstHostSpanEv d PointerEv dCanReadEv f PointerEv eCanReadEv f FillEy eCopyE e RK eSt16initializer_listIyE f S1_ eNSt3__16vectorIyNS2_9allocatorIyEEEE f ExtendERKS1_ fResizeEmy f HostCanWriteEv fDeviceAccessEv f C gD iaSEOS1_ i1 j8HostSpanEv l4 m6 n 2E g1E h mji hSt16initializer_listIjEi hRKNSt3__16vectorIjNS2_9allocatorIjEEEEi hOS1_ i mji hSt16initializer_listIjEi hRKNSt3__16vectorIjNS2_9allocatorIjEEEEi iOS1_ i
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4ReadEPvm 8PeekReadEPvm C 4 ߀8PeekReadEPvm ǁ 2EPNS0_16PeekableInStreamE Ӏ1EPNS0_16PeekableInStreamE ـ ReadEPvm SeekEm ́TakeEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE Ӂ Json SparsePage ˺ExtendPathEPNS_11PathElementEjffi UnwindPathEPNS_11PathElementEjj ț Writer Object String Number ѐReader 4SaveENS_4JsonE 5VisitEPKNS_ 9JsonArrayE 1 ȃ8JsonNullE 0Json ܃1Json ObjectE NumberE StringE IntegerE BooleanE DummyJsonObjectEv LoadFeatureTypeERKNSt3__16vectorINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS5_IS7_EEEEPNS1_INS_11FeatureTypeENS5_ISC_EEEE GradientBooster6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterEPKNS_17LearnerModelParamE C ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE 2EO ˇ1EO ۈ S0_ ՈNSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE S0_ NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Ċi ʊ Ѓ Json SparsePage12GetTransposeEi ObjecteqERKNS_5ValueE StringeqERKNS_5ValueE NumbereqERKNS_5ValueE Reader5ErrorENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ixE ߌaSERKNS_5ValueE 4SaveEPNS_10JsonWriterE č RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i JsonArray GPUMetric15CreateGPUMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE predictor Predictor TreeParam11__MANAGER__Ev C ďixE aSERKNS_5ValueE Ő4SaveEPNS_10JsonWriterE ː 2EOS0_ ڏ1EOS0_ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Й О П ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Ցi ۑ Щ Json EllpackPage ObjFunction6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE TreeUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE LearnerImpl14kRandSeedMagicE Integer Boolean Ҙ ixE ݔaSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json ֕EllpackPage4SizeEv IntegereqERKNS_5ValueE BooleaneqERKNS_5ValueE д JsonNull ParseStrERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE MetaInfo ixE aSERKNS_5ValueE Ƙ4SaveEPNS_10JsonWriterE ̘ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i е JsonNulleqERKNS_5ValueE MetaInfo ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ֙i ܙ 5ParseEv 1 4LoadEv 9ParseNullEv ś 0 ߚ1Parse 2ParseBooleanEv ݛ3kMaxNumLengthE SkipSpacesEv ParseArrayEv ˛ StringEv ObjectEv ћNumberEv כ Ё Ћ Json data tree 4 aSERKS0_ LoadE DumpES0_PNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NS_10StringViewE PNS_10JsonReaderE Э C 1 9AllReduceEPNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_IiNS9_IiEEEE 8MakeCutsEPNS0_13HistogramCutsE ƣ 2ENSt3__16vectorImNS2_9allocatorImEEEEiNS0_4SpanIKNS_11FeatureTypeELm18446744073709551615EEEbi 1ENSt3__16vectorImNS2_9allocatorImEEEEiNS0_4SpanIKNS_11FeatureTypeELm18446744073709551615EEEbi 4CalcColumnSizeERKNS_10SparsePageEjm 1 6GatherSketchInfoERKNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_ImNS9_ImEEEESG_PNS3_INS6_5EntryENS9_ISH_EEEE LoadBalanceERKNS_10SparsePageEjm PushRowPageERKNS_10SparsePageERKNS_8MetaInfoENS0_4SpanIfLm18446744073709551615EEE 2 1 6engine 5c_api9All op7ReducerINS0_ 4SerializeReducerFuncImplIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEEEvPKvPviRKN3MPI8DatatypeE ֠2SerializeReduceClosureIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEE6InvokeEPv ܠ 3 5BitORE SumE M mEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE axE ĨinE mEEvPKvPviRKN3MPI8DatatypeE ݪyEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE 5StartERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 4StopERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 15PrintStatisticsERKNSt3__13mapINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4pairImmEENS2_4lessIS9_EENS7_INSA_IKS9_SB_EEEEEE 5PrintEv Version DMatrix Learner RegTree 4 6StringERKNSt3__15tupleIJiiiEEE կ8kInvalidE ȓ LoadE S RKNS_4JsonE ߮PN4dmlc6StreamE Ы a elfEv ɯ veEPN meERKNSt3__15tupleIJiiiEEE ۯ S_4JsonE ï4dmlc6StreamE ϯ м 7xgboost S_16ParserFactoryRegI 4data19SparsePageFormatRegINS1_ Ȱ1 9MetricRegEE3GetEv 1 7CSCPageEEEE3GetEv ۱ 0SparsePageEEEE3GetEv ձ3SortedCSCPageEEEE3GetEv 1EllpackPageEEEE3GetEv 6GHistIndexMatrixEEEE3GetEv 6CreateI ײD 4LoadERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbbS9_ PvS2_FvS2_EFiS2_EEEPS0_T_T0_PT1_PT2_fi NS_4data1 i ۳NSt3__112basic_stringIcNSC_11char_traitsIcEENSC_9allocatorIcEEEE 2 0CS 6DataTableAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE Ǹ1FileAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE 5 Ӹ DenseAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE ArrayAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE RAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE CAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE CSRArrayAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPS0_PT_fiRKNSt3__112basic_stringIcNSD_11char_traitsIcEENSD_9allocatorIcEEEE ź 4Push 7PushCSCERKS0_ INS_4data ERKS0_ 1 2 ļ 7 5CS 6FileAdapterBatchEEEyRKT_fi DenseAdapterBatchEEEyRKT_fi ArrayAdapterBatchEEEyRKT_fi RAdapterBatchEEEyRKT_fi CAdapterBatchEEEyRKT_fi 0CSRArrayAdapterBatchEEEyRKT_fi 1DataTableAdapterBatchEEEyRKT_fi 5ClearEv 1 7SetInfoEPKc 6ExtendERKS0_bb 9kNumFieldE 1 ߾5SliceENS_6common4SpanIKiLm18446744073709551615EEE 7GetInfoEPKcPyNS_8DataTypeEPPKv 8ValidateEi 0SaveBinaryEPN4dmlc6StreamE 4GetFeatureInfoEPKcPNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEE 0LoadBinaryEPN4dmlc6StreamE 4SetFeatureInfoEPKcPS2_y PKvNS_8DataTypeEm RKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE Ч DMatrix14GetThreadLocalEv Learner19AllowLazyCheckPointEv RegTree 2Ev 1Ev 0Ev Tr LinearUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ConsoleLogger GraphvizParam11__MANAGER__Ev yLoadBinaryENSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEb ackerLoggerD eeGenerator6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_10FeatureMapEb 8fileiter 4 23GradientIndexPageSource5FetchEv 1 6detail1 5ResetEPv 4NextEPv г C D 12SetBaseRowIdEm 2E 1E v PNS_7DMatrixERKNS_10BatchParamE Л v PNS_7DMatrixERKNS_10BatchParamE 2Ev 1Ev HistIndexMatrix enericParameter 9PushBatchERKNS_10SparsePageEmmji 11ResizeIndexEmb 4InitE PNS_7DMatrixEiNS_6common4SpanIfLm18446744073709551615EEE RKNS_10SparsePageERKNS_6common13HistogramCutsEibi 7GetValueFromUint8EPvm 8GetValueFromUint 16EPvm 32EPvm 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ˀ 4LoadEPS1_m 6AppendEPS1_m π р Ԁ 1 4OpenERKNS0_3URIEPKcb 1 3ListDirectoryERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE GetPathInfoERKNS0_3URIE OpenForReadERKNS0_3URIEb Ą N ZN PF F 4dmlc 7xgboost St3__1 5rabit ߂ 9parameter1 8DataIterIN 1 7istream 5ErrorE 6 4data 2io 7FieldEntryNumericINS0_10FieldEntryI 4FieldEntryBaseINS0_10FieldEntryI 0FieldEntryI 6FieldAccessEntryE xEExEE yEEyEE mEEmEE ٢fEEfEE ئiEEiEE dEEdEE jEEjEE ۇ xEExEE yEEyEE mEEmEE fEEfEE ߦiEEiEE NSt3__1 bEEbEE dEEdEE jEEjEE ۇ jEE yEE fEE dEE N mEE ҢbEE xEE iEE ܇ ܇ ܇ ݇ ݇ އ 4 6 1 9 8JsonNullE Đ5 ϓ7 3 20LearnerConfigurationE data tree 1 6detail19SingleBatchDataIterINS0_ 2 5 2 0CS 6 1FileAdapterE ۖ9 8PageSourceIncMixInINS_ 3 7SparsePageDMatrixE IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE CSRArrayAdapterE އ 7xgboost4data S 1 2 6FileAdapterBatchEEE 5CS 7 އ D ArrayAdapterE MatrixProxyE enseAdapterE ߇ 2 1 6__ 0__ 3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE 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+4 0 9basic_ 3 5 7 ם8 1__ 6__ 2__match_charIcEE default_deleteIN basic_ifstreamIcNS_11char_traitsIcEEEE 7xgboost S_13__empty_stateIcEEEE 1 7 4data 6common16PartitionBuilderILm2048EE9BlockInfoEEE 7BatchIteratorImplINS1_ 6GHistIndexMatrixEEE 3SortedCSCPageEEE 1EllpackPageEEE 1 7CSCPageEEEEE 0SparsePageEEEEE 3SortedCSCPageEEEEE 6GHistIndexMatrixEEEEE 1EllpackPageEEEEE RAdapterE CAdapterE 1 2 5CS 7 RAdapterBatchEEE CAdapterBatchEEE RAdapterBatchEEE CAdapterBatchEEE 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE т 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE ArrayAdapterBatchEEE DenseAdapterBatchEEE ArrayAdapterBatchEEE DenseAdapterBatchEEE DataTableAdapterE ʂSparsePage 5 6engine utils1 c_api1 9MemoryFixSizeBufferE 8MemoryBufferStreamE 0 ̓2 Х SeekStreamE ParamErrorE InputSplitE E 5InBufE DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE CSCPageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE Ӟ DMatrixEEE ŅCSCPageEEE ڞ 1 ؆5CacheENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE 23GradientIndexPageSourceENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ơ 2 5CSRArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE DMatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE 1 ݈5CacheEEE 23GradientIndexPageSourceEEE ͡ 2 5CSRArrayAdapterEEE Ċ DMatrixProxyEEE ArrayAdapterEEE shared_ptrIN7xgboost4data1 މ__ 2 5CSRArrayAdapterEEE ˊ0CSRAdapterEEE ArrayAdapterEEE DenseAdapterEEE 8kFloatPow5InvSplitE 5kFloatPow5SplitE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE common Ōlinear metric MetricE 1 ،6ColumnI 6PeekableInStreamE 5FixedSizeStreamE 1DenseColumnI 2SparseColumnI basic_filebufIcNS_11char_traitsIcEEEE __empty_stateIcEE 0Json ̎1 7 5GradientBoosterE 2ConfigurableE ɥ3 StringE ObjectE ۏNumberE ReaderE ҐWriterE ِ JsonArrayE ԏLearnerIOE predictor12CPUPredictorE PredictorE Json LearnerImplE ObjFunctionE TreeUpdaterE IntegerE BooleanE ː 1 Ñ4data1 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t3__16vectorINS_4data17RowBlockContainerI 8RowBlockI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE ѱ _ t3__16vectorINS_4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE j y fEEEE iEEEE xEEEE 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ಉ 4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEEE j y fEEEEE iEEEEE xEEEEE 糉 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIjfEEE_ vE_ 浉 BasicRowIterI LibSVMParserI j y fEE iEE xEE fEE iEE xEE fEE iEE xEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE ͷ fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl 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yfEENS1_9allocatorIS5_EEEEEE ҉ jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE Ӊ jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE ԉ jfEC1EPNS0_10ParserImplIjfEEEUl yfEC1EPNS0_10ParserImplIyfEEEUl PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ Չ ։ fEE iEE xEE ؉ fEE iEE xEE ؉ fEE iEE xEE ؉ ؉ ؉ ى PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ډ ۉ PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ ܉ ݉ މ jfEE yfEE މ ߉ j y fEE iEE xEE ߉ fEE iEE xEE ߉ ߉ ߉ 1 23IndexedRecordIOSplitterE 5 8ThreadedInputSplitE 6 2LineSplitterE 4InputSplitBaseE 0FileS SingleFileSplitE LocalFileSystemE 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS7_EEFvvEEE 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS2_5ChunkEE_ vE_ CachedInputSplitE RecordIOSplitterE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE vE_NS_9allocatorIS5_EEFvvEEE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ vE_ end_stateIcEE lookaheadIcNS_12regex_traitsIcEEEE alternateIcEE match_anyIcEE nodeIcEE loopIcEE owns_one_stateIcEE back_ref_icaseIcNS_12regex_traitsIcEEEE has_one_stateIcEE word_boundaryIcNS_12regex_traitsIcEEEE match_char_icaseIcNS_12regex_traitsIcEEEE back_ref_collateIcNS_12regex_traitsIcEEEE owns_two_statesIcEE repeat_one_loopIcEE ystemE treamE N ZN F PF 4dmlc 7xgboost St3__1 5rabit Ď 5ErrorE 9parameter1 8DataIterIN 6 1 7istream 4data 2io 6FieldAccessEntryE 4FieldEntryBaseINS0_10FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI 0FieldEntryI Ⱦ iEEiEE xEExEE jEEjEE yEEyEE fEEfEE dEEdEE bEEbEE NSt3__1 mEEmEE ؾ iEEiEE xEExEE jEEjEE yEEyEE fEEfEE dEEdEE mEEmEE iEE xEE jEE yEE fEE dEE bEE N mEE п 还 5 4 6 ˕1 9 8JsonNullE 7 3 20LearnerConfigurationE ValueE ModelE 1 2 6__
+9basic_ 5 8 0 4basic_ifstreamIcNS_11char_traitsIcEEEE 3 7 1__ 6__ 2__match_charIcEE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ܍Ę basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ Ř basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ ɘ 7xgboost4data S 1 2 6FileAdapterBatchEEE 5CS Չ7 ʘ data tree 1 6detail19SingleBatchDataIterINS0_ 2 5 2 Ԁ0CS ъ6 1FileAdapterE 9 8PageSourceIncMixInINS_ ɧ3 7SparsePageDMatrixE IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE ̀CSRArrayAdapterE ʘ D ArrayAdapterE MatrixProxyE enseAdapterE ˘ 0__ 3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE 6__end_marked_subexpressionIcEE 1__empty_non_own_stateIcEE shared_ptr_ match_char_collateIcNS_12regex_traitsIcEEEE bracket_expressionIcNS_12regex_traitsIcEEEE pointerIPN emplaceIN7xgboost Λ 7xgboost S_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7 ԏ4data ѐ6common16PartitionBuilderILm2048EE9BlockInfoENS_14default_deleteIS5_EENS_9allocatorIS5_EEEE 7BatchIteratorImplINS1_ 6GHistIndexMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE Ȩ3SortedCSCPageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ٩1EllpackPageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 0SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 3SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 1EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE lj6GHistIndexMatrixEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE Ή̘ ̘ ͘ Θ Θ RAdapterBatchEEE CAdapterBatchEEE Ϙ 1 2 5CS 7 RAdapterBatchEEE ʊCAdapterBatchEEE Ϙ RAdapterE CAdapterE Ϙ 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE И 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE И И ArrayAdapterBatchEEE DenseAdapterBatchEEE ј ArrayAdapterBatchEEE DenseAdapterBatchEEE ј ј Ҙ Ҙ Ҙ Ә Ә Ә Ԙ Ԙ DataTableAdapterE ՍSparsePage ֘ StreamE ParserI ט 0 2 SeekStreamE ParamErrorE ͏InputSplitE ט 5 ֎6engine utils1 c_api1 9MemoryFixSizeBufferE 8MemoryBufferStreamE ט ؘ E 5InBufE Əژ ۘ ۘ DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ʐCSCPageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ĩܘ 1 ݑ5CacheENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE 23GradientIndexPageSourceENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE 2 5CSRArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE DMatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ݘ ݘ shared_ptrIN7xgboost4data1 ۓ__ 2ArrayAdapterEEE 5CSRArrayAdapterEEE ݘ ޘ ޘ 7xgboost ؔ5rabit6engine13AllreduceBaseE 4dmlc2io 6common1 1 9 8JsonNullE 7 4 3gbm 5FixedSizeStreamE 6PeekableInStreamE ĕ ޘ ߘ common linear MetricE metric 1 6ColumnI 6PeekableInStreamE ٖ5FixedSizeStreamE 1DenseColumnI 2SparseColumnI ߘ ߘ basic_filebufIcNS_11char_traitsIcEEEE __empty_stateIcEE 0Json ɗ1Json ObjectE StringE NumberE ReaderE WriterE JsonArrayE 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3any8TypeInfoINSt3__110shared_ptrIN7xgboost4data1 2io15LocalFileSystem11GetInstanceEvE8instance 5LogMessageFatal5Entry11ThreadLocalEvE6result ́6ThreadLocalStoreI 芛 VZN RZN7xgboost13TextGenerator9BuildTreeERKNS_7RegTreeEE13kTreeTemplate_ 4dmlc Ȃ7xgboost K7xgboost 1 3any8TypeInfoINSt3__110shared_ptrIN7xgboost4data1 2io15LocalFileSystem11GetInstanceEvE8instance 5LogMessageFatal5Entry11ThreadLocalEvE6result 6ThreadLocalStoreI N 16XGBAPIErrorEntryE3GetEvE4inst 7xgboost St3__13mapIPKN7xgboost7 5rabit6engine16ThreadLocalEntryEE3GetEvE4inst 19 22XGBAPIThreadLocalEntryEE3GetEvE4inst Ո6common22RandomThreadLocalEntryEE3GetEvE4inst LogCallbackRegistryEE3GetEvE4inst GlobalConfigurationEE3GetEvE4inst Έ N 16XGBAPIErrorEntryE3GetEvE4inst 7xgboost ÇSt3__13mapIPKN7xgboost7 5rabit6engine16ThreadLocalEntryEE3GetEvE4inst 19LogCallbackRegistryEE3GetEvE4inst Lj22XGBAPIThreadLocalEntryEE3GetEvE4inst ܈6common22RandomThreadLocalEntryEE3GetEvE4inst ȋ ، 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__ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_18LogisticRegressionEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSA_EEEvSE_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..118 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_18LogisticRegressionEE13PredTransformEPNS2_16HostDeviceVectorIfEEEUlmNS3_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSC_EEEvSG_DpPT_EUlmE_JmEEEvT_DpT0_ __ZN7xgboost3obj18LogisticRegression12ProbToMarginEf __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED1Ev __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED0Ev __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEPNS0_6__baseIS9_EE __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7destroyEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE18destroy_deallocateEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEclEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE6targetERKSt9type_info __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE11target_typeEv __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEED1Ev __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEED0Ev __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE10LoadConfigERKNS_4JsonE __ZNK7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE10SaveConfigEPNS_4JsonE __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS4_INS_6detail20GradientPairInternalIfEEEE __ZNK7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE17DefaultEvalMetricEv __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE13PredTransformEPNS_16HostDeviceVectorIfEE __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E10LaunchCUDAILPv0EJS9_SI_SA_SA_SA_EEEvSP_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS9_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSL_ISI_Lm18446744073709551615EEENSL_IKfLm18446744073709551615EEESP_SP_E_E9LaunchCPUIJSA_SJ_SB_SB_SB_EEEvSQ_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..122 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_16PseudoHuberErrorEE11GetGradientERKNS2_16HostDeviceVectorIfEERKNS2_8MetaInfoEiPNSB_INS2_6detail20GradientPairInternalIfEEEEEUlmNS3_4SpanIfLm18446744073709551615EEENSN_ISK_Lm18446744073709551615EEENSN_IKfLm18446744073709551615EEESR_SR_E_E9LaunchCPUIJSC_SL_SD_SD_SD_EEEvSS_DpPT_EUlmE_JmEEEvT_DpT0_ __ZZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E9LaunchCPUIJS9_SI_SA_SA_SA_EEEvSP_DpPT_ENKUlmE_clEm __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_16PseudoHuberErrorEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E10LaunchCUDAILPv0EJS9_EEEvSD_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_16PseudoHuberErrorEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSA_EEEvSE_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..124 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_16PseudoHuberErrorEE13PredTransformEPNS2_16HostDeviceVectorIfEEEUlmNS3_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSC_EEEvSG_DpPT_EUlmE_JmEEEvT_DpT0_ __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED1Ev __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED0Ev __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEPNS0_6__baseIS9_EE __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7destroyEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE18destroy_deallocateEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEclEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE6targetERKSt9type_info __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE11target_typeEv __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEED1Ev __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEED0Ev __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE10LoadConfigERKNS_4JsonE __ZNK7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE10SaveConfigEPNS_4JsonE __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS4_INS_6detail20GradientPairInternalIfEEEE 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GCC_except_table18 GCC_except_table19 GCC_except_table20 GCC_except_table21 GCC_except_table22 GCC_except_table23 GCC_except_table25 GCC_except_table26 GCC_except_table28 GCC_except_table30 GCC_except_table32 GCC_except_table34 GCC_except_table35 GCC_except_table38 GCC_except_table39 GCC_except_table40 GCC_except_table41 GCC_except_table42 GCC_except_table43 GCC_except_table44 GCC_except_table45 GCC_except_table46 GCC_except_table47 GCC_except_table48 GCC_except_table49 GCC_except_table50 GCC_except_table51 GCC_except_table52 GCC_except_table54 GCC_except_table55 GCC_except_table56 GCC_except_table57 GCC_except_table58 GCC_except_table59 GCC_except_table60 GCC_except_table61 GCC_except_table62 GCC_except_table63 GCC_except_table64 GCC_except_table65 GCC_except_table66 GCC_except_table67 GCC_except_table68 GCC_except_table69 GCC_except_table70 GCC_except_table71 GCC_except_table72 GCC_except_table73 GCC_except_table74 GCC_except_table75 GCC_except_table76 GCC_except_table77 GCC_except_table79 GCC_except_table81 GCC_except_table82 GCC_except_table83 GCC_except_table84 GCC_except_table85 GCC_except_table86 GCC_except_table87 GCC_except_table88 GCC_except_table89 GCC_except_table91 GCC_except_table92 GCC_except_table93 GCC_except_table94 GCC_except_table95 GCC_except_table96 GCC_except_table97 GCC_except_table98 GCC_except_table99 GCC_except_table100 GCC_except_table103 GCC_except_table104 GCC_except_table116 GCC_except_table118 GCC_except_table119 GCC_except_table120 GCC_except_table121 GCC_except_table122 GCC_except_table133 GCC_except_table137 GCC_except_table138 GCC_except_table147 GCC_except_table148 GCC_except_table149 GCC_except_table150 GCC_except_table151 GCC_except_table152 GCC_except_table153 GCC_except_table154 GCC_except_table157 GCC_except_table188 GCC_except_table193 GCC_except_table194 GCC_except_table195 GCC_except_table196 GCC_except_table197 GCC_except_table198 GCC_except_table199 GCC_except_table200 GCC_except_table201 GCC_except_table202 GCC_except_table203 GCC_except_table204 GCC_except_table205 GCC_except_table206 GCC_except_table207 GCC_except_table208 GCC_except_table209 GCC_except_table210 GCC_except_table211 GCC_except_table212 GCC_except_table213 GCC_except_table214 GCC_except_table215 GCC_except_table216 GCC_except_table217 GCC_except_table218 GCC_except_table219 GCC_except_table236 GCC_except_table237 GCC_except_table238 GCC_except_table249 GCC_except_table256 GCC_except_table257 GCC_except_table263 GCC_except_table266 GCC_except_table267 GCC_except_table268 GCC_except_table269 GCC_except_table271 GCC_except_table272 GCC_except_table275 GCC_except_table278 GCC_except_table279 GCC_except_table280 GCC_except_table281 GCC_except_table282 GCC_except_table283 GCC_except_table284 GCC_except_table285 GCC_except_table286 GCC_except_table289 GCC_except_table290 GCC_except_table292 GCC_except_table295 GCC_except_table298 GCC_except_table299 GCC_except_table300 GCC_except_table303 GCC_except_table318 GCC_except_table325 GCC_except_table330 GCC_except_table12 GCC_except_table24 GCC_except_table0 GCC_except_table1 GCC_except_table2 GCC_except_table3 GCC_except_table33 GCC_except_table114 GCC_except_table115 GCC_except_table125 GCC_except_table126 GCC_except_table127 GCC_except_table128 GCC_except_table129 GCC_except_table155 GCC_except_table156 GCC_except_table166 GCC_except_table167 GCC_except_table168 GCC_except_table169 GCC_except_table170 GCC_except_table239 GCC_except_table248 GCC_except_table250 GCC_except_table251 GCC_except_table252 GCC_except_table291 GCC_except_table293 GCC_except_table319 GCC_except_table320 GCC_except_table321 GCC_except_table331 GCC_except_table332 GCC_except_table333 GCC_except_table334 GCC_except_table360 GCC_except_table361 GCC_except_table362 GCC_except_table371 GCC_except_table372 GCC_except_table373 GCC_except_table374 GCC_except_table375 GCC_except_table401 GCC_except_table402 GCC_except_table403 GCC_except_table428 GCC_except_table7 GCC_except_table110 GCC_except_table131 GCC_except_table134 GCC_except_table135 GCC_except_table5 GCC_except_table37 GCC_except_table78 GCC_except_table90 GCC_except_table36 GCC_except_table27 GCC_except_table80 GCC_except_table29 GCC_except_table31 GCC_except_table53 GCC_except_table101 GCC_except_table143 GCC_except_table145 GCC_except_table146 GCC_except_table221 GCC_except_table223 GCC_except_table228 GCC_except_table253 GCC_except_table261 GCC_except_table262 GCC_except_table264 GCC_except_table102 GCC_except_table105 GCC_except_table106 GCC_except_table107 GCC_except_table109 GCC_except_table113 GCC_except_table142 GCC_except_table174 GCC_except_table175 GCC_except_table176 GCC_except_table179 GCC_except_table182 GCC_except_table185 GCC_except_table186 GCC_except_table192 GCC_except_table224 GCC_except_table225 GCC_except_table229 GCC_except_table235 GCC_except_table241 GCC_except_table242 GCC_except_table245 GCC_except_table246 GCC_except_table258 GCC_except_table259 GCC_except_table274 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GCC_except_table417 GCC_except_table419 GCC_except_table424 GCC_except_table425 GCC_except_table426 GCC_except_table427 GCC_except_table429 GCC_except_table430 GCC_except_table431 GCC_except_table432 GCC_except_table434 GCC_except_table435 GCC_except_table436 GCC_except_table437 GCC_except_table438 GCC_except_table439 GCC_except_table440 GCC_except_table441 GCC_except_table442 GCC_except_table443 GCC_except_table450 GCC_except_table455 GCC_except_table456 GCC_except_table457 GCC_except_table458 GCC_except_table462 GCC_except_table463 GCC_except_table464 GCC_except_table465 GCC_except_table466 GCC_except_table467 GCC_except_table468 GCC_except_table469 GCC_except_table470 GCC_except_table471 GCC_except_table472 GCC_except_table473 GCC_except_table474 GCC_except_table475 GCC_except_table477 GCC_except_table478 GCC_except_table479 GCC_except_table480 GCC_except_table481 GCC_except_table482 GCC_except_table483 GCC_except_table484 GCC_except_table485 GCC_except_table486 GCC_except_table184 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