vendor/libxgboost.arm64.dylib in xgb-0.5.3 vs vendor/libxgboost.arm64.dylib in xgb-0.6.0

- old
+ new

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T7m +' + Ն +CBRRq@TqT`@@(a`@@(a + +Bvi@@ !g(a*fj~ _qT @@ !g(a*f + _qT3@ )G)@?T{POOWN_MgLoK#Jm+Im3Hm;GmC_w($@ +,@@@@(a@`@!@(a`__WO{|($@ TDiU} ȪT`Rh +Z@`TbbTt{COBWA_Ĩ_p  ^9h6@tt@tv@AT^r8(7`T87r8(6]T`@t_֕{R( +A"{_( +A"(__ֆ{R EA|{_O{C)@ %?T`"{AO¨_?T{AO¨_ @@`4{AO¨_ +@%_g_WO{4X@TWR8R9u . +R?qHZhT^@9 @?qHTR5b96@ @9?qT #ɚ_AT? T?T R @8jQ_%qT)R @9jQ_%qT -RJ}J+ JQ @8lQ)qCT{DOCWB_AgŨ_֕g_WO{ aG@  "aT/cC#5@(C!$5RC!R>!6R:@ ]@9* 1@_qa2A!BR.C̶@8_9h6@=;ҍ B͍?aTPcC# @H C\!$5RYCV!R!7R@ ]@9* 1@_qaA!BRC@(_9h6 @@oo7m4 cAcRBRRERҬ<Ama( ` b@ )aG)@?T@b{GOFWE_DgC_@oo6Fm58 CP@@7wCH@@8w ʳt4_# mo _WO{C aG@=397aT;/f ;@(!$5bR!Ro!\8BRk;@ ]@9* 1@_qacA!BR_;@;8_9h6@nl;tRHUr#o+=[Cc#` B O!R;@#Em`"*Z )aG)@?aTN!N{QOPWO_NoM#Lm_֙! 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*ddddddddddddddddddddddddddddddddddddddddddd dddCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC$CCC9C -)>Cg3/7: #'7+/;3!HHH[/#-N4dmlc2io14InputSplitBaseENSt3__111__end_stateIcEENSt3__16__nodeIcEENSt3__120__shared_ptr_pointerIPNS_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEENSt3__114default_deleteINS_13__empty_stateIcEEEENSt3__113__empty_stateIcEENSt3__116__owns_one_stateIcEENSt3__115__has_one_stateIcEENSt3__110__l_anchorIcEENSt3__110__r_anchorIcEENSt3__115__word_boundaryIcNS_12regex_traitsIcEEEENSt3__111__lookaheadIcNS_12regex_traitsIcEEEENSt3__123__match_any_but_newlineIcEENSt3__118__match_char_icaseIcNS_12regex_traitsIcEEEENSt3__120__match_char_collateIcNS_12regex_traitsIcEEEENSt3__112__match_charIcEENSt3__116__back_ref_icaseIcNS_12regex_traitsIcEEEENSt3__118__back_ref_collateIcNS_12regex_traitsIcEEEENSt3__110__back_refIcEENSt3__120__bracket_expressionIcNS_12regex_traitsIcEEEENSt3__128__begin_marked_subexpressionIcEENSt3__126__end_marked_subexpressionIcEENSt3__16__loopIcEENSt3__117__owns_two_statesIcEENSt3__117__repeat_one_loopIcEENSt3__111__alternateIcEENSt3__121__empty_non_own_stateIcEENSt3__111__match_anyIcEEN4dmlc2io10FileSystemEN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamE/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccCheck failed: ec == std::errc(): Unknown global parameters: { }Check failed: eXGBoost distributed mode detected, will split data among workersDMatrix/Booster has not been intialized or has already been disposed.Check failed: p_mCheck failed: mCurrent DMatrix type does not support set data.nthreadstatic_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structurebinary saving only supported by SimpleDMatrixCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupBooster has not been intialized or has already been disposed.DMatrix has not been intialized or has already been disposed.iteration_beginiteration_endstrict_shapejsonstr.size() > 2str[0] == '{'rwCheck failed: p_outtextfeature_namefeature_typeUnknown field for Booster feature info:allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset IteratorAdapterCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support./Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Not implemented./Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.hget<Array const>(jinterface).size() == 1Column: typestrn_bits == num_rowsShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.Check failed: array.find("mask") == array.cend()Masked array is not yet supported.streamversionMissing `version' field for array interfaceMissing `typestr' field for array interfacetypestr.size() == 3typestr.front() != '>'shapeMissing `shape' field for array interfaceMissing `data' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' are supported.`typestr' should be of format <endian><type><size of type in bytes>.Big endian is not supported. is not supported.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. -Please verify the `__cuda_array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html -Or open an issue.j_shape.size() == 2Only 1-D and 2-D arrays are supported.shape.first * shape.second == 0Empty data with non-zero shape.Empty data passed in.j_shape.size() == 1type_length == 1mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.stridesstrides.size() == 1get<Integer>(strides.at(0)) == type_lengthMemory should be contigious.j_strides.size() <= 2Check failed: validInvalid strides in array.Only dimensional array is valid.Check failed: num_rows == 1 || num_cols == 1Array should be a vector instead of matrix.missing/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hInvalid missing value: gblineardartgbtreegbtree_train_paramnum_parallel_treeUnknown booster:chunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rowswrite position exceed fixed buffer size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer lengthfid == static_cast<int>(names_.size())iqintfloatunknown feature type, use i for indicator and q for quantityCannot find argument 'get<Integer const>(config["cache_id"]) == 0Cache ID is not supported yetCheck failed: p_predtNaN0E0;unknown;unknown;0;0;;/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccsibling.size() == sizeparent.size() == sizecut.Values().size() > 0Ucurent_bin_size == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hbins_type_size == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.hsizeof(BinIdxType) == bins_type_size_/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptrbatch_size < offset_vec.size()ibegin + inst.size() == iendCheck failed: false/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.ccpointer_ <= buffer_.size()Check failed: outfile://://Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedrb/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc\\\"\b\f\n\r\t\u%04xNumberArrayObject of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first.Unknown construct, around character position: , " "\0Unknown escapenullExpecting null value "null"ch != -1cursor_.Pos(): raw_str_.size():Expecting digitbeg <= size_Expecting: "", got: "EOF"\0"HostSketchContainer/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cccolumns_size_.size() != 0count <= total_entriesPushRowPagesketches_.size() == info.num_col_worker_segments.size() >= 1AllReducenum_cuts.size() == 0n_columns == sketches_.size()Number of columns differs across workersCheck failed: reduced[i].dataCheck failed: a.dataCheck failed: a.data && reduced[fid].datacuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max()cut_size > cuts->cut_ptrs_.HostVector().back()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hinvalid init parameterCheck failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps))base_rowid < group_ptr.back()Row: is not found in any group.mingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0Check failed: worker_feature.data()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()aft_loss_distributionnormalextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time model optional, default=, requiredInvalid Input: '' is not presented/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h has already been registered in Enum ( exisit!Enums: ), statisticcountelapsed/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor: ========From rank: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccInvaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again.fi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccInput data contains `inf` or `nan`num_rownum_colnum_nonzerolabelsgroup_ptrweightsbase_marginlabels_lower_boundlabels_upper_boundfield_cnt == kNumFieldWrong number of fieldscategoricalAll feature_types must be one of {int, float, i, q, categorical}.major == 1Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.Check failed: fi->Read(&num_field)MetaInfo: invalid formatnum_field >= expectedMetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.)MetaInfo: the given binary file contains extra fields which will be ignored.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.labelUnknown data typeweightWeights must be positive values.qidCheck failed: non_dec`qid` must be sorted in non-decreasing order along with data.label_lower_boundlabel_upper_boundfeature_weightsFeature weight must be greater than 0.Unknown key for MetaInfo: Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info.size == this->num_col_Length of must be equal to number of columns.Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.group_ptr_.back() == num_row_Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.weights_.Size() == num_row_Size of weights must equal to number of rows.labels_.Size() == num_row_Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weigths.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..r-XGBOOST_TEST_NPARTLoad part of data partsauto matrix with entries loaded from csvlibsvmNo format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format=No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csvEncountered parser error: -.group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from self_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.hfloat32float64bool8int32int8int16int64/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hUnknown data table type.Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: MetaInfo: Invalid format. Check failed: strm->Read(&name)name == expected_name Expected field: , got: Check failed: strm->Read(&type_val)Check failed: type == expected_typeExpected field of type: got field type: Check failed: strm->Read(&is_scalar)Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: strm->Read(field)Check failed: !is_scalar to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCouldn't remove external memory cache file ; you may want to remove it manually.row.pagepage->Size() == 0Check failed: qids.empty() || qids.size() == info.num_row_SparsePageSource Finished writing to cache_shards.size() != 0U.fmt-rawCache file exists already; Is there another DMatrix with the same cache prefix? It can be caused by previously used DMatrix that hasn't been collected by language environment garbage collector. Otherwise please remove it manually./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hname_shards.size() == format_shards.size()thread constructor failedSparsePageWriter Finished writing to Unknown format type info.name_shards.size() != 0UCheck failed: finfo->Read(&tmagic)tmagic == kMagicinvalid format, magic number mismatchCheck failed: fi->Read(&format)Invalid page format/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)unique_lock::unlock: not lockedCheck failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Check failed: out_data_ != NULLCalling Value at beginning or end?Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroyNo threadCheck failed: mutex_.try_lock()Multiple threads attempting to use prefetcherkey >= builder_base_row_offset/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required.ellpack.page/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page_source.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPageSource is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same<AdapterT, CSCAdapter>::value)Expecting CSCAdapterCheck failed: param != BatchParam{}Batch parameter is not initialized.param.gpu_id >= 0param.max_bin >= 2Check failed: in_stream->Read(&tmagic)invalid input file format/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hCheck failed: page_ != nullptr/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hCheck failed: source_ != nullptr.col.pageWriting to in MB/s, writtenCSCPageSource: Finished writing to Check failed: *out_page == nullptrCheck failed: qrecycle_.Pop(out_page).sorted.col.pageSortedCSCPageSource: Finished writing to /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Raw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage filefi->Read(dmlc::BeginPtr(data_vec), (page->data).Size() * sizeof(Entry)) == (page->data).Size() * sizeof(Entry)fid + 1 < disk_offset_.size()disk_offset_[fid] > curr_offsetfi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i], size_to_read * sizeof(Entry)) == size_to_read * sizeof(Entry)Check failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.ccoffset + n_rows + 1 <= in_offset.size()h_offset.size() > 0n_entries == in_offset.at(offset + n_rows) - in_offset.at(offset)out->Size() != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cclayer_begin == 0Linear booster does not support prediction range.layer_end == 0GBLinearTrainParamLinear booster, implement generalized linear model.updatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hget<String>(in["name"]) == "gblinear"modelgpu_coord_descent/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(&param_, sizeof(param_)) == sizeof(param_)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster.DoBoostPredictBatchPredictBatchInternalbase_margin.size() == nsize * ngroupInplace predict is not supported by current booster.gblinear does not support prediction of leaf index { "bias": [, ], "weight": [ ] }bias: +&-4 ") N5rabit5c_api11ReadWrapperEN5rabit5c_api12WriteWrapperE1@ 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*fffffffffffffffffffffffffffffffffffffffffff fff).38=j[ !%(,"&*.26:!III[/#-N4dmlc2io14InputSplitBaseENSt3__111__end_stateIcEENSt3__16__nodeIcEENSt3__120__shared_ptr_pointerIPNS_13__empty_stateIcEENS_10shared_ptrIS2_E27__shared_ptr_default_deleteIS2_S2_EENS_9allocatorIS2_EEEENSt3__110shared_ptrINS_13__empty_stateIcEEE27__shared_ptr_default_deleteIS2_S2_EENSt3__113__empty_stateIcEENSt3__116__owns_one_stateIcEENSt3__115__has_one_stateIcEENSt3__110__l_anchorIcEENSt3__110__r_anchorIcEENSt3__115__word_boundaryIcNS_12regex_traitsIcEEEENSt3__111__lookaheadIcNS_12regex_traitsIcEEEENSt3__123__match_any_but_newlineIcEENSt3__118__match_char_icaseIcNS_12regex_traitsIcEEEENSt3__120__match_char_collateIcNS_12regex_traitsIcEEEENSt3__112__match_charIcEENSt3__116__back_ref_icaseIcNS_12regex_traitsIcEEEENSt3__118__back_ref_collateIcNS_12regex_traitsIcEEEENSt3__110__back_refIcEENSt3__120__bracket_expressionIcNS_12regex_traitsIcEEEENSt3__128__begin_marked_subexpressionIcEENSt3__126__end_marked_subexpressionIcEENSt3__16__loopIcEENSt3__117__owns_two_statesIcEENSt3__117__repeat_one_loopIcEENSt3__111__alternateIcEENSt3__121__empty_non_own_stateIcEENSt3__111__match_anyIcEEN4dmlc2io10FileSystemEN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamE/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been initialized or has already been disposed.static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0U: slice does not support group structureCheck failed: ec == std::errc()Unknown global parameters: { }Cannot find argument 'Check failed: e (XGBoost distributed mode detected, will split data among workers/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset IteratorAdapterCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.missingcache_prefixnthread/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Not implemented.Check failed: p_mCheck failed: mCurrent DMatrix type does not support set data./Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.hget<Array const>(jinterface).size() == 1Column: typestrn_bits == num_rowsShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.Check failed: array.find("mask") == array.cend()Masked array is not yet supported.streamversionMissing `version' field for array interfaceMissing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4shapeMissing `shape' field for array interfaceMissing `data' field for array interfaceOnly version <= 3 of `__cuda_array_interface__/__array_interface__' are supported.`typestr' should be of format <endian><type><size of type in bytes>.map::at: key not foundCheck failed: sizeof(long double) == 16128-bit floating point is not supported on current platform.- is not supported.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. +Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html +Or open an issue.j_shape.size() == 2Only 1-D and 2-D arrays are supported.shape.first * shape.second == 0Empty data with non-zero shape.Empty data passed in.j_shape.size() == 1type_length == 1mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.stridesstrides.size() == 1get<Integer>(strides.at(0)) == type_lengthMemory should be contigious.j_strides.size() <= 2Check failed: validInvalid strides in array. strides: (), shape: ()Only dimensional array is valid.Check failed: num_rows == 1 || num_cols == 1Array should be a vector instead of matrix./Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hInvalid missing value: binary saving only supported by SimpleDMatrixCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupgblineardartgbtreegbtree_train_paramnum_parallel_treeUnknown booster:Booster has not been initialized or has already been disposed.DMatrix has not been initialized or has already been disposed.iteration_beginiteration_endntree_limitCheck failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.strict_shapechunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rowsget<Integer const>(config["cache_id"]) == 0Cache ID is not supported yetCheck failed: p_predtjsonstr.size() > 2str[0] == '{'rwread can not have position excceed buffer lengthCheck failed: p_outtextcustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.fqfeature_map.Size() == n_featuresfid == static_cast<int>(names_.size())iintfloatcunknown feature type, use i for indicator and q for quantityfeature_namefeature_typeUnknown field for Booster feature info:importance_typefeature_mapfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()idx < names_.size()FeatureMap feature index exceed boundNaN0E0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccCheck failed: falsesibling.size() == sizeparent.size() == size;unknown;unknown;0;0;;/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.ccpointer_ <= buffer_.size()Check failed: outfile://://rbOpening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hNot implemented/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc\\\"\b\f\n\r\t\u%04xNumberArrayUnknown construct, around character position: , " "\0"Unknown escapenullExpecting null value "null"ch != -1cursor_.Pos(): raw_str_.size():Expecting digitObject of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first.beg <= size_Expecting: "", got: "EOF"\0"HostSketchContainer/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cccolumns_size_.size() != 0n_threads_ >= 1count <= total_entriesPushRowPagesketches_.size() == n_columnsweights.size() == info.num_row_worker_segments.size() >= 1AllReducenum_cuts.size() == 0n_columns == sketches_.size()Number of columns differs across workerscuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max()cut_size > cuts->cut_ptrs_.HostVector().back()group_ptr.size() >= 2group_ptr.back() == n_sampleshessian.size() == info.num_row_group_ptr.back() == hessian.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hinvalid init parameterCheck failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps))mingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0Check failed: reduced[i].dataCheck failed: worker_feature.data()Check failed: a.dataCheck failed: a.data && reduced[fidx].data/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()aft_loss_distributionnormalextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time modelRequired parameter of optional, default=key Enum ( exisit!Enums: ), No conversion could be performed*p == ')'Invalid NAN literal/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor (): ========/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccInvaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again.fi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccInput data contains `inf` or `nan`All feature_types must be one of {int, float, i, q, c}.labelUnknown data typeLabel contains NaN, infinity or a value too large.weightWeights must be positive values.base_marginqidCheck failed: non_dec`qid` must be sorted in non-decreasing order along with data.label_lower_boundlabel_upper_boundfeature_weightsFeature weight must be greater than 0.Unknown key for MetaInfo: matrix with entries loaded from transpose.offset.Size() == num_columns + 1self_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/iterative_device_dmatrix.hSlicing DMatrix is not supported for Device DMatrix./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptrkey >= builder_base_row_offsetfloat32float64bool8int32int8int16int64/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hUnknown data table type.num_rownum_colnum_nonzerolabelsgroup_ptrweightslabels_lower_boundlabels_upper_boundfield_cnt == kNumFieldWrong number of fieldsmajor == 1Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.Check failed: fi->Read(&num_field)MetaInfo: invalid formatnum_field >= expectedMetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.)MetaInfo: the given binary file contains extra fields which will be ignored.MetaInfo: Invalid format. Check failed: strm->Read(&name)name == expected_name Expected field: , got: Check failed: strm->Read(&type_val)Check failed: type == expected_typeExpected field of type: got field type: Check failed: strm->Read(&is_scalar)Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: strm->Read(field)Check failed: !is_scalar to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info.size == this->num_col_Length of must be equal to number of columns.Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.group_ptr_.back() == num_row_Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.weights_.Size() == num_row_Size of weights must equal to number of rows.labels_.Size() == num_row_Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weigths.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.hcache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..rXGBOOST_TEST_NPARTLoad part of data partsautocsvlibsvmNo format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format=No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csvEncountered parser error: +.group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()Check failed: parser_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.cccut.Values().size() > 0Ucurent_bin_size == common::kUint32BinsTypeSizen_threads >= 1row_ptr.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.hbatch_size < offset_vec.size()ibegin + inst.size() == iendrawRaw GHistIndex binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccuts_.Values().size() != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hCheck failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const &f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.fi->Tell() == offsetCheck failed: fmt->Read(page.get(), fi.get())/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type format.front() == '.'thread constructor failedCheck failed: !cache_info_->writtenCheck failed: page_this->count_ == this->n_batches_this->count_ >= 1source_->Iter() == this->count_Check failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.count_ == n_batches_count_ >= 1Check failed: proxy_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.hUnknown type: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./any.hCheck failed: type_ != nullptrThe any container is empty requested=Check failed: *(type_->ptype_info) == typeid(T)The stored type mismatch stored=/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same<AdapterT, CSCAdapter>::value)Expecting CSCAdapter/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hCheck failed: param != BatchParam{}Batch parameter is not initialized.param.gpu_id >= 0param.max_bin >= 2param.gpu_id == -1batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid input file formattmagic == kMagicinvalid format, magic number mismatch.row.page/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_DMatrix-rinfo_.num_col_ != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hMake cache:Check failed: proxy_handleInvalid proxy handle..col.pagethis->Info().num_col_ != 0.sorted.col.pagethis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size().gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_Check failed: kv.secondCouldn't remove external memory cache file ; you may want to remove it manuallySlicing DMatrix is not supported for external memory.iter_.Next() == 1Must have at least 1 batch.Raw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage filen_bytes == (page->data).Size() * sizeof(Entry)Check failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cclayer_begin == 0Linear booster does not support prediction range.layer_end == 0GBLinearTrainParamLinear booster, implement generalized linear model.updatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.get<String>(in["name"]) == "gblinear"modelgpu_coord_descentcoord_descentLoading configuration on a CPU only machine. Changing updater to `coord_descent`./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(&param_, sizeof(param_)) == sizeof(param_)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster.DoBoostPredictBatchPredictBatchInternalbase_margin.size() == nsize * ngroupInplace predict is not supported by current booster.gblinear does not support prediction of leaf index { "bias": [ ], "weight": [ ] }bias: weight: -gblinear_train_paramCheck failed: this->updater_DeprecatedGBLinearModelParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type cpu_predictorGBTree/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccDANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.Check failed: this->configured_Check failed: tparam_.GetInitialised()Using updaters: Tree method is automatically selected to be 'approx' for distributed training.Tree method is automatically set to 'approx' since external-memory data matrix is used.Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'.Using tree method: grow_histmaker,prunegrow_colmaker,pruneTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.grow_quantile_histmakergrow_gpu_histUnknown tree_method () detectedBoostNewTreesngroup != 0in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsups.size() == updaters_.size()Internal Error: mismatched updater sequence. -Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree()Updater: `` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.CommitModelget<String>(in["name"]) == "gbtree"Loading from a raw memory buffer on CPU only machine. Changing predictor to auto.predictortree_methodLoading from a raw memory buffer on CPU only machine. Changing tree_method to hist.specified_updaterdefaultprocess_typeCheck failed: configured_Check failed: p_gbtreelayer_end > layer_beginstep >= 1Check failed: this->model_.trees_to_update.empty()out_preds->version == 0Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.GBTreeModelParamGBTreeTrainParamDartTrainParamTree booster, gradient boosted trees.Tree booster, dart./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin <= tree_endXGBoost version not compiled with OneAPI support.tparam_.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0Unsupported data type for inplace predict.Check failed: successtree_begin == 0Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead. in_it < tree_endnum_treesNumber of features used for training and prediction.Reserved option for vector tree.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.updater_seqTree updater sequence.updateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.gpu_predictoroneapi_predictorPredictor algorithm typeapproxexactChoice of tree construction method. has not been registered in sample_typeuniformDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.trueget<String>(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == out_preds->predictions.Size()dart_train_paramdrop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast<int32_t>(trees.size())fi->Read(&param, sizeof(param)) == sizeof(param)GBTree: invalid model filefi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast<int>(trees.size())gbtree_model_paramtreestree_infoGlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoostLearnerModelParamLegacyLearnerTrainParamGenericParametergpu_ideval_metricnum_roundCONFIG-offset:Learner/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: IsA<Object>(in)learnerlearner_model_paramobjectivegradient_boosterboosterattributesfeature_namesfeature_types[] +Check failed: !model_.weight.empty()Model is not initializedimportance_type == "weight"gblinear only has `weight` defined for feature importance./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hvec->size() == shape[0] * shape[1]gblinear_train_paramCheck failed: this->updater_DeprecatedGBLinearModelParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type cpu_predictorGBTree/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccDANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.Check failed: this->configured_Check failed: tparam_.GetInitialised()Using updaters: Tree method is automatically selected to be 'approx' for distributed training.Tree method is automatically set to 'approx' since external-memory data matrix is used.Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'.Using tree method: grow_histmaker,prunegrow_colmaker,pruneTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.grow_quantile_histmakergrow_gpu_histUnknown tree_method () detectedups.size() == updaters_.size()Internal Error: mismatched updater sequence. +Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree()Updater: `` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.CommitModelget<String>(in["name"]) == "gbtree"Loading from a raw memory buffer on CPU only machine. Changing predictor to auto.predictortree_methodLoading from a raw memory buffer on CPU only machine. Changing tree_method to hist.specified_updaterdefaultprocess_typeCheck failed: configured_Check failed: p_gbtreethis->model_.learner_model_param->num_feature != 0layer_trees != 0layer_end > layer_beginstep >= 1this->model_.trees_to_update.size() == this->model_.trees.size()Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees.Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.GBTreeModelParamGBTreeTrainParamDartTrainParamTree booster, gradient boosted trees.Tree booster, dart.BoostNewTreesngroup != 0in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsout_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin <= tree_endXGBoost version not compiled with OneAPI support.tparam_.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0Unsupported data type for inplace predict.Check failed: successCurrent Predictor: gpu_predictortree_begin == 0Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.gaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: in_it < tree_endnum_treesNumber of features used for training and prediction.size_leaf_vectorReserved option for vector tree.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.updater_seqTree updater sequence.updateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.oneapi_predictorPredictor algorithm typeapproxexactChoice of tree construction method. has not been registered in sample_typeDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.learning_rateLearning rate(step size) of update.trueget<String>(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == out_preds->predictions.Size()dart_train_paramdrop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast<int32_t>(trees.size())fi->Read(&param, sizeof(param)) == sizeof(param)GBTree: invalid model filefi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast<int>(trees.size())gbtree_model_paramtreestree_infoGlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoostLearnerModelParamLegacyLearnerTrainParamGenericParametergpu_ideval_metricallocator<T>::allocate(size_t n) 'n' exceeds maximum supported sizenum_roundCONFIG-offset:Learner/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: IsA<Object>(in)learnerlearner_model_paramobjectivegradient_boosterboosterattributesfeature_namesfeature_types[] Stack trace: - [bt] (_Z%zu/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from to num_classbase_score__', Possible Arguments: ----------------- - : Check failed: !this->need_configuration_Call Configure before saving model.Check failed: ret.ec == std::errc()Configure/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/generic_parameters.h -n_gpus: n_gpusCheck failed: matrix.firstCheck failed: !matrix.second.ref.expired()num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max())Unfortunately, XGBoost does not support data matrices with features or greatermparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface? (multi:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7_paramInvalid parameter "" contains whitespace. -Parameters: { "", } might not be used. + [bt] (_Z%zu/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from to num_featurenum_classbase_score__ : Check failed: !this->need_configuration_Call Configure before saving model.Check failed: ret.ec == std::errc()ConfigureCheck failed: matrix.firstCheck failed: !matrix.second.ref.expired()Check failed: num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max())Unfortunately, XGBoost does not support data matrices with features or greatermparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface? vs. 0multi:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7_paramInvalid parameter "" contains whitespace. +Parameters: { ", } might not be used. - This may not be accurate due to some parameters are only used in language bindings but - passed down to XGBoost core. Or some parameters are not used but slip through this - verification. Please open an issue if you find above cases. + This could be a false alarm, with some parameters getting used by language bindings but + then being mistakenly passed down to XGBoost core, or some parameter actually being used + but getting flagged wrongly here. Please open an issue if you find any such cases. -UpdateOneIterPredictRawGetGradientCheck failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterEvalOneIterbinary:logisticerrorlogloss1.3.0binary:logitrawauc1.4.0merrorStarting in XGBoost , the default evaluation metric used with the objective '' was changed from '' to ''. Explicitly set eval_metric if you'd like to restore the old behavior.multiple_predictions <= 1Perform one kind of prediction at a time.Unsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.Read(&header[0], 4) == 4Ufi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)SAVED_PARAM_count_poisson_max_delta_stepmulti:metricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibility;begin_layer >= 0learner_train_paramgeneric_paramModelConfigfp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_ +UpdateOneIterPredictRawGetGradientCheck failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterEvalOneIterbinary:logisticerrorlogloss1.3.0binary:logitrawauc1.4.0merrorStarting in XGBoost , the default evaluation metric used with the objective '' was changed from '' to ''. Explicitly set eval_metric if you'd like to restore the old behavior.multiple_predictions <= 1Perform one kind of prediction at a time.Unsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.Read(&header[0], 4) == 4Ufi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)SAVED_PARAM_count_poisson_max_delta_stepmulti:metricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibility;this->learner_model_param_.num_feature != 0begin_layer >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scorelearner_train_paramgeneric_paramModelConfigfp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_ If you are loading a serialized model (like pickle in Python) generated by older XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. There's a simple script for helping the process. See: @@ -10328,1640 +10483,2393 @@ https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for reference to the script, and more details about differences between saving model and serializing. -fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0Global bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.Invalid Parameter format for expect Required parameter of No conversion could be performed*p == ')'Invalid NAN literalvalue should be smaller equal to int (non-negative)', valid values are: enum_back_map_.count(value) != 0UValue not found in enum declareddsplitcolrowData split mode for distributed training.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableGradient booster used for training.reg:squarederrorObjective function used for obtaining gradient.) -falseRandom number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode.Number of threads to use.n_jobsThe primary GPU device ordinal.fail_on_invalid_gpu_idFail with error when gpu_id is invalid.gpu_page_sizeGPU page size when running in external memory mode.validate_parametersEnable checking whether parameters are used or not. - Deprecated. Single process multi-GPU training is no longer supported. - Please switch to distributed training with one process per GPU. - This can be done using Dask or Spark. See documentation for details./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater LinearTrainParamLearning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaCoordinateParamcoord_descentUpdate linear model according to coordinate descent algorithm.top_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.linear_train_paramcoordinate_paramCoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: UpdateFeatureUpdate linear model according to shotgun coordinate descent algorithm./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. -Supported options are: {cyclic, shuffle}WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.ccCheck failed: !labels.empty()labels.size() == predts.size()n_classes != 0auc <= 1.0finfo.group_ptr_.size() >= 2Receiver Operating Characteristic Area Under the Curve./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.hinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels_.Size()auc <= 1Total AUC across groups: , valid groups: Dataset contains only positive or negative samples.rmseRooted mean square error.rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.mpheMean Pseudo Huber error.Negative loglikelihood for logistic regression.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.Binary classification error.tweedie-nlogliktweedie-nloglik@rho for tweedie regression./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cupreds.Size() == info.labels_.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classification%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)tweedie-nloglik@/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builderMulticlass classification error.mloglossMulticlass negative loglikelihood./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cupreds.Size() == 0Check failed: preds.Size() % info.labels_.Size() == 0label and prediction size not matchnclass >= 1Umlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class)MultiClassEvaluation: label must be in [0, num_class), num_class= but found in labelamsAMS metric for higgs.aucprArea under PR curve for both classification and rank.preprecision@k for rank.ndcgndcg@k for rank.mapmap@k for rank.cox-nloglikNegative log partial likelihood of Cox proportioanl hazards model./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !distributedmetric AMS do not support distributed evaluationbest-ams-ratio=info.labels_.Size() != 0Ulabel set cannot be emptylabel size predict size not matchgptr.back() == info.labels_.Size()EvalAucPR: group structure must match number of predictiondat[1] > 0.0fAUC-PR: the dataset only contains pos or neg samplesdat[0] <= dat[1]AUC-PR: AUC > 1.0Check failed: !auc_errorAUC-PR: error in calculation%u[-]?gptr.size() != 0Umust specify group when constructing rank filegptr.back() == preds.Size()EvalRank: group structure must match number of predictionCox metric does not support distributed evaluationaft-nloglikNegative log likelihood of Accelerated Failure Time model.interval-regression-accuracyaft_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Check failed: metric_AFT metric must be configured first, with distribution type and scalesurvival:aftAFT loss function/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distribution/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: binary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedpreds.size=, label.size=SoftmaxMultiClassParamSoftmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Number of output class in the multi-class classification.softmax_multiclass_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size())SoftmaxMultiClassObj: label size and pred size does not match. +fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0write position exceed fixed buffer size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hGlobal bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier. but value=' is not presentedOut of range value for , value='/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hpos <= value.length()Some trailing characters could not be parsed: ''Out of range value/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h for Parameter exceed bound [ should be greater equal to optional, default=, requiredint (non-negative)', valid values are: , enum_back_map_.count(value) != 0UValue not found in enum declareddsplitcolrowData split mode for distributed training.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableGradient booster used for training.reg:squarederrorObjective function used for obtaining gradient.1Random number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode.Number of threads to use.n_jobsThe primary GPU device ordinal.fail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not.Alias LinearTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater Learning rate of each update.L2 regularization on weights.L1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaCoordinateParamUpdate linear model according to coordinate descent algorithm.top_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.linear_train_paramcoordinate_paramCoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: UpdateFeatureUpdate linear model according to shotgun coordinate descent algorithm./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. +Supported options are: {cyclic, shuffle}WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.ccCheck failed: !labels.empty()labels.size() == predts.size()n_classes != 0auc <= 1.0finfo.group_ptr_.size() >= 2Receiver Operating Characteristic Area Under the Curve./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.hinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels_.Size()auc <= 1Total AUC across groups: , valid groups: Dataset contains only positive or negative samples.rmseRooted mean square error.rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.mpheMean Pseudo Huber error.Negative loglikelihood for logistic regression.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.Binary classification error.tweedie-nlogliktweedie-nloglik@rho for tweedie regression./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cupreds.Size() == info.labels_.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classification%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)tweedie-nloglik@/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builderMulticlass classification error.mloglossMulticlass negative loglikelihood./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cupreds.Size() == 0Check failed: preds.Size() % info.labels_.Size() == 0label and prediction size not matchnclass >= 1Umlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class)MultiClassEvaluation: label must be in [0, num_class), num_class= but found in labelamsAMS metric for higgs.aucprArea under PR curve for both classification and rank.preprecision@k for rank.ndcgndcg@k for rank.mapmap@k for rank.cox-nloglikNegative log partial likelihood of Cox proportional hazards model./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !distributedmetric AMS do not support distributed evaluationbest-ams-ratio=info.labels_.Size() != 0Ulabel set cannot be emptylabel size predict size not matchgptr.back() == info.labels_.Size()EvalAucPR: group structure must match number of predictiondat[1] > 0.0fAUC-PR: the dataset only contains pos or neg samplesdat[0] <= dat[1]AUC-PR: AUC > 1.0Check failed: !auc_errorAUC-PR: error in calculation%u[-]?gptr.size() != 0Umust specify group when constructing rank filegptr.back() == preds.Size()EvalRank: group structure must match number of predictionCox metric does not support distributed evaluationaft-nloglikNegative log likelihood of Accelerated Failure Time model.interval-regression-accuracyaft_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Check failed: metric_AFT metric must be configured first, with distribution type and scalesurvival:aftAFT loss function/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distribution/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: binary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedpreds.size=, label.size=SoftmaxMultiClassParamSoftmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Number of output class in the multi-class classification.softmax_multiclass_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size())SoftmaxMultiClassObj: label size and pred size does not match. label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class).Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.ccUnknown objective function: `` -LambdaRankParamPairwise rank objective.LambdaRank with NDCG as objective.LambdaRank with MAP as objective.rank:pairwiserank:ndcgrank:mapnum_pairsampleNumber of pair generated for each instance.fix_list_weightNormalize the weight of each list by this value, if equals 0, no effect will happenlambda_rank_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuCheck failed: gptr.size() != 0 && gptr.back() == info.labels_.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: Computing gradients on CPU.RegLossParamRegression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Regression Pseudo Huber error.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.reg:linearPoissonRegressionParamPossion regression for count data.survival:coxCox regression for censored survival data (negative labels are considered censored).reg:gammaGamma regression for severity data.TweedieRegressionParamreg:tweedieTweedie regression for insurance data.reg:squaredlogerrorreg:logisticreg:pseudohubererrorscale_pos_weightScale the weight of positive examples by this factorreg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuLoss: label must be greater than -1 for rmsle so that log(label + 1) can be valid.label must be in [0,1] for logistic regression/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror.Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression.poisson_regression_paramPoissonRegression: label must be nonnegativeCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!GammaRegression: label must be positive.tweedie_variance_powerTweedie variance power. Must be between in range [1, 2).tweedie_regression_paramTweedieRegression: label must be nonnegativeMake predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.learner_model_param->num_output_group != 0out_preds->Size() == nIgnoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farm->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.hCheck failed: type_ != nullptrThe any container is empty requested=Check failed: *(type_->ptype_info) == typeid(T)The stored type mismatch stored=ncolumns != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()[Internal error]: DMatrix: has expired.Unknown predictor type unordered_map::at: key not found/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: -newsize != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:TreeParamTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccUnknown Model Builder:Dump text representation of treeDump json representation of treeGraphvizParamdotDump graphviz representation of treeCheck failed: node.IsLeaf()Check failed: fi->Read(&param, sizeof(TreeParam)) == sizeof(TreeParam)param.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast<int>(deleted_nodes_.size()) == param.num_deletedparam.num_nodes == static_cast<int>(nodes_.size())param.num_nodes == static_cast<int>(stats_.size())param.deprecated_num_roots == 1categories_segmentscategories_sizescategories_nodescategoriesj_end - j_begin != 0std::numeric_limits<bst_cat_t>::min() != max_catthis->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodestree_paramn_nodes != 0loss_changesloss_changes.size() == n_nodessum_hessiansum_hessian.size() == n_nodesbase_weightsbase_weights.size() == n_nodesleft_childrenlefts.size() == n_nodesright_childrenrights.size() == n_nodesparentsparents.size() == n_nodessplit_indicesindices.size() == n_nodessplit_conditionsconds.size() == n_nodesdefault_leftdefault_left.size() == n_nodessplit_typen_nodes == split_categories_segments_.size()static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodesnum_nodesget<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)this->node_mean_values_.size() > 0Uunique_path[i].pweight == 0Unique path must have zero weight:%02d:%02d:%02d) DMLC_LOG_STACK_TRACE_DEPTH/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits<int>::max()number of nodes in the tree exceed 2^31 vs. num_featureNumber of features used in tree construction.num_deletedsize_leaf_vectorSize of leaf vector, reserved for vector tree but value=', for Parameter exceed bound [ should be greater equal to optional, default=map::at: key not foundkey learning_rateLearning rate(step size) of update.min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.max_binif using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightdefault_directionlearnrightDefault direction choice when encountering a missing valueMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methodgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_epsEXP Param: Sketch accuracy of approximate algorithm.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsplit_evaluatorelastic_net,monotonicThe criteria to use for ranking splitssparse_thresholdpercentage threshold for treating a feature as sparseenable_feature_groupingif >0, enable feature grouping to ameliorate work imbalance among worker threadsmax_conflict_ratewhen grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary.max_search_groupwhen grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined.alphagammaetaOut of range value for , value='pos <= value.length()Some trailing characters could not be parsed: 'Out of range value/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h10Alias {nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}pos != std::string::npos/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hidx < names_.size()FeatureMap feature index exceed bound{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{cond}{left}{right}{missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}Unknown feature map type.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} +LambdaRankParamPairwise rank objective.LambdaRank with NDCG as objective.LambdaRank with MAP as objective.rank:pairwiserank:ndcgrank:mapnum_pairsampleNumber of pair generated for each instance.fix_list_weightNormalize the weight of each list by this value, if equals 0, no effect will happenlambda_rank_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuCheck failed: gptr.size() != 0 && gptr.back() == info.labels_.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: Computing gradients on CPU.RegLossParamRegression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Regression Pseudo Huber error.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.reg:linearPoissonRegressionParamPoisson regression for count data.survival:coxCox regression for censored survival data (negative labels are considered censored).reg:gammaGamma regression for severity data.TweedieRegressionParamreg:tweedieTweedie regression for insurance data.reg:squaredlogerrorreg:logisticreg:pseudohubererrorscale_pos_weightScale the weight of positive examples by this factorreg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuLoss: label must be greater than -1 for rmsle so that log(label + 1) can be valid.label must be in [0,1] for logistic regression/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror.Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression.poisson_regression_paramPoissonRegression: label must be nonnegativeCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!GammaRegression: label must be positive.tweedie_variance_powerTweedie variance power. Must be between in range [1, 2).tweedie_regression_paramTweedieRegression: label must be nonnegativeMake predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.learner_model_param->num_output_group != 0out_preds->Size() == nIgnoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farm->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.hncolumns != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()[Internal error]: DMatrix: has expired.unordered_map::at: key not foundUnknown predictor type /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: +With error: +newsize != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:TreeParamTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccUnknown Model Builder:{,}Dump text representation of treeDump json representation of treeGraphvizParamdotDump graphviz representation of treeCheck failed: node.IsLeaf()categories_segmentscategories_sizescategories_nodescategoriesj_end - j_begin != 0std::numeric_limits<bst_cat_t>::min() != max_catthis->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodesunique_path[i].pweight == 0Unique path must have zero weight:%02d:%02d:%02d) DMLC_LOG_STACK_TRACE_DEPTH/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits<int>::max()number of nodes in the tree exceed 2^31fi->Read(&param, sizeof(TreeParam)) == sizeof(TreeParam)param.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast<int>(deleted_nodes_.size()) == param.num_deletedparam.num_nodes == static_cast<int>(nodes_.size())param.num_nodes == static_cast<int>(stats_.size())param.deprecated_num_roots == 1tree_paramn_nodes != 0loss_changesloss_changes.size() == n_nodessum_hessiansum_hessian.size() == n_nodesbase_weightsbase_weights.size() == n_nodesleft_childrenlefts.size() == n_nodesright_childrenrights.size() == n_nodesparentsparents.size() == n_nodessplit_indicesindices.size() == n_nodessplit_conditionsconds.size() == n_nodesdefault_leftdefault_left.size() == n_nodessplit_typen_nodes == split_categories_segments_.size()static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodes', Possible Arguments: +---------------- +num_nodesget<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)mean_values->size() > 0UNumber of features used in tree construction.num_deletedSize of leaf vector, reserved for vector treeInvalid Parameter format for expect Invalid Input: 'value should be smaller equal to /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h has already been registered in min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.max_binif using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)min_child_weightMinimum sum of instance weight(hessian) needed in a child.reg_lambdaL2 regularization on leaf weightreg_alphaL1 regularization on leaf weightdefault_directionlearnrightDefault direction choice when encountering a missing valueMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methoduniformgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_epsEXP Param: Sketch accuracy of approximate algorithm.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsplit_evaluatorelastic_net,monotonicThe criteria to use for ranking splitssparse_thresholdpercentage threshold for treating a feature as sparsemax_conflict_ratewhen grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary.max_search_groupwhen grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined.alphagammaeta) +false{nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}pos != std::string::npos/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{cond}{left}{right}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}Unknown feature map type.Check failed: is_categorical in feature map is numerical but tree node is categorical.Check failed: is_numerical in feature map is categorical but tree node is numerical.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} {left} -{right}{stat}{nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}, "gain": {loss_chg}, "cover": {sum_hess}{{properties} {stat}, "children": [{left}, {right} +{right}{stat}{nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}] "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}, "gain": {loss_chg}, "cover": {sum_hess}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties} { "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {newline}{indent}{nodes}{newline}yes_color#0000FFEdge color when meets the node condition.#FF0000Edge color when doesn't meet the node condition.rankdirPassed to graphiz via graph_attr.condition_node_paramsConditional node configurationleaf_node_paramsLeaf node configurationgraph_attrsAny other extra attributes for graphviz `graph_attr`.Failed to parse graphviz parameters: - With error: -" graph [ {key}="{value}" ] + " graph [ {key}="{value}" ] {key}{value}The following parameters for graphviz are not recognized: -/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.hch == '{'Error at, Expect '{' but get ' Line , around ^``ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'ch == '\"', Expect '"' but get ' \ unknown string escape \, Expect '"' but reach end of line {nid} [ label="{fname}{<}{cond}" {params}] -{<}< {nid} -> {child} [label="{branch}" color="{color}"] -yesno, missing{child}{color} {nid} [ label="leaf={leaf-value}" {params}] +/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.hch == '{'Error at, Expect '{' but get ' Line , around ^``ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'ch == '\"', Expect '"' but get ' \ unknown string escape \, Expect '"' but reach end of line {nid} [ label="{fname}:{cond}" {params}] + {nid} -> {child} [label="{branch}" color="{color}"] +noyes, missing{child}{color} {nid} [ label="{fname}{<}{cond}" {params}] +{<}< {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}{parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} -{nodes}}{rankdir}{graph_attrs}/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater ColMakerTrainParamgrow_colmakerGrow tree with parallelization over columns.opt_dense_colEXP Param: speed optimization for dense column.train_paramcolmaker_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.hMax leaves and max depth cannot both be unconstrained.this->max_depth <= 31max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes.Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0fmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.ridx < position_.size()ridx exceed bound ridx= pos=/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hgrow_local_histmakerTree constructor that uses approximate histogram construction.grow_histmakerTree constructor that uses approximate global of histogram construction./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.hfi.Read(&this->size, sizeof(this->size)) == sizeof(this->size)fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=Check failed: nbig < nquantile: too many large chunk] rmin=, rmax=, wmin=, v=/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.ccCheck failed: param_.max_depth > 0max_depth must be larger than 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.hnode2workindex_[nid] == static_cast<int>(wid)summary_array_.size() == sketchs_.size()offset == -2this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1fid * 2 + 1 < fminmax_.size()FeatHelper fid exceed query bound ret > 0UCheck failed: tree[nid].IsLeaf()sketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=fid * 2 < fminmax_.size()fv=, hist.size=hist[]=, hist.last=istart != hist.sizethis->qexpand_.size() == 1Uthis->wspace_.rptr.back() == this->wspace_.cut.size()prunePruner that prune the tree according to statistics.TreePrunerPrunerUpdate/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cctree pruning end, extra nodes, pruned nodes, max_depth=Check failed: !tree[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1Quantile::BuilderUpdateSubtractionTrickUpdatePredictionCache/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccout_preds.size() > 0UCheck failed: (param_.max_depth > 0 || param_.max_leaves > 0)max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity.max_depth cannot be 0 (unlimited) when grow_policy is depthwise.row_indices.size() + unused_rows_.size() == info.num_row_min_nbins_per_feature > 0UEvaluateSplitsApplySplit(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max())InitNewNodeBuildLocalHistogramsSplitSiblingsBuildNodeStatsExpandWithLossGuideCPUHistMakerTrainParamGmatInitializationgrow_fast_histmaker(Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram.Grow tree using quantized histogram./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0UCheck failed: e.begin != nullptrn_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.end/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/column_matrix.hgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_valgmat_bin_size == kUint32BinsTypeSizebins_type_size_ == kUint32BinsTypeSizecpu_hist_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hAttempted 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.htmlsingle_precision_histogramUse single precision to build histograms.SyncHistogramsSyncHistogramsAllreduce/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hbegin < end/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.hend > beginnid < nodes_i < first_dimension_.size()i < ranges_.size()AddHistRowsrow_ptr_[nid] == kMaxCheck failed: (*p_last_tree_)[nid].IsLeaf()id != kMaxcut_ptr[fid] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max())cut_ptr[fid + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max())nodes == targeted_hists.size()tid < nthreads_grow_fast_histmaker is deprecated, use grow_quantile_histmaker instead.QuantileHistMakerrefreshRefresher that refreshes the weight and statistics according to data./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits<unsigned>::max()Syncher that synchronize the tree in all distributed nodes.NULLworkerrabit_reduce_buffer256MBDMLC_TASK_IDDMLC_ROLEDMLC_NUM_ATTEMPTDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_WORKER_CONNECT_RETRY%[^=]=%smapred_tip_idmapreduce_task_idhadoop_mode is set but cannot find mapred_task_idrabit_task_idrabit_hadoop_modemapred_task_id%drabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksrabit_world_size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccRabit Module currently only work with dmlc workercan only call Init oncestartFailed to shutdown due to%sprintrabit_tracker_urirabit_tracker_portrabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delayConnect to (failed): [] -ConnectRetry connect to ip(retry time ): [ReConnectLink failure 1ReConnectLink failure 2sync::Invalid tracker message, init failureReConnectLink failure 3task connected to the trackerReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelemed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specifiedReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14ReConnectLink failure 15ReConnectLink: bad sockettcp no delay is not implemented on non unix platformscannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkfail to get host nameSocket::Close double close the socket or close without createerror during send SendStr%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}Createcannot obtain address of %sDoes not support IPv6Socket %s Error:%sSendAllRecvAll0.0.0.0TryBindHostGetSockErrorerror during send RecvStrAcceptSetNonBlock-1SetNonBlock-2SetKeepAlivetoo large type_nbytes=%lu, buffer_size=%lu/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutPollReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkAllgatherRing failedAllreduce failedBroadcast failedunknown enum_opunknown data_typeFailed to initialize RABIT.Failed to shutdown RABIT worker.Read pickle stringnot implementedDataType does not support bitwise or operationthe rabit has not been initializedcannot initialize reduce handle twicemust intialize handle to call AllReduceLibSVMParserParamLibFMParserParamCSVParserParamlibfmformatFile formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: TryLoadCache()failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hCheck failed: fi->Read(&label)Bad RowBlock formatCheck failed: fi->Read(&weight)Check failed: fi->Read(&qid)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))MB read, MB/secfinish reading at %g MB/seclabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.hchunk.size != 0Uqid:Check failed: out->label.size() + 1 == out->offset.size()Check failed: base <= 10 && base >= 2sign == true/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"Check failed: out->field.size() == out->index.size()/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hparam_.format == "csv"Check failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsDelimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()hdfs://viewfs:///Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfss3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol stdinCheck failed: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type .split.part/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.hname_cache.size() == 1Uonly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1InputSplit do not support write/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedCheck failed: this->InitCachedIter()Failed to initialize CachedIterResetPartition is not supported in CachedInputSplit/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != end/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0invalid record io format(reinterpret_cast<size_t>(begin) & 3UL) == 0U(reinterpret_cast<size_t>(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->endInvalid RecordIO Format(reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U(reinterpret_cast<size_t>(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagic/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index files/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: files_[i].size % align_bytes == 0file do not align by bytesCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is neededfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[file offset not calculated correctlyC/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectoryCouldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory ..stdoutwbCheck failed: allow_null LocalFileSystem::Open "": Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteCheck failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET)%TTTTTTTTTTTTTTTTTTT|6DwDT`ud#Ltl7ED(7T@dCJL(P,0TUWtY\x]_,bxdhi<ln|pTsLw y`|~llܿ$`lpllP\L8|T4 H` XTXhH`\ x0,@|pP0D H dtX|88l #|' ,)8/<1T 3tH66=8DHElGS@TT U,UPDVtV|W\H^ Pa\f@goq tlv0xt܇<hT ,,\pP84ءTī,Įt,hP(P,XP8plT\dTl0X T,|`L#$%x)\+@ H<H`XII O0Zi`jTms0`($XH(إ$ hPd0Hxd0L8hddd0(XL$Dt`X0h` - - \ ( P0\@dp!"#$(*0+`,../0\45 d7P9:;<@BC@DFpXZ\\bd\eht uL<yd|Ĉ@px 0x`Ш0`Ե |̿ PP<Hdt(td,Tpp0P 4#$$&',8,p`.13 D4 Hl O XS T -X -Z -] -Tbd @ct s u ,v 8{ ( LT ̇ h  L   ,DТ|Ч$ĺDd P \(D|d<|< (Tt<  -P| pd$\&(+p/|82>XDAS`[enps0u $!<P!ܖ!!p"LT#t|##ԫ#%h&l8&8`&&L&p'<''',(((H(ph(H(((@((L)\),)\))))*8+0x+< +"+' ,(,,h)<,+h,+|,.,2,3, =4->P-XH4.`Kt.HL.P.0R.lR. 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eekableInStreamE5FixedSizeStreamE~~0Json1JsonObjectEёStringENumberEReaderEWriterE~JsonArrayEPredictorE~~0Json1JsonStringEΒObjectENumberEReaderEWriterE~JsonArrayEPredictorE~~~~IntegerEBooleanE֓~IntegerEēBooleanEܓ~~~~~~~~~DMatrixELearnerERegTreeE~DMatrixE̔LearnerERegTreeE—~data1tree17QuantileHistMakerȗ3SimpleDMatrixE7SparsePageDMatrixE~data1Еtree17QuantileHistMaker3SimpleDMatrixE7SparsePageDMatrixE~~~16GBTreeE3GBLinearModelEǖ1GBTreeModelE~16GBTreeE3GBLinearModelE1GBTreeModelE~~~~7BuilderIܗEfEEdEEE7BuilderIfEEdEE123IndexedRecordIOSplitterE2LineSplitterEʙ6RecordIOSplitterE4InputSplitBaseE0FileSystemE5LocalFileSystemE΀123IndexedRecordIOSplitterE2LineSplitterEٚ6RecordIOSplitterE4InputSplitBaseE0FileSystemE5LocalFileSystemEЀЀррҀҀԀ13any8TypeInfoINSt3__110shared_ptrIN7xgboost4data12io15LocalFileSystem11GetInstanceEvE8instance5LogMessageFatal5Entry11ThreadLocalEvE6resultܜ6ThreadLocalStoreIVZNRZN7xgboost13TextGenerator9BuildTreeERKNS_7RegTreeEE13kTreeTemplate_4dmlc؝7xgboost©K7xgboost1Ş3any8TypeInfoINSt3__110shared_ptrIN7xgboost4data1Ϫ2io15LocalFileSystem11GetInstanceEvE8instance5LogMessageFatal5Entry11ThreadLocalEvE6result6ThreadLocalStoreIϡИN16XGBAPIErrorEntryE3GetEvE4inst7xgboostSt3__13mapIPKN7xgboost75rabit6engine16ThreadLocalEntryEE3GetEvE4inst1922XGBAPIThreadLocalEntryEE3GetEvE4inst6common22RandomThreadLocalEntryEE3GetEvE4instLogCallbackRegistryEE3GetEvE4instȡGlobalConfigurationEE3GetEvE4instޣ蘁N16XGBAPIErrorEntryE3GetEvE4inst7xgboostӢSt3__13mapIPKN7xgboost75rabit6engine16ThreadLocalEntryEE3GetEvE4inst19LogCallbackRegistryEE3GetEvE4instף22XGBAPIThreadLocalEntryEE3GetEvE4inst6common22RandomThreadLocalEntryEE3GetEvE4instșDMatrixENS3_22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4instLearnerENS3_DMatrixENS3_22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4instLearnerENS3_ؚۧ19PredictionContainerENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4instԧ22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4inst19PredictionContainerENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4inst22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4instЛ蛁20ArrayInterfaceErrors9DimensionEiE3str1ش2ArrayAdapterEEEE8get_typeEvE2tpȪ5CSRArrayAdapterEEEE8get_typeEvE2tp2ArrayAdapterEEEE8get_typeEvE2tp5CSRArrayAdapterEEEE8get_typeEvE2tpȜ6metricë19EvalError4NameEvE4name18EvalTweedieNLogLik4NameEvE4nameء6metric19EvalError4NameEvE4name18EvalTweedieNLogLik4NameEvE4name37GraphvizGeneratorTextGeneratorJsonGenerator97IntegerERKNS_7RegTreeEijE16kIntegerTemplate10QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplate8ͱIndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplate37GraphvizGeneratorTextGeneratorԯJsonGeneratorط9ǰ7IntegerERKNS_7RegTreeEijE16kIntegerTemplate10QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplateƱ8IndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplateئঁNodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13kNodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13kاLeafTemplateStatTemplateٳুLeafTemplateҳStatTemplateTextGenerator9BuildTreeERKNS_7RegTreeEJsonGenerator9ijE13kNodeTemplateѴE13kTreeTemplate37GraphvizGeneratorTextGenerator9BuildTreeERKNS_7RegTreeEJsonGenerator9ijE13kNodeTemplateߵE13kTreeTemplateȨШ97IntegerERKNS_7RegTreeEijE16kIntegerTemplate10QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplateù8ѹIndicatorERKNS_7RegTreeEijE18kIndicatorTemplateѷPlainNodeERKNS_7RegTreeEijE13kNodeTemplate訁9˸7IntegerERKNS_7RegTreeEijE16kIntegerTemplate10QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplateʹ8IndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplateNodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13k߼ȩNodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13kੁ詁SplitNodeERKNS_7RegTreeEijE18kSplitNodeTemplateBuildTreeERKNS_7RegTreeEijE13kNodeTemplateSplitNodeERKNS_7RegTreeEijE18kSplitNodeTemplateؼBuildTreeERKNS_7RegTreeEijE13kNodeTemplateƽLeafTemplateStatTemplateLeafTemplateStatTemplateȪઁ誁C1ERKNS_10FeatureMapERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEbE14kGraphTemplateپ9BuildTreeERKNS_7RegTreeEC1ERKNS_10FeatureMapERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEbE14kGraphTemplate9BuildTreeERKNS_7RegTreeE9PlainNodeERKNS_7RegTreeEijE13k8LeafNodeERKNS_7RegTreeEijE13kLeafTemplateNodeTemplateEdgeTemplate9PlainNodeERKNS_7RegTreeEijE13k8LeafNodeERKNS_7RegTreeEijE13kLeafTemplateNodeTemplateEdgeTemplateȫુ諁ijE13kNodeTemplateE13kTreeTemplateijE13kNodeTemplateE13kTreeTemplateEvalBinaryAUC__EGammaDeviance__E3__make_MetricReg_4__make_MetricReg_2__make_MetricReg_6__make_MetricReg_LogLoss__E9__make_MetricReg_8__make_MetricReg_Precision__ERMSE__EMNDCG__ERMSLE__EError__EAucPR__EMAAMS__ECox__EE__EP__EAPE__EPHE__EGammaNLogLik__EMultiLogLoss__EMatchError__EAFTNLogLik__El$x 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 -  -(ņ  -@k  -Pɇ  -ψ  -  - '  -8W  -H  -  -V  -  -0x  -@  -  -  -ņ  -(k  -8ɇ  -#  -[  -'  - W  -0  -  -΋  -  -  -(g  -Ō  -  -7  -O  -   -x  -J  -  -  -  -p]  -  -ŏ  -ݏ  -;  -h  -  -  -m  -  -`  -xӑ  -1  -  -  -XՒ  -p  -K  -  -Γ  -P  -h  -xj  -ɔ  -  -H  -`0  -p  -  -u  -  -PŖ  -h  -0,  -  -ؗ  -;  -a  -x  -  -.  -M  -`l  -p  -  -  -^  -X  -p  -<  -P  -  -X  -p  -֝  -  -I  -X  -h  -  -!  -?  -e  -@  -P  -`  -  -Ɵ  -`  -x  -&  -E  -8d  -:  -  -  -   -0  -{  -  -0ף  -H  -X  -  -  -(  -@2  -g  -@  -PƤ  -h  -  -Q  -  -  -ۥ  -   -8 <  -` f  -p  - ͦ  -  - &  - U  -  -  -@ ܧ  -X V  -  - 3  - y  -  - I  -@ Ҫ  -X L  -  - )  - o  -  -  -`  -x  -   - ף  -  -  -`  -x ڬ  -  - ٭  -H  -`  -p  -  - e  -  -P  -h  - Ư  -@  -p   - *  - L  - o  -  -   -8 ڰ  -P   - O  -  -   - c  -  -0 E  -@  -   - X  - |  +{nodes}}{rankdir}{graph_attrs}/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater ColMakerTrainParamgrow_colmakerGrow tree with parallelization over columns.opt_dense_colEXP Param: speed optimization for dense column.train_paramcolmaker_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.hMax leaves and max depth cannot both be unconstrained.this->max_depth <= 31max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes.Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0fmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.ridx < position_.size()ridx exceed bound ridx= pos=/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hgrow_local_histmakerTree constructor that uses approximate histogram construction.grow_histmakerTree constructor that uses approximate global of histogram construction./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.hfi.Read(&this->size, sizeof(this->size)) == sizeof(this->size)fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=Check failed: nbig < nquantile: too many large chunk] rmin=, rmax=, wmin=, v=/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.ccCheck failed: param_.max_depth > 0max_depth must be larger than 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.hnode2workindex_[nid] == static_cast<int>(wid)summary_array_.size() == sketchs_.size()offset == -2this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1fid * 2 + 1 < fminmax_.size()FeatHelper fid exceed query bound ret > 0UCheck failed: tree[nid].IsLeaf()sketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=fid * 2 < fminmax_.size()fv=, hist.size=hist[]=, hist.last=istart != hist.sizethis->qexpand_.size() == 1Uthis->wspace_.rptr.back() == this->wspace_.cut.size()prunePruner that prune the tree according to statistics.TreePrunerPrunerUpdate/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cctree pruning end, extra nodes, pruned nodes, max_depth=Check failed: !tree[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1UpdatePredictionCache/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccout_preds.Size() > 0Uout_preds.DeviceIdx() == GenericParameter::kCpuIdCheck failed: (param_.max_depth > 0 || param_.max_leaves > 0)max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity.max_depth cannot be 0 (unlimited) when grow_policy is depthwise.InitSamplingrow_indices.size() == info.num_row_min_nbins_per_feature > 0UCPUHistMakerTrainParamgrow_fast_histmaker(Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram.Grow tree using quantized histogram.Quantile::BuilderUpdateExpandTreeEvaluateSplits/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/hist_util.hid != kMaxApplySplitupper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max())/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hbegin < end/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == true/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/../common/column_matrix.hsizeof(BinIdxType) == bins_type_size_any_missing == any_missing_i < first_dimension_.size()i < ranges_.size()(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.hCheck failed: e.begin != nullptrn_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endSplitSiblingsnodes_for_subtraction_trick_.size() == nodes_for_explicit_hist_build_.size()access element that is not in the setnodes == targeted_hists.size()row_ptr_[nid] == kMaxnid < nodes_tid < nthreads_end > begin/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hCheck failed: !features.empty()cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max())cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max())elem_of_each_node_.size() == 0UGmatInitializationgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_valgmat_bin_size == kUint32BinsTypeSizebins_type_size_ == kUint32BinsTypeSizecpu_hist_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hAttempted 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.htmlsingle_precision_histogramUse single precision to build histograms.Check failed: (*p_last_tree_)[nid].IsLeaf()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hmax_bin_per_feat >= 2exponent <= modgrow_fast_histmaker is deprecated, use grow_quantile_histmaker instead.QuantileHistMakerrefreshRefresher that refreshes the weight and statistics according to data./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits<unsigned>::max()Syncher that synchronize the tree in all distributed nodes.rabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_reduce_bufferDMLC_WORKER_CONNECT_RETRYrabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delay/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccConnect to (failed): [] +ConnectRetry connect to ip(retry time ): [ReConnectLink failure 1ReConnectLink failure 2sync::Invalid tracker message, init failureReConnectLink failure 3NULLtask connected to the trackerReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specifiedReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14ReConnectLink failure 15ReConnectLink: bad sockettcp no delay is not implemented on non unix platformscannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkworker256MBDMLC_NUM_ATTEMPT%[^=]=%smapred_tip_idmapreduce_task_idhadoop_mode is set but cannot find mapred_task_idmapred_task_id%drabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc workercan only call Init oncestartfail to get host nameFailed to shutdown due to%sprint%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}Createcannot obtain address of %sDoes not support IPv6Socket %s Error:%sSendAllRecvAllerror during send SendStrSocket::Close double close the socket or close without create0.0.0.0TryBindHostGetSockErrorerror during send RecvStrAcceptSetNonBlock-1SetNonBlock-2SetKeepAlivetoo large type_nbytes=%lu, buffer_size=%lu/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutPollReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkAllgatherRing failedAllreduce failedBroadcast failedunknown enum_opunknown data_typeDataType does not support bitwise or operationFailed to initialize RABIT.Failed to shutdown RABIT worker.Read pickle stringnot implementedthe rabit has not been initializedcannot initialize reduce handle twicemust initialize handle to call AllReduceLibSVMParserParamLibFMParserParamCSVParserParamlibfmformat/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: TryLoadCache()failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hCheck failed: fi->Read(&label)Bad RowBlock formatCheck failed: fi->Read(&weight)Check failed: fi->Read(&qid)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))MB read, MB/secfinish reading at %g MB/seclabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at File formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.hchunk.size != 0Uqid:Check failed: out->label.size() + 1 == out->offset.size()Check failed: base <= 10 && base >= 2sign == true/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"Check failed: out->field.size() == out->index.size()/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hparam_.format == "csv"Check failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsDelimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()hdfs://viewfs:///Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfss3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol stdinCheck failed: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type .split.part/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.hname_cache.size() == 1Uonly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1InputSplit do not support write/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)unique_lock::unlock: not lockedCheck failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Check failed: out_data_ != NULLCalling Value at beginning or end?Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroyNo thread/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedCheck failed: this->InitCachedIter()Failed to initialize CachedIterResetPartition is not supported in CachedInputSplit/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != end/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0invalid record io format(reinterpret_cast<size_t>(begin) & 3UL) == 0U(reinterpret_cast<size_t>(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->endInvalid RecordIO Format(reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U(reinterpret_cast<size_t>(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagic/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index files/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: files_[i].size % align_bytes == 0file do not align by bytesCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is neededfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[file offset not calculated correctlyC/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectoryCouldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: stdoutwbCheck failed: allow_null LocalFileSystem::Open "": LocalFileSystem.ListDirectory ..Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteCheck failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET)&TTTTTTTTTTTTTTTTTTT@!+L@<R$ <bX<r(+<?eL+4T/؃2d4|7 ;=Hx>\8HpDJM؆P ShxVtY\@l_Tadapilq̈r8t(w@0}\Xt܇8 0xȴ||Է0(8L̹p`܏ LtT,,h@|xp8tdԕ@ Pxp|H P p dؘ@`, t $$P'p)؛+@T./<2؜\36<$7L=?(BD0HGDI(<KO@RU(VXZx\aHb fLhj,tl p4xrxu8y |0|@؏LX4ī\l| D T$خ<\<X$lܰı4h``زP0$ \жd4PDԹ,.L 6|8;̻ =ܻ? 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IFinalizeGetTrackerPrintBroadcastAllLCheckPointVersionNumbernitsDistributed`Գ`RWorldSizeProcessorNameingPrevRankank```ܴ````gatherreduce``oadCheckPointinkTag````GetEngineEvAllgatherEPvmmmm`CD8TypeSizeERKN3MPI8DatatypeE4InitEPFvPKvPviRKN3MPI8DatatypeEEm9AllreduceEPvmmPFvS2_ES2_2Ev1Ev``2Ev1Ev`````````76LibFMParserParam11__MANAGER__Ev4CSVParserParam11__MANAGER__Ev8CreateLibSVMParserI5CreateCSVParserILibSVMParserParam11__MANAGER__EvCreateLibFMParserI```jyfEEE3GetEviEEE3GetEvxEEE3GetEv```fEEE3GetEviEEE3GetEvxEEE3GetEv```jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjjyfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj``jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjjyfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj``jyfEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjjiEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjjxEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj`fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjjiEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjjxEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj``aaaRowBlockIterIThreadedIterINS_2io14InputSplitBase5ChunkEE14NotImplementedEvjyfE6CreateEPKcjjS3_iE6CreateEPKcjjS3_xE6CreateEPKcjjS3_afE6CreateEPKcjjS3_iE6CreateEPKcjjS3_xE6CreateEPKcjjS3_bcdefParserIStream6CreateEPKcS2_bjyfE6CreateEPKcjjS3_iE6CreateEPKcjjS3_xE6CreateEPKcjjS3_gfE6CreateEPKcjjS3_iE6CreateEPKcjjS3_xE6CreateEPKcjjS3_ggggg123IndexedRecordIOSplitter0FileSystem2LineSplitter16RecordIOSplitter14InputSplitBase5LocalFileSystem11GetInstanceERKNS0_3URIE22ListDirectoryRecursiveERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEEmInputSplit6CreateEPKcSeekStream13CreateForReadEPKcbS2_jjS2_bimbjjS2_mԢmmmn7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE5SeekRecordBeginEPNS_6StreamE9FindLastRecordBeginEPKcS3_nnn7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE5SeekRecordBeginEPNS_6StreamE9FindLastRecordBeginEPKcS3_nnn194ResetPartitionEjj3ReadIndexFileEPNS0_10FileSystemERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE15SeekRecordBeginEPNS_6StreamE9FindLastRecordBeginEPKcS3_nnoNextBatchExEPNS0_14InputSplitBase5ChunkEmBeforeFirstEvoNextReadChunkEPvPmBatchEPNS_10InputSplit4BlobEmChunkEPNS_10InputSplit4BlobEooԕoooo41D8StripEndENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEc9ReadChunkEPvPm5ChunkInitEPNS0_10FileSystemEPKcmbReadEPvmСo7InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb4ResetPartitionEjj1BeforeFirstEv3ConvertToURIsERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE6ExtractNextChunkEPNS_10InputSplit4BlobEPNS1_5ChunkEooo2Ev1Ev0Evoooĺoooo4LoadEPS1_m6AppendEPS1_mooorr14OpenERKNS0_3URIEPKcb13ListDirectoryERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEEGetPathInfoERKNS0_3URIEOpenForReadERKNS0_3URIEbsssȞs8kFloatPow5InvSplitE5kFloatPow5SplitE}}}7xgboost5rabit6engine13AllreduceBaseE4dmlc2io6common1198JsonNullE743gbm6PeekableInStreamE5FixedSizeStreamE}}}0Json1JsonStringEObjectENumberEReaderEWriterE}JsonArrayEPredictorE}}}IntegerEBooleanE}}}}}}}DMatrixELearnerERegTreeE}datatree17QuantileHistMaker23GradientIndexPageSourceE1~3SimpleDMatrixE7SparsePageDMatrixE~~16GBTreeE3GBLinearModelE1GBTreeModelE~~~ܼ~~~~E7BuilderIfEEdEEӛ123IndexedRecordIOSplitterE2LineSplitterE6RecordIOSplitterE4InputSplitBaseE0FileSystemE5LocalFileSystemE7xgboost5rabit6engine13AllreduceBaseE4dmlc2io6common1198JsonNullE743gbm6PeekableInStreamE5FixedSizeStreamE7xgboost5rabit6engine13AllreduceBaseE4dmlc2io6common1198JsonNullE743gbm6PeekableInStreamE5FixedSizeStreamE††Æ0Json1JsonObjectEStringENumberEReaderEWriterEƆJsonArrayEPredictorEƆdž0Json1JsonStringEObjectENumberEReaderEWriterEdžJsonArrayEPredictorEdžȆȆȆIntegerEBooleanEɆIntegerEBooleanEɆɆʆʆˆˆˆ̆̆DMatrixELearnerERegTreeEцDMatrixELearnerERegTreeE҆datatree17QuantileHistMaker23GradientIndexPageSourceE1؆datatree17QuantileHistMaker23GradientIndexPageSourceE1ن3SimpleDMatrixE7SparsePageDMatrixE߆3SimpleDMatrixE7SparsePageDMatrixE16GBTreeE3GBLinearModelE1GBTreeModelE聇16GBTreeE3GBLinearModelE1GBTreeModelEȂ،7BuilderIEfEEdEEȑE7BuilderIfEEdEEȒ123IndexedRecordIOSplitterE2LineSplitterE6RecordIOSplitterE4InputSplitBaseE0FileSystemE5LocalFileSystemE123IndexedRecordIOSplitterE2LineSplitterE6RecordIOSplitterE4InputSplitBaseE0FileSystemE5LocalFileSystemE13any8TypeInfoINSt3__110shared_ptrIN7xgboost4data1Ɏ2io15LocalFileSystem11GetInstanceEvE8instance6ThreadLocalStoreI5LogMessageFatal5Entry11ThreadLocalEvE6resultNԁ16XGBAPIErrorEntryE3GetEvE4inst7xgboostSt3__13mapIPKN7xgboost7݈5rabit6engine16ThreadLocalEntryEE3GetEvE4inst1922XGBAPIThreadLocalEntryEE3GetEvE4inst6common22RandomThreadLocalEntryEE3GetEvE4instψLogCallbackRegistryEE3GetEvE4inst݃GlobalConfigurationEE3GetEvE4instVZNRZN7xgboost13TextGenerator9BuildTreeERKNS_7RegTreeEE13kTreeTemplate_4dmlcل7xgboostK7xgboostڐ1ƅ3any8TypeInfoINSt3__110shared_ptrIN7xgboost4data12io15LocalFileSystem11GetInstanceEvE8instance6ThreadLocalStoreI5LogMessageFatal5Entry11ThreadLocalEvE6resultN16XGBAPIErrorEntryE3GetEvE4instȈ7xgboostSt3__13mapIPKN7xgboost75rabit6engine16ThreadLocalEntryEE3GetEvE4inst19LogCallbackRegistryEE3GetEvE4inst22XGBAPIThreadLocalEntryEE3GetEvE4inst6common22RandomThreadLocalEntryEE3GetEvE4instֈؘЙ虉DMatrixENS3_22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4instډLearnerENS3_DMatrixENS3_22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4instފLearnerENS3_Ț19PredictionContainerENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4inst22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4inst19PredictionContainerENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4inst22XGBAPIThreadLocalEntryENS1_4lessIS6_EENS1_9allocatorINS1_4pairIKS6_S7_EEEEEEE3GetEvE4inst؛20ArrayInterfaceErrors9DimensionEiE3strŽ12ArrayAdapterEEEE8get_typeEvE2tp5CSRArrayAdapterEEEE8get_typeEvE2tp2ArrayAdapterEEEE8get_typeEvE2tp5CSRArrayAdapterEEEE8get_typeEvE2tpМ6metric1Ñ9EvalError4NameEvE4nameӐ18EvalTweedieNLogLik4NameEvE4name؜6metric19EvalError4NameEvE4name18EvalTweedieNLogLik4NameEvE4name37GraphvizGeneratorTextGeneratorJsonGeneratorś9Œ17IntegerERKNS_7RegTreeEijE16kIntegerTemplate8IndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplateØ3̓7GraphvizGeneratorTextGeneratorJsonGenerator917IntegerERKNS_7RegTreeEijE16kIntegerTemplate8IndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplateʘ1CategoricalERKNS_7RegTreeEijE13kNodeTemplate0QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplate1CategoricalERKNS_7RegTreeEijE13kNodeTemplate0QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplateН؝NodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13kјNodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13kОLeafTemplateStatTemplate؞LeafTemplateStatTemplateTextGenerator9BuildTreeERKNS_7RegTreeEJsonGenerator9١ijE13kNodeTemplateE13kTreeTemplate37GraphvizGeneratorTextGenerator9BuildTreeERKNS_7RegTreeEJsonGenerator9âijE13kNodeTemplateE13kTreeTemplateȟ917IntegerERKNS_7RegTreeEijE16kIntegerTemplate8IndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplateˡ917IntegerERKNS_7RegTreeEijE16kIntegerTemplate8IndicatorERKNS_7RegTreeEijE18kIndicatorTemplatePlainNodeERKNS_7RegTreeEijE13kNodeTemplateҡ1CategoricalERKNS_7RegTreeEijE17kCategoryTemplate0QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplate1CategoricalERKNS_7RegTreeEijE17kCategoryTemplate0QuantitiveERKNS_7RegTreeEijE19kQuantitiveTemplateؠNodeStatERKNS_7RegTreeEiE13kStatTemplateLeafNodeERKNS_7RegTreeEijE13kࠉNodeStatERKNS_7RegTreeEiE13kStatTemplateġLeafNodeERKNS_7RegTreeEijE13k֣SplitNodeERKNS_7RegTreeEijE18kSplitNodeTemplateBuildTreeERKNS_7RegTreeEijE13kNodeTemplateSplitNodeERKNS_7RegTreeEijE18kSplitNodeTemplateBuildTreeERKNS_7RegTreeEijE13kNodeTemplateLeafTemplateϣStatTemplateLeafTemplateStatTemplateءࡉC1ERKNS_10FeatureMapERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEbE14kGraphTemplate9BuildTreeERKNS_7RegTreeEC1ERKNS_10FeatureMapERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEbE14kGraphTemplate9BuildTreeERKNS_7RegTreeE11CategoricalERKNS_7RegTreeEijE14kLabelTemplate98LeafNodeERKNS_7RegTreeEijE13kLeafTemplate11CategoricalERKNS_7RegTreeEijE14kLabelTemplate98LeafNodeERKNS_7RegTreeEijE13kLeafTemplateآBuildEdgeILbߨPlainNodeERKNS_7RegTreeEijE13kNodeTemplateլ1EEENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS_7RegTreeEiibE13kEdgeTemplate0EEENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS_7RegTreeEiibE13kEdgeTemplateࢉBuildEdgeILbPlainNodeERKNS_7RegTreeEijE13kNodeTemplateܬ1EEENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS_7RegTreeEiibE13kEdgeTemplateά0EEENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS_7RegTreeEiibE13kEdgeTemplateأijE13kNodeTemplateE13kTreeTemplateࣉijE13kNodeTemplateޭE13kTreeTemplate6__make_MetricReg_3__make_MetricReg_4__make_MetricReg_2__make_MetricReg_9__make_MetricReg_8__make_MetricReg_Precision__EEvalAUC__ELogLoss__ERMSE__EܯMŰNDCG__ERMSLE__EError__EAucPR__EMAAMS__ECox__EE__EP__EAPE__E۰PHE__EGammaNLogLik__EMultiLogLoss__EMatchError__E߱AFTNLogLik__EW 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