vendor/x86_64-linux/libxgboost.so in xgb-0.7.0 vs vendor/x86_64-linux/libxgboost.so in xgb-0.7.1

- old
+ new

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valid/__w/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been initialized or has already been disposed.Current DMatrix type does not support set data.binary saving only supported by SimpleDMatrixCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.` is deprecated, please use `XGBoosterSaveModelToBuffer` instead.Unknown field for Booster feature info:DMatrix has not been initialized or has already been disposed.Check failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.Check failed: std::string(schema->format) == "+s"Column data type not supported by XGBoostCheck failed: !schema_->columns.empty()Cannot import record batch without a schemaFeatureMap feature index exceed boundfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()XGBoost distributed mode detected, /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/../common/threading_utils.hInvalid thread limit for OpenMP.static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureCheck failed: ec == std::errc()Stack trace: -_Z +basic_string::basic_string [bt] () %s: __pos (which is %zu) > this->size() (which is %zu)%02d:%02d:%02d] DMLC_LOG_STACK_TRACE_DEPTH%zuUSE_CUDAUSE_NCCLUSE_RMMCheck failed: mCheck failed: p_outjsonubjCheck failed: outInvalid input pointerBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPGCC_VERSIONDEBUGfeature_namefeature_typedeprecatedUnknown format: `iteration_beginiteration_endntree_limittrainingstrict_shapestr.size() >= 3str[0] == '{'str[str.size() - 2] == '}'text+sCheck failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falseimportance_typefeature_mapfeature_namestree_idxidx < names_.size()will split data among workersautocache_prefixlimit >= 1nthreadCheck failed: e%ldUnknown global parameters: { /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCannot reset RecordBatchesIterAdapterCheck failed: at_first_Cannot reset IteratorAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer lengthwrite position exceed fixed buffer size/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.basic_string::appendOnly %d dimensional array is valid.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaDatetimeObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. -Or open an issue.-/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h -Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.htmlÔ1Åÿ43Åÿô1Åÿ43Åÿ43Åÿ43Åÿ2Åÿ43Åÿ42ÅÿT2Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿt2Åÿ”2Åÿ43Åÿ43Åÿ´2Åÿ43Åÿ43ÅÿÔ2Åÿ43Åÿ43Åÿ43Åÿô2Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ43Åÿ3Åÿ,0Åÿmap::at vs. vector::_M_default_appendNot implemented./__w/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hstd::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rowsCheck failed: chunksize == groupsUnknown prediction type:'wÅÿ'wÅÿàtÅÿàtÅÿtÅÿtÅÿ0uÅÿ/__w/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from gradient_boosterlearnergblinearnum_parallel_treegbtree_model_paramstoiUnknown booster:Invalid missing value: typestrMissing `version' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.Missing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4`typestr' should be of format <endian><type><size of type in bytes>.Missing `shape' field for array interfaceMissing `data' field for array interfaceArgument `` is required for `Empty data passed in.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)Empty data with non-zero shape.maskj_shape.size() == 1type_length == 1stridesstrides.size() == 1Memory should be contiguous.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)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.get<Integer>(strides.at(0)) == type_lengthcache_idCache ID is not supported yetCheck failed: p_predtget<Integer const>(config["cache_id"]) == 0/__w/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast<int>(names_.size())unknown feature type, use i for indicator and q for quantityfloatcustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.feature_map.Size() == n_features%luj_strides.size() == j_shape.size()stride and shape don't match.i < DCheck failed: array.find("mask") == array.cend()Masked array is not yet supported.stream%Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿð%Äÿ&Äÿ&Äÿà%Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&ÄÿÐ%Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&ÄÿÀ%Äÿ&Äÿ&Äÿ°%Äÿ %Äÿ&Äÿ%Äÿ&Äÿ&Äÿ€%Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿ&Äÿx%ÄÿXGBoosterFeatureScoreXGBoosterGetModelRawXGBoosterSaveModelToBufferInplacePredictImplInplacePredictImplXGBoosterPredictFromDMatrixXGDMatrixCreateFromCallbackCheck failed: m == 1 || n == 1n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.get<Array const>(array).size() == 1Column: vector::_M_range_insertCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from St19__shared_ptr_accessIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN4dmlc5ErrorEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEEN7xgboost4data12ArrayAdapterEN4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEEN7xgboost4data15CSRArrayAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEEN7xgboost4data16DataTableAdapterEN4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEEN7xgboost4data18ArrowColumnarBatchEN4dmlc8DataIterISt6vectorISt10unique_ptrIN7xgboost4data18ArrowColumnarBatchESt14default_deleteIS5_EESaIS8_EEEEN7xgboost4data24RecordBatchesIterAdapterEN7xgboost4data12DMatrixProxyEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryIjEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEEN4dmlc9parameter10FieldEntryImEEN7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EESt15_Sp_counted_ptrIPN7xgboost7DMatrixELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DMatrixProxyELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt10shared_ptrIN7xgboost4data12ArrayAdapterEESt15_Sp_counted_ptrIPN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt10shared_ptrIN7xgboost4data15CSRArrayAdapterEEÀ?0Cð?àCÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ À@ˆj€„Є×eÍ _ èvH¢”@åœ0ļ4&õk€à7yà؅W4ÈNgmÁ=‘`äX@Œµx¯PïâÖä’ÕMÏð€öJáÇ- ´ÙyCx”(,*‹¹42·ô­ç„þäqÙ0/ç'|!×æúà1Û錠9Y>R$°ˆï³nµµ¸`œÉF"ã¦x|Øê›Ð+‚MÇraB¶â yÏùdiWC¸±¡*ÓÎfJœô‡‚¿¤\Ãñ)c÷æ7ú]µ` àÄxõãxÈöÖ²K}ÏYÆïqž\Cð·k Æ3Tì¥gffffff¹…ëQ¸ú~j¼t“*ËǺ¸"<Ú8b->Nc{-è½1Ò+¯¦üµx#Œ¸c^-¥µ ú‚K®nïuö7ßX%Y^ø_G„zK`æeq ]šp ÁæJ¨á&¡g…Õ¹çë€ o"ö¥¬4£%µ‘Q½Ãéêtt—œ«´íSò`؈$C(çàÓ¦ƒí¸f¹×HŽR”¬Ðl Û©#j¦ðÕ+vŸv=´¼ïIJ+10ó¼¦óQ”¯=ìÚѬ©¿—Vâ§ðºÿèZb™u¦Ì)jõˆ}ìÖ‡î]:1VÓ¾äa'½ƒþmi¥—œKË$!Q¬ã¢¢Pç@9žjç>ÎÜ`KUìËqçÕ݉ [¾q‰üBø1ŽÊh?“(ØÒÔSÌuÙYQ!†à"G®ÚMk€µ9X ‰3‘ŽóîDÛ¸N ö‹â“ØÖÄ<±C­¼.µ’ž{ýÒ$ÄÛ~üþÛƒ6ÿÉ/“lŠ#˜©ó¨#Õ‚F!00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899€/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.ccvector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()basic_string::substrfile://Opening pointer_ <= buffer_.size()/__w/ml-builds/ml-builds/xgboost/src/common/io.cc/__w/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamE/__w/ml-builds/ml-builds/xgboost/src/common/json.cc can not be indexed by Integer. can not be indexed by string./__w/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hHigh precision number is not supported.Check failed: ret.ec == std::errc()` is not supported for typed array.NumberNullF32ArrayU8ArrayI32ArrayI64ArrayObject of type , around character position: , "beg <= size_\n\0Unknown construct Unknown escapeExpecting null value "null"Expecting digitf64 is not supported.\\\"\b\f\r\t\u%04xch != -1cursor_.Pos(): raw_str_.size():X1Èÿp1Èÿˆ1Èÿ 1Èÿ¸1ÈÿÐ1Èÿè1Èÿ2Èÿ2Èÿ(1Èÿ@1ÈÿÌŒÈÿ¤ÈÿÈÿlŒÈÿÈÿ,ŽÈÿlŽÈÿÈÿÈÿŒŽÈÿÈÿÈÿÈÿÈÿÈÿÈÿ¬ŽÈÿlŒÈÿüÈÿÈÿÈÿÈÿÈÿlÈÿœÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿ´ÈÿÈÿÈÿÈÿÈÿÌŒÈÿÈÿÈÿÜÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿÈÿ„Èÿvector::_M_realloc_insertExpecting: "", got: "EOF"\0"N7xgboost5ValueEN7xgboost10JsonStringEN7xgboost9JsonArrayEN7xgboost10JsonObjectEN7xgboost10JsonNumberEN7xgboost11JsonIntegerEN7xgboost8JsonNullEN7xgboost11JsonBooleanEN7xgboost10JsonReaderEN7xgboost10JsonWriterEN7xgboost9UBJReaderEN7xgboost9UBJWriterEN7xgboost14JsonTypedArrayIfLNS_5Value9ValueKindE7EEEN7xgboost14JsonTypedArrayIhLNS_5Value9ValueKindE8EEEN7xgboost14JsonTypedArrayIiLNS_5Value9ValueKindE9EEEN7xgboost14JsonTypedArrayIlLNS_5Value9ValueKindE10EEEÿÿÿÿÿ€€ÿvalue for Parameter exceed bound [ should be greater equal to should be smaller equal to Required parameter is not presentedInvalid Parameter format for expect but value=' optional, default=, required/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.hNo conversion could be performed/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hSome trailing characters could not be parsed: '*p == ')'Invalid NAN literalOut of range valuepos <= value.length()Out of range value for , value='key has already been registered in N4dmlc10ParamErrorE®Åt ¼¾LÊZÂSg€?ÜÇY@™v–? A€PesudoHuberParamThe delta term in Pseudo-Huber l/__w/ml-builds/ml-builds/xgboost/src/common/quantile.cc/__w/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than Maximum cateogry should not be lesser than the total number of categories./__w/ml-builds/ml-builds/xgboost/src/common/threading_utils.hhessian.size() == info.num_row_group_ptr.back() == hessian.size()weights.size() == info.num_row_Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps))max_cat + 1 >= n_categoriesfeature_ptr.front() == 0rank_size == totaln_threads >= 1group_ptr.size() >= 2group_ptr.back() == n_samplesinvalid init parameter/__w/ml-builds/ml-builds/xgboost/src/common/quantile.hbase_rowid < group_ptr.back()Row: is not found in any group.sketch->temp.size < max_size, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=invalid maximum size max_size=src.size == 0this->size == 0mingap=, maxgap=, wgap=bug in combineCheck failed: size <= sa.size + sb.size check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=] rmin=, rmax=, wmin=, v=Check failed: nbig < nquantile: too many large chunkCheck failed: worker_feature.data()count <= total_entriesn_threads_ >= 1sketches_.size() == n_columnsCheck failed: a.dataCheck failed: a.data && reduced[fidx].dataworker_segments.size() >= 1sketch->temp.size <= max_size, stemp.size=Finalize: invalid maximum size, max_size=n_columns == sketches_.size()num_cuts.size() == 0Number of columns differs across workerscuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max()cut_size > cuts->cut_ptrs_.HostVector().back()columns_size_.size() != 0Check failed: reduced[i].dataPushColPageHostSketchContainerMakeCutsPushRowPageAllReduceSketchContainerImplMakeCutsPushRowPageAllReduceSketchContainerImpl€Ko;oƒ:A€¿_ð¿€µøä>/__w/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/__w/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0¬ùÌÿôùÌÿìøÌÿÜùÌÿúÌÿSt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN7xgboost16HostDeviceVectorIjEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE½7†5€/Invalid Input: '', valid values are: /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.henum_back_map_.count(value) != 0UValue not found in enum declared/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././strtonum.hEnum exisit!) -Enums: ), N4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEEaft_loss_distribChoice of distribution for the noise term in Accelerated FailureScaling factor used to scale the distribution in Accelerated Fai/sys/fs/cgroup/cpu/cpu.cfs_quota_us/sys/fs/cgroup/cpu/cpu.cfs_period_usTimer for calls @ us======== Monitor ( ======== did not get stopped properly.€„.A/__w/ml-builds/ml-builds/xgboost/src/common/time/__w/ml-builds/ml-builds/xgboost/src/common/version.ccfi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)Invaid version format in loaded JSON object: ÿÿÿÿÿÿÿÿÿÿÿÿIncorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it agai/__w/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Unknown data type for getting meta info.All feature_types must be one of {int, float, i, q, c}./__w/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.hself_offset.size() == other_offset.size() must be equal to number of columns.group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.Size of weights must equal to number of rows.Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification.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=csvCheck failed: array.valid.Size() == 0Meta info like label or weight can not have missing value.this->num_col_ == that.num_col_Number of columns must be consistent across batches.transpose.offset.Size() == num_columns + 1Check failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.this->labels.Size() % this->num_row_ == 0Label contains NaN, infinity or a value too large.`qid` must be sorted in non-decreasing order along with data.Weights must be positive values.Feature weight must be greater than 0.Check failed: strm->Read(&name)Check failed: strm->Read(&type_val)Check failed: type == expected_typeCheck failed: strm->Read(&is_scalar)Check failed: strm->Read(field)Check failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)Number of columns is expected to be 1.Check failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field)Binary DMatrix generated by XGBoost: Please process and save your data in current version: Check failed: fi->Read(&num_field)MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains MetaInfo: the given binary file contains extra fields which will be ignored.¨=ÎÿÀ=Îÿà=Îÿ>Îÿ>Îÿ8>ÎÿP>Îÿp>Îÿˆ>Îÿ=Îÿ€=ÎÿÌ@Îÿä@ÎÿAÎÿ$AÎÿ<AÎÿ\AÎÿtAÎÿ”AÎÿ¬AÎÿ$@Îÿ¤@ÎÿðCÎÿDÎÿ(DÎÿHDÎÿ`DÎÿ€DÎÿ˜DÎÿ¸DÎÿÐDÎÿHCÎÿÈCÎÿ GÎÿ$GÎÿ<GÎÿ\GÎÿtGÎÿ”GÎÿ¬GÎÿÌGÎÿìGÎÿ|FÎÿìFÎÿIÎÿ0IÎÿPIÎÿ€IÎÿ IÎÿÐIÎÿøIÎÿ JÎÿ@JÎÿ°HÎÿàHÎÿD`Îÿ$`Îÿä_ÎÿÌ_Îÿt_Îÿô^ÎÿT_Îÿ”_Îÿ¬_Îÿô^ÎÿT_ÎÿhbÎÿHbÎÿbÎÿèaÎÿˆaÎÿaÎÿhaÎÿ¨aÎÿÈaÎÿaÎÿhaÎÿ\dÎÿ<dÎÿücÎÿäcÎÿŒcÎÿ cÎÿlcÎÿ¬cÎÿÄcÎÿ cÎÿlcÎÿ€fÎÿ`fÎÿ fÎÿfÎÿ eÎÿeÎÿ€eÎÿÀeÎÿàeÎÿeÎÿ€eÎÿthÎÿThÎÿhÎÿügÎÿ¤gÎÿ$gÎÿ„gÎÿÄgÎÿÜgÎÿ$gÎÿ„gÎÿ˜jÎÿxjÎÿ8jÎÿjÎÿ¸iÎÿ0iÎÿ˜iÎÿØiÎÿøiÎÿ0iÎÿ˜iÎÿœlÎÿ„mÎÿ<mÎÿmÎÿülÎÿ,lÎÿ„lÎÿ¼lÎÿÜlÎÿ,lÎÿ„lÎÿ€nÎÿPoÎÿoÎÿønÎÿØnÎÿ nÎÿhnÎÿ nÎÿ¸nÎÿ nÎÿhnÎÿ„qÎÿ\qÎÿ4qÎÿ qÎÿäpÎÿÄpÎÿ¤pÎÿ„pÎÿ\pÎÿôoÎÿ¤qÎÿ(tÎÿtÎÿØsÎÿ°sÎÿˆsÎÿhsÎÿHsÎÿ(sÎÿsÎÿrÎÿHtÎÿÌvÎÿ¤vÎÿ|vÎÿTvÎÿ,vÎÿ vÎÿìuÎÿÌuÎÿ¬uÎÿ4uÎÿìvÎÿPyÎÿ(yÎÿyÎÿØxÎÿ°xÎÿxÎÿpxÎÿPxÎÿ(xÎÿÀwÎÿpyÎÿÔ{Îÿ¬{Îÿ„{Îÿ\{Îÿ4{Îÿ{ÎÿôzÎÿÔzÎÿ¬zÎÿDzÎÿô{Îÿx~ÎÿP~Îÿ(~Îÿ~ÎÿØ}Îÿ¸}Îÿ˜}Îÿx}ÎÿX}Îÿà|Îÿ˜~Îÿì„ÎÿÌ„Îÿ¤„Îÿ„„Îÿ\„Îÿ<„Îÿ„ÎÿüƒÎÿ܃Îÿ|ƒÎÿ …ÎÿP‡Îÿ(‡Îÿ‡Îÿ؆Îÿ°†Îÿ†Îÿp†ÎÿP†Îÿ0†Îÿà…Îÿp‡Îÿ” Ïÿ´ ÏÿÔ Ïÿü Ïÿ$ -ÏÿD -Ïÿd -Ïÿ„ -Ïÿ¤ -ÏÿìÏÿd Ïÿø Ïÿ Ïÿ8 Ïÿ` Ïÿˆ Ïÿ¨ ÏÿÈ Ïÿè ÏÿÏÿP ÏÿÈ ÏÿlÏÿŒÏÿ¬ÏÿÔÏÿüÏÿ$ÏÿLÏÿlÏÿŒÏÿ¼Ïÿ<ÏÿÏÿ0ÏÿPÏÿxÏÿ ÏÿÀÏÿàÏÿÏÿ ÏÿxÏÿèÏÿ Device that data is on: `gpu_id` for XGBoost: num_rownum_colnum_nonzerolabelsgroup_ptrlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightslabel_lower_boundlabel_upper_boundUnknown float field name: Unknown uint32 field name: matrix with entries loaded from self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()size == this->num_col_Length of Unknown feature info name: group_ptr_.back() == num_row_weights_.Size() == num_row_labels.Shape(0) == num_row_Unknown feature info: .rXGBOOST_TEST_NPARTLoad part of data parts.group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from csvlibsvm . Encountered parser error: -that.group_ptr_.size() != 0Check failed: t.CContiguous()Incorrect size for labels.Check failed: non_decUnknown key for MetaInfo: Unknown data typeMetaInfo: Invalid format for name == expected_name Expected field: , got: Expected field of type: got field type: Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: !is_scalar to be a vector; got a scalarshape.second == 1 to be a tensor; got a scalar is no longer supported. again.major == 1minor >= 6MetaInfo: invalid formatnum_field >= expectedfields.)/__w/ml-builds/ml-builds/xgboost/src/data/../data/simple_batch_iterator.hCheck failed: page_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: page_/__w/ml-builds/ml-builds/xgboost/src/data/../data/iterative_device_dmatrix.hSlicing DMatrix is not supported for Device DMatrix./__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()/__w/ml-builds/ml-builds/xgboost/src/data/../common/common.hfloat32float64bool8int32int8int16int64Unknown data table type./__w/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h¬ÜÏÿŒÝÏÿÝÏÿŒÜÏÿlÜÏÿLÜÏÿ¼ÝÏÿCheck failed: valid_query_group/__w/ml-builds/ml-builds/xgboost/src/data/validation.hInvalid group structure.Check failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.std::future_error: /__w/ml-builds/ml-builds/xgboost/src/data/../data/array_interface.h¼ûÏÿÔûÏÿôûÏÿüÏÿDüÏÿlüÏÿŒüÏÿ´üÏÿÜüÏÿtûÏÿ”ûÏÿ/__w/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hthis->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification.shape[i] == r.Shape(i)<Check failed: parser_qidkey >= builder_base_row_offset¸hÐÿiÐÿ`iÐÿˆiÐÿ¸iÐÿÐgÐÿðiÐÿäyÐÿ$zÐÿdzÐÿ¼zÐÿÔzÐÿôzÐÿÌyÐÿ4{ÐÿL{Ðÿ{ÐÿÌyÐÿH|Ðÿh|Ðÿ|Ðÿ¸|ÐÿØ|Ðÿ}Ðÿ(}ÐÿP}Ðÿp}ÐÿÐ{Ðÿ|Ðÿ¼‚Ðÿ¤‚ÐÿL‚Ðÿ<‚Ðÿ,‚Ðÿ‚ÐÿÜÐÿüÐÿÜÐÿ ‚ÐÿÜÐÿÀÐÿ Ðÿ`ÐÿHÐÿ0Ðÿ ÐÿÐÿð€ÐÿØ€ÐÿØÐÿ€ÐÿÔˆÐÿôˆÐÿT‰Ðÿ,‰ÐÿD‰Ðÿ‰ÐÿÄ…Ðÿ´‰ÐÿÔ‰ÐÿĉÐÿÄ…Ðÿ`ˆÐÿ@ˆÐÿ(ˆÐÿˆÐÿà‡ÐÿÀ‡Ðÿ ‡Ðÿ€‡Ðÿ`‡Ðÿè…ÐÿxˆÐÿformat.front() == '.'/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type fi->Tell() == offsetRead a page in seconds.Check failed: fmt->Read(page.get(), fi.get())Check failed: !cache_info_->written MB written in /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././any.hCheck failed: type_ != nullptrCheck failed: *(type_->ptype_info) == typeid(T)The any container is empty requested=The stored type mismatch stored=Check failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.Check failed: validInput data contains `inf` or `nan`/__w/ml-builds/ml-builds/xgboost/src/data/../data/proxy_dmatrix.hUnknown type: Check failed: proxy_count_ == n_batches_this->count_ == this->n_batches_source_->Iter() == this->count_this->count_ >= 1”;Ñÿ¬;ÑÿÄ;Ñÿü;ÑÿL;Ñÿ;Ñÿ4;Ñÿ|;Ñÿd;Ñÿ;Ñÿ4;Ñÿ`<Ñÿx<Ñÿ˜<Ñÿ¸<ÑÿØ<Ñÿø<Ñÿ=Ñÿ8=ÑÿX=Ñÿ<Ñÿ8<ÑÿN7xgboost7DMatrixEN7xgboost4data11FileAdapterEN7xgboost4data22IterativeDeviceDMatrixEN7xgboost17BatchIteratorImplINS_11EllpackPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEENSt13__future_base13_State_baseV2ENSt13__future_base21_Async_state_commonV2EN7xgboost4data20SparsePageSourceImplINS_11EllpackPageEEEN7xgboost4data18PageSourceIncMixInINS_11EllpackPageEEEN7xgboost4data17EllpackPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt23_Sp_counted_ptr_inplaceIN7xgboost10SparsePageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEEÿÿÿÿÿÿïP?eÍÍAø/__w/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.cc/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.hcurent_bin_size == common::kUint32BinsTypeSizeCheck failed: columns_ibegin + inst.size() == iendcut.Values().size() > 0Urow_ptr.size() == 0Check failed: !sorted_sketch/__w/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.hbatch_size < offset_vec.size()vector<bool>::_M_fill_insertgmat_bin_size == kUint32BinsTypeSizebins_type_size_ == kUint32BinsTypeSize/__w/ml-builds/ml-builds/xgboost/src/data/../common/quantile.hSortedSketchContainerSt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_10SparsePageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13SortedCSCPageEEELN9__gnu_cxx12_Lock_policyE2EE@Raw GHistIndex binary data format./__w/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizeN7xgboost4data16SparsePageFormatINS_16GHistIndexMatrixEEEN7xgboost4data19GHistIndexRawFormatE*N7xgboost4dataUlvE_E/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././any.h/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.hN7xgboost17BatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost4data20SparsePageSourceImplINS_16GHistIndexMatrixEEEN7xgboost4data18PageSourceIncMixInINS_16GHistIndexMatrixEEEN7xgboost4data23GradientIndexPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost16GHistIndexMatrixELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost16GHistIndexMatrixESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device./__w/ml-builds/ml-builds/xgboost/src/data/array_interface.h˜ÓÿÓÿÄÓÿÓÿÓÿÓÿIÓÿÓÿdÓÿðÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿ¼ÓÿÓÿÓÿèÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿÓÿ@ÓÿlÓÿ/__w/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: r != BatchParam{}Batch parameter is not initialized.Generating new Gradient Index.batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid format, magic number mismatchoffset_vec.back() == info_.num_nonzero_param.gpu_id >= 0param.max_bin >= 2param.gpu_id == -1invalid input file formattmagic == kMagic/__w/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_Column is empty/__w/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the column¤ÔÿÌÔÿDÔÿŒÔÿÔÔÿ$ÔÿtÔÿÄÔÿÔÿdÔÿ´ÔÿCheck failed: (std::is_same<AdapterT, CSCAdapter>::value)Expecting CSCAdapterN7xgboost4data13SimpleDMatrixEN7xgboost4data6ColumnEN7xgboost4data15PrimitiveColumnIaEEN7xgboost4data15PrimitiveColumnIhEEN7xgboost4data15PrimitiveColumnIsEEN7xgboost4data15PrimitiveColumnItEEN7xgboost4data15PrimitiveColumnIiEEN7xgboost4data15PrimitiveColumnIjEEN7xgboost4data15PrimitiveColumnIlEEN7xgboost4data15PrimitiveColumnImEEN7xgboost4data15PrimitiveColumnIfEEN7xgboost4data15PrimitiveColumnIdEEN7xgboost17BatchIteratorImplINS_10SparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEEN7xgboost17BatchIteratorImplINS_7CSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEEN7xgboost17BatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_16GHistIndexMatrixEEESt15_Sp_counted_ptrIPN7xgboost7CSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost13SortedCSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost11EllpackPageELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIaEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIhEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIsEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnItEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIiEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIjEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIlEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnImEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIfEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIdEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_7CSCPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_16GHistIndexMatrixEEELN9__gnu_cxx12_Lock_policyE2EEÿÿÿÿÿÿÿÿ€G€G€G€G/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_Check failed: ghist_index_source_.row.pageCheck failed: proxy_handleInvalid proxy handle.iter_.Next() == 1Must have at least 1 batch.DMatrix-rinfo_.num_col_ != 0this->Info().num_col_ != 0.sorted.col.page.gradient_index.pagecuts.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory./__w/ml-builds/ml-builds/xgboost/src/data/./../common/common.hCouldn't remove external memory cache file ; you may want to remove it manually/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././././any.h/__w/ml-builds/ml-builds/xgboost/src/data/./proxy_dmatrix.hCheck failed: kv.secondthis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size()Make cache:/__w/ml-builds/ml-builds/xgboost/src/data/./../common/quantile.hSortedSketchContainerN7xgboost4data20SparsePageSourceImplINS_10SparsePageEEEN7xgboost4data16SparsePageSourceEN7xgboost4data20SparsePageSourceImplINS_7CSCPageEEEN7xgboost4data18PageSourceIncMixInINS_7CSCPageEEEN7xgboost4data13CSCPageSourceEN7xgboost4data20SparsePageSourceImplINS_13SortedCSCPageEEEN7xgboost4data18PageSourceIncMixInINS_13SortedCSCPageEEEN7xgboost4data19SortedCSCPageSourceEN7xgboost4data17SparsePageDMatrixENSt13__future_base7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENSt13__future_base7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost4data5CacheELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data23GradientIndexPageSourceELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data16SparsePageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data13CSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data19SortedCSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost7CSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13SortedCSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccCheck failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()Raw binary data format.page->offset.Size() != 0UInvalid SparsePage filen_bytes == (page->data).Size() * sizeof(Entry)N7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_Egpu_coord_descentLinear booster does not support prediction range.Linear booster, implement generalized linear model.layer_begin == 0GBLinearTrainParam/__w/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster.Inplace predict is not supported by current booster./__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccgblinear does not support prediction of leaf indexmodel/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(&param_, sizeof(param_)) == sizeof(param_)DoBoostget<String>(in["name"]) == "gblinear"/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hCheck failed: !model_.weight.empty()gblinear doesn't support number of trees for feature importance.gblinear only has `weight` defined for feature importance.Model is not initializedCheck failed: trees.empty()importance_type == "weight"PredictBatchInternalbase_margin.Size() == nsize * ngroupPredictBatch { "bias": [ ], "weight": [ ] }bias: +tâëšH³ÐH»è O¾ÿH‹|$èc5¾ÿH‹»`H;»h…-þÿÿL‹ƒxI‹@øL‹¸èL‹°ðèq¾ÿL‹‹xM‹YøMQøL‰“xI“øL‰›hIÃàL‰›`H‰“péäýÿÿL‰âH‰ï踡¾ÿH‹|$é-ýÿÿH‹5Ç,,L‰ÿÿÐI‰Áé±ûÿÿH‰ÅH‹»H9<$tè•p¾ÿH‹»H…ÿtèäd¾ÿH‹»ÐHÃàH9ßtèlp¾ÿH‰ïèä°¾ÿH‰ÅëÛH‰ÃL‰ç蓾ÿH‰ßèÌ°¾ÿL‰çH‰Åè¡—¾ÿL‰ÿè™—¾ÿHƒ»0tˆH‹³xL‹»XLnM9ýv#I‹?IƒÇè 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‡­Iƒü…3A¶ˆUPL‰eHBÆ H‹kHH‹EL}L‰{HH¸àH‰C8H‰C0H‹D$(dH3%(H‰{@…^HƒÄ8[]A\A]A^A_ÃDH‹s(I‰îI)öIÁþMnIƒÆO6L9‡åH…Ò„ÌHlH¾ÿÿÿÿÿÿÿH9õ‡ H<íè¾ÿL‹SHH‹s(I‰éM)ñI‰ÇIÑéIRN4ÈH9òt H)òL‰÷è»M¾ÿH‹;IÁåèß¾ÿL‰;H‰kM‹Kl.øL‰s(H‰kHL‰[IÃàL‰[ H‹UH‰S8HÂàH‰S@éÚýÿÿHt$1ÒH‰ïèQ[¾ÿL‹D$H‰EL‰EL‰êL‰þH‰Ç覾ÿL‹l$H‹Eéþÿÿ„L)òHƒÅIÁåHÑêL4ÑH‰êH)òL9ö†ßH9õ„^ÿÿÿL‰÷èþL¾ÿéQÿÿÿf„Ht$ H}@1ÒèÐZ¾ÿH‹L$ H‰E@H‰MPL‰âL‰öH‰Çè%¾ÿL‹d$ H‹E@é+þÿÿ€Ht$H} 1ÒèZ¾ÿL‹T$H‰E L‰U0L‰êL‰öH‰Çèå ¾ÿL‹l$H‹E éŸýÿÿ€M…í„?ýÿÿéÿÿÿfM…ä„Ëýÿÿë‚DM…íL‰ø„lýÿÿë¯f½éBþÿÿfDH9õ„þÿÿL‰ïH)×L÷èL¾ÿélþÿÿH=è(¾ÿH‰Çèpì½ÿH‹[HH‹{è#¾ÿèN?¾ÿèI#¾ÿè4é½ÿH=]èø¾ÿI‰ÅH‹}H9|$tèõ¾ÿL‰èë¸H=9èÔ¾ÿH‹} I‰ÅI9ÿtÓèÓ¾ÿëÌI‰Æè9L¾ÿL‰÷èAX¾ÿHƒìHƒÄÃbasic_string::_M_construct null not valid/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support./__w/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been initialized or has already been disposed.Current DMatrix type does not support set data.binary saving only supported by SimpleDMatrixCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.` is deprecated, please use `XGBoosterSaveModelToBuffer` instead.p_m->Info().num_col_ <= std::numeric_limits<unsigned>::max()/__w/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/../common/threading_utils.hUnknown field for Booster feature info:DMatrix has not been initialized or has already been disposed.Check failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.get<Integer const>(config["cache_id"]) == 0Invalid input type for inplace predict.Check failed: std::string(schema->format) == "+s"Column data type not supported by XGBoostCheck failed: !schema_->columns.empty()Cannot import record batch without a schemaFeatureMap feature index exceed boundfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size() is deprecated. Use `XGQuantileDMatrixCreateFromCallback` instead.XGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workersInvalid thread limit for OpenMP.static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureCheck failed: array.find("mask") == array.cend()Masked array is not yet supported.Check failed: ec == std::errc()Stack trace: +_Z +basic_string::basic_string [bt] () %s: __pos (which is %zu) > this->size() (which is %zu)%02d:%02d:%02d] DMLC_LOG_STACK_TRACE_DEPTH%zuUSE_CUDAUSE_NCCLUSE_RMMInvalid pointer argument: c_interface_strCheck failed: mfnameout_lenout_dptrjson_parametersbufCheck failed: p_outname_strjsonubjBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPGCC_VERSIONDEBUGUSE_FEDERATEDout_strout_indptrout_indicesout_dataCheck failed: page.pagen_threads >= 1dtraingradhessfeature_namefeature_typeout_resultdeprecatedUnknown format: `out_featuresdmatsevnamesc_json_configiteration_beginiteration_endntree_limittrainingstrict_shapeout_dimout_shapecache_idCache ID is not supported yetCheck failed: p_predtCheck failed: proxyarray_interfacestr.size() >= 3str[0] == '{'fmaptextftype+sCheck failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falseimportance_typefeature_mapfeature_namestree_idxidx < names_.size()out_n_featuresout_scoresautocache_prefixlimit >= 1nthreadnextresetCheck failed: _refInvalid handle to ref.max_binnbatchCheck failed: ejson_str%ldUnknown global parameters: { /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCannot reset RecordBatchesIterAdapterCheck failed: at_first_Cannot reset IteratorAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer lengthwrite position exceed fixed buffer sizebasic_string::appendOnly %d dimensional array is valid.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaDatetimeObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. +Or open an issue.-/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h +Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.htmlœáÀÿüâÀÿ¼áÀÿüâÀÿüâÀÿüâÀÿÜáÀÿüâÀÿüáÀÿâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿ<âÀÿ\âÀÿüâÀÿüâÀÿ|âÀÿüâÀÿüâÀÿœâÀÿüâÀÿüâÀÿüâÀÿ¼âÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿüâÀÿÜâÀÿôßÀÿmap::at/__w/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hIncorrect type for: ``, expecting: ``, got: ``. vs. vector::_M_default_appendCheck failed: chunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast<bst_ulong>(1), std::multiplies<>{}) == chunksize * rowss+Áÿs+Áÿ )Áÿ )Áÿ¼(Áÿ¼(Áÿ\)ÁÿNot implemented.Check failed: pp_mInvalid DMatrix handleCheck failed: p_m/__w/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from gradient_boosterlearnergblinearnum_parallel_treegbtree_model_paramstoiUnknown booster:Invalid missing value: Argument `missing` is required.Argument `` is required for `Missing `version' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.Missing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4`typestr' should be of format <endian><type><size of type in bytes>.Missing `shape' field for array interfaceMissing `data' field for array interfacetypestrEmpty data passed in.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)Empty data with non-zero shape.maskj_shape.size() == 1type_length == 1stridesstrides.size() == 1Memory should be contiguous.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)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.get<Integer>(strides.at(0)) == type_length/__w/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast<int>(names_.size())unknown feature type, use i for indicator and q for quantityfloatcustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.feature_map.Size() == n_features%luout_modelsj_strides.size() == j_shape.size()stride and shape don't match.i < Dð¿¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿÐÀ¿ÿàÀ¿ÿàÀ¿ÿÀÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿ°À¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿ À¿ÿàÀ¿ÿàÀ¿ÿÀ¿ÿ€À¿ÿàÀ¿ÿpÀ¿ÿàÀ¿ÿàÀ¿ÿ`À¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿàÀ¿ÿXÀ¿ÿXGBoosterFeatureScoreXGBoosterGetModelRawXGBoosterSaveModelToBufferInplacePredictImplXGBoosterPredictFromDMatrixXGDMatrixCreateFromArrowCallbackXGDeviceQuantileDMatrixCreateFromCallbackXGDMatrixCreateFromCallbackCheck failed: m == 1 || n == 1n_bits == nstreamShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.get<Array const>(array).size() == 1Column: vector::_M_range_insertCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN4dmlc5ErrorEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEEN7xgboost4data12ArrayAdapterEN4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEEN7xgboost4data15CSRArrayAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEEN7xgboost4data16DataTableAdapterEN4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEEN7xgboost4data18ArrowColumnarBatchEN4dmlc8DataIterISt6vectorISt10unique_ptrIN7xgboost4data18ArrowColumnarBatchESt14default_deleteIS5_EESaIS8_EEEEN7xgboost4data24RecordBatchesIterAdapterEN7xgboost4data12DMatrixProxyEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryIjEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEEN4dmlc9parameter10FieldEntryImEEN7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EESt15_Sp_counted_ptrIPN7xgboost7DMatrixELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DMatrixProxyELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13ExtSparsePageEEELN9__gnu_cxx12_Lock_policyE2EEÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ øÀ?0Cð?àCÀ/__w/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hUnknown allreduce operationUnknown data type¼ŠÂÿ ‹Âÿ\‹Âÿ¬‹Âÿü‹ÂÿLŒÂÿŠÂÿlŠÂÿ=1=0Failed to initialize RabitWorld size is less than 1.Rank is less than 0./__w/ml-builds/ml-builds/xgboost/src/collective/communicator.h is greater than world_size - 1: XGBOOST_COMMUNICATORrabitfederatedUnknown communicator type /__w/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support.N7xgboost10collective12CommunicatorEN7xgboost10collective16NoOpCommunicatorEN7xgboost10collective17RabitCommunicatorEXGBOOST_COMMUNICxgboost_communic/__w/ml-builds/ml-builds/xgboost/src/collective/socket.ccgetsockopt(handle_, SOL_SOCKET, SO_DOMAIN, reinterpret_cast<char *>(&domain), &len)static_cast<std::int32_t>(socket.Domain()) == static_cast<std::int32_t>(addr.Domain())Check failed: !this->IsClosed()str.size() < std::numeric_limits<std::int32_t>::max()this->SendAll(&len, sizeof(len)) == sizeof(len)this->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to get addr info for: 127.0.0.10.0.0.0::1::Unknown IA family.sendFailed to send string length.bytes == str.size()Failed to send string.recvFailed to recv string length.bytes == lenFailed to recv string./__w/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.h): Failed to call `system::CloseSocket(handle_)`: @ˆj€„Є×eÍ _ èvH¢”@åœ0ļ4&õk€à7yà؅W4ÈNgmÁ=‘`äX@Œµx¯PïâÖä’ÕMÏð€öJáÇ- ´ÙyCx”(,*‹¹42·ô­ç„þäqÙ0/ç'|!×æúà1Û錠9Y>R$°ˆï³nµµ¸`œÉF"ã¦x|Øê›Ð+‚MÇraB¶â yÏùdiWC¸±¡*ÓÎfJœô‡‚¿¤\Ãñ)c÷æ7ú]µ` àÄxõãxÈöÖ²K}ÏYÆïqž\Cð·k Æ3Tì¥gffffff¹…ëQ¸ú~j¼t“*ËǺ¸"<Ú8b->Nc{-è½1Ò+¯¦üµx#Œ¸c^-¥µ ú‚K®nïuö7ßX%Y^ø_G„zK`æeq ]šp ÁæJ¨á&¡g…Õ¹çë€ o"ö¥¬4£%µ‘Q½Ãéêtt—œ«´íSò`؈$C(çàÓ¦ƒí¸f¹×HŽR”¬Ðl Û©#j¦ðÕ+vŸv=´¼ïIJ+10ó¼¦óQ”¯=ìÚѬ©¿—Vâ§ðºÿèZb™u¦Ì)jõˆ}ìÖ‡î]:1VÓ¾äa'½ƒþmi¥—œKË$!Q¬ã¢¢Pç@9žjç>ÎÜ`KUìËqçÕ݉ [¾q‰üBø1ŽÊh?“(ØÒÔSÌuÙYQ!†à"G®ÚMk€µ9X ‰3‘ŽóîDÛ¸N ö‹â“ØÖÄ<±C­¼.µ’ž{ýÒ$ÄÛ~üþÛƒ6ÿÉ/“lŠ#˜©ó¨#Õ‚F!00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899€vector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast<size_t>(n_columns)column_sizes_tloc[i].size() == static_cast<size_t>(n_columns)/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.ccentries_per_column.size() == info.num_col_Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps))invalid init parameter/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachableSortedSketchContainerSt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_10SparsePageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13SortedCSCPageEEELN9__gnu_cxx12_Lock_policyE2EEš™™™™™)Að¿@/__w/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()basic_string::substrCheck failed: outfile://Opening pointer_ <= buffer_.size()/__w/ml-builds/ml-builds/xgboost/src/common/io.cc/__w/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamE/__w/ml-builds/ml-builds/xgboost/src/common/json.cc can not be indexed by Integer. can not be indexed by string./__w/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hHigh precision number is not supported.Check failed: ret.ec == std::errc()` is not supported for typed array.NumberNullF32ArrayU8ArrayI32ArrayI64ArrayObject of type , around character position: , "beg <= size_\n\0Unknown construct Unknown escapeExpecting null value "null"Expecting digitf64 is not supported.\\\"\b\f\r\t\u%04xch != -1cursor_.Pos(): raw_str_.size():(ÒÄÿ@ÒÄÿXÒÄÿpÒÄÿˆÒÄÿ ÒÄÿ¸ÒÄÿÐÒÄÿèÒÄÿøÑÄÿÒÄÿL.Åÿ$/Åÿ”.Åÿì-Åÿ”.Åÿ¬/Åÿì/Åÿ”.Åÿ”.Åÿ 0Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ,0Åÿì-Åÿ|1Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿì.Åÿ1Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ41Åÿ”.Åÿ”.Åÿ”.Åÿ”.ÅÿL.Åÿ”.Åÿ”.Åÿ\/Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ”.Åÿ1Åÿvector::_M_realloc_insertExpecting: "", got: "EOF"\0"N7xgboost5ValueEN7xgboost10JsonStringEN7xgboost9JsonArrayEN7xgboost10JsonObjectEN7xgboost10JsonNumberEN7xgboost11JsonIntegerEN7xgboost8JsonNullEN7xgboost11JsonBooleanEN7xgboost10JsonReaderEN7xgboost10JsonWriterEN7xgboost9UBJReaderEN7xgboost9UBJWriterEN7xgboost14JsonTypedArrayIfLNS_5Value9ValueKindE7EEEN7xgboost14JsonTypedArrayIhLNS_5Value9ValueKindE8EEEN7xgboost14JsonTypedArrayIiLNS_5Value9ValueKindE9EEEN7xgboost14JsonTypedArrayIlLNS_5Value9ValueKindE10EEEÿÿÿÿÿ€€ÿ/__w/ml-builds/ml-builds/xgboost/src/common/common.hvalue for Parameter exceed bound [ should be greater equal to should be smaller equal to Required parameter is not presentedInvalid Parameter format for expect but value=' optional, default=, required/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.hNo conversion could be performed/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hSome trailing characters could not be parsed: '*p == ')'Invalid NAN literalOut of range valuepos <= value.length()Out of range value for , value='key has already been registered in N4dmlc10ParamErrorE®Åt ¼¾LÊZÂSg€?ÜÇY@™v–? A€PesudoHuberParamThe delta term in Pseudo-Huber l/__w/ml-builds/ml-builds/xgboost/src/common/quantile.cc/__w/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than Maximum cateogry should not be lesser than the total number of categories.hessian.size() == info.num_row_group_ptr.back() == hessian.size()weights.size() == info.num_row_max_cat + 1 >= n_categoriesfeature_ptr.front() == 0rank_size == total/__w/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hUnknown data table type.øÆÿ‚ÆÿH‚ÆÿX‚Æÿx‚Æÿ¸ÆÿØÆÿd„Æÿ|„Æÿœ„ÆÿÄ„Æÿì„Æÿ…Æÿ4…Æÿ\…Æÿ„…Æÿ„Æÿ<„Æÿbase_rowid < group_ptr.back()Row: is not found in any group.sketch->temp.size < max_size, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=invalid maximum size max_size=group_ptr.size() >= 2group_ptr.back() == n_samplessrc.size == 0this->size == 0mingap=, maxgap=, wgap=bug in combineCheck failed: size <= sa.size + sb.size check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=] rmin=, rmax=, wmin=, v=Check failed: nbig < nquantile: too many large chunkà¶Æÿ ·Æÿ`·Æÿ¸·ÆÿзÆÿð·ÆÿȶÆÿ0¸ÆÿH¸Æÿ¸ÆÿȶÆÿCheck failed: worker_feature.data()worker_segments.size() >= 1Check failed: a.dataCheck failed: a.data && reduced[fidx].datasketch->temp.size <= max_size, stemp.size=Finalize: invalid maximum size, max_size=count <= total_entriesn_threads_ >= 1sketches_.size() == n_columns@SÈÿSÈÿðRÈÿØRÈÿÀRÈÿ°RÈÿ˜RÈÿ€RÈÿhRÈÿàQÈÿ SÈÿfloat32float64bool8int32int8int16int64n_columns == sketches_.size()num_cuts.size() == 0Number of columns differs across workerscuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max()cut_size > cuts->cut_ptrs_.HostVector().back()columns_size_.size() != 0Check failed: reduced[i].data¨ZÉÿ€ZÉÿPZÉÿ(ZÉÿøYÉÿÈYÉÿ YÉÿxYÉÿHYÉÿxXÉÿÐZÉÿŒ]Éÿ\]Éÿ,]Éÿ]ÉÿÔ\Éÿ¤\Éÿt\ÉÿL\Éÿ\Éÿl[Éÿ¼]ÉÿPushColPageHostSketchContainerMakeCutsPushRowPageAllReduceSketchContainerImplMakeCutsPushRowPageAllReduceSketchContainerImpl€Ko;oƒ:_A€¿ÿÿÿÿÿÿøä>/__w/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/__w/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0\Êÿ¤ÊÿœÊÿŒÊÿÌÊÿSt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN7xgboost16HostDeviceVectorIjEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE½7†5€/Invalid Input: '', valid values are: /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.henum_back_map_.count(value) != 0UValue not found in enum declared/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././strtonum.hEnum exisit!) +Enums: ), N4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEEaft_loss_distribChoice of distribution for the noise term in Accelerated FailureScaling factor used to scale the distribution in Accelerated Fai/sys/fs/cgroup/cpu/cpu.cfs_quota_us/sys/fs/cgroup/cpu/cpu.cfs_period_usTimer for calls @ us======== Monitor ( ======== did not get stopped properly.€„.A/__w/ml-builds/ml-builds/xgboost/src/common/time/__w/ml-builds/ml-builds/xgboost/src/common/version.ccfi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)Invaid version format in loaded JSON object: ÿÿÿÿÿÿÿÿÿÿÿÿIncorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it agai/__w/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Unknown data type for getting meta info.All feature_types must be one of {int, float, i, q, c}./__w/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.hself_offset.size() == other_offset.size()cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification.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=csvgroup_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.Size of weights must equal to number of rows.Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector. must be equal to number of columns.this->num_col_ == that.num_col_Number of columns must be consistent across batches.transpose.offset.Size() == num_columns + 1Check failed: array.valid.Size() == 0Meta info like label or weight can not have missing value.Check failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.this->labels.Size() % this->num_row_ == 0Label contains NaN, infinity or a value too large.`qid` must be sorted in non-decreasing order along with data.Weights must be positive values.Feature weight must be greater than 0.Check failed: strm->Read(&name)Check failed: strm->Read(&type_val)Check failed: type == expected_typeCheck failed: strm->Read(&is_scalar)Check failed: strm->Read(field)Check failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)Number of columns is expected to be 1.Check failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field)Binary DMatrix generated by XGBoost: Please process and save your data in current version: Check failed: fi->Read(&num_field)MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains MetaInfo: the given binary file contains extra fields which will be ignored.ˆÙËÿ ÙËÿÀÙËÿàÙËÿøÙËÿÚËÿ0ÚËÿPÚËÿhÚËÿàØËÿ`ÙËÿ¬ÜËÿÄÜËÿäÜËÿÝËÿÝËÿ<ÝËÿTÝËÿtÝËÿŒÝËÿÜËÿ„ÜËÿÐßËÿèßËÿàËÿ(àËÿ@àËÿ`àËÿxàËÿ˜àËÿ°àËÿ(ßËÿ¨ßËÿìâËÿãËÿãËÿ<ãËÿTãËÿtãËÿŒãËÿ¬ãËÿÌãËÿ\âËÿÌâËÿ`åËÿ€åËÿ åËÿÐåËÿðåËÿ æËÿHæËÿpæËÿæËÿåËÿ0åËÿÄüËÿ¤üËÿdüËÿDüËÿäûËÿ\ûËÿÄûËÿüËÿ$üËÿ\ûËÿÄûËÿ¸þËÿ˜þËÿXþËÿ@þËÿèýËÿhýËÿÈýËÿþËÿ þËÿhýËÿÈýËÿ¬ÌÿŒÌÿLÌÿ4ÌÿÜÿËÿ\ÿËÿ¼ÿËÿüÿËÿÌÿ\ÿËÿ¼ÿËÿÐÌÿ°ÌÿpÌÿPÌÿðÌÿhÌÿÐÌÿÌÿ0ÌÿhÌÿÐÌÿôÌÿÔÌÿ”ÌÿtÌÿÌÿŒÌÿôÌÿ4ÌÿTÌÿŒÌÿôÌÿèÌÿÈÌÿˆÌÿpÌÿÌÿ˜ÌÿøÌÿ8ÌÿPÌÿ˜ÌÿøÌÿìÌÿÔ ÌÿŒ Ìÿl ÌÿL Ìÿ|ÌÿÔÌÿ Ìÿ, Ìÿ|ÌÿÔÌÿÐ +Ìÿ  Ìÿ` ÌÿH Ìÿ( Ìÿp +Ìÿ¸ +Ìÿð +Ìÿ Ìÿp +Ìÿ¸ +Ìÿô ÌÿÌ Ìÿ¤ Ìÿ| ÌÿT Ìÿ4 Ìÿ Ìÿô ÌÿÔ Ìÿ\ ÌÿÌÿ˜ÌÿpÌÿHÌÿ ÌÿøÌÿØÌÿ¸Ìÿ˜ÌÿxÌÿÌÿ¸ÌÿÌÿôÌÿÌÌÿ¤Ìÿ|Ìÿ\Ìÿ<ÌÿÌÿôÌÿŒÌÿ<Ìÿ ÌÿxÌÿPÌÿ(ÌÿÌÿàÌÿÀÌÿ ÌÿxÌÿÌÿÀÌÿDÌÿÌÿôÌÿÌÌÿ¤Ìÿ„ÌÿdÌÿDÌÿ$Ìÿ¬ÌÿdÌÿÈÌÿ ÌÿxÌÿPÌÿ(ÌÿÌÿèÌÿÈÌÿ Ìÿ8ÌÿèÌÿ<!Ìÿ!Ìÿô ÌÿÔ Ìÿ¬ ÌÿŒ Ìÿl ÌÿL Ìÿ, ÌÿÌÌÿ\!Ìÿ #Ìÿx#ÌÿP#Ìÿ(#Ìÿ#Ìÿà"ÌÿÀ"Ìÿ "Ìÿ€"Ìÿ0"ÌÿÀ#ÌÿÄ“Ìÿä“Ìÿ”Ìÿ,”ÌÿT”Ìÿt”Ìÿ””Ìÿ´”ÌÿÔ”Ìÿ“Ìÿ”“Ìÿ(—ÌÿH—Ìÿh—Ìÿ—Ìÿ¸—ÌÿØ—Ìÿø—Ìÿ˜Ìÿ8˜Ìÿ€–Ìÿø–ÌÿœšÌÿ¼šÌÿÜšÌÿ›Ìÿ,›ÌÿT›Ìÿ|›Ìÿœ›Ìÿ¼›Ìÿì™ÌÿlšÌÿ@žÌÿ`žÌÿ€žÌÿ¨žÌÿОÌÿðžÌÿŸÌÿ0ŸÌÿPŸÌÿ¨ÌÿžÌÿ Device that data is on: `gpu_id` for XGBoost: num_rownum_colnum_nonzerolabelsgroup_ptrbase_marginlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightslabel_lower_boundlabel_upper_boundUnknown float field name: Unknown uint32 field name: matrix with entries loaded from self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size().rLoad part of data partscsvlibsvm . Encountered parser error: +group_ptr_.back() == num_row_weights_.Size() == num_row_labels.Shape(0) == num_row_size == this->num_col_Length of Check failed: infoUnknown feature info name: Unknown feature info: that.group_ptr_.size() != 0Check failed: t.CContiguous()Incorrect size for labels.Check failed: non_decUnknown key for MetaInfo: Check failed: keyMetaInfo: Invalid format for name == expected_name Expected field: , got: Expected field of type: got field type: Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: !is_scalar to be a vector; got a scalarshape.second == 1 to be a tensor; got a scalar is no longer supported. again.major >= 1minor >= 6MetaInfo: invalid formatnum_field >= expectedfields.)/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: page_/__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()/__w/ml-builds/ml-builds/xgboost/src/data/../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hLcÍÿ,dÍÿ¼cÍÿ,cÍÿ cÍÿìbÍÿ\dÍÿCheck failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.Check failed: valid_query_group/__w/ml-builds/ml-builds/xgboost/src/data/validation.hInvalid group structure.std::future_error: /__w/ml-builds/ml-builds/xgboost/src/data/../data/array_interface.h/__w/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hthis->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification.shape[i] == r.Shape(i)à¢Íÿ£Íÿ(£ÍÿP£Íÿp£Íÿ˜£ÍÿÀ£Íÿè£Íÿ¤Íÿh¢Íÿ°¢ÍÿT©Íÿ<©Íÿä¨ÍÿÔ¨ÍÿĨÍÿ´¨Íÿt¦Íÿ”¨Íÿt¨Íÿ¤¨Íÿt¦ÍÿX¨Íÿ8¨Íÿø§Íÿà§ÍÿȧÍÿ¸§Íÿ §Íÿˆ§Íÿp§Íÿp¦Íÿ¨Íÿkey >= builder_base_row_offsetÐÁÍÿÂÍÿxÂÍÿ ÂÍÿÐÂÍÿèÀÍÿÃÍÿ\×Íÿ|×ÍÿÜ×Íÿ´×ÍÿÌ×Íÿ¤×ÍÿLÔÍÿ<ØÍÿ\ØÍÿLØÍÿLÔÍÿèÖÍÿÈÖÍÿ°ÖÍÿÖÍÿhÖÍÿHÖÍÿ(ÖÍÿÖÍÿèÕÍÿpÔÍÿ×Íÿformat.front() == '.'/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.hUnknown format type fi->Tell() == offsetRead a page in seconds.Check failed: fmt->Read(page.get(), fi.get())Check failed: !cache_info_->written MB written in /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././././any.hCheck failed: type_ != nullptrCheck failed: *(type_->ptype_info) == typeid(T)The any container is empty requested=The stored type mismatch stored=<Check failed: parser_qidCheck failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.Check failed: validInput data contains `inf` or `nan`/__w/ml-builds/ml-builds/xgboost/src/data/../data/proxy_dmatrix.hUnknown type: count_ == n_batches_Check failed: proxy_this->count_ == this->n_batches_source_->Iter() == this->count_this->count_ >= 1ìªÎÿ«Îÿ«ÎÿT«Îÿ¤ªÎÿtªÎÿŒªÎÿÔªÎÿ¼ªÎÿtªÎÿŒªÎÿ¸«ÎÿЫÎÿð«Îÿ¬Îÿ0¬ÎÿP¬Îÿp¬Îÿ¬Îÿ°¬Îÿh«Îÿ«ÎÿN7xgboost10SparsePageEN7xgboost7DMatrixEN7xgboost4data11FileAdapterEN7xgboost17BatchIteratorImplINS_11EllpackPageEEENSt13__future_base13_State_baseV2ENSt13__future_base21_Async_state_commonV2ESt10shared_ptrIN7xgboost4data15CSRArrayAdapterEESt10shared_ptrIN7xgboost4data12ArrayAdapterEEN7xgboost4data20SparsePageSourceImplINS_11EllpackPageEEEN7xgboost4data18PageSourceIncMixInINS_11EllpackPageEEEN7xgboost4data17EllpackPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt23_Sp_counted_ptr_inplaceIN7xgboost10SparsePageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEEP?eÍÍA/__w/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.cc/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalCheck failed: p_fmat->SingleColBlock()Check failed: columns_Check failed: !sorted_sketchrow_ptr.size() == 0/__w/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizevector<bool>::_M_fill_insertÔIÐÿ¼IÐÿdIÐÿTIÐÿDIÐÿ4IÐÿTGÐÿIÐÿôHÐÿ$IÐÿTGÐÿØHÐÿ¸HÐÿxHÐÿ`HÐÿHHÐÿ8HÐÿ HÐÿHÐÿðGÐÿPGÐÿ˜HÐÿ¼SÐÿŒSÐÿ\SÐÿ4SÐÿSÐÿÔRÐÿ¤RÐÿ|RÐÿLRÐÿÜQÐÿìSÐÿ@QÐÿQÐÿàPÐÿ¸PÐÿˆPÐÿXPÐÿ(PÐÿPÐÿÐOÐÿ`OÐÿpQÐÿRaw GHistIndex binary data format.N7xgboost4data16SparsePageFormatINS_16GHistIndexMatrixEEEN7xgboost4data19GHistIndexRawFormatE*N7xgboost4dataUlvE_E/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././any.h/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hN7xgboost17BatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost4data20SparsePageSourceImplINS_16GHistIndexMatrixEEEN7xgboost4data18PageSourceIncMixInINS_16GHistIndexMatrixEEEN7xgboost4data23GradientIndexPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost16GHistIndexMatrixELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost16GHistIndexMatrixESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccpage.row_ptr.size() == h_offset.size()/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/hist_util.href_->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.Inconsistent max_bin between Quantile DMatrix and Booster:Check failed: !param.regen && param.hess.empty()`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.Data must has at least 1 column.accumulated_rows == Info().num_row_proxy->Info().labels.Size() == 0Iterative DMatrix must have at least 1 batch.All batch should be on the same device.Check failed: ref_Check failed: p_cutsCheck failed: ellpack_n_features == num_cols()n_features >= 1Check failed: p_sketchrbegin == Info().num_row_d == batch_param_.gpu_idäŸÑÿ´ŸÑÿœŸÑÿ„ŸÑÿtŸÑÿdŸÑÿLŸÑÿ4ŸÑÿŸÑÿdžÑÿÄŸÑÿ€¢ÑÿÀ¢Ñÿ¨¢Ñÿh¢ÑÿP¢Ñÿ(¢Ñÿ¢Ñÿ8¢Ñÿ¢ÑÿP¡Ñÿ¢ÑÿT¥Ñÿ”¥Ñÿ|¥Ñÿ<¥Ñÿ$¥Ñÿü¤Ñÿì¤Ñÿ ¥ÑÿܤÑÿ$¤Ñÿd¥Ñÿ(¨Ñÿh¨ÑÿP¨Ñÿ¨Ñÿø§ÑÿЧÑÿÀ§Ñÿà§Ñÿ°§Ñÿø¦Ñÿ8¨ÑÿLïÐÿ”ïÐÿ|ïÐÿ4ïÐÿïÐÿôîÐÿäîÐÿïÐÿÔîÐÿ$îÐÿ\ïÐÿ€òÐÿÈòÐÿ°òÐÿhòÐÿPòÐÿ(òÐÿòÐÿ8òÐÿòÐÿXñÐÿòÐÿ´õÐÿüõÐÿäõÐÿœõÐÿ„õÐÿ\õÐÿLõÐÿlõÐÿ<õÐÿŒôÐÿÄõÐÿ0ùÐÿùÐÿèøÐÿÐøÐÿÀøÐÿ°øÐÿ øÐÿpøÐÿˆøÐÿÐ÷ÐÿùÐÿ¤ÑÿtÑÿ\ÑÿDÑÿ4ÑÿÑÿôÑÿäÑÿÑÿdÑÿ„Ñÿ/__w/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hSlicing DMatrix is not supported for Quantile DMatrix.Check failed: proxy_handleInvalid proxy handle.Check failed: typedOnly `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.¬¸Ñÿœ¸Ñÿ|¸Ñÿd¸ÑÿL¸Ñÿ<¸Ñÿ,¸Ñÿ¸Ñÿü·Ñÿ\·Ñÿ¼¸Ñÿ/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.hCheck failed: !std::isnan(sparse_thresh)|ÒÿLÒÿÒÿìÒÿ¼ÒÿŒÒÿ\Òÿ,ÒÿüÒÿ\Òÿ¬Òÿ+ÒÿÐ*Òÿ *Òÿp*Òÿ@*Òÿ*Òÿà)Òÿ°)Òÿ€)Òÿà'Òÿ0+Òÿ„AÒÿTAÒÿ$AÒÿô@ÒÿÄ@Òÿ”@Òÿd@Òÿ4@Òÿ@Òÿd>Òÿ´AÒÿøWÒÿÈWÒÿ˜WÒÿhWÒÿ8WÒÿWÒÿØVÒÿ¨VÒÿxVÒÿèTÒÿ(XÒÿSt14default_deleteIN7xgboost16GHistIndexMatrixEEN7xgboost4data16IterativeDMatrixEN7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost17BatchIteratorImplINS_13ExtSparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13ExtSparsePageEEESt19_Sp_counted_deleterIPN7xgboost16GHistIndexMatrixESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_16GHistIndexMatrixEEELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13ExtSparsePageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEš™™™™™É?/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device./__w/ml-builds/ml-builds/xgboost/src/data/array_interface.h¸ÖÒÿØÒÿäÖÒÿØÒÿØÒÿØÒÿ×ÒÿØÒÿ!ÔÒÿ<×ÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿ4ÖÒÿ`ÖÒÿØÒÿØÒÿh×ÒÿØÒÿØÒÿ”×ÒÿØÒÿØÒÿØÒÿÀ×ÒÿØÒÿØÒÿØÒÿØÒÿØÒÿØÒÿì×ÒÿŒÖÒÿSt19__shared_ptr_accessIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt12__shared_ptrIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EE/__w/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: r != BatchParam{}Batch parameter is not initialized.Generating new Gradient Index.batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid format, magic number mismatchoffset_vec.back() == info_.num_nonzero_param.gpu_id >= 0param.max_bin >= 2param.gpu_id == -1Check failed: castedinvalid input file formattmagic == kMagic/__w/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h/__w/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_Column is emptyCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnŒÎÓÿ´ÎÓÿ,ÏÓÿtÏÓÿ¼ÏÓÿ ÐÓÿ\ÐÓÿ¬ÐÓÿüÐÓÿLÑÓÿœÑÓÿCheck failed: (std::is_same<AdapterT, CSCAdapter>::value)Expecting CSCAdapterN7xgboost7CSCPageEN7xgboost13SortedCSCPageEN7xgboost4data6ColumnEN7xgboost4data15PrimitiveColumnIaEEN7xgboost4data15PrimitiveColumnIhEEN7xgboost4data15PrimitiveColumnIsEEN7xgboost4data15PrimitiveColumnItEEN7xgboost4data15PrimitiveColumnIiEEN7xgboost4data15PrimitiveColumnIjEEN7xgboost4data15PrimitiveColumnIlEEN7xgboost4data15PrimitiveColumnImEEN7xgboost4data15PrimitiveColumnIfEEN7xgboost4data15PrimitiveColumnIdEEN7xgboost4data13SimpleDMatrixEN7xgboost17BatchIteratorImplINS_10SparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEEN7xgboost17BatchIteratorImplINS_7CSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEEN7xgboost17BatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEESt15_Sp_counted_ptrIPN7xgboost7CSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost13SortedCSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost11EllpackPageELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIaEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIhEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIsEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnItEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIiEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIjEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIlEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnImEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIfEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIdEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_7CSCPageEEELN9__gnu_cxx12_Lock_policyE2EEÿÿÿÿÿÿÿÿ€G€G€G€G/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_Check failed: ghist_index_source_.row.pageiter_.Next() == 1Must have at least 1 batch.DMatrix-rinfo_.num_col_ != 0this->Info().num_col_ != 0.sorted.col.page.gradient_index.pagecuts.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory./__w/ml-builds/ml-builds/xgboost/src/data/./../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/./proxy_dmatrix.hCouldn't remove external memory cache file ; you may want to remove it manuallyCan not obtain a single CSR page for external memory DMatrixCheck failed: kv.secondthis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size()Make cache:N7xgboost4data20SparsePageSourceImplINS_10SparsePageEEEN7xgboost4data16SparsePageSourceEN7xgboost4data20SparsePageSourceImplINS_7CSCPageEEEN7xgboost4data18PageSourceIncMixInINS_7CSCPageEEEN7xgboost4data13CSCPageSourceEN7xgboost4data20SparsePageSourceImplINS_13SortedCSCPageEEEN7xgboost4data18PageSourceIncMixInINS_13SortedCSCPageEEEN7xgboost4data19SortedCSCPageSourceEN7xgboost4data17SparsePageDMatrixENSt13__future_base7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENSt13__future_base7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost4data5CacheELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data23GradientIndexPageSourceELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data16SparsePageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data13CSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data19SortedCSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost7CSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13SortedCSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccCheck failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()Raw binary data format.page->offset.Size() != 0UInvalid SparsePage filen_bytes == (page->data).Size() * sizeof(Entry)N7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_Egpu_coord_descentLinear booster does not support prediction range.Linear booster, implement generalized linear model.layer_begin == 0GBLinearTrainParam/__w/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster.Inplace predict is not supported by current booster./__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccgblinear does not support prediction of leaf indexmodel/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(&param_, sizeof(param_)) == sizeof(param_)DoBoostget<String>(in["name"]) == "gblinear"/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hCheck failed: !model_.weight.empty()gblinear doesn't support number of trees for feature importance.gblinear only has `weight` defined for feature importance.Model is not initializedCheck failed: trees.empty()importance_type == "weight"PredictBatchInternalbase_margin.Size() == nsize * ngroupPredictBatch { "bias": [ ], "weight": [ ] }bias: weight: -shotguntolerancemax_row_perbatchlong (non-negative)Maximum rows per batch.Update algorithm for linear model. One of shotgun/coord_descentStop if largest weight update is smaller than this number./__w/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hGBLineargblinear_train_paramCheck failed: this->updater_N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_EN7xgboost3gbm13GBLinearModelEDeprecatedGBLine/__w/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type /__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccTree 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'.Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.XGBoost version not compiled with OneAPI support.Data is on CUDA device, but XGBoost is not compiled with CUDA support.tree_end <= model_.trees.size()ups.size() == updaters_.size() mismatched updater sequence. -get<String>(in["name"]) == "gbtree"this->model_.learner_model_param->num_feature != 0this->model_.trees_to_update.size() == this->model_.trees.size() trees remain. Slice the model before making update if you only want to update a portion of trees.Check failed: !updaters_.front()->CanModifyTree()can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.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()in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsLoading from a raw memory buffer on CPU only machine. Changing predictor to auto.Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist.Tree booster, gradient boosted trees.Using tree method: grow_histmakergrow_colmaker,prunegrow_quantile_histmakerUnknown tree_method () detectedCheck failed: configured_Check failed: cpu_predictor_out_preds->version == 0Invalid number of trees.CommitModelInternal Error: Specified updaters: Actual updaters: Check failed: p_gbtreelayer_trees != 0layer_end > layer_beginstep >= 1Not all trees are updated, in_it < tree_endGBTreeModelParamGBTreeTrainParamDartTrainParamUpdater: `` GBTreeUsing updaters: BoostNewTreesngroup != 0gbtree_train_paramspecified_updaterTree booster, dart.xÖÿ8ÖÿPÖÿ¨Öÿ`Öÿ Öÿ*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE0_Eweight_drop/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin <= tree_endmodel_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0tree_begin == 0Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.cpu_predictorgpu_predictorCheck failed: successCurrent Predictor: Unsupported data type for inplace predict.Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == out_preds->predictions.Size()drop trees, weight = get<String>(in["name"]) == "dart"Check failed: p_dartCheck failed: p_dart->weight_drop_.empty()num_treessize_leaf_vectorNumber 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. has not been registered in Alias updater_seqTree updater sequence.process_typedefaultupdateoneapi_predictorPredictor algorithm typetree_methodapproxexactgpu_histWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.Choice of tree construction method.sample_typeuniformweightednormalize_typeforestrate_dropone_dropbooleanskip_droplearning_rateetaDifferent types of sampling algorithm.Different types of normalization algorithm.Fraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.Probability of skipping the dropout during a boosting iteration.Learning rate(step size) of update.idx <= total_n_treesInvalid tree index.total_gaintotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: dart_train_paramvector::reserveN4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN4dmlc9parameter10FieldEntryIN7xgboost13PredictorTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartEðAð;à?/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast<int32_t>(trees.size())fi->Read(&param, sizeof(param)) == sizeof(param)fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast<int>(trees.size())GBTree: invalid model fileCheck failed: ctx_N7xgboost3gbm11GBTreeModelEgbtree_model_parGlobalConfiguratFlag to print out detailed breakdown of runtime.Whether to use RAPIDS Memory Manager to allocate GPU memory in XCheck failed: n_classes <= 1 || n_targets <= 1Multi-class multi-output is not yet supported. n_classes:, n_targets:LearnerModelParamLegacyGenericParameterLearnerTrainParam/__w/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective function/__w/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: !this->need_configuration_ : Check failed: this->obj_Inconsistent labels.Check failed: n_targets == t || 1 == tCheck failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.Starting in XGBoost used with the objective '' was changed from '' to ', the default evaluation metric '. Explicitly set eval_metric if you'd like to restore the old behavior.Configlearner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: gbm_ != nullptrPredict must happen after Load or configurationmultiple_predictions <= 1Perform one kind of prediction at a time.BoostOneIterUpdateOneIterPredictRawPredictionsGetGradientGradientsEvalOneIterbinary:logitraw1.4.0aucbase_scoreGlobal bias of the model.num_featureint (non-negative)num_classnum_targetNumber 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.seedlongrandom_stateseed_per_iterationNumber of threads to use.n_jobsfail_on_invalid_gpu_idvalidate_parametersRandom number seed during training.Seed PRNG determnisticly via iterator number.The primary GPU device ordinal.Fail with error when gpu_id is invalid.Enable checking whether parameters are used or not.dsplitdisable_default_eval_metricobjectivereg:squarederrorData split mode for distributed training.Flag to disable default metric. Set to >0 to disableGradient booster used for training.Objective function used for obtaining gradient.Call Configure before saving model.learner_model_paramattributesCheck failed: matrix.first features or greatermparam_.num_feature != 0Check failed: !matrix.second.ref.expired()num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max())Unfortunately, XGBoost does not support data matrices with 0 feature is supplied. Are you using raw Booster interface?multi:softprobnum_output_groupmulti:softmaxcount:poisson0.7learner_train_parammetricsgeneric_param__Cannot find argument '', Possible Arguments: +shotguntolerancemax_row_perbatchlong (non-negative)Maximum rows per batch.Update algorithm for linear model. One of shotgun/coord_descentStop if largest weight update is smaller than this number./__w/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hGBLineargblinear_train_paramCheck failed: this->updater_N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_Eboosted_roundsN7xgboost3gbm13GBLinearModelEDeprecatedGBLinedeprecated_num_fint (non-negativdeprecated_num_o/__w/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type /__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccTree 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'.Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.Check failed: !updaters_.empty()XGBoost version not compiled with OneAPI support.Data is on CUDA device, but XGBoost is not compiled with CUDA support.tree_end <= model_.trees.size()get<String>(in["name"]) == "gbtree"ups.size() == updaters_.size() mismatched updater sequence. +this->model_.learner_model_param->num_feature != 0this->model_.trees_to_update.size() == this->model_.trees.size() trees remain. Slice the model before making update if you only want to update a portion of trees.Check failed: !updaters_.front()->CanModifyTree()can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.Check failed: this->configured_Check failed: tparam_.GetInitialised()Current objective doesn't support external memory.in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsDANGER 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.Loading from a raw memory buffer on CPU only machine. Changing predictor to auto.Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist.Tree booster, gradient boosted trees.Using tree method: grow_histmakergrow_colmaker,prunegrow_quantile_histmakerUnknown tree_method () detectedCheck failed: configured_Check failed: cpu_predictor_out_preds->version == 0Invalid number of trees.CommitModelInternal Error: Specified updaters: Actual updaters: Check failed: p_gbtreelayer_trees != 0layer_end > layer_beginstep >= 1Not all trees are updated, GBTreeModelParamGBTreeTrainParamDartTrainParamUpdater: `` Using updaters: BoostNewTreesngroup != 0GBTreegbtree_train_paramspecified_updaterTree booster, dart.èÖÿ¨ ÖÿÀÖÿ ÖÿÐ Öÿ Öÿ*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE0_E/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.hweight_droptree_begin <= tree_endmodel_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0tree_begin == 0Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.cpu_predictorgpu_predictorCheck failed: successCurrent Predictor: Unsupported data type for inplace predict.get<String>(in["name"]) == "dart"Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == p_out_preds->predictions.Size()/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hdrop trees, weight = in_it < tree_endCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()num_treessize_leaf_vectorNumber 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. has not been registered in Alias updater_seqTree updater sequence.process_typedefaultupdateoneapi_predictorPredictor algorithm typetree_methodapproxexactgpu_histWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.Choice of tree construction method.sample_typeuniformweightednormalize_typeforestrate_dropone_dropbooleanskip_droplearning_rateetaDifferent types of sampling algorithm.Different types of normalization algorithm.Fraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.Probability of skipping the dropout during a boosting iteration.Learning rate(step size) of update.idx <= total_n_treesInvalid tree index.total_gaintotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: dart_train_paramvector::reserveN4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN4dmlc9parameter10FieldEntryIN7xgboost13PredictorTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartEðAð;à?/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast<int32_t>(trees.size())fi->Read(&param, sizeof(param)) == sizeof(param)fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast<int>(trees.size())GBTree: invalid model fileCheck failed: ctx_N7xgboost3gbm11GBTreeModelEgbtree_model_parGlobalConfiguratFlag to print out detailed breakdown of runtime.Whether to use RAPIDS Memory Manager to allocate GPU memory in XCheck failed: n_classes <= 1 || n_targets <= 1Multi-class multi-output is not yet supported. n_classes:Check failed: common::AsConst(base_score_).Data()->HostCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead()Check failed: base_score_.Data()->HostCanRead()Model is not yet initialized (not fitted).Check failed: base_score_.Data()->DeviceCanRead(), n_targets:base_score_.Size() == 1LearnerModelParamLegacyGenericParameterLearnerTrainParam/__w/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective function/__w/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: !this->need_configuration_ : Check failed: this->obj_Inconsistent labels.Check failed: n_targets == t || 1 == tCheck failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.Check failed: ret.ec == std::errc{}num_featurenum_classnum_targetboost_from_averageCheck failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0ConfigCheck failed: ptrlearner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: gbm_ != nullptrPredict must happen after Load or configurationmultiple_predictions <= 1Perform one kind of prediction at a time.BoostOneIterUpdateOneIterPredictRawPredictionsGetGradientGradientsEvalOneIterCall Configure before saving model.learner_model_paramobjectiveattributesGlobal bias of the model.int (non-negative)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.seedlongrandom_stateseed_per_iterationNumber of threads to use.n_jobsfail_on_invalid_gpu_idvalidate_parametersRandom number seed during training.Seed PRNG determnisticly via iterator number.The primary GPU device ordinal.Fail with error when gpu_id is invalid.Enable checking whether parameters are used or not.dsplitdisable_default_eval_metricreg:squarederrorData split mode for distributed training.Flag to disable default metric. Set to >0 to disableGradient booster used for training.Objective function used for obtaining gradient.Check failed: matrix.first features or greatermparam_.num_feature != 0Check failed: !matrix.second.ref.expired()num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max())Unfortunately, XGBoost does not support data matrices with 0 feature is supplied. Are you using raw Booster interface?multi:softprobnum_output_groupmulti:softmaxcount:poisson0.7learner_train_parammetricsgeneric_param;binf__Cannot find argument '', Possible Arguments: ---------------- -Check failed: IsA<Object>(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. +Check failed: IsA<Object>(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. 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. -;binfLearnernum_roundCONFIG-offset:this->learner_model_param_.num_feature != 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0begin_layer >= 0best_iterationbest_scoreUnsupported prediction type:Invalid parameter "" contains whitespace. -Parameters: { ", } might not be used. - - 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. - -Configurefixed_size->Read(&buffer[0], size) == sizebs64header != "bs64"fp.Read(&header[0], 4) == 4UInvalid model formatSAVED_PARAM_count_poisson_max_delta_stepmulti:Base64 format is no longer supported in brick.fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityInvalid serialization file.sz > 0fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)N4dmlc12SerializableEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13DataSplitModeEEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplEÿÿÿÿ/__w/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater Learning rate ofL2 regularizatioL1 regularizatiofeature_selectorFeature selection or ordering meLinearTrainParamUpdate linear model according to coordinate descent algorithm.CoordinateParam/__w/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeaturetop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features./__w/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: CoordinateUpdater]Ûÿ1]Ûÿi]Ûÿ \Ûÿñ\Ûÿcoordinate_paramN7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*N7xgboost6linearUlvE_E€€ÿÿÿÿÿÿÿÿUpdate linear model according to shotgun coordinate descent algorithm./__w/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. -Supported options are: {cyclic, shuffle}™ÒÛÿ·ÒÛÿðÒÛÿqÓÛÿ<ÒÛÿN7xgboost6linear14ShotgunUpdaterE*N7xgboost6linearUlvE_EWARNING: DEBUG: INFO: aucpr/__w/ml-builds/ml-builds/xgboost/src/metric/../common/common.hlabels.Size() == predts.size()Receiver Operating Characteristic Area Under the Curve.Area under PR curve for both classification and rank.labels.Size() != 0auc <= 1.0f/__w/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples./__w/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/metric/auc.ccinfo.group_ptr_.size() >= 2n_classes != 0labels.Shape(1) == 1AUC doesn't support multi-target model.èÝÿ ÝÿHÝÿ0ÝÿHÝÿinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()Dataset is empty, or contains only positive or negative samples.auc <= 1Total AUC across groups: , valid groups: auc <= local_areaPR-AUC supports only binary relevance for learning to rank.N7xgboost6MetricEN7xgboost6metric7EvalAUCINS0_10EvalROCAUCEEEN7xgboost6metric10EvalROCAUCEN7xgboost6metric7EvalAUCINS0_9EvalPRAUCEEEN7xgboost6metric9EvalPRAUCE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E?mphegamma-nloglikgamma-deviancepoisson-nloglikmapemaermslermse/__w/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cuCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)sscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricMean absolute percentage error.Negative loglikelihood for logistic regression.Negative loglikelihood for poisson regression.Residual deviance for gamma regression.Negative log-likelihood for gamma regression.tweedie-nloglik@rho for tweedie regression.%fRooted mean square error.Rooted mean square log error.Mean absolute error.Mean Pseudo-huber error.Binary classification error.tweedie-nlogliktweedie-nloglik@info.labels.Shape(0) == info.num_row_slope for pseudo huber cannot be 0.slope != 0.0pseduo_huber_parampreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0N7xgboost6metric15PseudoErrorLossEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMAPEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E*N7xgboost6metricUlPKcE9_E@$tI$tÉ••æ$ ÷Æ°>/__w/ml-builds/ml-builds/xgboost/src/metric/metric.ccCannot find a GPU metric builder for metric . Resorting to the CPU builderUnknown metric function /src/metric/metrmerrormloglossMulticlass classification error.Multiclass negative loglikelihood./__w/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cuCheck failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchmlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class)MultiClassEvaluation: label must be in [0, num_class),preds.Size() == 0nclass >= 1U num_class= but found in labelN7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_EŽ]Bcox-nloglikNegative log partial likelihood of Cox proportional hazards model./__w/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@gptr.size() != 0Ugptr.back() == preds.Size()Check failed: tparam_label size predict size not matchmust specify group when constructing rank fileEvalRank: group structure must match number of prediction%u[-]?preAMS metric for higgs.precision@k for rank.ndcg@k for rank.map@k for rank.Check failed: !distributedCox metric does not support distributed evaluationmetric AMS do not support distributed evaluationbest-ams-ratio=Dnáÿ4oáÿ„náÿlnáÿtoáÿÈÚáÿxÙáÿ ÛáÿÛáÿØÝáÿN7xgboost6metric14EvalRankConfigEN7xgboost6metric7EvalAMSEN7xgboost6metric8EvalRankEN7xgboost6metric13EvalPrecisionEN7xgboost6metric8EvalNDCGEN7xgboost6metric7EvalMAPEN7xgboost6metric7EvalCoxE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E$@aft-nloglikinterval-regression-accuracyNegative log likelihood of Accelerated Failure Time model./__w/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuCheck failed: metric_AFT metric must be configured first, with distribution type and scaleUnknown probability distributionaft_loss_parampreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()N7xgboost6metric20AFTNLogLikDispatcherEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common18NormalDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common20LogisticDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common19ExtremeDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_30EvalIntervalRegressionAccuracyEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_Eê-™—q='ö“ @Í;fž ö?/__w/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /__w/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndataNumber of weights should be equal to number of data points.info.weights_.Size() == ndataUnrecognized distributionsurvival:aftAFT loss functionN7xgboost11ObjFunctionEN7xgboost3obj6AFTObjE*N7xgboost3objUlvE_E.À.@¼‰Ø—²Òœ<@@pApÁHinge loss. Expects labels to be in [0,1f]/__w/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedinfo.labels.Size() != 0Ulabel set cannot be emptypreds.size=, label.size=binary:hingeN7xgboost3obj8HingeObjE*N7xgboost3objUlvE_E€Number of output class in the multi-class classification.Softmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.SoftmaxMultiClassParam/__w/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. -SoftmaxMultiClassObj: label must be in [0, num_class).label.Size() * num_class: preds.Size(): softmax_multiclass_paramN7xgboost3obj20SoftmaxMultiClassObjE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E ²Òœ<Objective candidate: Unknown objective function: `` -/__w/ml-builds/ml-builds/xgboost/src/objective/objective.ccLambdaRank with NDCG as objective.LambdaRank with MAP as objective.num_pairsamplefix_list_weightNumber of pair generated for each instance.Normalize the weight of each list by this value, if equals 0, no effect will happenLambdaRankParamrank:pairwisePairwise rank objective.rank:ndcgrank:map/__w/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuComputing gradients on CPU.Check failed: gptr.size() != 0 && gptr.back() == info.labels.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: lambda_rank_paramN7xgboost3obj13LambdaRankObjINS0_28PairwiseLambdaWeightComputerEEEN7xgboost3obj13LambdaRankObjINS0_24NDCGLambdaWeightComputerEEEN7xgboost3obj13LambdaRankObjINS0_23MAPLambdaWeightComputerEEE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_Eff±Â/__w/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cureg:linear is now deprecated in favor of reg:squarederror.info.labels.Size() == preds.Size()info.weights_.Size() == info.num_row_Scale the weight of positive examples by this factorMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.Tweedie variance power. Must be between in range [1, 2).Regression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.Regression Pseudo Huber error.Poisson regression for count data.Cox regression for censored survival data (negative labels are considered censored).Gamma regression for severity data.Tweedie regression for insurance data.Invalid shape of labels.RegLossParamscale_pos_weightPoissonRegressionParamTweedieRegressionParamtweedie_variance_powerreg:squaredlogerrorreg:logisticbinary:logisticreg:linearreg:pseudohubererrorreg:tweedie/__w/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: survival:coxreg:gamma/__w/ml-builds/ml-builds/xgboost/src/objective/../common/common.hPoissonRegression: label must be nonnegativeGammaRegression: label must be positive.TweedieRegression: label must be nonnegativetweedie_regression_parampoisson_regression_paramCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!reg_loss_paramlabel must be in [0,1] for logistic regressionlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.N7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_Eff±Bïÿ¿€?€?€?€?Make predictions using CPU./__w/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farbase_margin.Shape(1) == ngroupPèÿ@èÿ(èÿèÿøèÿèèÿÐèÿ¸èÿ èÿÀèÿ`èÿìèÿÄèÿ\èÿLèÿ<èÿ,èÿÄ èÿ èÿüèÿèÿÄ èÿ èÿèÿpèÿPèÿ@èÿ0èÿ èÿèÿð èÿÀ èÿ°èÿ¤xèÿtxèÿ$xèÿxèÿxèÿôwèÿÄrèÿÔwèÿÄwèÿäwèÿÄrèÿhwèÿXwèÿ8wèÿwèÿwèÿØvèÿÈvèÿèvèÿ°vèÿÈrèÿxwèÿncolumns != 0/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.hm->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupN7xgboost9predictor12CPUPredictorESt10shared_ptrIN7xgboost4data12DenseAdapterEESt10shared_ptrIN7xgboost4data10CSRAdapterEE*N7xgboost9predictorUlPKNS_16GenericParameterEE_E?/__w/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()model.learner_model_param->num_output_group != 0Unknown predictor type _Map_base::at[Internal error]: DMatrix: has expired.%umargin.Shape(0) == n_samplesmargin.Shape(1) == n_groupsInvalid shape of base_margin. Expected: (N7xgboost9PredictorEnewsize != 0With error: +Learnernum_roundCONFIG-offset:this->learner_model_param_.num_feature != 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0begin_layer >= 0best_iterationbest_scoreUnsupported prediction type:Invalid parameter "" contains whitespace. +Parameters: { ", } are not used. +Configurefixed_size->Read(&buffer[0], size) == sizebs64header != "bs64"fp.Read(&header[0], 4) == 4UInvalid model formatSAVED_PARAM_count_poisson_max_delta_stepmulti:Base64 format is no longer supported in brick.fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)data_.Size() == detail::CalcSize(shape_)Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityInvalid serialization file.sz > 0fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)N4dmlc12SerializableEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13DataSplitModeEEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplEÿÿÿÿ/__w/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater Learning rate ofL2 regularizatioL1 regularizatiofeature_selectorFeature selection or ordering meLinearTrainParamUpdate linear model according to coordinate descent algorithm.CoordinateParam/__w/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeaturetop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features./__w/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: CoordinateUpdater‡Ûÿ¡‡ÛÿÙ‡Ûÿ}†Ûÿa‡Ûÿcoordinate_paramN7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*N7xgboost6linearUlvE_E€€ÿÿÿÿÿÿÿÿUpdate linear model according to shotgun coordinate descent algorithm./__w/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. +Supported options are: {cyclic, shuffle}ýÛÿ7ýÛÿpýÛÿñýÛÿ¼üÛÿN7xgboost6linear14ShotgunUpdaterE*N7xgboost6linearUlvE_EWARNING: DEBUG: INFO: aucaucpr/__w/ml-builds/ml-builds/xgboost/src/metric/../common/common.hlabels.Size() == predts.size()Receiver Operating Characteristic Area Under the Curve.Area under PR curve for both classification and rank.labels.Size() != 0auc <= 1.0f/__w/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples./__w/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/metric/auc.ccinfo.group_ptr_.size() >= 2n_classes != 0labels.Shape(1) == 1AUC doesn't support multi-target model.ð&Ýÿ((ÝÿP'Ýÿ8'ÝÿP(Ýÿinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()Dataset is empty, or contains only positive or negative samples.auc <= 1Total AUC across groups: , valid groups: auc <= local_areaPR-AUC supports only binary relevance for learning to rank.N7xgboost6MetricEN7xgboost6metric7EvalAUCINS0_10EvalROCAUCEEEN7xgboost6metric10EvalROCAUCEN7xgboost6metric7EvalAUCINS0_9EvalPRAUCEEEN7xgboost6metric9EvalPRAUCE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E?mphegamma-nloglikgamma-deviancepoisson-nloglikmapemaermslermse/__w/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cuCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)sscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricMean absolute percentage error.Negative loglikelihood for logistic regression.Negative loglikelihood for poisson regression.Residual deviance for gamma regression.Negative log-likelihood for gamma regression.tweedie-nloglik@rho for tweedie regression.%fRooted mean square error.Rooted mean square log error.Mean absolute error.Mean Pseudo-huber error.Binary classification error.tweedie-nlogliktweedie-nloglik@info.labels.Shape(0) == info.num_row_slope for pseudo huber cannot be 0.slope != 0.0pseudo_huber_parampreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0N7xgboost6metric15PseudoErrorLossEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMAPEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E*N7xgboost6metricUlPKcE9_E@$tI$tÉ••æ$ ÷Æ°>/__w/ml-builds/ml-builds/xgboost/src/metric/metric.ccCannot find a GPU metric builder for metric . Resorting to the CPU builderUnknown metric function /src/metric/metrmerrormloglossMulticlass classification error.Multiclass negative loglikelihood./__w/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cuCheck failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchmlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class)MultiClassEvaluation: label must be in [0, num_class),preds.Size() == 0nclass >= 1U num_class= but found in labelN7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_EŽ]Bcox-nloglikNegative log partial likelihood of Cox proportional hazards model./__w/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@gptr.size() != 0Ugptr.back() == preds.Size()Check failed: tparam_label size predict size not matchmust specify group when constructing rank fileEvalRank: group structure must match number of prediction%u[-]?preAMS metric for higgs.precision@k for rank.ndcg@k for rank.map@k for rank.Check failed: !collective::IsDistributed()Cox metric does not support distributed evaluationmetric AMS do not support distributed evaluationbest-ams-ratio=<’áÿ,“áÿ|’áÿd’áÿl“áÿÀþáÿpýáÿÿáÿÿáÿÐâÿN7xgboost6metric14EvalRankConfigEN7xgboost6metric7EvalAMSEN7xgboost6metric8EvalRankEN7xgboost6metric13EvalPrecisionEN7xgboost6metric8EvalNDCGEN7xgboost6metric7EvalMAPEN7xgboost6metric7EvalCoxE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E$@aft-nloglikinterval-regression-accuracyNegative log likelihood of Accelerated Failure Time model./__w/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuCheck failed: metric_AFT metric must be configured first, with distribution type and scaleUnknown probability distributionaft_loss_parampreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()N7xgboost6metric20AFTNLogLikDispatcherEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common18NormalDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common20LogisticDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common19ExtremeDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_30EvalIntervalRegressionAccuracyEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_Eê-™—q='ö“ @Í;fž ö?/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.cc/__w/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hCheck failed: alpha >= 0 && alpha <= 1Check failed: h_row_set.empty()begin_pos <= sorted_pos.size()Check failed: !position.empty()h_node_ptr.back() <= info.num_row_/__w/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.hk + 1 < h_node_ptr.size()k >= 0nptr.size() > 0n_unique == n_leafø-äÿ8.äÿÈ,äÿ .äÿ`.äÿ/__w/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /__w/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndataNumber of weights should be equal to number of data points.info.weights_.Size() == ndataUnrecognized distributionsurvival:aftAFT loss functionN7xgboost3obj6AFTObjE*N7xgboost3objUlvE_E.À.@¼‰Ø—²Òœ<@@pApÁHinge loss. Expects labels to be in [0,1f]/__w/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedinfo.labels.Size() != 0Ulabel set cannot be emptypreds.size=, label.size=binary:hingeN7xgboost3obj8HingeObjE*N7xgboost3objUlvE_E€Number of output class in the multi-class classification.Softmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.SoftmaxMultiClassParam/__w/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. +SoftmaxMultiClassObj: label must be in [0, num_class).label.Size() * num_class: preds.Size(): softmax_multiclass_paramN7xgboost3obj20SoftmaxMultiClassObjE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E ²Òœ<Objective candidate: Unknown objective function: `` +Check failed: base_score/__w/ml-builds/ml-builds/xgboost/src/objective/objective.ccN7xgboost11ObjFunctionELambdaRank with NDCG as objective.LambdaRank with MAP as objective.num_pairsamplefix_list_weightNumber of pair generated for each instance.Normalize the weight of each list by this value, if equals 0, no effect will happenLambdaRankParamrank:pairwisePairwise rank objective.rank:ndcgrank:map/__w/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuComputing gradients on CPU.Check failed: gptr.size() != 0 && gptr.back() == info.labels.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: lambda_rank_paramN7xgboost3obj13LambdaRankObjINS0_28PairwiseLambdaWeightComputerEEEN7xgboost3obj13LambdaRankObjINS0_24NDCGLambdaWeightComputerEEEN7xgboost3obj13LambdaRankObjINS0_23MAPLambdaWeightComputerEEE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_Eff±Â/__w/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cureg:linear is now deprecated in favor of reg:squarederror.info.weights_.Size() == info.num_row_info.labels.Size() == preds.Size()Scale the weight of positive examples by this factorMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.Tweedie variance power. Must be between in range [1, 2).Regression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.Regression Pseudo Huber error.Poisson regression for count data.Cox regression for censored survival data (negative labels are considered censored).Gamma regression for severity data.Tweedie regression for insurance data.Invalid shape of labels.RegLossParamscale_pos_weightPoissonRegressionParamTweedieRegressionParamtweedie_variance_powerreg:squaredlogerrorreg:logisticbinary:logisticbinary:logitrawreg:linearreg:pseudohubererrorreg:tweedieMean absoluate error./__w/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: /__w/ml-builds/ml-builds/xgboost/src/objective/../common/common.hsurvival:coxreg:gammareg:absoluteerrorStringView{get<String const>(in["name"])} == StringView{"reg:absoluteerror"}PoissonRegression: label must be nonnegativeGammaRegression: label must be positive.TweedieRegression: label must be nonnegativepoisson_regression_paramtweedie_regression_paramCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!t_v.Shape(1) != 0Sum of weights is close to 0.0, skipping base score estimation.reg_loss_paramlabel must be in [0,1] for logistic regressionlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.N7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionEN7xgboost3obj17MeanAbsoluteErrorE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_E*N7xgboost3objUlvE10_Eff±Bïÿ¿€?€?€?€?íµ ÷Æ°>Make predictions using CPU./__w/ml-builds/ml-builds/xgboost/src/predictor/../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farbase_margin.Shape(1) == ngroup0bêÿ bêÿbêÿðaêÿØaêÿÈaêÿ°aêÿ˜aêÿ€aêÿ `êÿ@bêÿncolumns != 0m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.Inplace predict accepts only DMatrixProxy as input.out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupŒëÿ\ëÿ ëÿüëÿìëÿÜëÿ´ëÿ¼ëÿ¬ëÿÌëÿ´ëÿPëÿ@ëÿ ëÿëÿðëÿÀëÿ°ëÿÐëÿ˜ëÿ°ëÿ`ëÿt ëÿT ëÿ ëÿ ëÿôëÿäëÿŒëÿÄëÿ´ëÿÔëÿŒëÿh$ëÿX$ëÿ8$ëÿÈ$ëÿ˜$ëÿ°$ëÿø#ëÿ $ëÿ$ëÿh ëÿx$ëÿN7xgboost9predictor12CPUPredictorESt10shared_ptrIN7xgboost4data12DenseAdapterEESt10shared_ptrIN7xgboost4data10CSRAdapterEE*N7xgboost9predictorUlPKNS_16GenericParameterEE_E?/__w/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()model.learner_model_param->num_output_group != 0Unknown predictor type _Map_base::at[Internal error]: DMatrix: has expired.%umargin.Shape(0) == n_samplesmargin.Shape(1) == n_groupsInvalid shape of base_margin. Expected: (N7xgboost9PredictorEnewsize != 0With error: /__w/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: -€_KCPUHistMakerTraisingle_precisionUse single precision to build histd::floor(d) == d/__w/ml-builds/ml-builds/xgboost/src/tree/param.ccFound floating point number in interaction constraintsUnknown value type for interaction constraint:/__w/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccparam.num_nodes == static_cast<int>(nodes_.size())param.num_nodes == static_cast<int>(stats_.size())param.deprecated_num_roots == 1Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.fi->Read(&param, sizeof(TreeParam)) == sizeof(TreeParam)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_deletedthis->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodesget<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodesDump text representation of treeDump json representation of treeDump graphviz representation of treebasic_string::replaceUnknown Model Builder:{param.num_nodes != 0mean_values->size() > 0UCheck failed: node.IsLeaf()categories_segmentscategories_sizescategories_nodesunique_path[i].pweight == 0Unique path must have zero weightGraphvizParamTreeParamtree_paramsplit_indicessplit_typeloss_changessum_hessianbase_weightsleft_childrenright_childrenparentssplit_conditionsdefault_leftdot Line , around ^`/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././json.h, Expect '"' but reach end of line ch == '\"'Error at, Expect '"' but get 'unknown string escape \ch == ','ch == ':', Expect ':' but get ', JSON object expect '}' or ',' 'Check failed: is_numerical in feature map is categorical but tree node is numerical.Check failed: is_categoricalUnknown feature map type. in feature map is numerical but tree node is categorical.ü)êÿ¤)êÿT)êÿ¤)êÿL*êÿpos != std::string::npos/__w/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits<int>::max()number of nodes in the tree exceed 2^31ch == '{', Expect '{' but get 'j_end - j_begin != 0std::numeric_limits<bst_cat_t>::min() != max_catyes_color#0000FFno_color#FF0000rankdircondition_node_paramsleaf_node_paramsLeaf node configurationgraph_attrsEdge color when meets the node condition.Edge color when doesn't meet the node condition.Passed to graphiz via graph_attr.Conditional node configurationAny other extra attributes for graphviz `graph_attr`.num_nodesnum_deletedNumber of features used in tree construction.Size of leaf vector, reserved for vector treemin_split_lossmax_depthmax_leavesmax_bingrow_policydepthwiselossguidemax_cat_to_onehotmin_child_weightreg_lambdareg_alphasubsamplesampling_methodgradient_basedcolsample_bynodecolsample_bylevelcolsample_bytreesketch_epssketch_ratiocache_optrefresh_leafmonotone_constraintsinteraction_constraintssparse_thresholddoubleMinimum loss reduction required to make a further partition.Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policyMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featureTree 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)Maximum number of categories to use one-hot encoding based split.Minimum 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 constraintRow subsample ratio of training instance.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)Subsample ratio of columns, resample on each node (split).Subsample ratio of columns, resample on each level.Subsample ratio of columns, resample on each tree construction.EXP Param: Sketch accuracy of approximate algorithm.EXP Param: Sketch accuracy related parameter of approximate algorithm.EXP Param: Cache aware optimization.Whether the refresh updater needs to update leaf values.Constraint of variable monotonicityConstraints 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 informationpercentage threshold for treating a feature as sparse,gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}{fname}{cond}{left}{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}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}{nid}:[{fname}] yes={yes},no={no}{yes}{no} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{ "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {leaf}{stat}{newline}{indent}{nodes}{newline}{indent}{{properties} {stat}, "children": [{left}, {right} +€_Kstd::floor(d) == d/__w/ml-builds/ml-builds/xgboost/src/tree/param.ccFound floating point number in interaction constraintsUnknown value type for interaction constraint:/__w/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccparam.num_nodes == static_cast<int>(nodes_.size())param.num_nodes == static_cast<int>(stats_.size())param.deprecated_num_roots == 1Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.fi->Read(&param, sizeof(TreeParam)) == sizeof(TreeParam)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_deletedthis->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodesget<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodesDump text representation of treeDump json representation of treeDump graphviz representation of treebasic_string::replaceUnknown Model Builder:{param.num_nodes != 0mean_values->size() > 0UCheck failed: node.IsLeaf()categories_segmentscategories_sizescategories_nodesunique_path[i].pweight == 0Unique path must have zero weightGraphvizParamTreeParamtree_paramsplit_indicessplit_typeloss_changessum_hessianbase_weightsleft_childrenright_childrenparentssplit_conditionsdefault_leftdot Line , around ^`/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././json.h, Expect '"' but reach end of line ch == '\"'Error at, Expect '"' but get 'unknown string escape \ch == ','ch == ':', Expect ':' but get ', JSON object expect '}' or ',' 'Check failed: is_numerical in feature map is categorical but tree node is numerical.Check failed: is_categoricalUnknown feature map type. in feature map is numerical but tree node is categorical.d“ìÿ “ìÿ¼’ìÿ “ìÿ´“ìÿpos != std::string::npos/__w/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits<int>::max()number of nodes in the tree exceed 2^31ch == '{', Expect '{' but get 'j_end - j_begin != 0std::numeric_limits<bst_cat_t>::min() != max_catyes_color#0000FFno_color#FF0000rankdircondition_node_paramsleaf_node_paramsLeaf node configurationgraph_attrsEdge color when meets the node condition.Edge color when doesn't meet the node condition.Passed to graphiz via graph_attr.Conditional node configurationAny other extra attributes for graphviz `graph_attr`.num_nodesnum_deletedNumber of features used in tree construction.Size of leaf vector, reserved for vector treemin_split_lossmax_depthmax_leavesgrow_policydepthwiselossguidemax_cat_to_onehotmax_cat_thresholdmin_child_weightreg_lambdareg_alphasubsamplesampling_methodgradient_basedcolsample_bynodecolsample_bylevelcolsample_bytreesketch_ratiocache_optrefresh_leafmonotone_constraintsinteraction_constraintssparse_thresholddoubleMinimum loss reduction required to make a further partition.Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policyMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featureTree 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)Maximum number of categories to use one-hot encoding based split.Maximum number of categories considered for split. Used only by partition-basedsplits.Minimum 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 constraintRow subsample ratio of training instance.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)Subsample ratio of columns, resample on each node (split).Subsample ratio of columns, resample on each level.Subsample ratio of columns, resample on each tree construction.EXP Param: Sketch accuracy related parameter of approximate algorithm.EXP Param: Cache aware optimization.Whether the refresh updater needs to update leaf values.Constraint of variable monotonicityConstraints 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 informationpercentage threshold for treating a feature as sparse,gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}{fname}{cond}{left}{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}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}{nid}:[{fname}] yes={yes},no={no}{yes}{no} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{ "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {leaf}{stat}{newline}{indent}{nodes}{newline}{indent}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties}{tabs}{nid}:leaf={leaf}{stats},cover={cover}{stats}{parent}{stat} {left} {right}{parent}{nodes} , missingyesno{child}{color}{branch} {nid} -> {child} [label="{branch}" color="{color}"] {<}{params} {nid} [ label="{fname}{<}{cond}" {params}] @@ -5172,4235 +5491,4529 @@ graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs}, "gain": {loss_chg}, "cover": {sum_hess}n_nodes != 0sum_hessian.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesloss_changes.size() == n_nodesbase_weights.size() == n_nodesdefault_left.size() == n_nodesn_nodes == split_categories_segments.size()" edge{key}{value} graph [ {key}="{value}" ] The following parameters for graphviz are not recognized: Failed to parse graphviz parameters: - N7xgboost7RegTreeEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryISt6vectorIiSaIiEEEES5_EEN4dmlc9parameter10FieldEntryISt6vectorIiSaIiEEEEN7xgboost13TreeGeneratorEN7xgboost13TextGeneratorEN7xgboost13JsonGeneratorEN7xgboost17GraphvizGeneratorE*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE_E*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE0_E*N7xgboostUlRKNS_10FeatureMapENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE1_EðÿðH£gàYÃü? Èë…óÌ@„×—A ?š™™™™™É?/__w/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater Tree constructor that uses approximate histogram construction for each node./__w/ml-builds/ml-builds/xgboost/src/tree/../common/categorical.h/__w/ml-builds/ml-builds/xgboost/src/tree/param.hMax leaves and max depth cannot both be unconstrained.max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes.this->max_depth <= 31/__w/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/threading_utils.hnthreads >= 1begin < endi < ranges_.size()i < first_dimension_.size()/__w/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/row_set.hbegin + n_left + n_right == e.endn_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.end/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.hn_left + n_right == row_set_collection_[nidx].Size()/__w/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/hist_util.hid != kMax/__w/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hout_preds.DeviceIdx() == GenericParameter::kCpuIdout_preds.Size() > 0UCheck failed: p_last_treepart.Size() == n_nodes/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccout_preds.Size() == data->Info().num_row_Check failed: !features.empty()elem_of_each_node_.size() == 0Un_total_bins == page.cut.TotalBins()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hcut_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())hist_paramend > beginnid < nodes_row_ptr_[nid] == kMaxtid < nthreads_nodes == targeted_hists.size()n_nodes > 0n_batches_ >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/transform.hUpdatePositionEvaluateSplitsCheck failed: param.sampling_method != TrainParam::kGradientBasedGradient based sampling is not supported for approx tree method.BuildHistogramInitRootInitDataBuildHistogramInitRootInitDataUpdatePredictionCacheUpdatePredictionCacheGlobalApproxUpdater*N7xgboost4treeUlNS_7ObjInfoEE_EFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost11TreeUpdaterEN7xgboost4tree19GlobalApproxUpdaterEPFbRKN7xgboost4tree14CPUExpandEntryES3_ESt15_Sp_counted_ptrIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost6common13ColumnSamplerESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEÿÿÿgrow_colmakerGrow tree with parallelization over columns.ColMakerTrainParam/__w/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccCheck failed: this->ctx_Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0ridx < position_.size()ridx exceed bound ridx= pos=opt_dense_coldefault_directionlearnrightEXP Param: speed optimization for dense column.Default direction choice when encountering a missing valuecolmaker_train_paramfmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.param_.max_depth > 0exact tree method doesn't support unlimited depth./__w/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training.N7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlNS_7ObjInfoEE_Egrow_local_histmakerfi.Read(&this->size, sizeof(this->size)) == sizeof(this->size)fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry)Tree constructor that uses approximate histogram construction./__w/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.ccfv=, hist.size=hist[]=, hist.last=/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h/__w/ml-builds/ml-builds/xgboost/src/tree/./param.hret > 0U/__w/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.hFeatHelper fid exceed query bound fid * 2 + 1 < fminmax_.size()fid * 2 < fminmax_.size()Check failed: param_.max_depth > 0max_depth must be larger than 0node2workindex_[nid] == static_cast<int>(wid)istart != hist.sizeCheck failed: tree[nid].IsLeaf()summary_array_.size() == sketchs_.size()this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1offset == -2N7xgboost4tree9BaseMakerEN7xgboost4tree9HistMakerEN7xgboost4tree11CQHistMakerEZN7xgboost4tree11CQHistMaker10CreateHistERKSt6vectorINS_6detail20GradientPairInternalIfEESaIS5_EEPNS_7DMatrixERKS2_IjSaIjEERKNS_7RegTreeEEUlvE_*N7xgboost4treeUlNS_7ObjInfoEE_EÿÿÿÿÿÿÿÿÿÿÿÿPruner that prune the tree according to statistics.nid >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: !tree[pid].IsLeaf()tree pruning end, extra nodes, pruned nodes, max_depth=N7xgboost4tree10TreePrunerE*N7xgboost4treeUlNS_7ObjInfoEE_E/src/tree/update/__w/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max())Grow tree using quantized histogram./__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/categorical.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../param.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hexponent <= mod/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/row_set.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/hist_util.hQuantile::Builder/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/evaluate_splits.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/histogram.h/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/column_matrix.hsizeof(BinIdxType) == bins_type_size_any_missing == any_missing_/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/partition_builder.hany_missing == truecpu_hist_train_paramAttempted 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.htmlrow_ptr.size() >= 2/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/transform.hbase_rowid == gmat.base_rowidExpandTreeInitSamplingInitDataUpdatePredictionCacheUpdateTreeExpandTreeInitSamplingInitDataUpdatePredictionCacheUpdateTree*N7xgboost4treeUlNS_7ObjInfoEE_EN7xgboost4tree17QuantileHistMakerEN7xgboost6common6ColumnIhEEN7xgboost6common11DenseColumnIhLb1EEEN7xgboost6common12SparseColumnIhEEN7xgboost6common6ColumnItEEN7xgboost6common11DenseColumnItLb1EEEN7xgboost6common12SparseColumnItEEN7xgboost6common6ColumnIjEEN7xgboost6common11DenseColumnIjLb1EEEN7xgboost6common12SparseColumnIjEEN7xgboost6common11DenseColumnIhLb0EEEN7xgboost6common11DenseColumnItLb0EEEN7xgboost6common11DenseColumnIjLb0EEE<refreshRefresher that refreshes the weight and statistics according to data./__w/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits<unsigned>::max()N7xgboost4tree13TreeRefresherEZN7xgboost4tree13TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKSt6vectorIPNS_7RegTreeESaISC_EEEUlvE0_*N7xgboost4treeUlNS_7ObjInfoEE_ESyncher that synchronize the tree in all distributed nodes.syncN7xgboost4tree11TreeSyncherE*N7xgboost4treeUlNS_7ObjInfoEE_Erabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_buffer%lu%cinvalid format for %sDMLC_WORKER_CONNECT_RETRYrabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_enable_tcp_no_delayCreateSocket %s Error:%scannot obtain address of %sDoes not support IPv6Connect to (failed): [] -ConnectSendAllReConnectLink failure 1RecvAllReConnectLink failure 2ReConnectLink failure 3error during send SendStrFailed to shutdown due toworker256MBDMLC_NUM_ATTEMPTtask connected to the trackerReConnectLink failure 4 got new rank 0.0.0.0TryBindHostGetSockErrorReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8error during send RecvStrReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure 14AcceptReConnectLink failure 15ReConnectLink: bad socketSetNonBlock-1SetNonBlock-2SetKeepAlivefailed in ReconnectLink %[^=]=%smapred_tip_idmapreduce_task_idmapred_task_idrabit_num_trialmapred_map_tasksmapreduce_job_mapscan only call Init oncefail to get host namestartneed to assume rank structurePoll timeoutPollwrite ptr boundary checkAllreduce: boundary check[%d] read_ptr boundary checkbuffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorrabit_reduce_ring_mincount should be greater than 0invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}rabit_timeout_sec should be non negative secondRetry connect to ip(retry time sync::Invalid tracker message, init failureSocket::Close double close the socket or close without create/__w/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccmust keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rankReConnectLink fail to bind the ports specifiedReConnectLink failure, link rank inconsistentOverride a link that is activecannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkhadoop_mode is set but cannot find mapred_task_idhadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc worker/__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.htoo large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkBroadcast: root should be smaller than world sizeAllgatherRing failedBroadcast failedAllreduce failedN5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseE/rabit/src/allreDMLC_NUM_ATTEMPTDMLC_TRACKER_URIDMLC_TRACKER_PORDMLC_WORKER_CONNFailed to shutdown RABIT worker.DataType does not support bitwise or operationFailed to initialize RABIT.unknown data_typeunknown enum_opˆ­öÿX­öÿ(­öÿø¬öÿȬöÿ˜¬öÿ¨öÿ¨öÿ©öÿè¨öÿ¸¨öÿˆ¨öÿX¨öÿ(¨öÿø§öÿH©öÿxªöÿHªöÿªöÿè©öÿ¸©öÿˆ©öÿX©öÿ¨ªöÿØ«öÿ¨«öÿx«öÿH«öÿ«öÿèªöÿ¸ªöÿ¬öÿ0¯öÿ0¯öÿ¨®öÿ¨®öÿø®öÿø®öÿ¨®öÿø®öÿnot implementedRead pickle stringN5rabit5c_api11ReadWrapperEN5rabit5c_api12WriteWrapperEthe rabit has not been initializedcannot initialize reduce handle twicemust initialize handle to call AllReduce/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hLibSVMParserParamLibFMParserParamCSVParserParamCheck failed: TryLoadCache()failed to build cache file finish reading at /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: out_data_ != NULLCalling Value at beginning or end?/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNextNo thread/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.h.split.partname_cache.size() == 1UInvalid uri argument format for key in arg for value in arg name_args.size() == 1U/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.honly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Check failed: std::getline(is, kv.second)/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hCheck failed: base <= 10 && base >= 2sign == trueUnknown data type /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccFile 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.libfmIf >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_columndelimiterweight_columnColumn index (0-based) that will put into label.Delimiter used in the csv file.Column index that will put into instance weights./__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hlabel.size() + 1 == offset.size()Check failed: offset.back() == value.size() || value.size() == 0offset.back() == index.size()param_.format == "csv"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hCheck failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsCheck failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"Check failed: fi->Read(&label)Check failed: fi->Read(&weight)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))Bad RowBlock formatCheck failed: fi->Read(&qid)MB read,finish reading at %g MB/sec/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Delimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()Check failed: out->field.size() == out->index.size()qid:/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/././text_parser.hchunk.size != 0UNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEEN4dmlc12ScopedThreadEN4dmlc8DataIterINS_8RowBlockIjfEEEEN4dmlc12RowBlockIterIjfEEN4dmlc8DataIterINS_8RowBlockImfEEEEN4dmlc12RowBlockIterImfEEN4dmlc8DataIterINS_8RowBlockIjiEEEEN4dmlc12RowBlockIterIjiEEN4dmlc8DataIterINS_8RowBlockImiEEEEN4dmlc12RowBlockIterImiEEN4dmlc8DataIterINS_8RowBlockIjlEEEEN4dmlc12RowBlockIterIjlEEN4dmlc8DataIterINS_8RowBlockImlEEEEN4dmlc12RowBlockIterImlEEN4dmlc6ParserIjfEEN4dmlc6ParserImfEEN4dmlc6ParserIjiEEN4dmlc6ParserImiEEN4dmlc6ParserIjlEEN4dmlc6ParserImlEEN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEEN4dmlc4data11DiskRowIterIjfEEN4dmlc4data12BasicRowIterIjfEEN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEEN4dmlc4data11DiskRowIterImfEEN4dmlc4data12BasicRowIterImfEEN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEEN4dmlc4data11DiskRowIterIjiEEN4dmlc4data12BasicRowIterIjiEEN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEEN4dmlc4data11DiskRowIterImiEEN4dmlc4data12BasicRowIterImiEEN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEEN4dmlc4data11DiskRowIterIjlEEN4dmlc4data12BasicRowIterIjlEEN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEEN4dmlc4data11DiskRowIterImlEEN4dmlc4data12BasicRowIterImlEEN4dmlc4data10ParserImplIjfEEN4dmlc4data14TextParserBaseIjfEEN4dmlc4data12LibSVMParserIjfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc4data14ThreadedParserIjfEEN4dmlc4data10ParserImplImfEEN4dmlc4data14TextParserBaseImfEEN4dmlc4data12LibSVMParserImfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc4data14ThreadedParserImfEEN4dmlc4data11LibFMParserIjfEEN4dmlc4data11LibFMParserImfEEN4dmlc4data9CSVParserIjfEEN4dmlc4data9CSVParserImfEEN4dmlc4data10ParserImplIjiEEN4dmlc4data14TextParserBaseIjiEEN4dmlc4data9CSVParserIjiEEN4dmlc4data10ParserImplImiEEN4dmlc4data14TextParserBaseImiEEN4dmlc4data9CSVParserImiEEN4dmlc4data10ParserImplIjlEEN4dmlc4data14TextParserBaseIjlEEN4dmlc4data9CSVParserIjlEEN4dmlc4data10ParserImplImlEEN4dmlc4data14TextParserBaseImlEEN4dmlc4data9CSVParserImlEEZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlPPSt6vectorINS0_17RowBlockContainerIjfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlvE0_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlvE0_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjfEEE_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImfEEE_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjiEEE_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImiEEE_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjlEEE_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImlEEE_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerIjfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerImfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfsPlease compile with DMLC_USE_S3=1 to use S3Please compile with DMLC_USE_AZURE=1 to use AzureSingleFileSplit: fail to open invalid input parameter for InputSplit::Createneed to pass index file to use IndexedRecordIOhdfs://viewfs://s3://http://https://azure://unknown filesystem protocol stdinCheck failed: fp_ != NULLCheck failed: part < nsplitindexed_recordiounknown input split type rbBeforeFirst is not supported/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hResetPartition is not supported in CachedInputSplit/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1InputSplit do not support writeCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeCheck failed: this->InitCachedIter()Failed to initialize CachedIterFvvEN4dmlc10InputSplitEN4dmlc2io15SingleFileSplitEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc2io16CachedInputSplitEN4dmlc2io18ThreadedInputSplitEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_PFvvEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE0_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != endN4dmlc2io12LineSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0(reinterpret_cast<size_t>(begin) & 3UL) == 0U(reinterpret_cast<size_t>(end) & 3UL) == 0UCheck failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end(reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U(reinterpret_cast<size_t>(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: p[0] == RecordIOWriter::kMagicinvalid record io formatCheck failed: p >= pbegin + 2Invalid RecordIO FormatCheck failed: cflag == 1UN4dmlc2io16RecordIOSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccIndexedRecordIOSplitter does not support multiple index filesexpanded_list.size() == 1ulN4dmlc2io23IndexedRecordIOSplitterE/__w/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()file offset not calculated correctlyThis could due to compiler version, g++-4.9 is neededCannot find any files that matches the URI pattern Check failed: files_[i].size % align_bytes == 0Unexpected end of regex when escaping.Unexpected end of regex when reading control code.Unexpected end of regex when ascii character.curr=,begin=,end=,fileptr=,fileoffset=offset[ bad regex files_.size() != 0Ufile do not align by bytesNULSOHSTXETXEOTENQACKalertbackspacenewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1exclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodonetwothreefourfivesixsevenninesemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atJRSVWleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrlgraphlowerprintpunctupperxdigitUnexpected escape character.Unexpected end of regex when in an open parenthesis.Invalid special open parenthesis.Unexpected end of character class.Unexpected end of regex when in bracket expression.Unexpected character class open bracket.Unexpected character in brace expression.^$\.*+?()[]{}|.[\*^$.[\()*+?{|^$.[\*^$ + N7xgboost7RegTreeEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryISt6vectorIiSaIiEEEES5_EEN4dmlc9parameter10FieldEntryISt6vectorIiSaIiEEEEN7xgboost13TreeGeneratorEN7xgboost13TextGeneratorEN7xgboost13JsonGeneratorEN7xgboost17GraphvizGeneratorE*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE_E*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE0_E*N7xgboostUlRKNS_10FeatureMapENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE1_EðÿðH£gàYÃü? Èë…óÌ@„×—A ?/__w/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater /__w/ml-builds/ml-builds/xgboost/src/tree/../data/proxy_dmatrix.hTree constructor that uses approximate histogram construction for each node./__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/hist_util.hid != kMax/__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/threading_utils.hbegin < endi < ranges_.size()i < first_dimension_.size()end > beginnid < nodes_/__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/row_set.hbegin + n_left + n_right == e.endn_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.end/__w/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.h(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()row_ptr_[nid] == kMaxtid < nthreads_upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max())nthreads >= 1elem_of_each_node_.size() == 0U/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccn_total_bins == page.cut.TotalBins()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h/__w/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hout_preds.DeviceIdx() == GenericParameter::kCpuIdout_preds.Size() > 0UCheck failed: p_last_treepart.Size() == n_nodesout_preds.Size() == data->Info().num_row_Check failed: !features.empty()nodes == targeted_hists.size()base_rowid == gmat.base_rowid/__w/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == trueCheck failed: tree[node.node_id].IsLeaf()ptr_offset <= row_set.Data()->size()partition > 0cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max())cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max())n_nodes > 0n_batches_ >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/transform.hUpdatePositionEvaluateSplitsCheck failed: param.sampling_method != TrainParam::kGradientBasedGradient based sampling is not supported for approx tree method.LeafPartitionBuildHistogramInitRootInitDataUpdatePredictionCacheGlobalApproxUpdater*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_EFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost11TreeUpdaterEN7xgboost4tree19GlobalApproxUpdaterEN7xgboost6common6ColumnIhEEN7xgboost6common16SparseColumnIterIhEEPFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost6common6ColumnItEEN7xgboost6common16SparseColumnIterItEEN7xgboost6common6ColumnIjEEN7xgboost6common16SparseColumnIterIjEESt23_Sp_counted_ptr_inplaceIN7xgboost6common13ColumnSamplerESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoELN9__gnu_cxx12_Lock_policyE2EEÿÿÿgrow_colmakerGrow tree with parallelization over columns.ColMakerTrainParam/__w/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccCheck failed: this->ctx_Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0ridx < position_.size()ridx exceed bound ridx= pos=opt_dense_coldefault_directionlearnrightEXP Param: speed optimization for dense column.Default direction choice when encountering a missing valuecolmaker_train_paramfmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.param_.max_depth > 0exact tree method doesn't support unlimited depth./__w/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training.N7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_EPruner that prune the tree according to statistics.nid >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()tree pruning end, extra nodes, pruned nodes, max_depth=N7xgboost4tree10TreePrunerE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_E/src/tree/update/__w/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/histogram.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/transform.hGrow tree using quantized histogram.exponent <= modrow_ptr.size() >= 2Quantile::Builder/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/hist_util.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/row_set.h/__w/ml-builds/ml-builds/xgboost/src/tree/./common_row_partitioner.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/evaluate_splits.h/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/partition_builder.hInitDataInitSamplingUpdatePredictionCacheUpdateTreeExpandTreeLeafPartition*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_EN7xgboost4tree17QuantileHistMakerE<refreshRefresher that refreshes the weight and statistics according to data./__w/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits<unsigned>::max()N7xgboost4tree13TreeRefresherE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_ESyncher that synchronize the tree in all distributed nodes.syncN7xgboost4tree11TreeSyncherE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_E/__w/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccrabit_reduce_ring_mincount should be greater than 0invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}rabit_timeout_sec should be non negative second +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 failuretracker.Send(xgboost::StringView{task_id}) == task_id.size()must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rankbind(handle_, handle, sizeof(std::remove_reference_t<decltype(addr.Handle())>))getsockname(handle_, reinterpret_cast<sockaddr *>(&res_addr), &addrlen)ReConnectLink fail to bind the ports specifiedgetsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast<char *>(&error), &len)ReConnectLink failure, link rank inconsistentOverride a link that is activesetsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast<char *>(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast<char *>(&tcp_no_delay), sizeof(tcp_no_delay))cannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkhadoop_mode is set but cannot find mapred_task_idhadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc workergethostname(&buf[0], HOST_NAME_MAX)too large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkBroadcast: root should be smaller than world sizerabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_buffer%lu%cinvalid format for %sDMLC_WORKER_CONNECT_RETRYrabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_enable_tcp_no_delayConnecting to (failed): [] +ReConnectLink failure 3shutdownFailed to shutdown due toprintworker256MBDMLC_NUM_ATTEMPTtask connected to the trackerReConnectLink failure 4 got new rank listen(handle_, backlog)ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure 14acceptReConnectLink failure 15ReConnectLink: bad socketfcntlfailed in ReconnectLink %[^=]=%smapred_tip_idmapreduce_task_idmapred_task_idrabit_num_trialmapred_map_tasksmapreduce_job_mapscan only call Init oncestartneed to assume rank structurewrite ptr boundary checkAllreduce: boundary check[%d] read_ptr boundary checkbuffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorPoll timeoutFailed to poll./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hAllgatherRing failedBroadcast failedAllreduce failedN5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseE/rabit/src/allreDMLC_NUM_ATTEMPTDMLC_TRACKER_URIDMLC_TRACKER_PORDMLC_WORKER_CONNFailed to shutdown RABIT worker.DataType does not support bitwise or operationFailed to initialize RABIT.unknown data_typeunknown enum_op(ÅöÿøÄöÿÈÄöÿ˜ÄöÿhÄöÿ8Äöÿ ¿öÿ ¿öÿ¸ÀöÿˆÀöÿXÀöÿ(Àöÿø¿öÿÈ¿öÿ˜¿öÿèÀöÿÂöÿèÁöÿ¸ÁöÿˆÁöÿXÁöÿ(ÁöÿøÀöÿHÂöÿxÃöÿHÃöÿÃöÿèÂöÿ¸ÂöÿˆÂöÿXÂöÿ¨ÃöÿÐÆöÿÐÆöÿHÆöÿHÆöÿ˜Æöÿ˜ÆöÿHÆöÿ˜Æöÿthe rabit has not been initialized/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hLibSVMParserParamLibFMParserParamCSVParserParamCheck failed: TryLoadCache()failed to build cache file finish reading at /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: out_data_ != NULLCalling Value at beginning or end?/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNextNo thread.split.partname_cache.size() == 1UInvalid uri argument format for key in arg for value in arg name_args.size() == 1U/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.honly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Check failed: std::getline(is, kv.second)Check failed: base <= 10 && base >= 2sign == trueUnknown data type /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccFile 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.libfmIf >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_columndelimiterweight_columnColumn index (0-based) that will put into label.Delimiter used in the csv file.Column index that will put into instance weights./__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hlabel.size() + 1 == offset.size()Check failed: offset.back() == value.size() || value.size() == 0offset.back() == index.size()param_.format == "csv"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hCheck failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsCheck failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"Check failed: fi->Read(&label)Check failed: fi->Read(&weight)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))Bad RowBlock formatCheck failed: fi->Read(&qid)MB read,finish reading at %g MB/sec/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Delimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()Check failed: out->field.size() == out->index.size()qid:/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/././text_parser.hchunk.size != 0UNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEEN4dmlc12ScopedThreadEN4dmlc8DataIterINS_8RowBlockIjfEEEEN4dmlc12RowBlockIterIjfEEN4dmlc8DataIterINS_8RowBlockImfEEEEN4dmlc12RowBlockIterImfEEN4dmlc8DataIterINS_8RowBlockIjiEEEEN4dmlc12RowBlockIterIjiEEN4dmlc8DataIterINS_8RowBlockImiEEEEN4dmlc12RowBlockIterImiEEN4dmlc8DataIterINS_8RowBlockIjlEEEEN4dmlc12RowBlockIterIjlEEN4dmlc8DataIterINS_8RowBlockImlEEEEN4dmlc12RowBlockIterImlEEN4dmlc6ParserIjfEEN4dmlc6ParserImfEEN4dmlc6ParserIjiEEN4dmlc6ParserImiEEN4dmlc6ParserIjlEEN4dmlc6ParserImlEEN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEEN4dmlc4data11DiskRowIterIjfEEN4dmlc4data12BasicRowIterIjfEEN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEEN4dmlc4data11DiskRowIterImfEEN4dmlc4data12BasicRowIterImfEEN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEEN4dmlc4data11DiskRowIterIjiEEN4dmlc4data12BasicRowIterIjiEEN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEEN4dmlc4data11DiskRowIterImiEEN4dmlc4data12BasicRowIterImiEEN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEEN4dmlc4data11DiskRowIterIjlEEN4dmlc4data12BasicRowIterIjlEEN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEEN4dmlc4data11DiskRowIterImlEEN4dmlc4data12BasicRowIterImlEEN4dmlc4data10ParserImplIjfEEN4dmlc4data14TextParserBaseIjfEEN4dmlc4data12LibSVMParserIjfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc4data14ThreadedParserIjfEEN4dmlc4data10ParserImplImfEEN4dmlc4data14TextParserBaseImfEEN4dmlc4data12LibSVMParserImfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc4data14ThreadedParserImfEEN4dmlc4data11LibFMParserIjfEEN4dmlc4data11LibFMParserImfEEN4dmlc4data9CSVParserIjfEEN4dmlc4data9CSVParserImfEEN4dmlc4data10ParserImplIjiEEN4dmlc4data14TextParserBaseIjiEEN4dmlc4data9CSVParserIjiEEN4dmlc4data10ParserImplImiEEN4dmlc4data14TextParserBaseImiEEN4dmlc4data9CSVParserImiEEN4dmlc4data10ParserImplIjlEEN4dmlc4data14TextParserBaseIjlEEN4dmlc4data9CSVParserIjlEEN4dmlc4data10ParserImplImlEEN4dmlc4data14TextParserBaseImlEEN4dmlc4data9CSVParserImlEEZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlPPSt6vectorINS0_17RowBlockContainerIjfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlvE0_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlvE0_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjfEEE_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImfEEE_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjiEEE_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImiEEE_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjlEEE_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImlEEE_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerIjfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerImfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfsPlease compile with DMLC_USE_S3=1 to use S3Please compile with DMLC_USE_AZURE=1 to use AzureSingleFileSplit: fail to open invalid input parameter for InputSplit::Createneed to pass index file to use IndexedRecordIOhdfs://viewfs://s3://http://https://azure://unknown filesystem protocol stdinCheck failed: fp_ != NULLCheck failed: part < nsplitindexed_recordiounknown input split type rbBeforeFirst is not supported/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hResetPartition is not supported in CachedInputSplit/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1InputSplit do not support writeCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeCheck failed: this->InitCachedIter()Failed to initialize CachedIterFvvEN4dmlc10InputSplitEN4dmlc2io15SingleFileSplitEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc2io16CachedInputSplitEN4dmlc2io18ThreadedInputSplitEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_PFvvEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE0_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != endN4dmlc2io12LineSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0(reinterpret_cast<size_t>(begin) & 3UL) == 0U(reinterpret_cast<size_t>(end) & 3UL) == 0UCheck failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end(reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U(reinterpret_cast<size_t>(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: p[0] == RecordIOWriter::kMagicinvalid record io formatCheck failed: p >= pbegin + 2Invalid RecordIO FormatCheck failed: cflag == 1UN4dmlc2io16RecordIOSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccIndexedRecordIOSplitter does not support multiple index filesexpanded_list.size() == 1ulN4dmlc2io23IndexedRecordIOSplitterE/__w/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()file offset not calculated correctlyThis could due to compiler version, g++-4.9 is neededCannot find any files that matches the URI pattern Check failed: files_[i].size % align_bytes == 0Unexpected end of regex when escaping.Unexpected end of regex when reading control code.Unexpected end of regex when ascii character.curr=,begin=,end=,fileptr=,fileoffset=offset[]= bad regex files_.size() != 0Ufile do not align by bytesNULSOHSTXETXEOTENQACKalertbackspacenewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1exclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodonetwothreefourfivesixsevenninesemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atJSVWleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrlgraphlowerpunctupperxdigitUnexpected escape character.Unexpected end of regex when in an open parenthesis.Invalid special open parenthesis.Unexpected end of character class.Unexpected end of regex when in bracket expression.Unexpected character class open bracket.Unexpected character in brace expression.^$\.*+?()[]{}|.[\*^$.[\()*+?{|^$.[\*^$ .[\()*+?{|^$ -Nothing to repeat before a quantifier.Number of NFA states exceeds limit. Please use shorter regex string, or use smaller brace expression, or make _GLIBCXX_REGEX_STATE_LIMIT larger.Unexpected back-reference in polynomial mode.Back-reference index exceeds current sub-expression count.Back-reference referred to an opened sub-expression.hýÿØýÿýÿxýÿXýÿ0ýÿˆýÿPýÿýÿ¸ýÿhýÿýÿ0ýÿäýÿ ýÿDýÿtýÿ„ýÿ¤ýÿÄýÿlýÿ”ýÿÌýÿäýÿ$ýÿ”ýÿInvalid character class.Unexpected token in brace expression.Unexpected end of brace expression.Invalid range in brace expression.Invalid collate element.Invalid equivalence class.Unexpected dash in bracket expression. For POSIX syntax, a dash is not treated literally only when it is at beginning or end.Invalid range in bracket expression.Character is expected after a dash.Unexpected character in bracket expression.Parenthesis is not closed.N4dmlc2io14InputSplitBaseESt23_Sp_counted_ptr_inplaceINSt8__detail4_NFAINSt7__cxx1112regex_traitsIcEEEESaIS5_ELN9__gnu_cxx12_Lock_policyE2EENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1ELb1EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1EEEww error: LocalFileSystem.GetPathInfo: stdoutwbCheck failed: allow_null LocalFileSystem::Open "": ../__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink LocalFileSystem.ListDirectory Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET)Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamElstat(path.c_str(), &sb) == 0Couldn't remove file ~TemporaryDirectory(): /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hdmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectorySuccessfully deleted temporary directory Could not remove temporary directory N4dmlc2io10FileSystemE; ´€XËÀÿ(´\ÁÿP´8\ÁÿØb\ÁÿP;Œ\Áÿ¨S¶\Áÿø­à\Áÿˆ½ 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BBBD°JÂÿSBBŽE B(ŒD0†A8ƒM@U8G0J(B BBBøJÂÿ (JÂÿ%HƒX( <JÂÿ< HJÂÿ%HƒX4X \JÂÿYGŒA†D ƒnABCÃÆÌH ƒ†Œ „ÿÀÿ¤ ÿÀÿ2Fƒ]Ã0À ´ÿÀÿÐBŒA†D ƒG0c - AABK ô PÁÿ>AƒR bD$ -pÁÿ÷A†F CŒƒç - A < -ØIÂÿŽAƒˆX -ÜJÂÿ6l -KÂÿ6€ -4KÂÿEHƒxœ -JÂÿ‡Oƒs¸ -LKÂÿAƒ{Ô -ÀKÂÿzOƒfLÔ$LÂÿ Ç™BBŽE B(ŒA0†A8ƒDPÏ -8I0A(B BBBN h$ä`Âÿ{™BBŽB B(ŒA0†A8ƒDPØ +Nothing to repeat before a quantifier.Number of NFA states exceeds limit. Please use shorter regex string, or use smaller brace expression, or make _GLIBCXX_REGEX_STATE_LIMIT larger.Unexpected back-reference in polynomial mode.Back-reference index exceeds current sub-expression count.Back-reference referred to an opened sub-expression.€ïüÿðòüÿ¨òüÿòüÿpòüÿHòüÿ ñüÿhñüÿ0ñüÿÐðüÿ€ïüÿ¨ïüÿHïüÿüýÿ$ýÿ\ýÿŒýÿœýÿ¼ýÿÜýÿ„ýÿ¬ýÿäýÿüýÿ<ýÿ¬ýÿInvalid character class.Unexpected token in brace expression.Unexpected end of brace expression.Invalid range in brace expression.Invalid collate element.Invalid equivalence class.Unexpected dash in bracket expression. For POSIX syntax, a dash is not treated literally only when it is at beginning or end.Invalid range in bracket expression.Character is expected after a dash.Unexpected character in bracket expression.Parenthesis is not 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error: LocalFileSystem.GetPathInfo: stdoutwbCheck failed: allow_null LocalFileSystem::Open "": ../__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink LocalFileSystem.ListDirectory Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET)Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamElstat(path.c_str(), &sb) == 0Couldn't remove file ~TemporaryDirectory(): /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hdmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectorySuccessfully deleted temporary directory Could not remove temporary directory N4dmlc2io10FileSystemE;œ¾Ò¨¼ÿ¸¾¦¼ÿà¾H¦¼ÿD +r¦¼ÿXœ¦¼ÿ0Ʀ¼ÿjð¦¼ÿø„§¼ÿèD§¼ÿxn§¼ÿ8€§¼ÿL°ž¨¼ÿØ°¨¼ÿìÚ¨¼ÿÜ&਼ÿÔ(þ©¼ÿÄ<(ª¼ÿÜ\Rª¼ÿTdXª¼ÿ 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B(ŒC0†A8ƒM€Rˆ\HˆA€WˆoH˜D I€eˆaD˜D I€GˆaD˜D I€GˆbD˜D I€Pˆ^D˜D LˆaD˜D Lˆ^D˜D LˆbD˜D I€w +8A0A(B BBBA Pœh jãÿ¦¯KBBŽB B(ŒD0†A8ƒD°S +8A0A(B BBBB œðh|mãÿ¢ƒKBBŒA †A(ƒDp^xv€DˆDD˜D D¨D°IpYxu€DˆDD˜D D¨D°Mx{€DˆDD˜D D¨D°IpM(A ABBèiŒnãÿTçJBBŽG B(ŒC0†A8ƒM R¨\°H¨A X¨G°D¸DÀDÈDÐDØDàI V¨u°D¸DÀDÈDÐDØDàI W¨s°D¸DÀDÈDÐDØDàM¨|°D¸DÀDÈDÐDØDàI w +8A0A(B BBBA è|jpãÿVÿIBBŽG B(ŒC0†A8ƒG M¨d°L¨A W¨G°D¸DÀDÈDÐDØDàI V¨u°D¸DÀDÈDÐDØDàI W¨u°D¸DÀDÈDÐDØDàM¨|°D¸DÀDÈDÐDØDàI w +8A0A(B BBBA œhktqãÿ¢IBBŒA †A(ƒDp^xv€DˆDD˜D D¨D°IpYxu€DˆDD˜D D¨D°Mx{€DˆDD˜D D¨D°IpM(A ABBØl„rãÿ_{HBBŽB B(ŒA0†A8ƒD m¨C°D¸DÀDÈDÐDØDàI Y¨x°D¸DÀDÈDÐDØDàI W¨u°D¸DÀDÈDÐDØDàM¨|°D¸DÀDÈDÐDØDàI u8A0A(B BBBèältãÿT£GBBŽG B(ŒC0†A8ƒM R¨\°H¨A X¨G°D¸DÀDÈDÐDØDàI V¨u°D¸DÀDÈDÐDØDàI W¨s°D¸DÀDÈDÐDØDàM¨|°D¸DÀDÈDÐDØDàI w +8A0A(B BBBA <Ðm|uãÿ!»FBBŒD †A(ƒGPN +(A ABBD ˜nlvãÿ£FBBŒA †A(ƒDPoXb`DhDpIPHXc`DhDpIPHXc`DhDpIPQX^`DhDpMX_`DhDpMX``DhDpMXd`DhDpIPM(A ABBð¬nðwãÿ¦ FBBŽG B(ŒC0†A8ƒG€MˆdLˆA€WˆoH˜D I€fˆaD˜D I€HˆbD˜D I€HˆbD˜D I€Qˆ]D˜D MˆaD˜D Mˆ^D˜D MˆcD˜D I€w +8A0A(B BBBA 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