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e0HMXH ]H IH$@UH HL A@H9H ]@UH HE t e HM8pH ]H 10@UH HL A@HH ]@UH HE t e HMPH ]H H ٮH HH@UH HL A@HH ]H @H 0H0Hp@UH HE te H H ]@UH HE t"e L BA@HkH ]@UH HE t e HM8pH ]@UH HE te HmH ]@UH HE t e HMh@H ]@UH HL A@H0H ]@UH HE @t e HM`H ]H Q H i H@UH HE te H}H ]@UH HE t"e L A@HH ]@UH HE te H H ]@UH HE te HH ]@UH HL A@HPIH ]@UH HE %te HMh{H ]@UH HE0te0HMH ]H q @UH HL A@HH ]H  H Y @UH HE0te0HH ]@UH HE0te0HH ]H@UH HE0te0HMH ]@UH HE0te0HH ]Hd@UH HL PA@H0yH ]@UH HE0 t e0HM`H ]H p H ٩` H@tH Q@ H 0 HPH @UH HE$te$H Hu]H ]H HH%Y@UH HE$te$HM0H]H ]HDH4HPHxHdHDHHHtH dHTHDH4H$HHpH8TH8HH(tHH`HHHPH H@HH0HH HHHHHHHpHH8H0H8HH @UH HE0te0HPH ]@UH HE0te0H]H ]@UH HL A@H H ]@UH HE8t e8HM@H ]@UH HE t e HMhH ]@UH HE te HH ]HHP@UH HL A@H H ]@UH HE t e HM8 H ]H`H@UH HE te HH ]@UH HE te HH ]H0Hp@UH HL A@H0H ]@UH HE t e HMX H ]@UH HE t e HMhH ]@UH HHM`H ]H H lH@tUH `H lH@WH 10H ٥TlH H yH iHpHsHDH4HH0HpHH`HDH4H0sHdH`sH`HHDHPrHPdHPH`HT$SUH8HLEHHU0HSLHU8H33HT$SUH(HLE HUxHMpSLHU(HMp=V33HT$UH HLExHU HMp33aHT$UH HLEXHUhHMP331H@drHHTrH8DrH(4rH $rHT$UH HHMPHLEXHUh:33HT$UH HHM`ʖHLEhHUx 33yHT$SUH(HLE HU(HMp!HMp舖HLExHţ334HT$SUH(HLE HU(HMpQHMp8HLExHu33H`kH`HPsHp@UH HHM`]H ]HhdHhHPlHhH @HtSHHHHsHHpHH8MlH@pH@H@pHHHpHHHXpHHHp`HHHH`kH kHPdHXDRH@4RH@HHPH@HhH@HmiH@H]mHpHQHpH=bHpHjHpHH0HPH`TH`H@H`H0H HHHptHpdHHxiHHiH`iH0tmHdmHlHHx$HtlHhiHTlHHHHHtHT@UH HHH ]H$HPtl@UH HL `lAXHH ]H kHH4hHgHgHpHP$kH0HPtH@k@UH HL kAXHH ]H gHpdCH@HpjHpHpHH@UH HHjH ]HTdHjH8fHfHpfH$HHHHpDjH4jHHHhLHLHAHeHPAH i@UH HL iAXH@H ]HeH@UH HHM8H ]HpDAH4AH8$HT$SUH(HLHU(HKHύHLHU0|33{HT$SUH(HLE HU(HMp!HMp舍HLExHŅ334HpdHxHHxHH8hH(H H(HH(HH(H -H(H8MH0H0cH0H @UH HꋅtHM@H ]@UH HꋅtHM@H ]HHH0HP鄘H0HHtH@dcH@HPcH@H0@cHT$UH HHM@ZHLEHHUP誗33 HT$UH HHM@*HLEHHUP33H84HH H `H ]H8HH8HHH8H=Y@UH HꋅtHM@H ]@UH HHM8H ]HT$SUWH0HH}`HOtHu'HM0eHM0zHU0HbHM0MHOzHH0_][H`HPH`H H`H8H`H@c@UH H꺀HMhH ]H`HhH`H-@UH H@HH ]HTcHx`H4cHx$HHcH_H_H $Hh_HdcH TcH0HHD_Hc@UH HL cAXH`)H ]H^H@H@UH HPHH ]Hp$@UH HEDteDH]bH ]@UH HL @bAXHiH ]HdaH0HH@UH HL aAXHpH ]H]HP4H$@UH HL paAXH H ]Hd]H`HH@H@\H@\Hd\Hxd\HHh`@UH H꺈HMpH ]HHPt^H84HH dAH8HT$SUH(HLE0HU(HLHU Hg33H HPH aHPH@HPHhHPH-HPH HPHHPH@UH HE$t e$HM`0H ]HhrH(HHHrHHġH $HptbH4HT$SUH8HtHDžHDžHDžƅA<H#H词AAHHpHwH/GHH`HRHHGHqHHt:HHHr!H'LAI+HAHwIDHH8][H dHT$SUH(Hhs$HDž8HDžHHDžPƅ8@&HDžH<HDžP?ٚ ߚH@  H0 H8@<H8AAH8HMH+oHHEHHM@HRHHEHMHpHPHt:HH8Hr!H'LAI+HAHwI3CqHH(][H@t`HH\HH H`D`HH\HHH0`H HP\H HHPH0\HPHHT$UH HHM@HLEHHUP:33H a`@UH HHHM@MH ]H0H0H@H0H H0H8@.H0H-.H0HH0H(}H0HxH01H0HH0HH0H0HH0H@H@H@HH@H8HHH HH0HHXHHČH@UH HE@te@HH;H ]HHH%8@UH HE@te@HH ]@UH HE@te@HH%;H ]HHH%7HH%;HH0HmH0HH0HH0HX@H0tHH(/H@THHT$SUVWAVH HHH@uYH,AHbH@H,HƨH.@HHTD@HHH;t!!!F"!f!!!D!~!!!R!!!!Z!!!N!!!!z!!!8!!!!n!!!b!!!*!r!!!!!&!>!j!!!^!!!:!z!! !b!!!!:!h!!!!!!!6!X!!! !Z!!!(!n!!!&!j!!!D!x!!!!H!!!"Z"""","J"h"""""""" "*"F"t"""""*"X""""z!!x!B!!!t!2!!!r!!!!! !,!N!r!!!!!6!Z!n!!!!N!!!!!"""""""2""T"j"""|"b"J"8","<""" "to""  s9F " " "L" "" "v" " " " " " "n "f " " "$ "* "2 "8 "@ "H "N "X " " " "X" ""Z " "x " "" " " "0 " "$ "> "` "| "" " " " " "" " "("""" " " """ " "v "d " """"" """"P " " " " "`"< "j"4"B"??PPP0!!!!@$`$ $#$&`&(&(,*=?@68@4;9`0P2.`A`CCCCCDEGN M`KpIPRPTV[WY`\\ \@\a `\P^yupskgiepq`yobdypwmy{`@`} { @0@P@@@Е0p0p0 t?> B-XpUnknown exception-Xp.Xpbad array new lengthstring too long.Xp: /Xpbad castfalseinvalid stoi argumentstoi argument out of rangemap/set too long/Xp:] ['@U778p8"8(8 887*hR`0OOZ1X777788"8(878 88701Rfloatint`Check failed: , H2Xp : nthread(3Yp~:9P9O3Y~HH6O(4Y`~JH7O4PTP~:97O(5PT0~:96O5PY@~:97O(6PT ~:96O6OO0OOpWOOOOOOOOOOfid == static_cast(names_.size())D:\a\ml-builds\ml-builds\xgboost\include\xgboost/feature_map.hFeatureMap feature index exceed boundidx < names_.size()unknown feature type, use i for indicator and q for quantitychunksize == groupsD:\a\ml-builds\ml-builds\xgboost\src\c_api\c_api_utils.hUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast(1), std::multiplies<>{}) == chunksize * rowslearnergradient_boostergblineardartgbtreegbtree_model_paramnum_parallel_treeUnknown booster:missingArgument `missing` is required.Invalid missing value: rIncorrect number of feature names.custom_feature_names.size() == n_featuresfeature_names.size() == n_featuresIncorrect number of feature types.feature_types.size() == n_featuresffeature_map.Size() == n_featuresInvalid DMatrix handleCheck failed: pp_mCheck failed: p_mD:\a\ml-builds\ml-builds\xgboost\rabit\include\rabit/internal/utils.h 70PZ0awrite position exceed fixed buffer size7 Z aread can not have position excceed buffer lengthXGBoost version not compiled with GPU support.D:\a\ml-builds\ml-builds\xgboost\src\common\common.hMemory should be contiguous.`typestr' should be of format .Only dimensional array is valid.Only version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaObjectStringUnicodeOther Or open an issue.https://docs.scipy.org/doc/numpy/reference/arrays.interface.htmlof your input data complies to: Please verify the `__cuda_array_interface__/__array_interface__' in `typestr' of input array.Invalid type code: D:\a\ml-builds\ml-builds\xgboost\src\data\array_interface.h is not supported.-versionMissing `version' field for array interfacetypestrMissing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4shapeMissing `shape' field for array interfacedataMissing `data' field for array interfacemaskj_shape.size() == 1mask with bitfield type should be of 1 byte per bitfield.type_length == 1mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.stridesstrides.size() == 1get(strides.at(0)) == type_lengthEmpty data with non-zero shape.size == 09 SOOO08WaP;SOOO9PXaP<ROOO0;UaP>PSOOO<@WaP?ROOO0>VaPD:\a\ml-builds\ml-builds\xgboost\src\c_api\../data/adapter.hASOOO? XaPASOOOFormat string cannot be emptyCheck failed: format_strColumn data type not supported by XGBoostCheck failed: falseCheck failed: std::string(schema->format) == "+s"Check failed: columns.empty()AVCannot import non-existent record batchCheck failed: rb_Cannot import record batch without a schemaCheck failed: !schema_->columns.empty()BROOO BZapCannot reset RecordBatchesIterAdapterCheck failed: at_first_D:\a\ml-builds\ml-builds\xgboost\src\common\threading_utils.hC00hkW`@kkkD:\a\ml-builds\ml-builds\xgboost\src\data\proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Not implemented.USE_RMMoutInvalid pointer argument: D:\a\ml-builds\ml-builds\xgboost\src\c_api\c_api.ccBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPDEBUGUSE_FEDERATED+tRjson_strCheck failed: ec == std::errc()Unknown global parameters: { }Check failed: eXGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workersfnameCT@ap`.` is required for `Argument `configXGDMatrixCreateFromCallbackcache_prefixnextreset is deprecated. Use `XGQuantileDMatrixCreateFromCallback` instead.XGDeviceQuantileDMatrixCreateFromCallbackInvalid handle to ref.Check failed: _refmax_binDMatrix/Booster has not been initialized or has already been disposed.c_interface_strCurrent DMatrix type does not support set data.Check failed: mindptrindicesc_json_configXGDMatrixCreateFromArrowCallbacknbatchslice does not support group structurestatic_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Ubinary saving only supported by SimpleDMatrixfieldout_featureslenCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupout_lenout_dptrout_indptrout_indicesout_datap_m->Info().num_col_ <= std::numeric_limits::max()Check failed: page.pagedmatsjson_parametersout_strdtrainCheck failed: dtrgradhessevnamesout_resultBooster has not been initialized or has already been disposed.DMatrix has not been initialized or has already been disposed.XGBoosterPredictFromDMatrixtypeiteration_beginiteration_endntree_limitOnly one of the `ntree_limit` or `iteration_range` can be specified.Check failed: iteration_end == 0`ntree_limit` is deprecated, use `iteration_range` instead.trainingstrict_shapeout_dimout_shapeCache ID is not supported yetget(config["cache_id"]) == 0InplacePredictImplCheck failed: p_predtInvalid input type for inplace predict.Check failed: proxyarray_interfacestr.size() >= 3str[0] == '{'jsonwbufjson_configXGBoosterSaveModelToBufferformatdeprecatedUnknown format: `` is deprecated, please use `XGBoosterSaveModelToBuffer` instead.XGBoosterGetModelRawCheck failed: p_outout_modelstextfmapftypesuccesskeyfeaturesfeature_namefeature_typeUnknown field for Booster feature info:XGBoosterFeatureScoreimportance_typefeature_mapfeature_namesout_n_featuresfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()out_scoresname_strCannot reset IteratorAdapterinvalid map keyCheck failed: impl_ != nullptrD:\a\ml-builds\ml-builds\xgboost\include\xgboost/data.h to Number of columns between batches changed from Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsColumn: get(array).size() == 1Input pointer misalignment.ptr % alignment == 0XGBoost doesn't support internal broadcasting.Shape of bit mask doesn't match data shape. n_bits == nMasked array is not yet supported.Check failed: mask_it == array.cend() || IsA(mask_it->second)streaminvalid vector subscript128-bit floating point is not supported on current platform.Check failed: sizeof(long double) == 16invalid string positionEmpty data passed in.DPT`, got: ``, expecting: `Incorrect type for: `DPTi < Dstride and shape don't match.j_strides.size() == j_shape.size()vector too longInvalid cast, from D:\a\ml-builds\ml-builds\xgboost\include\xgboost/json.h) vs. (n_threads >= 1EPTEPTFPTFPTGPTGPTCheck failed: m == 1 || n == 1', Possible Arguments: Cannot find argument '---------------- ?CHPYOOOOOOOXGBOOST_COMMUNICATORxgboost_communicator is less than 1.World size D:\a\ml-builds\ml-builds\xgboost\src\collective\communicator.h is less than 0.Rank . is greater than world_size - 1: rabitfederatedUnknown communicator type HPYkk??@?IPY kP==1=0Failed to initialize RabitD:\a\ml-builds\ml-builds\xgboost\src\collective\rabit_communicator.hUnknown data typeXGBoost is not compiled with Federated Learning support.D:\a\ml-builds\ml-builds\xgboost\src\collective\communicator.ccUnknown communicator type.Unknown allreduce operationIPYP`generic`JPY`systemunknown error`: Failed to call `D:\a\ml-builds\ml-builds\xgboost\include\xgboost/collective/socket.hUnknown IA family.getsockopt(handle_, SOL_SOCKET, SO_PROTOCOL_INFO, reinterpret_cast(&info), &len)sendrecvsystem::CloseSocket(handle_)socketFailed to get addr info for: D:\a\ml-builds\ml-builds\xgboost\src\collective\socket.cc0.0.0.0::Check failed: !this->IsClosed()str.size() < std::numeric_limits::max()Failed to send string length.this->SendAll(&len, sizeof(len)) == sizeof(len)Failed to send string.bytes == str.size()Failed to recv string length.this->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to recv string.bytes == lenstatic_cast(socket.Domain()) == static_cast(addr.Domain())00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899gffffffQ~jt*Ǻ"<8b->Nc{-1+x#c^- Knu7X%Y^_GzK`eq]p J&gչ o"4%QttS`؈$C(ӦfHRl ۩#j+vv=IJ+10Q=ѬVZbu)j}և]:1VӾa'miK$!Q㢢P@9j>`KUq݉ [qB1h?(SuYQ!"GMk9X 3D۸N <C.{$~ۃ6/l##ՂF!@je _vH@0ļ4&k7y؅W4Ngm=`X@xPMJ- yCx(,*42q0/'|!1錠9Y>R$n`F"x|+MraB ydiWC*fJ\)c7]`xxֲK}Yq\Ck 3TNaN0E0?D:\a\ml-builds\ml-builds\xgboost\src\common\quantile.hSortedSketchContainerD:\a\ml-builds\ml-builds\xgboost\src\common\hist_util.hUnreachableentries_per_column.size() == info.num_col_D:\a\ml-builds\ml-builds\xgboost\src\common\hist_util.ccinvalid init parameterCheck failed: n * limit_size >= maxnCheck failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))entries_per_columns.size() == static_cast(n_columns)column_sizes_tloc[i].size() == static_cast(n_columns)?@)AD:\a\ml-builds\ml-builds\xgboost\src\common\host_device_vector.ccSize() == other.Size()J\://LpNot implementedD:\a\ml-builds\ml-builds\xgboost\src\common\io.hM 0 xKpp8p KPD:\a\ml-builds\ml-builds\xgboost\src\common\io.ccCheck failed: out failed: Opening file:// \ "\r\n\\\t\"D:\a\ml-builds\ml-builds\xgboost\include\xgboost/string_view.hbeg <= size_MvkPWPhcPf`=QExpecting: "", got: "EOF"\0" "N~PY`P0P PPvrZPhcPf`= QQ~PY@p`@@ 0PCheck failed: ret.ec == std::errc()D:\a\ml-builds\ml-builds\xgboost\src\common\json.cc\b\f\u%04xNumberArrayU8ArrayI32ArrayI64Array can not be indexed by string.Object of type can not be indexed by Integer.N`<}:94OO;}:9 4OO`<}:9p4OP<~:95OUnknown construct, around character position: , "\0Unknown escapenullExpecting null value "null"raw_str_.size():cursor_.Pos(): ch != -1Expecting digit` is not supported for typed array.f64 is not supported.High precision number is not supported.bad allocationQXpRXpRXpOut of range valueNo conversion could be performedSOO?OOO has already been registered in key D:\a\ml-builds\ml-builds\xgboost\dmlc-core\include\dmlc/parameter.h, required' but value=' expect Invalid Parameter format for S0е@ p@ , value='Out of range value for pos <= value.length()Some trailing characters could not be parsed: 'huber_slopeThe delta term in Pseudo-Huber loss.PesudoHuberParam exceed bound [ for Parameter value should be greater equal to should be smaller equal to is not presented of Required parameter optional, default=infinitynanInvalid NAN literal*p == ')'D:\a\ml-builds\ml-builds\xgboost\dmlc-core\include\dmlc\./strtonum.hv?Y@ A LInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than D:\a\ml-builds\ml-builds\xgboost\src\common\categorical.hMaximum cateogry should not be lesser than the total number of categories.max_cat + 1 >= n_categoriesgroup_ptr.size() >= 2group_ptr.back() == n_samples, stemp.sizeinvalid maximum size max_size=sketch->temp.size < max_size, size=, naxt_goal=, sum_total=INFO: rmax=, stemp.size=Finalize: invalid maximum size, max_size=sketch->temp.size <= max_sizefloat32float64bool8int32int8int16int64Unknown data table type.D:\a\ml-builds\ml-builds\xgboost\src\common\../data/adapter.hhessian.size() == info.num_row_D:\a\ml-builds\ml-builds\xgboost\src\common\quantile.ccgroup_ptr.back() == hessian.size()feature_ptr.front() == 0rank_size == totalHostSketchContainerPushColPageweights.size() == info.num_row_, n= check quantile stats, nbig=, chunk=, range=, maxsize= srcsize=quantile: too many large chunkCheck failed: nbig < n, wgap=, maxgap=mingap=bug in combineCheck failed: size <= sa.size + sb.sizesrc.size == 0this->size == 0, v=, wmin=, rmax=] rmin=SketchContainerImplcolumns_size_.size() != 0n_threads_ >= 1worker_segments.size() >= 1Check failed: reduced[i].dataAllReduceNumber of columns differs across workersn_columns == sketches_.size()num_cuts.size() == 0PushRowPagesketches_.size() == n_columnsCheck failed: a.dataCheck failed: a.data && reduced[fidx].datacuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > cuts->cut_ptrs_.HostVector().back()count <= total_entriesCheck failed: worker_feature.data()'7o:>AK_75features.size() > 0D:\a\ml-builds\ml-builds\xgboost\src\common\random.ccT array.size() == weights.size()D:\a\ml-builds\ml-builds\xgboost\src\common\random.h75AOD:\a\ml-builds\ml-builds\xgboost\dmlc-core\include\dmlc\./parameter.hXU0 @ @    Invalid Input: '', valid values are: optional, default=) exisit!(Enum Enums: ), Value not found in enum declaredenum_back_map_.count(value) != 0UV0p @ @    aft_loss_distributionnormalextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time model did not get stopped properly.Timer for D:\a\ml-builds\ml-builds\xgboost\src\common\timer.ccus calls @ s, ========): ======== Monitor (.AInvaid version format in loaded JSON object: D:\a\ml-builds\ml-builds\xgboost\src\common\version.ccIncorrect 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) 8WDD:\a\ml-builds\ml-builds\xgboost\src\data\../data/adapter.hW`DECheck failed: parser_D:\a\ml-builds\ml-builds\xgboost\src\data\file_iterator.hlabelqidweightCheck failed: !type_.empty()Invalid group structure.Check failed: valid_query_groupD:\a\ml-builds\ml-builds\xgboost\src\data\validation.hnum_rownum_colnum_nonzerolabelsgroup_ptrweightsbase_marginlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightsD:\a\ml-builds\ml-builds\xgboost\src\data\data.ccAll feature_types must be one of {int, float, i, q, c}. again.Please process and save your data in current version: is no longer supported. Binary DMatrix generated by XGBoost: major >= 1minor >= 6MetaInfo: invalid formatCheck failed: fi->Read(&num_field)fields.) fields, but the binary file only contains MetaInfo: insufficient number of fields (expected at least num_field >= expectedMetaInfo: the given binary file contains extra fields which will be ignored.Incorrect size of base margin vector.this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.Check failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for labels.this->labels.Size() % this->num_row_ == 0Label contains NaN, infinity or a value too large.Check failed: valid`qid` must be sorted in non-decreasing order along with data.Check failed: non_decWeights must be positive values.label_lower_boundlabel_upper_boundFeature weight must be greater than 0.Unknown key for MetaInfo: Check failed: t.CContiguous()Check failed: keyUnknown float field name: Unknown uint32 field name: Unknown data type for getting meta info. must be equal to number of columns.Length of size == this->num_col_Check failed: infoUnknown feature info name: Unknown feature info: Number of columns must be consistent across batches.this->num_col_ == that.num_col_that.group_ptr_.size() != 0`gpu_id` for XGBoost: Device that data is on: Data is resided on a different device than `gpu_id`. Check failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == deviceSize of weights must equal to number of groups when ranking group is used.group_ptr_.size() == weights_.Size() + 1Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.group_ptr_.back() == num_row_Size of weights must equal to number of rows.weights_.Size() == num_row_Size of labels must equal to number of rows.labels.Shape(0) == num_row_Size of label_lower_bound must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of feature_weights must equal to number of columns.feature_weights.Size() == num_col_Size of label_upper_bound must equal to number of rows.labels_upper_bound_.Size() == num_row_num_nonzero_ <= num_col_ * num_row_Size of base margin must be a multiple of number of rows.base_margin_.Size() % num_row_ == 0 entries loaded from matrix with Only one `#` is allowed in file path for cache file specification.cache_file.find('#') == std::string::npos.rauto partsLoad part of data libsvmConsider providing a uri parameter: filename?format= . No format parameter is provided in input uri, but found file extension: Consider providing a uri parameter like: filename?format=csvChoosing default parser in dmlc-core. No format parameter is provided in input uri. Encountered parser error: transpose.offset.Size() == num_columns + 1other_data.size(): self_data.size(): self_offset.size() == other_offset.size()beg <= data.size()ptr < offset.size()key >= builder_base_row_offsetInput data contains `inf` or `nan`<MetaInfo: Invalid format for Check failed: strm->Read(&name), got: Expected field: name == expected_nameCheck failed: strm->Read(&type_val)got field type: Expected field of type: Check failed: type == expected_typeCheck failed: strm->Read(&is_scalar) to be a scalar; got a vectorExpected field Check failed: is_scalarCheck failed: strm->Read(field) to be a tensor; got a scalarCheck failed: !is_scalarCheck failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field) to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)Number of columns is expected to be 1.shape.second == 1Inconsistent size after modification.this->Data()->Size() == detail::CalcSize(this->shape_)D:\a\ml-builds\ml-builds\xgboost\include\xgboost/linalg.hMeta info like label or weight can not have missing value.Check failed: array.valid.Size() == 0shape[i] == r.Shape(i)Internal Error: XGBoost is not compiled with CUDA but EllpackPage is requiredD:\a\ml-builds\ml-builds\xgboost\src\data\ellpack_page.ccCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizeD:\a\ml-builds\ml-builds\xgboost\src\data\../common/hist_util.hhit_count.size() == n_bins_totalD:\a\ml-builds\ml-builds\xgboost\src\data\gradient_index.hCheck failed: p_fmat->SingleColBlock()D:\a\ml-builds\ml-builds\xgboost\src\data\gradient_index.ccCheck failed: !sorted_sketchrow_ptr.size() == 0Check failed: columns_HYPmYPmHZPmvector too long[  0`pZЉrawRaw GHistIndex binary data format.h\p\p]Xp _Fail to schedule the chore!_`^`O_0OThis function cannot be called on a default constructed task``Oa`Pa`Pbroken promisefuture already retrievedpromise already satisfiedno state`bPYP`futureD:\a\ml-builds\ml-builds\xgboost\src\data\sparse_page_source.hformat.front() == '.'count_ == source_->Iter()D:\a\ml-builds\ml-builds\xgboost\src\data\gradient_index_page_source.cccuts_.Values().size() != 0Check failed: !cache_info_->written seconds. MB written in fi->Tell() == offsetCheck failed: fmt->Read(page.get(), fi.get())Read a page in Check failed: !at_end_total batches:n_prefetch_batches > 0fetch_it < cache_info_->offset.size()Sparse DMatrix assumes forward iteration.std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesbPk?Xc 0Unknown format type D:\a\ml-builds\ml-builds\xgboost\src\data\sparse_page_writer.hc0`XddPXe pe0XfPTgk?f` 0h@k?h@p8i@@`j0i0j0pP?eAgpu_idInvalid proxy handle.Check failed: proxy_handleCheck failed: typed0l0  0  @      kInconsistent max_bin between Quantile DMatrix and Booster:D:\a\ml-builds\ml-builds\xgboost\src\data\iterative_dmatrix.hOnly `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.Check failed: !param.regen && param.hess.empty()Slicing DMatrix is not supported for Quantile DMatrix.l Pi i  @ D:\a\ml-builds\ml-builds\xgboost\src\common\../data/gradient_index.hIterative DMatrix must have at least 1 batch.D:\a\ml-builds\ml-builds\xgboost\src\data\iterative_dmatrix.ccAll batch should be on the same device.d == batch_param_.gpu_idCheck failed: ref_Check failed: p_cutsInvalid ref DMatrix, different number of features.ref_->Info().num_col_ == n_featuresUnknown type: Inconsistent number of columns.n_features == num_cols()Something went wrong during iteration.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Data must has at least 1 column.n_features >= 1accumulated_rows == Info().num_row_Check failed: p_sketchrbegin == Info().num_row_proxy->Info().labels.Size() == 00m Pi i  @ Check failed: ellpack_`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.m Pi i  @ page.row_ptr.size() == h_offset.size()Check failed: page_ != nullptrD:\a\ml-builds\ml-builds\xgboost\src\data\simple_batch_iterator.hCheck failed: !std::isnan(sparse_thresh)0n n@  0oPT  requested=The any container is emptyCheck failed: type_ != nullptrD:\a\ml-builds\ml-builds\xgboost\dmlc-core\include\dmlc/any.h stored=The stored type mismatchCheck failed: *(type_->ptype_info) == typeid(T)?Not implemented on device.Check failed: on_hostD:\a\ml-builds\ml-builds\xgboost\src\data\proxy_dmatrix.cco PT0p PTpD0qDD:\a\ml-builds\ml-builds\xgboost\src\data\adapter.hrPm * p  psPmp *   sPmP +  p ptPm0 +   tPm ,  ` puPm ,   uPm -  @ pvPmp p.  @ vPm -  @ pwPm p-  @ w0 0 1 & @ ' p ` `   0 px  i  @ x  i  @ py  i  @ Batch parameter is not initialized.Check failed: r != BatchParam{}D:\a\ml-builds\ml-builds\xgboost\src\data\simple_dmatrix.ccparam.gpu_id >= 0param.max_bin >= 2Generating new Gradient Index.param.gpu_id == -1batch_param_.hess.data() == param.hess.data()Check failed: castedinvalid input file formatCheck failed: in_stream->Read(&tmagic)invalid format, magic number mismatchtmagic == kMagicoffset_vec.back() == info_.num_nonzero_D:\a\ml-builds\ml-builds\xgboost\src\data\./simple_batch_iterator.hColumn is emptyCheck failed: data_Column is empty or out-of-bound index of the columnCheck failed: data_ && row_idx < length_yP pz z p{ { p|@ | p} }p p~ Expecting CSCAdapterCheck failed: (std::is_same::value)~PTpPT@7 PTdata_.Size() == detail::CalcSize(shape_)CCCC; you may want to remove it manuallyCouldn't remove external memory cache file Multiple threads attempting to use Sparse DMatrix.Check failed: lock_.try_lock()P @ O ` O pp @   ` p p Check failed: proxy_Must have at least 1 batch.iter_.Next() == 1count_ == n_batches_count_ >= 1 @ O ` O  @   ` O x @   `   @ @ O ` O X @   ` O  @   ` P  this->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size() @ O ` O @ @   ` O Ц @   `  @0 k0  ` 0 `  p Ч   Check failed: kv.secondD:\a\ml-builds\ml-builds\xgboost\src\data\./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Can not obtain a single CSR page for external memory DMatrixMake cache:DMatrix-rinfo_.num_col_ != 0D:\a\ml-builds\ml-builds\xgboost\src\data\sparse_page_dmatrix.cc.row.pageCheck failed: sparse_page_source_.col.pagethis->Info().num_col_ != 0.sorted.col.page.gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_this->count_ == this->n_batches_this->count_ >= 1source_->Iter() == this->count_Check failed: page_@ k?8 k? k?(  (  (P P PT(PT k? `  @k?h k? ` @ @k?H` k?h` `  @k?H   p ȑ0    H  p `  ` ȒР `   ГP  С  ؔ    0 p`  P p  @ p@ P  Check failed: page.offset.Size() != 0 && offset_vec[0] == 0D:\a\ml-builds\ml-builds\xgboost\src\data\sparse_page_raw_format.ccoffset_vec.back() == page.data.Size()Invalid SparsePage filepage->offset.Size() != 0Un_bytes == (page->data).Size() * sizeof(Entry) P  Raw binary data format.` ` p  p@    p    0 p p  P   { "bias": [, "weight": [ ], } ]bias: weight: D:\a\ml-builds\ml-builds\xgboost\src\gbm\../common/common.hSlice is not supported by current booster.D:\a\ml-builds\ml-builds\xgboost\include\xgboost/gbm.hD:\a\ml-builds\ml-builds\xgboost\src\gbm\../common/threading_utils.hInplace predict is not supported by current booster.gpu_coord_descentcoord_descentupdaterD:\a\ml-builds\ml-builds\xgboost\src\gbm\gblinear.cc8"     p   @ P ` `    @   p p" Ȟx P  namemodelget(in["name"]) == "gblinear"gblinear_train_paramCheck failed: this->updater_DoBoostPredictBatchgblinear does not support prediction of leaf indexModel is not initializedCheck failed: !model_.weight.empty()gblinear doesn't support number of trees for feature importance.Check failed: trees.empty()gblinear only has `weight` defined for feature importance.importance_type == "weight"PredictBatchInternalbase_margin.Size() == nsize * ngroup@# @# P# `# pLinear booster, implement generalized linear model.p# m  p fi->Read(¶m_, sizeof(param_)) == sizeof(param_)D:\a\ml-builds\ml-builds\xgboost\src\gbm\gblinear_model.hlong (non-negative)stringp? `Z W ?pL `I V V P0@ `[ W p@ M `I V W @e  e shotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.Linear booster does not support prediction range.layer_begin == 0GBLinearTrainParamint (non-negative)deprecated_num_featuredeprecated_num_output_groupboosted_roundsDeprecatedGBLinearModelParam0s Ph pk `I p p   Unknown gbm type D:\a\ml-builds\ml-builds\xgboost\src\gbm\gbm.ccy      @ @      0  p @  p P  P  Check failed: p_dartCheck failed: p_dart->weight_drop_.empty()weight_dropget(in["name"]) == "dart"dart_train_paramCheck failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == p_out_preds->predictions.Size()weight = trees, drop cpu_predictorgpu_predictorD:\a\ml-builds\ml-builds\xgboost\src\gbm\gbtree.hdeque too longؤ   @ pXP P ` p p` ` ` z    @    @ p 0  p @  p     model_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0in_it < tree_endD:\a\ml-builds\ml-builds\xgboost\src\gbm\gbtree.ccTree booster, gradient boosted trees.Tree booster, dart.Check failed: configured_Invalid number of trees.tree_end <= model_.trees.size()Unsupported data type for inplace predict.Current Predictor: Check failed: successInvalid tree index.idx <= total_n_treesgaintotal_gaincovertotal_cover{"weight", "total_gain", "total_cover", "gain", "cover"}, got: Unknown feature importance type, expected one of: Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.tree_begin == 0Predict 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.   true1boolean has not been registered in Alias إH | @y ?@a `I  y 0y size_leaf_vectorH P`num_treesNumber of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.Reserved option for vector tree.XGBoost version not compiled with OneAPI support.80p @ @    Ч0p @ @    h0p @ @    updater_seqgrow_colmaker,pruneTree updater sequence.process_typedefaultupdateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.predictoroneapi_predictorPredictor algorithm typetree_methodapproxexacthistgpu_histChoice of tree construction method.sample_typeuniformDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.one_dropWhether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.learning_rateLearning rate(step size) of update.etatree_begin <= tree_endGBTreeDANGER 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_histmakerTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.grow_quantile_histmakergrow_gpu_hist) detectedUnknown tree_method (Check failed: !updaters_.empty()BoostNewTreesngroup != 0Current objective doesn't support external memory.must have exactly ngroup * nrow gpairsin_gpair->Size() % ngroup == 0U ups.size() == updaters_.size() mismatched updater sequence. Internal Error: Specified updaters: Actual updaters: Set `process_type` to `update` if you want to update existing trees.can not be used to create new trees. ` Updater: `Check failed: !updaters_.front()->CanModifyTree()Set `process_type` to `default` if you want to build new trees.can not be used to modify existing trees. Check failed: up->CanModifyTree()boosting rounds can not exceed previous training roundsNo more tree left for updating. For updating existing trees, model_.trees.size() < model_.trees_to_update.size()Mismatching size between number of rows from input data and size of gradient vector.gpair->Size() == p_fmat->Info().num_row_CommitModelget(in["name"]) == "gbtree"gbtree_train_paramLoading from a raw memory buffer on CPU only machine. Changing predictor to auto. Loading from a raw memory buffer (like pickle in Python, RDS in R) on a CPU-only machine. Consider using `save_model/load_model` instead. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing. Changing `tree_method` to `hist`.Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.specified_updaterCheck failed: p_gbtreethis->model_.learner_model_param->num_feature != 0layer_trees != 0layer_end > layer_beginstep >= 1 trees remain. Slice the model before making update if you only want to update a portion of trees.Not all trees are updated, this->model_.trees_to_update.size() == this->model_.trees.size()out_preds->version == 0Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.GBTreeModelParamGBTreeTrainParamDartTrainParam?J@Aparam.num_trees == static_cast(trees.size())D:\a\ml-builds\ml-builds\xgboost\src\gbm\gbtree_model.ccGBTree: invalid model filefi->Read(¶m, sizeof(param)) == sizeof(param)fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast(trees.size())Check failed: ctx_treestree_infoverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoostGlobalConfiguration0.7eval_metric0@ `[  1 M `I V  @e  e Random number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number.Number of threads to use.n_jobsThe primary GPU device ordinal.fail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not.H* OO) OOOOOOOOOOOOOOOOOOOOOOOOOOOOOx( OO( OOD:\a\ml-builds\ml-builds\xgboost\src\common/io.hfixed_size->Read(&buffer[0], size) == sizenum_featureȫ0p @ @    Model is not yet initialized (not fitted).Check failed: ret.ec == std::errc{}D:\a\ml-builds\ml-builds\xgboost\src\learner.ccbase_scorenum_classnum_targetboost_from_averageGlobal bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.Number of target for multi-target regression.Whether we should calculate the base score from training data., n_targets:Multi-class multi-output is not yet supported. n_classes:Check failed: n_classes <= 1 || n_targets <= 1Check failed: common::AsConst(base_score_).Data()->HostCanRead()base_score_.Size() == 1Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead()dsplitcolrowData split mode for distributed training.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableboosterGradient booster used for training.objectivereg:squarederrorObjective function used for obtaining gradient.LearnerModelParamLegacyLearnerTrainParamGenericParameter If you are loading a serialized model (like pickle in Python, RDS in R) generated by older XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing. `@) OO07 OOOOOOOOOO0  o @ 0m X m  n  n OOOX n  o (  `  ( OOCheck failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)LearnerConfigurelearner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: IsA(in)learner_train_parammetricsgeneric_paramCall Configure before saving model.Check failed: !this->need_configuration_learner_model_param_paramnum_output_group" contains whitespace.Invalid parameter " Parameters: { ", } are not used. Check failed: matrix.firstCheck failed: !matrix.second.ref.expired() features or greaterUnfortunately, XGBoost does not support data matrices with num_col <= static_cast(std::numeric_limits::max())0 feature is supplied. Are you using raw Booster interface?mparam_.num_feature != 0multi:softprobmulti:softmaxmax_delta_stepcount:poissonCheck failed: this->obj_Inconsistent labels.Check failed: n_targets == t || 1 == t, configuration from parameter:Inconsistent configuration of num_target. Configuration result from input data:Check failed: n_targets == 1 || n_targets == mparam_.num_targetH)   07 OOOOOOOO0  0  o @ 0m X m  n  n OOOX n  o (  ` ( u  num_roundCONFIG-offset:Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.attributesbs64Base64 format is no longer supported in brick.header != "bs64"binffp.Read(&header[0], 4) == 4UInvalid model formatBoostLearner: wrong model formatfi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)Check failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)SAVED_PARAM_count_poisson_max_delta_stepmulti:Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility;ModelConfigInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 08)   07 @- Z 0 s 0 / o 0  0  o @ 0m X m  n  n  Y Po X n  o (  ` ( u  Precondition violated; dsplit cannot be 'auto' in distributed modeCheck failed: tparam_.dsplit != DataSplitMode::kAutoColumn-wise data split is currently not supported.this->learner_model_param_.num_feature != 0begin_layer >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scoreUpdateOneIterPredictRawPredictionsGetGradientGradientsBoostOneIterEvalOneIterPerform one kind of prediction at a time.multiple_predictions <= 1Unsupported prediction type:Predict must happen after Load or configurationCheck failed: gbm_ != nullptrNumber of columns does not match number of features in booster.learner_model_param_.num_feature == p_fmat->Info().num_col_learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: ptr?Learning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaalphaUnknown linear updater D:\a\ml-builds\ml-builds\xgboost\src\linear\linear_updater.ccLinearTrainParam(PY?Q`<QR(PY?RWRS(^Xp^unknown coordinate selector: D:\a\ml-builds\ml-builds\xgboost\src\linear\coordinate_common.hmae_gCoordinateUpdaterlinear_train_paramcoordinate_paramUpdateFeature0 n n0n@npUpdate linear model according to coordinate descent algorithm.D:\a\ml-builds\ml-builds\xgboost\src\linear\../common/threading_utils.htop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.CoordinateParamSupported options are: {cyclic, shuffle}Unsupported feature selector for shotgun updater. D:\a\ml-builds\ml-builds\xgboost\src\linear\updater_shotgun.cc8PpUpdate linear model according to shotgun coordinate descent algorithm.WARNING: DEBUG: INFO: info.group_ptr_.size() >= 2n_classes != 0aucAUC doesn't support multi-target model.labels.Shape(1) == 1Receiver Operating Characteristic Area Under the Curve.P?@?aucprD:\a\ml-builds\ml-builds\xgboost\src\metric\../common/threading_utils.hArea under PR curve for both classification and rank.@@@Pppleast 2 pairs of samples.. Calculating AUC value requires at Invalid group with less than 3 samples is found on worker D:\a\ml-builds\ml-builds\xgboost\src\metric\auc.hPR-AUC supports only binary relevance for learning to rank.info.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size(), valid groups: Total AUC across groups: auc <= 1auc <= local_areaDataset is empty, or contains only positive or negative samples.D:\a\ml-builds\ml-builds\xgboost\src\metric\auc.cc`?@? auc <= 1.0flabels.Size() != 0labels.Size() == predts.size()??Rooted mean square log error.عPT?@? #Mean absolute error.`PT?@?0#&Mean absolute percentage error.PT?@?&*Negative loglikelihood for logistic regression.Mean Pseudo-huber error.pPT?@?*0.Negative loglikelihood for poisson regression.PT?@?@.1Residual deviance for gamma regression.PT?@?15Negative log-likelihood for gamma regression.PT?@?5@9Binary classification error.PT?@?P9=tweedie-nlogliktweedie-nloglik@rho for tweedie regression.hint: use merror or mlogloss for multi-class classificationlabel and prediction size not match, preds.Size() == info.labels.Size()info.labels.Shape(1) != 0%frmsermslemaemapelogloss@PT0>? =PB=mphepseudo_huber_paramFFFPFp`F`FpFFpFFFGp G G0GpGpGGGGpGGG0Hp@H@HPHHpinfo.labels.Shape(0) == info.num_row_D:\a\ml-builds\ml-builds\xgboost\src\metric\elementwise_metric.cuslope for pseudo huber cannot be 0.slope != 0.0HHHHpunable to parse the threshold value for the error metricsscanf(param, "%f", &threshold_) == 1errorIIIPIppoisson-nloglikgamma-deviancegamma-nloglik`I`IpIIptweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: param != nullptrtweedie variance power must be in interval [1, 2)Check failed: rho_ < 2 && rho_ >= 1tweedie-nloglik@IIIIpȸPT?@?IpMRooted mean square error.PPT?@?M0Q$ư>@Unknown metric function D:\a\ml-builds\ml-builds\xgboost\src\metric\metric.cc. Resorting to the CPU builderCannot find a GPU metric builder for metric xжp  0pp`=?@?merror`=?@?mlogloss in label but found num_class=MultiClassEvaluation: label must be in [0, num_class),Check failed: label_error >= 0 && label_error < static_cast(n_class)Multiclass classification error.Multiclass negative loglikelihood.preds.Size() == 0D:\a\ml-builds\ml-builds\xgboost\src\metric\multiclass_metric.culabel and prediction size not matchCheck failed: preds.Size() % info.labels.Size() == 0 use logloss for binary classificationmlogloss and merror are only used for multi-class classification,nclass >= 1U0amsAMS metric for higgs.preprecision@k for rank.ndcgndcg@k for rank.mapmap@k for rank.Negative log partial likelihood of Cox proportional hazards model.p?@?``AMS must be in format ams@kD:\a\ml-builds\ml-builds\xgboost\src\metric\rank_metric.ccams@metric AMS do not support distributed evaluationCheck failed: !collective::IsDistributed()best-ams-ratio=P?@?O8plabel size predict size not matchmust specify group when constructing rank filegptr.size() != 0UEvalRank: group structure must match number of predictiongptr.back() == preds.Size()PpCheck failed: tparam_%u[-]?8``ppX?@??@?p p?@?80p PY?@?@Cox metric does not support distributed evaluationcox-nloglik$@interval-regression-accuracy*)&$`$P$aft-nloglikAFT metric must be configured first, with distribution type and scaleCheck failed: metric_D:\a\ml-builds\ml-builds\xgboost\src\metric\survival_metric.cundata == labels_upper_bound.Size()@/?@ +@,P$` / /0/p/p/?@ +/P$2222pP/?@ +2P$Unknown probability distributionaft_loss_paramNegative log likelihood of Accelerated Failure Time model.PT?@5@6 9preds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()-q=-DT!@Check failed: quantiles.empty() || quantiles.size() == n_leafD:\a\ml-builds\ml-builds\xgboost\src\objective\adaptive.hCheck failed: tree[nidx].IsLeaf()D:\a\ml-builds\ml-builds\xgboost\src\objective\../common/stats.hbegin_pos <= sorted_pos.size()D:\a\ml-builds\ml-builds\xgboost\src\objective\adaptive.ccnptr.size() > 0n_unique == n_leafk + 1 < h_node_ptr.size()Check failed: h_row_set.empty()Check failed: !position.empty()h_node_ptr.back() <= info.num_row_Check failed: alpha >= 0 && alpha <= 1k >= 0multioutput is not supported by current objective functionD:\a\ml-builds\ml-builds\xgboost\include\xgboost/objective.hNot part of device code. WITH_CUDA: D:\a\ml-builds\ml-builds\xgboost\src\objective\../common/transform.h@p`=)PpP$?0(`?info.labels_lower_bound_.Size() == ndataD:\a\ml-builds\ml-builds\xgboost\src\objective\aft_obj.cuinfo.labels_upper_bound_.Size() == ndataNumber of weights should be equal to number of data points.info.weights_.Size() == ndataUnrecognized distributionsurvival:aftAFT loss functionؗҜ<@@.@.pPY?`?P@p0(?label set cannot be emptyinfo.labels.Size() != 0UD:\a\ml-builds\ml-builds\xgboost\src\objective\hinge.cu, label.size=preds.size=labels are not correctly providedbinary:hingeHinge loss. Expects labels to be in [0,1f]Softmax for multi-class classification, output probability distribution.p``p0(p?  0p ppreds.Size(): num_class: label.Size() * num_class: SoftmaxMultiClassObj: label size and pred size does not match. Check failed: preds.Size() == (static_cast(param_.num_class) * info.labels.Size())D:\a\ml-builds\ml-builds\xgboost\src\objective\multiclass_obj.cuSoftmaxMultiClassObj: label must be in [0, num_class).softmax_multiclass_paramSoftmax for multi-class classification, output class index.Number of output class in the multi-class classification.SoftmaxMultiClassParamObjective candidate: ` Unknown objective function: `D:\a\ml-builds\ml-builds\xgboost\src\objective\objective.ccCheck failed: base_score gradients on CPU.Computing rank:pairwiserank:ndcgrank:map0 /,(),?p0(p)?Pairwise rank objective.(0 /3(0,?p0(p)?LambdaRank with NDCG as objective.0 /p9(@6,?p0(p)?LambdaRank with MAP as objective.lambda_rank_paramD:\a\ml-builds\ml-builds\xgboost\src\objective\rank_obj.cugroup pointer back: labels size: group ponter size: group structure not consistent with #rowsCheck failed: gptr.size() != 0 && gptr.back() == info.labels.Size()8;;;<p < <0<p<p8<<<<pnum_pairsampleNumber of pair generated for each instance.fix_list_weightNormalize the weight of each list by this value, if equals 0, no effect will happenLambdaRankParamffBreg:linear is now deprecated in favor of reg:squarederror.reg:linear`PT ==?p0(p?  0pp  0pp  0ppreg:pseudohubererrorЯЯ p00@pRegression Pseudo Huber error.кpP0.p0(?PP`pPoissonRegression: label must be nonnegativeллppoisson_regression_paramPoisson regression for count data.pPY??pp0(?@@PpMetaInfo::LabelArgsort failed!CoxRegression: labels must be in sorted order, Check failed: last_abs_y <= abs_ysurvival:coxCox regression for censored survival data (negative labels are considered censored).??5p0(?t_v.Shape(1) != 0GammaRegression: label must be positive.reg:gammalabel must be greater than -1 for rmsle so that log(label + 1) can be valid.reg:squaredlogerrorbase_score must be in (0,1) for logistic loss, got: Check failed: base_score > 0.0f && base_score < 1.0fD:\a\ml-builds\ml-builds\xgboost\src\objective\./regression_loss.hlabel must be in [0,1] for logistic regressionreg:logisticbinary:logisticbinary:logitrawGamma regression for severity data.P@P`p0(?TweedieRegression: label must be nonnegativereg:tweedietweedie_regression_paramTweedie regression for insurance data.PY? #?pInvalid shape of labels.D:\a\ml-builds\ml-builds\xgboost\src\objective\regression_obj.cuinfo.weights_.Size() == info.num_row_info.labels.Size() == preds.Size()Sum of weights is close to 0.0, skipping base score estimation.reg:absoluteerrorStringView{get(in["name"])} == StringView{"reg:absoluteerror"}Mean absoluate error.reg_loss_param` 0pMp0(p?Regression with squared error.@`000Qp0(p?Regression with root mean squared logarithmic error.`0pMp0(p?Logistic regression for probability regression task.P`00*p0( p?Logistic regression for binary classification task.`0*p0(p?Logistic regression for classification, output score before logistic transformation.scale_pos_weightScale the weight of positive examples by this factorRegLossParamMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.PoissonRegressionParamtweedie_variance_powerTweedie variance power. Must be between in range [1, 2).TweedieRegressionParamư>PY?`Pout_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupD:\a\ml-builds\ml-builds\xgboost\src\predictor\cpu_predictor.ccInplace predict accepts only DMatrixProxy as input.size_leaf_vector is enforced to 0 so farmodel.param.size_leaf_vector == 0Number of columns in data must equal to trained model.m->NumColumns() == model.learner_model_param->num_featurebase_margin.Shape(1) == ngroupncolumns != 0D:\a\ml-builds\ml-builds\xgboost\src\predictor\../common/threading_utils.h `pMake predictions using CPU.?Check failed: container_.find(m) != container_.cend()D:\a\ml-builds\ml-builds\xgboost\src\predictor\predictor.cc has expired.[Internal error]: DMatrix: Check failed: container_.at(m).ref.lock()Unknown predictor type model.learner_model_param->num_output_group != 0invalid unordered_map key)Invalid shape of base_margin. Expected: (margin.Shape(0) == n_samplesmargin.Shape(1) == n_groupsunordered_map/set too longinvalid hash bucket countWith error: Failed to parse feature interaction constraint: D:\a\ml-builds\ml-builds\xgboost\src\tree\constraints.ccnewsize != 0Found floating point number in interaction constraintsstd::floor(d) == dD:\a\ml-builds\ml-builds\xgboost\src\tree\param.ccUnknown value type for interaction constraint:, Expect '"' but get 'Error atch == '\"'unknown string escape \, Expect '"' but reach end of line , Expect '{' but get 'ch == '{', JSON object expect '}' or ',' 'ch == ',', Expect ':' but get 'ch == ':'Failed to parse graphviz parameters: " edge graph [ {key}="{value}" ] {key}{value}The following parameters for graphviz are not recognized: {nid} [ label="{fname}{<}{cond}" {params}] {<}{params} {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}dotDump graphviz representation of tree Line , around ^`0OOO0OЏpos != std::string::nposD:\a\ml-builds\ml-builds\xgboost\src\tree\tree_model.cc in feature map is numerical but tree node is categorical.Check failed: is_categorical in feature map is categorical but tree node is numerical.Check failed: is_numericalUnknown feature map type. {nid} -> {child} [label="{branch}" color="{color}"] , missingnoyes{child}{color}x 0p@@P`pxppPp` ```Џ{tabs}{nid}:leaf={leaf}{stats},cover={cover}{tabs}{nid}{leaf}{cover}{stats}{nid}:[{fname}] yes={yes},no={no}{fname}{yes}{cond}{left}{right}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{parent}{stat} {left} {right}{stat}{nodes}Dump text representation of treex0000Џ { "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} "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}{indent}{newline}{indent}{nodes}{newline}Dump json representation of treeyes_colorno_colorcondition_node_paramsleaf_node_paramsgraph_attrs@PpD:\a\ml-builds\ml-builds\xgboost\dmlc-core\include\dmlc\./json.hdouble0@ `[ `Ф`I @X Xnum_nodesNumber of features used in tree construction.num_deletedSize of leaf vector, reserved for vector treeD:\a\ml-builds\ml-builds\xgboost\include\xgboost/tree_model.hnumber of nodes in the tree exceed 2^31param.num_nodes < std::numeric_limits::max()@PP?`I `min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)max_cat_to_onehotMaximum number of categories to use one-hot encoding based split.max_cat_thresholdMaximum number of categories considered for split. Used only by partition-basedsplits.min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methodgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsparse_thresholdpercentage threshold for treating a feature as sparsegammaTreeParamTrainParamUnknown Model Builder:{}#0000FFEdge color when meets the node condition.#FF0000Edge color when doesn't meet the node condition.rankdirPassed to graphiz via graph_attr.Conditional node configurationLeaf node configurationAny other extra attributes for graphviz `graph_attr`.GraphvizParamCheck failed: node.IsLeaf()fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam)param.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast(deleted_nodes_.size()) == param.num_deletedparam.num_nodes == static_cast(nodes_.size())param.num_nodes == static_cast(stats_.size())param.deprecated_num_roots == 1Please use JSON/UBJSON for saving models with categorical splits.Check failed: !HasCategoricalSplit()this->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodessize != 0categories_segmentscategories_sizescategories_nodescategoriesloss_changessplit_indicesstatic_cast(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodestree_paramget(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)split_typesum_hessianbase_weightsleft_childrenright_childrenparentssplit_conditionsdefault_leftmean_values->size() > 0U must have zero weightUnique path unique_path[i].pweight == 0j_end - j_begin != 0std::numeric_limits::min() != max_catn_nodes != 0loss_changes.size() == n_nodessum_hessian.size() == n_nodesbase_weights.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesdefault_left.size() == n_nodesn_nodes == split_categories_segments.size()?HgY?@חAUnknown tree updater D:\a\ml-builds\ml-builds\xgboost\src\tree\tree_updater.ccbegin < endi < first_dimension_.size()i < ranges_.size()n_nodes > 0elem_of_each_node_.size() == 0UD:\a\ml-builds\ml-builds\xgboost\src\common\row_set.hn_left == 0n_right == 0n_left + n_right == e.Size()Tree constructor that uses approximate histogram construction for each node.begin + n_left <= e.endbegin + n_left + n_right == e.endn_batches_ >= 1D:\a\ml-builds\ml-builds\xgboost\src\tree\hist/histogram.hlocal_block_ptr != (BlockInfo*)nullptrany_missing == true `[`[p[[p[[\ \pCheck failed: !features.empty()D:\a\ml-builds\ml-builds\xgboost\src\tree\hist/evaluate_splits.hid != kMaxrow_ptr_[nid] == kMaxbase_rowid == gmat.base_rowidCheck failed: tree[node.node_id].IsLeaf()D:\a\ml-builds\ml-builds\xgboost\src\tree\../common/partition_builder.hptr_offset <= row_set.Data()->size()D:\a\ml-builds\ml-builds\xgboost\src\tree\../common/transform.hout_preds.Size() > 0UCheck failed: p_last_treeout_preds.DeviceIdx() == GenericParameter::kCpuIdpart.Size() == n_nodesn_total_bins == page.cut.TotalBins()D:\a\ml-builds\ml-builds\xgboost\src\tree\updater_approx.cc0\\UpdatePredictionCacheout_preds.Size() == data->Info().num_row_BuildHistogramLeafPartitionUpdatePositionEvaluateSplitsf0^` ]kaeaGlobalApproxUpdatertrain_param fPTGradient based sampling is not supported for approx tree method.Check failed: param.sampling_method != TrainParam::kGradientBasedpartition > 0nodes == targeted_hists.size()cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())nthreads >= 1nid < nodes_tid < nthreads_end > beginupper_bound < static_cast(std::numeric_limits::max())D:\a\ml-builds\ml-builds\xgboost\src\tree\common_row_partitioner.h(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()Grow tree with parallelization over columns.жp( @НЛkkkcolmaker_train_paramgrow_colmaker pos=ridx=ridx exceed bound ridx < position_.size()Updater `grow_colmaker` or `exact` tree method doesn't support distributed training.D:\a\ml-builds\ml-builds\xgboost\src\tree\updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support external memory training.exact tree method doesn't support unlimited depth.param_.max_depth > 0fmat.Info().num_row_ == position_.size()gradient-based sampling is only support by GPU Hist.Only uniform sampling is supported, param_.sampling_method == TrainParam::kUniformSupport for `cache_opt' is removed in 1.0.0Check failed: param_.cache_optCheck failed: this->ctx_opt_dense_colEXP Param: speed optimization for dense column.default_directionlearnrightDefault direction choice when encountering a missing valueColMakerTrainParam  "k kTreePrunerprunePrunerUpdateCheck failed: tree[nid].IsLeaf()D:\a\ml-builds\ml-builds\xgboost\src\tree\updater_prune.ccCheck failed: !tree[pid].IsLeaf() pruned nodes, max_depth= extra nodes, tree pruning end, """#pPruner that prune the tree according to statistics.Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1Grow tree using quantized histogram..../p( /0^` ]k,`..exponent <= modD:\a\ml-builds\ml-builds\xgboost\src\tree\./updater_quantile_hist.hQuantile::Builderrow_ptr.size() >= 2D:\a\ml-builds\ml-builds\xgboost\src\tree\updater_quantile_hist.ccExpandTreeUpdateTreeInitSampling@pD:\a\ml-builds\ml-builds\xgboost\src\tree\../common/threading_utils.hRefresher that refreshes the weight and statistics according to data.0Ј0^` ]k`kPrefreshbatch.Size() < std::numeric_limits::max()D:\a\ml-builds\ml-builds\xgboost\src\tree\updater_refresh.ccРp8PY???kkkSyncher that synchronize the tree in all distributed nodes.WSAStartupCould not find a usable version of Winsock.dllgetsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len)ioctlsocket(handle_, FIONBIO, &mode)setsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&tcp_no_delay), sizeof(tcp_no_delay))acceptlisten(handle_, backlog)bind(handle_, handle, sizeof(std::remove_reference_t))getsockname(handle_, reinterpret_cast(&res_addr), &addrlen)gethostname(&buf[0], HOST_NAME_MAX)Poll timeoutD:\a\ml-builds\ml-builds\xgboost\rabit\include\rabit/internal/socket.hFailed to poll.PPp0 NULLAllgatherRing failedAllreduce failedBroadcast failedtoo large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkworker256MBrabit_reduce_bufferDMLC_TASK_IDDMLC_ROLEDMLC_NUM_ATTEMPTDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_WORKER_CONNECT_RETRY%[^=]=%smapred_tip_idmapreduce_task_idhadoop_mode is set but cannot find mapred_task_idrabit_task_idrabit_hadoop_modemapred_task_id%drabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksrabit_world_sizeRabit Module currently only work with dmlc workerD:\a\ml-builds\ml-builds\xgboost\rabit\src\allreduce_base.cccan only call Init oncestartshutdownFailed to shutdown due to%sprint%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}rabit_tracker_urirabit_tracker_portrabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delay] Connecting to (failed): [): [ Retry connecting to IP(retry time: tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic)tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic)sync::Invalid tracker message, init failureReConnectLink failure 3tracker.Send(xgboost::StringView{task_id}) == task_id.size() connected to the trackertask ReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specifiedReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14ReConnectLink failure 15ReConnectLink: bad socketcannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkunknown enum_opunknown data_typeFailed to initialize RABIT.Failed to shutdown RABIT worker.DataType does not support bitwise or operationthe rabit has not been initialized?OOOOi IOGOO_OO.part.splitonly one `#` is allowed in file path for cachefile specificationname_cache.size() == 1UD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io/uri_spec.h for key in arg Invalid uri argument formatCheck failed: std::getline(is, kv.first, '=') for value in arg Check failed: std::getline(is, kv.second)name_args.size() == 1U_^PPn`jiPIOO`l iOO_^PPn`jiPI`l i_^PPn`jiPI@k`l i_^ m  H kPl0@_^Onji0JqpsstdinrbSingleFileSplit: fail to open Check failed: fp_ != NULLD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1InputSplit do not support writeG@# %_*2INo thread@GOOOpG0Lhs^p^ Nl0iiGqFailed to initialize CachedIterCheck failed: this->InitCachedIter()D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io/cached_input_split.hResetPartition is not supported in CachedInputSplit has invalid cache file formatCheck failed: nread == sizeof(size)Check failed: fi_->Read(p->begin, size) == sizeX^^OPo0ki0K@rhdfs://viewfs://Please compile with DMLC_USE_HDFS=1 to use hdfsD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io.ccs3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol invalid input parameter for InputSplit::CreateCheck failed: part < nsplitindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type BeforeFirst is not supportedD:\a\ml-builds\ml-builds\xgboost\dmlc-core\include\dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)Calling Value at beginning or end?Check failed: out_data_ != NULLCheck failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != 0 || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroyMake sure you call BeforeFirst not inconcurrent with Next!Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceCheck failed: produce_end_.load(std::memory_order_acquire)tt`upvpXsst vp@t@t@uPvpX t t u@vpttt0vpX`t`tPu`vp`xOPwPxOOPxxPwPxxxOP xPwPxxx yOOO0LhsxPPx OOO pOp `OOOqid:Check failed: out->label.size() + 1 == out->offset.size()@ 0Lhs ЍP8  xPwPxxx ph 0xPwPxxx pchunk.size != 0UD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data\./text_parser.h@@`pxЦЦp`0px@@`p pOPwPxOOxPwPxxxO xPwPxxxCheck failed: out->field.size() == out->index.size()' as the delimiter to separate fields.Expected '' is not found in the line. Delimiter 'Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()OOO@pOlibfmH pcsvparam_.format == "csv"D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data/csv_parser.hMust have distinct columns for labels and instance weightsCheck failed: param_.label_column != param_.weight_column || param_.label_column < 0param_.format == "libfm"D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data/libfm_parser.hpOpPxOOXгxpPxxOCheck failed: base <= 10 && base >= 2sign == truexpPxxcannot call ParseNextD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data/parser.h0OOOparam_.format == "libsvm"D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data\./libsvm_parser.hpp`OPp`File formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights.LibSVMParserParamLibFMParserParamCSVParserParampOOO OOOH@OOOpOOOOOO0OOOUnknown data type D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data.cclabel.size() + 1 == offset.size()D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\data\./row_block.hoffset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 00;*;@Џ bytesfile do not align by Check failed: files_[i].size % align_bytes == 0D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io\input_split_base.ccCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size()This could due to compiler version, g++-4.9 is needed bad regex Cannot find any files that matches the URI pattern files_.size() != 0U,fileoffset=,fileptr=,end=,begin=curr=]=offset[file offset not calculated correctly()$^.*+?[]|\-{},:=! P@(o H,8 DHLXdp|HHнalnumalnumalphablankblankcntrlcntrldddigitdigitgraphgraphlowerlowerprintpunctpunctspacespacessupperupperwxdigitxdigitCheck failed: begin != endD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io\line_split.ccinvalid record io formatCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0D:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io\recordio_split.cc(reinterpret_cast(begin) & 3UL) == 0U(reinterpret_cast(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Invalid RecordIO FormatCheck failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end(reinterpret_cast(chunk->begin) & 3UL) == 0U(reinterpret_cast(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagicIndexedRecordIOSplitter does not support multiple index filesexpanded_list.size() == 1ulD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io\indexed_recordio_split.ccp`3"2P3 #FileStream.Write incompleteCheck failed: std::fwrite(ptr, 1, size, fp_) == sizeD:\a\ml-builds\ml-builds\xgboost\dmlc-core\src\io\local_filesys.ccCheck failed: !_fseeki64(fp_, pos, SEEK_SET) error: LocalFileSystem.GetPathInfo: /*LocalFileSystem.ListDirectory Invalid character sequence.": LocalFileSystem::Open "Check failed: fname_length > 0stdinstdoutfile://wbrrbCheck failed: allow_null>api-ms-win-core-synch-l1-2-0.dllkernel32.dllSleepConditionVariableCSWakeAllConditionVariable B D" "^d 8*8H"hx ph"<S@"P0"'''(0(0"@'1"@X(p(0(`1"@((((@*h*1"P)()H)p))1"@)1"@))p))2"@)**2"@)1"@)2"@)@2"***+(+(@*h*@2"@*2"@P+h+(+(@*h*2"++ +(,P,((@*h*,(@*h*2" @+P3"@x, ,P,((@*h*,(@*h*2"@P+"0--H-X-"@0-"----X-"@-"(..@.`.-X-"@(."....X-"@.8"0//H/`/X-8"@0///3"@/`"0/080.X-`"@0"0`000"@00103"@03"X101p11((@*h*3"@X14"112 20(4"@1"p2H22280.X-"@p223`"@2"P3(3h333"@P3H"33343H"@3"P4(4h443"@P4"44453"@4"P5(5h553"@P5H4"55563H4"@5p"P6(6h663p"@P6"6666"@6"H7 7`7710"@H7p4"777810p4"@74"X808p88894"@X84"@8889@5"M89P9`9@5"@89@5"8995"999:8::5"@95"@`:x:8::06"M:::06"@:06":;x6"X;0;p;;;<x6"@X;6"@;;;< 7"M8<P<`< 7"@8< 7"8<<h7"<<<=8==h7"@<7"@`=x=8==8"M===8"@=8"=>`8"X>0>p>>>?`8"@X>8"@>>>?8"M8?P?`?8"@8?8"8??89"???@8@@89"@?p9"@`@x@8@@9"M@@@9"@@9"@AHAXA0:"@0A0:"0AAx:"AAAAx:"@A:"HB B`BxBB:"@HB:"@BBB:"BB0"@CCXCpC60"@@C <"CCCCXA <"@C<"@DDXDpD/<"@@D<"DDDD/<"@D ="@EEXEpE/ ="@@E="EEEE/="@E="@FFXFpF/="@@FP>"FFFF/P>"@F>"@GGXGpG/>"@@G?"GGGG/?"@Gp?"@HHXHhHp?"@@H?"HHHHhH?"@H?"8IIPIhIhH?"@8III(@"@IX@"JI J8JIX@"@J@"J`JJJI@"@J@"KJ KPK((@*h*@"@K0A"KxKKK0(0A"@KA" LK8LhL((@*h*A"@ LA"LLLL0A"@LB"8MMPMpML0B"@8M8B"MMMM8B"@MhB"8NNPN`NhB"@8N"NNNN3"@N"0OOHO`O3"@0O"OOOO3"@OH"0PPHP`P3H"@0PB"PPPPMB"@PB"0QQHQ`Q`NB"@0Q"QQQQX-"@Q"0RRHRhRQX-"@0R"RRRRQX-"@R"@SSXShS"@@S("SSSS TThS("@SB"@HT`T TThSPC"@TTThSC"UTU0U/C"@UH"UXUUUUHVhSH"@UD"@V(VUHVhS`D"@pVVHVhSD"VVVWUUHVhSD"@V(E"`W8WxWW(E"@`WXE"WWWX0XXE"@W0:"M0AE"@XXXXE"@XXXE"@ Y8YXF"pYHYYYXXF"@pYXF"YYZ ZXXF"@YF"pZHZZZXF"@pZF"ZZ[ [H[F"@ZG"@p[[H[[[pG"@[G"\[(\@\[G"@\h"\h\\\.X-h"@\@"]\0]X]\.X-@"@]]]H"@]"]]^(^X-"@]H"@x^^P^I"^^^^P^I"@^PI"H_ 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