vendor/x86_64-darwin/libxgboost.dylib in xgb-0.7.0 vs vendor/x86_64-darwin/libxgboost.dylib in xgb-0.7.1
- old
+ new
@@ -1,2316 +1,2933 @@
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+Hx]E ] D UHSPHt H{D HD H[]f. UHSPHt H{D HD H[]f. UHHH?Ht
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+Hx]u> ]ÐUHAVSIHH5s H=s ~H=s Ht H=s 6> qs t H=xs !> t I~> [A^]UHSP1HHtHHQH5Gs H=Hs HHH)HH9rH[]f. D UHSPHt H{= H= H[]f. UHSPHt H{w= Ho= H[]f. UHAWAVAUATSPIIIH^H9t/Ct H{/= LkCt H{ = LM9uILIMgH[A\A]A^A_] UHAWAVAUATSPIIIH^H9t/Ct H{< LkCt H{< LM9uILIMgH[A\A]A^A_] UHAWAVATSIIH^H9t/Ct H{U< LcCt H{B< LM9uI>LM~)< [A\A^A_]fUHAWAVATSIIH^H9t/Ct H{; LcCt H{; LM9uI>LM~; [A\A^A_]fUHAWAVATSIIH^H9t/Ct H{; LcCt H{; LM9uI>LM~[A\A^A_]a; UHAWAVATSIIH^H9t/Ct H{5; LcCt H{"; LM9uI>LM~[A\A^A_]; UHAWAVAUATSPIIH H9t2ICt H{: LkCt H{: LM9uI?LM&: H[A\A]A^A_] UHAWAVAUATSPIIH H9t2ICt H{`: LkCt H{M: LM9uI?LM&5: H[A\A]A^A_] UHAWAVATSIIH^H9t/Ct H{9 LcCt H{9 LM9uI>LM~[A\A^A_]9 UHAWAVATSIIH^H9t/Ct H{9 LcCt H{9 LM9uI>LM~[A\A^A_]a9 UHAWAVAUATSPIILn8I9t=II}IHtH )9 HHuI} Ie Ht9 M9uI?LMf8H[A\A]A^A_]8 f. fUHAWAVATSIILfI9t;I|$IHtH 8 HHuI<$I$$ Ht8 M9uI>LM~[A\A^A_]u8 UHAWAVAUATSHIHUHL7MtHIp
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NSt3__111__alternateIcEE NSt3__121__empty_non_own_stateIcEE NSt3__111__match_anyIcEE N4dmlc2io10FileSystemE N4dmlc2io15LocalFileSystemE N4dmlc2io10FileStreamE USE_RMM allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.cc Invalid pointer argument: out BUILTIN_PREFETCH_PRESENT MM_PREFETCH_PRESENT USE_OPENMP CLANG_VERSION DEBUG USE_FEDERATED [ ] : : %02d:%02d:%02d
+ Stack trace:
+ [bt] ( ) _Z DMLC_LOG_STACK_TRACE_DEPTH %zu json_str false Check failed: ec == std::errc() Unknown global parameters: { , } /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.h Invalid cast, from to __ Cannot find argument ' ', Possible Arguments:
----------------
- : Check failed: e 0 ( vs. ) XGBoost distributed mode detected, will split data among workers /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h Check failed: at_first_ Cannot reset IteratorAdapter Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columns Number of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.h XGBoost version not compiled with GPU support. cache_prefix XGDMatrixCreateFromCallback nthread missing /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.h Invalid missing value: Argument ` ` is required for ` ` /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.h limit >= 1 Invalid thread limit for OpenMP. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.h Slicing DMatrix is not supported for Proxy DMatrix. Not implemented. Check failed: p_m Check failed: m Current DMatrix type does not support set data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h get<Array const>(array).size() == 1 Column: typestr n_bits == n Shape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting. stream version Missing `version' field for array interface Missing `typestr' field for array interface Check failed: typestr.size() == 3 || typestr.size() == 4 shape Missing `shape' field for array interface Missing `data' field for array interface Only version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported. `typestr' should be of format <endian><type><size of type in bytes>. map::at: key not found - is not supported. Bit field Boolean Integer Unsigned integer Floating point Complex floating point Timedelta Object String Unicode Other Invalid type code: in `typestr' of input array.
+ : Check failed: e Check failed: 0 ( vs. XGBoost federated mode detected, not splitting data among workers XGBoost distributed mode detected, will split data among workers fname /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h Check failed: at_first_ Cannot reset IteratorAdapter Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columns Number of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.h XGBoost version not compiled with GPU support. config cache_prefix XGDMatrixCreateFromCallback nthread next reset missing /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.h Argument `missing` is required. Invalid missing value: Argument ` ` is required for ` `. Incorrect type for: ` `, expecting: ` `, got: ` /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.h limit >= 1 Invalid thread limit for OpenMP. XGDeviceQuantileDMatrixCreateFromCallback is deprecated. Use `XGQuantileDMatrixCreateFromCallback` instead. Check failed: _ref max_bin /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.h Slicing DMatrix is not supported for Proxy DMatrix. Not implemented. DMatrix/Booster has not been initialized or has already been disposed. c_interface_str Check failed: p_m Check failed: m Current DMatrix type does not support set data. Check failed: c_interface indptr indices data c_json_config /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h get<Array const>(array).size() == 1 Column: typestr n_bits == n Shape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting. stream version Missing `version' field for array interface Missing `typestr' field for array interface Check failed: typestr.size() == 3 || typestr.size() == 4 shape Missing `shape' field for array interface Missing `data' field for array interface Only version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported. `typestr' should be of format <endian><type><size of type in bytes>. map::at: key not found - is not supported. Bit field Boolean Integer Unsigned integer Floating point Complex floating point Timedelta Object String Unicode Other Invalid type code: in `typestr' of input array.
Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html
-Or open an issue. i < D Check failed: m == 1 || n == 1 strides j_strides.size() == j_shape.size() stride and shape don't match. size == 0 Empty data with non-zero shape. Empty data passed in. j_shape.size() == 1 type_length == 1 mask with bitfield type should be of 1 byte per bitfield. mask with integer type should be of 1 byte per integer. mask must be of integer type or bit field type. strides.size() == 1 get<Integer>(strides.at(0)) == type_length Memory should be contiguous. Only dimensional array is valid. Check failed: array.find("mask") == array.cend() Masked array is not yet supported. +s Check failed: std::string(schema->format) == "+s" Check failed: columns.empty() Check failed: format_str Format string cannot be empty Check failed: false Column data type not supported by XGBoost Check failed: rb_ Cannot import non-existent record batch Check failed: !schema_->columns.empty() Cannot import record batch without a schema Cannot reset RecordBatchesIterAdapter [ ] : %02d:%02d:%02d
- Stack trace:
- [bt] ( _Z DMLC_LOG_STACK_TRACE_DEPTH %zu binary saving only supported by SimpleDMatrix Check failed: type >= 1 && type <= 4 XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead. group learner gradient_booster gblinear dart gbtree gbtree_model_param num_parallel_tree Unknown booster: Booster has not been initialized or has already been disposed. DMatrix has not been initialized or has already been disposed. XGBoosterPredictFromDMatrix iteration_begin iteration_end ntree_limit Check failed: iteration_end == 0 Only one of the `ntree_limit` or `iteration_range` can be specified. `ntree_limit` is deprecated, use `iteration_range` instead. strict_shape chunksize == groups Unknown prediction type: std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rows get<Integer const>(config["cache_id"]) == 0 Cache ID is not supported yet InplacePredictImpl Check failed: p_predt json ubj r str.size() >= 3 str[0] == '{' str[str.size() - 2] == '}' w write position exceed fixed buffer size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h format XGBoosterSaveModelToBuffer deprecated Unknown format: ` read can not have position excceed buffer length XGBoosterGetModelRaw ` is deprecated, please use `XGBoosterSaveModelToBuffer` instead. Check failed: p_out text custom_feature_names.size() == n_features Incorrect number of feature names. feature_names.size() == n_features feature_types.size() == n_features Incorrect number of feature types. f q feature_map.Size() == n_features /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h fid == static_cast<int>(names_.size()) i int float c unknown feature type, use i for indicator and q for quantity feature_name feature_type Unknown field for Booster feature info: importance_type XGBoosterFeatureScore feature_map feature_names features.size() <= scores.size() scores.size() % features.size() == 0ul features.size() == scores.size() idx < names_.size() FeatureMap feature index exceed bound NaN 0E0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.cc /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.cc Size() == other.Size() Size() == other.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc pointer_ <= buffer_.size() file:// :// rb Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.h Not implemented /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc Check failed: ret.ec == std::errc() \ \\ \" \b \f \n \r \t \u%04x Number Array U8Array Object of type can not be indexed by string. can not be indexed by Integer. Unknown construct , around character position: , " " \0
" Unknown escape null Expecting null value "null" ch != -1 cursor_.Pos(): raw_str_.size(): Expecting digit ` is not supported for typed array. f64 is not supported. High precision number is not supported. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/string_view.h beg <= size_ Expecting: " ", got: " EOF" \0" PesudoHuberParam huber_slope The delta term in Pseudo-Huber loss. Invalid Parameter format for expect but value=' Required parameter of is not presented Out of range value for , value=' /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h pos <= value.length() Some trailing characters could not be parsed: ' ' Out of range value No conversion could be performed /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h *p == ')' Invalid NAN literal value for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default= , required key has already been registered in PushRowPage /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cc n_threads_ >= 1 sketches_.size() == n_columns weights.size() == info.num_row_ ;unknown;unknown;0;0;; cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max() cut_size > cuts->cut_ptrs_.HostVector().back() SketchContainerImpl columns_size_.size() != 0 HostSketchContainer PushColPage count <= total_entries /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.h base_rowid < group_ptr.back() Row: is not found in any group. worker_segments.size() >= 1 AllReduce n_columns == sketches_.size() Number of columns differs across workers num_cuts.size() == 0 feature_ptr.front() == 0 rank_size == total /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/threading_utils.h n_threads >= 1 Check failed: reduced[i].data src.size == 0 this->size == 0 mingap= , maxgap= , wgap= Check failed: size <= sa.size + sb.size bug in combine Check failed: worker_feature.data() check quantile stats, nbig= , n= srcsize= , maxsize= , range= , chunk= Check failed: nbig < n quantile: too many large chunk ] rmin= , rmax= , wmin= , v= hessian.size() == info.num_row_ group_ptr.size() >= 2 group_ptr.back() == hessian.size() group_ptr.back() == n_samples /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/categorical.h Invalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than max_cat + 1 >= n_categories Maximum cateogry should not be lesser than the total number of categories. Check failed: a.data Check failed: a.data && reduced[fidx].data invalid init parameter Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)) sketch->temp.size < max_size invalid maximum size max_size= , stemp.size INFO: rmax= , sum_total= , naxt_goal= , size= sketch->temp.size <= max_size Finalize: invalid maximum size, max_size= , stemp.size= /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.cc features.size() > 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.h array.size() == weights.size() aft_loss_distribution normal extreme Choice of distribution for the noise term in Accelerated Failure Time model aft_loss_distribution_scale Scaling factor used to scale the distribution in Accelerated Failure Time model Invalid Input: ' ', valid values are: optional, default= /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h enum_back_map_.count(value) != 0U Value not found in enum declared Enum ( exisit! )
- Enums: ), /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc Timer for did not get stopped properly. s, calls @ us ======== Monitor ( ): ======== /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.cc Invaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again. fi->Read(&read[0], verstr.size()) == verstr.size() Check failed: fi->Read(&major) Check failed: fi->Read(&minor) Check failed: fi->Read(&patch) . /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.cc Check failed: valid Input data contains `inf` or `nan` All feature_types must be one of {int, float, i, q, c}. base_margin Check failed: this->base_margin_.Size() % this->num_row_ == 0 Incorrect size for base margin. label this->labels.Size() % this->num_row_ == 0 Incorrect size for labels. Label contains NaN, infinity or a value too large. qid Check failed: non_dec `qid` must be sorted in non-decreasing order along with data. weight Weights must be positive values. label_lower_bound label_upper_bound feature_weights Feature weight must be greater than 0. Unknown key for MetaInfo: Unknown data type matrix with entries loaded from transpose.offset.Size() == num_columns + 1 self_offset.size() == other_offset.size() self_data.size(): other_data.size(): beg <= data.size() ptr < offset.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.h Check failed: page_ != nullptr /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h key >= builder_base_row_offset float32 float64 bool8 int32 int8 int16 int64 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h Unknown data table type. num_row num_col num_nonzero labels group_ptr weights labels_lower_bound labels_upper_bound feature_types field_cnt == kNumField Wrong number of fields Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again. major == 1 minor >= 6 Check failed: fi->Read(&num_field) MetaInfo: invalid format num_field >= expected MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.) MetaInfo: the given binary file contains extra fields which will be ignored. MetaInfo: Invalid format for Check failed: strm->Read(&name) name == expected_name Expected field: , got: Check failed: strm->Read(&type_val) Check failed: type == expected_type Expected field of type: got field type: Check failed: strm->Read(&is_scalar) Check failed: is_scalar Expected field to be a scalar; got a vector Check failed: strm->Read(field) Check failed: !is_scalar to be a tensor; got a scalar Check failed: strm->Read(&(shape[i])) Check failed: strm->Read(&field) to be a vector; got a scalar Check failed: strm->Read(&shape.first) Check failed: strm->Read(&shape.second) shape.second == 1 Number of columns is expected to be 1. this->base_margin_.Size() % this->num_row_ == 0 Incorrect size of base margin vector. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.h this->Data()->Size() == detail::CalcSize(this->shape_) Inconsistent size after modification. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/validation.h Check failed: valid_query_group Invalid group structure. < Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info. size == this->num_col_ Length of must be equal to number of columns. Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_ Number of columns must be consistent across batches. that.group_ptr_.size() != 0 shape[i] == r.Shape(i) group_ptr_.size() == weights_.Size() + 1 Size of weights must equal to number of groups when ranking group is used. group_ptr_.back() == num_row_ Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data. weights_.Size() == num_row_ Size of weights must equal to number of rows. labels.Shape(0) == num_row_ Size of labels must equal to number of rows. labels_lower_bound_.Size() == num_row_ Size of label_lower_bound must equal to number of rows. feature_weights.Size() == num_col_ Size of feature_weights must equal to number of columns. labels_upper_bound_.Size() == num_row_ Size of label_upper_bound must equal to number of rows. num_nonzero_ <= num_col_ * num_row_ base_margin_.Size() % num_row_ == 0 Size of base margin must be a multiple of number of rows. Check failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == device Data is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: cache_file.find('#') == std::string::npos Only one `#` is allowed in file path for cache file specification. .r XGBOOST_TEST_NPART Load part of data parts auto csv libsvm No format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format= No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csv Encountered parser error:
- .group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.h Check failed: !type_.empty() Check failed: parser_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/iterative_device_dmatrix.h Slicing DMatrix is not supported for Device DMatrix. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.h Check failed: array.valid.Size() == 0 Meta info like label or weight can not have missing value. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.cc cut.Values().size() > 0U curent_bin_size == common::kUint32BinsTypeSize Check failed: !sorted_sketch SortedSketchContainer /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/quantile.h /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.h Check failed: impl_ != nullptr /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.h gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val gmat_bin_size == kUint32BinsTypeSize bins_type_size_ == kUint32BinsTypeSize batch_size < offset_vec.size() ibegin + inst.size() == iend row_ptr.size() == 0 Check failed: columns_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.h raw Raw GHistIndex binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h Check failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cc count_ == source_->Iter() cuts_.Values().size() != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h count_ == n_batches_ count_ >= 1 Check failed: lock_.try_lock() Multiple threads attempting to use Sparse DMatrix. Check failed: proxy_ Check failed: !at_end_ n_prefetch_batches > 0 total batches: fetch_it < cache_info_->offset.size() std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batches Sparse DMatrix assumes forward iteration. fi->Tell() == offset Check failed: fmt->Read(page.get(), fi.get()) Read a page in seconds. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h Unknown format type format.front() == '.' thread constructor failed /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h Unknown type: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./any.h Check failed: type_ != nullptr The any container is empty requested= Check failed: *(type_->ptype_info) == typeid(T) The stored type mismatch stored= Check failed: !cache_info_->written MB written in Check failed: page_ this->count_ >= 1 source_->Iter() == this->count_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.cc Check failed: on_host Not implemented on device. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.cc Check failed: (std::is_same<AdapterT, CSCAdapter>::value) Expecting CSCAdapter /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h param.gpu_id >= 0 param.max_bin >= 2 Check failed: r != BatchParam{} Batch parameter is not initialized. Generating new Gradient Index. param.gpu_id == -1 batch_param_.hess.data() == param.hess.data() Check failed: in_stream->Read(&tmagic) invalid input file format tmagic == kMagic invalid format, magic number mismatch offset_vec.back() == info_.num_nonzero_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.h Check failed: data_ && row_idx < length_ Column is empty or out-of-bound index of the column Check failed: data_ Column is empty .row.page /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc Check failed: sparse_page_source_ DMatrix -r info_.num_col_ != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.h Make cache: Check failed: proxy_handle Invalid proxy handle. .col.page this->Info().num_col_ != 0 .sorted.col.page this->page_->Size() == n_features_ this->page_->data.Size() == csr->data.Size() .gradient_index.page cuts.Values().size() != 0 Check failed: ghist_index_source_ Check failed: kv.second Couldn't remove external memory cache file ; you may want to remove it manually Slicing DMatrix is not supported for external memory. iter_.Next() == 1 Must have at least 1 batch. Raw binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.cc page->offset.Size() != 0U Invalid SparsePage file n_bytes == (page->data).Size() * sizeof(Entry) Check failed: page.offset.Size() != 0 && offset_vec[0] == 0 offset_vec.back() == page.data.Size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cc layer_begin == 0 Linear booster does not support prediction range. GBLinearTrainParam Linear booster, implement generalized linear model. updater shotgun Update algorithm for linear model. One of shotgun/coord_descent tolerance Stop if largest weight update is smaller than this number. max_row_perbatch Maximum rows per batch. string get<String>(in["name"]) == "gblinear" model gpu_coord_descent coord_descent Loading configuration on a CPU only machine. Changing updater to `coord_descent`. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.h fi->Read(¶m_, sizeof(param_)) == sizeof(param_) /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.h Slice is not supported by current booster. DoBoost PredictBatch PredictBatchInternal base_margin.Size() == nsize * ngroup /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.h Inplace predict is not supported by current booster. gblinear does not support prediction of leaf index { "bias": [ , ], "weight": [ ] } bias:
+Or open an issue. i < D Check failed: m == 1 || n == 1 strides j_strides.size() == j_shape.size() stride and shape don't match. size == 0 Empty data with non-zero shape. Empty data passed in. mask j_shape.size() == 1 type_length == 1 mask with bitfield type should be of 1 byte per bitfield. mask with integer type should be of 1 byte per integer. mask must be of integer type or bit field type. strides.size() == 1 get<Integer>(strides.at(0)) == type_length Memory should be contiguous. Only dimensional array is valid. Check failed: array.find("mask") == array.cend() Masked array is not yet supported. +s Check failed: std::string(schema->format) == "+s" Check failed: columns.empty() Check failed: format_str Format string cannot be empty Check failed: false Column data type not supported by XGBoost Check failed: rb_ Cannot import non-existent record batch Check failed: !schema_->columns.empty() Cannot import record batch without a schema nbatch XGDMatrixCreateFromArrowCallback Cannot reset RecordBatchesIterAdapter static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0U slice does not support group structure binary saving only supported by SimpleDMatrix field out_features len Check failed: type >= 1 && type <= 4 XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead. group out_len out_dptr Check failed: pp_m out_indptr out_indices out_data p_m->Info().num_col_ <= std::numeric_limits<unsigned>::max() Check failed: page.page /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.h Check failed: impl_ != nullptr n_threads >= 1 ;unknown;unknown;0;0;; dmats json_parameters out_str dtrain Check failed: dtr grad hess evnames out_result learner gradient_booster gblinear dart gbtree gbtree_model_param num_parallel_tree Unknown booster: Booster has not been initialized or has already been disposed. DMatrix has not been initialized or has already been disposed. type XGBoosterPredictFromDMatrix iteration_begin iteration_end ntree_limit Check failed: iteration_end == 0 Only one of the `ntree_limit` or `iteration_range` can be specified. `ntree_limit` is deprecated, use `iteration_range` instead. training strict_shape out_dim out_shape chunksize == groups Unknown prediction type: std::accumulate(shape.cbegin(), shape.cend(), static_cast<bst_ulong>(1), std::multiplies<>{}) == chunksize * rows get<Integer const>(config["cache_id"]) == 0 Cache ID is not supported yet InplacePredictImpl Check failed: p_predt Check failed: proxy Invalid input type for inplace predict. array_interface json ubj r str.size() >= 3 str[0] == '{' w buf write position exceed fixed buffer size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h json_config format XGBoosterSaveModelToBuffer deprecated Unknown format: ` ` read can not have position excceed buffer length XGBoosterGetModelRaw ` is deprecated, please use `XGBoosterSaveModelToBuffer` instead. Check failed: p_out text fmap /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h fid == static_cast<int>(names_.size()) i q int float c unknown feature type, use i for indicator and q for quantity out_models custom_feature_names.size() == n_features Incorrect number of feature names. feature_names.size() == n_features feature_types.size() == n_features Incorrect number of feature types. f feature_map.Size() == n_features ftype success key features feature_name feature_type Unknown field for Booster feature info: importance_type XGBoosterFeatureScore feature_map feature_names out_n_features features.size() <= scores.size() scores.size() % features.size() == 0ul features.size() == scores.size() out_scores idx < names_.size() FeatureMap feature index exceed bound name_str /Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.h World size is less than 1. Rank is less than 0. is greater than world_size - 1: . /Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.cc XGBoost is not compiled with Federated Learning support. XGBOOST_COMMUNICATOR rabit federated Unknown communicator type xgboost_communicator = =1 =0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.h Failed to initialize Rabit Unknown data type Unknown allreduce operation /Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/socket.cc Failed to get addr info for: 127.0.0.1 0.0.0.0 ::1 :: Check failed: !this->IsClosed() str.size() < std::numeric_limits<std::int32_t>::max() this->SendAll(&len, sizeof(len)) == sizeof(len) Failed to send string length. bytes == str.size() Failed to send string. send /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.h Failed to call ` `: this->RecvAll(&len, sizeof(len)) == sizeof(len) Failed to recv string length. bytes == len Failed to recv string. recv static_cast<std::int32_t>(socket.Domain()) == static_cast<std::int32_t>(addr.Domain()) socket system::CloseSocket(handle_) NaN 0E0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.cc gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.h Unreachable /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.cc entries_per_column.size() == info.num_col_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.h entries_per_columns.size() == static_cast<size_t>(n_columns) column_sizes_tloc[i].size() == static_cast<size_t>(n_columns) /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/threading_utils.h SortedSketchContainer invalid init parameter Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)) /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.cc Size() == other.Size() Size() == other.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc pointer_ <= buffer_.size() Check failed: out file:// :// rb Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.h Not implemented /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc Check failed: ret.ec == std::errc() \ \\ \" \b \f \n \r \t \u%04x Number Array U8Array Object of type can not be indexed by string. can not be indexed by Integer. Unknown construct , around character position: , " " \0 /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/string_view.h beg <= size_
" Unknown escape Expecting: " ", got: " EOF" \0" null Expecting null value "null" ch != -1 cursor_.Pos(): raw_str_.size(): Expecting digit ` is not supported for typed array. f64 is not supported. High precision number is not supported. /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/common.h PesudoHuberParam huber_slope The delta term in Pseudo-Huber loss. Invalid Parameter format for expect but value=' Required parameter of is not presented Out of range value for , value=' /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h pos <= value.length() Some trailing characters could not be parsed: ' ' Out of range value No conversion could be performed /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h *p == ')' Invalid NAN literal value for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default= , required key has already been registered in group_ptr.size() >= 2 group_ptr.back() == n_samples count <= total_entries mingap= , maxgap= , wgap= Check failed: size <= sa.size + sb.size bug in combine src.size == 0 this->size == 0 float32 float64 bool8 int32 int8 int16 int64 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/adapter.h Unknown data table type. SketchContainerImpl /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cc columns_size_.size() != 0 n_threads_ >= 1 base_rowid < group_ptr.back() Row: is not found in any group. worker_segments.size() >= 1 AllReduce n_columns == sketches_.size() Number of columns differs across workers num_cuts.size() == 0 feature_ptr.front() == 0 rank_size == total Check failed: reduced[i].data Check failed: worker_feature.data() PushRowPage sketches_.size() == n_columns weights.size() == info.num_row_ hessian.size() == info.num_row_ group_ptr.back() == hessian.size() cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max() cut_size > cuts->cut_ptrs_.HostVector().back() Check failed: a.data Check failed: a.data && reduced[fidx].data check quantile stats, nbig= , n= srcsize= , maxsize= , range= , chunk= Check failed: nbig < n quantile: too many large chunk ] rmin= , rmax= , wmin= , v= /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/categorical.h Invalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than max_cat + 1 >= n_categories Maximum cateogry should not be lesser than the total number of categories. HostSketchContainer PushColPage sketch->temp.size < max_size invalid maximum size max_size= , stemp.size INFO: rmax= , sum_total= , naxt_goal= , size= sketch->temp.size <= max_size Finalize: invalid maximum size, max_size= , stemp.size= /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.cc features.size() > 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.h array.size() == weights.size() aft_loss_distribution normal extreme Choice of distribution for the noise term in Accelerated Failure Time model aft_loss_distribution_scale Scaling factor used to scale the distribution in Accelerated Failure Time model Invalid Input: ' ', valid values are: optional, default= /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h enum_back_map_.count(value) != 0U Value not found in enum declared Enum ( exisit! )
+ Enums: ), /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc Timer for did not get stopped properly. s, calls @ us ======== Monitor ( ): ======== /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.cc Invaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again. fi->Read(&read[0], verstr.size()) == verstr.size() Check failed: fi->Read(&major) Check failed: fi->Read(&minor) Check failed: fi->Read(&patch) /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.cc Check failed: valid Input data contains `inf` or `nan` key >= builder_base_row_offset /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h num_row num_col num_nonzero labels group_ptr weights base_margin labels_lower_bound labels_upper_bound feature_types feature_weights field_cnt == kNumField Wrong number of fields All feature_types must be one of {int, float, i, q, c}. Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again. major >= 1 minor >= 6 Check failed: fi->Read(&num_field) MetaInfo: invalid format num_field >= expected MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.) MetaInfo: the given binary file contains extra fields which will be ignored. MetaInfo: Invalid format for Check failed: strm->Read(&name) name == expected_name Expected field: , got: Check failed: strm->Read(&type_val) Check failed: type == expected_type Expected field of type: got field type: Check failed: strm->Read(&is_scalar) Check failed: is_scalar Expected field to be a scalar; got a vector Check failed: strm->Read(field) Check failed: !is_scalar to be a tensor; got a scalar Check failed: strm->Read(&(shape[i])) Check failed: strm->Read(&field) to be a vector; got a scalar Check failed: strm->Read(&shape.first) Check failed: strm->Read(&shape.second) shape.second == 1 Number of columns is expected to be 1. this->base_margin_.Size() % this->num_row_ == 0 Incorrect size of base margin vector. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.h this->Data()->Size() == detail::CalcSize(this->shape_) Inconsistent size after modification. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.h Check failed: this->base_margin_.Size() % this->num_row_ == 0 Incorrect size for base margin. label this->labels.Size() % this->num_row_ == 0 Incorrect size for labels. Label contains NaN, infinity or a value too large. qid Check failed: non_dec `qid` must be sorted in non-decreasing order along with data. weight Weights must be positive values. label_lower_bound label_upper_bound Feature weight must be greater than 0. Unknown key for MetaInfo: Check failed: array.valid.Size() == 0 Meta info like label or weight can not have missing value. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/validation.h Check failed: valid_query_group Invalid group structure. Check failed: key < Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info. size == this->num_col_ Length of must be equal to number of columns. Check failed: info Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_ Number of columns must be consistent across batches. that.group_ptr_.size() != 0 shape[i] == r.Shape(i) group_ptr_.size() == weights_.Size() + 1 Size of weights must equal to number of groups when ranking group is used. group_ptr_.back() == num_row_ Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data. weights_.Size() == num_row_ Size of weights must equal to number of rows. labels.Shape(0) == num_row_ Size of labels must equal to number of rows. labels_lower_bound_.Size() == num_row_ Size of label_lower_bound must equal to number of rows. feature_weights.Size() == num_col_ Size of feature_weights must equal to number of columns. labels_upper_bound_.Size() == num_row_ Size of label_upper_bound must equal to number of rows. num_nonzero_ <= num_col_ * num_row_ base_margin_.Size() % num_row_ == 0 Size of base margin must be a multiple of number of rows. Check failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == device Data is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: matrix with entries loaded from cache_file.find('#') == std::string::npos Only one `#` is allowed in file path for cache file specification. .r auto Load part of data parts csv libsvm No format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format= No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csv Encountered parser error:
+ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.h Check failed: !type_.empty() Check failed: parser_ transpose.offset.Size() == num_columns + 1 self_offset.size() == other_offset.size() self_data.size(): other_data.size(): beg <= data.size() ptr < offset.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.cc Check failed: columns_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h Check failed: p_fmat->SingleColBlock() Check failed: !sorted_sketch /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.h hit_count.size() == n_bins_total row_ptr.size() == 0 raw Raw GHistIndex binary data format. Check failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cc count_ == source_->Iter() cuts_.Values().size() != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h Check failed: !at_end_ n_prefetch_batches > 0 total batches: fetch_it < cache_info_->offset.size() std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batches Sparse DMatrix assumes forward iteration. fi->Tell() == offset Check failed: fmt->Read(page.get(), fi.get()) Read a page in seconds. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h Unknown format type format.front() == '.' thread constructor failed count_ == n_batches_ count_ >= 1 Check failed: lock_.try_lock() Multiple threads attempting to use Sparse DMatrix. Check failed: proxy_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h Unknown type: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./any.h Check failed: type_ != nullptr The any container is empty requested= Check failed: *(type_->ptype_info) == typeid(T) The stored type mismatch stored= Check failed: !cache_info_->written MB written in Check failed: page_ this->count_ >= 1 source_->Iter() == this->count_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.cc Iterative DMatrix must have at least 1 batch. d == batch_param_.gpu_id gpu_id Check failed: proxy_handle Invalid proxy handle. Check failed: typed Check failed: ref_ Check failed: p_cuts ref_->Info().num_col_ == n_features Invalid ref DMatrix, different number of features. n_features == num_cols() Inconsistent number of columns. Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; }) Something went wrong during iteration. n_features >= 1 Data must has at least 1 column. accumulated_rows == Info().num_row_ Check failed: p_sketch rbegin == Info().num_row_ proxy->Info().labels.Size() == 0 /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.h Check failed: !std::isnan(sparse_thresh) Check failed: ellpack_ `sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.h Inconsistent max_bin between Quantile DMatrix and Booster: Check failed: !param.regen && param.hess.empty() Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.h Check failed: page_ != nullptr page.row_ptr.size() == h_offset.size() Slicing DMatrix is not supported for Quantile DMatrix. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.cc Check failed: on_host Not implemented on device. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.cc Check failed: (std::is_same<AdapterT, CSCAdapter>::value) Expecting CSCAdapter /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h param.gpu_id >= 0 param.max_bin >= 2 Check failed: r != BatchParam{} Batch parameter is not initialized. Generating new Gradient Index. param.gpu_id == -1 batch_param_.hess.data() == param.hess.data() Check failed: in_stream->Read(&tmagic) invalid input file format tmagic == kMagic invalid format, magic number mismatch offset_vec.back() == info_.num_nonzero_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.h Check failed: data_ && row_idx < length_ Column is empty or out-of-bound index of the column Check failed: data_ Column is empty DMatrix -r /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc info_.num_col_ != 0 .row.page Check failed: sparse_page_source_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.h Make cache: iter_.Next() == 1 Must have at least 1 batch. .col.page this->Info().num_col_ != 0 .sorted.col.page this->page_->Size() == n_features_ this->page_->data.Size() == csr->data.Size() .gradient_index.page cuts.Values().size() != 0 Check failed: ghist_index_source_ Check failed: kv.second Couldn't remove external memory cache file ; you may want to remove it manually Slicing DMatrix is not supported for external memory. Can not obtain a single CSR page for external memory DMatrix Raw binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.cc page->offset.Size() != 0U Invalid SparsePage file n_bytes == (page->data).Size() * sizeof(Entry) Check failed: page.offset.Size() != 0 && offset_vec[0] == 0 offset_vec.back() == page.data.Size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cc layer_begin == 0 Linear booster does not support prediction range. GBLinearTrainParam updater shotgun Update algorithm for linear model. One of shotgun/coord_descent tolerance Stop if largest weight update is smaller than this number. max_row_perbatch Maximum rows per batch. string Linear booster, implement generalized linear model. get<String>(in["name"]) == "gblinear" model gpu_coord_descent coord_descent Loading configuration on a CPU only machine. Changing updater to `coord_descent`. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.h fi->Read(¶m_, sizeof(param_)) == sizeof(param_) /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.h Slice is not supported by current booster. DoBoost PredictBatch PredictBatchInternal base_margin.Size() == nsize * ngroup /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.h Inplace predict is not supported by current booster. gblinear does not support prediction of leaf index { "bias": [ , ], "weight": [ ] } bias:
weight:
- Check failed: !model_.weight.empty() Model is not initialized Check failed: trees.empty() gblinear doesn't support number of trees for feature importance. importance_type == "weight" gblinear only has `weight` defined for feature importance. gblinear_train_param Check failed: this->updater_ DeprecatedGBLinearModelParam boosted_rounds /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc Unknown gbm type cpu_predictor GBTree /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.cc DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead. Check failed: this->configured_ Check failed: tparam_.GetInitialised() Using updaters: Tree method is automatically selected to be 'approx' for distributed training. Tree method is automatically set to 'approx' since external-memory data matrix is used. Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'. Using tree method: grow_histmaker grow_colmaker,prune Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker. grow_quantile_histmaker grow_gpu_hist Unknown tree_method ( ) detected ups.size() == updaters_.size() Internal Error: mismatched updater sequence.
- Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree() Updater: ` ` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees. Check failed: up->CanModifyTree() can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees. model_.trees.size() < model_.trees_to_update.size() No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training rounds gpair->Size() == p_fmat->Info().num_row_ Mismatching size between number of rows from input data and size of gradient vector. CommitModel get<String>(in["name"]) == "gbtree" gbtree_train_param Loading from a raw memory buffer on CPU only machine. Changing predictor to auto. predictor tree_method Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist. specified_updater default process_type Check failed: configured_ Check failed: p_gbtree this->model_.learner_model_param->num_feature != 0 layer_trees != 0 layer_end > layer_begin step >= 1 this->model_.trees_to_update.size() == this->model_.trees.size() Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees. Check failed: cpu_predictor_ Data is on CUDA device, but XGBoost is not compiled with CUDA support. GBTreeModelParam GBTreeTrainParam DartTrainParam Tree booster, gradient boosted trees. Tree booster, dart. BoostNewTrees ngroup != 0 in_gpair->Size() % ngroup == 0U must have exactly ngroup * nrow gpairs out_preds->version == 0 tree_end <= model_.trees.size() Invalid number of trees. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h tree_begin <= tree_end XGBoost version not compiled with OneAPI support. model_.param.num_parallel_tree != 0 model_.learner_model_param->num_output_group != 0 Check failed: success Current Predictor: gpu_predictor tree_begin == 0 Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead. Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead. Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead. gain total_gain cover total_cover Unknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: idx <= total_n_trees Invalid tree index. in_it < tree_end num_trees Number of features used for training and prediction. Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. size_leaf_vector Reserved option for vector tree. updater_seq Tree updater sequence. update Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model. oneapi_predictor Predictor algorithm type approx exact Choice of tree construction method. has not been registered in Alias sample_type uniform Different types of sampling algorithm. normalize_type forest Different types of normalization algorithm. rate_drop Fraction of trees to drop during the dropout. Whether at least one tree should always be dropped during the dropout. skip_drop Probability of skipping the dropout during a boosting iteration. learning_rate Learning rate(step size) of update. eta true 1 boolean get<String>(in["name"]) == "dart" weight_drop Check failed: p_dart Check failed: p_dart->weight_drop_.empty() Check failed: predictor i >= p_out_preds->version p_out_preds->predictions.Size() == predts.predictions.Size() predts.predictions.Size() == out_preds->predictions.Size() dart_train_param drop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.cc param.num_trees == static_cast<int32_t>(trees.size()) fi->Read(¶m, sizeof(param)) == sizeof(param) GBTree: invalid model file fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_trees param.num_trees == static_cast<int>(trees.size()) Check failed: ctx_ trees tree_info GlobalConfiguration verbosity Flag to print out detailed breakdown of runtime. use_rmm Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost /Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.cc Check failed: n_classes <= 1 || n_targets <= 1 Multi-class multi-output is not yet supported. n_classes: , n_targets: LearnerModelParamLegacy LearnerTrainParam GenericParameter gpu_id eval_metric num_round CONFIG-offset: Learner Check failed: IsA<Object>(in) Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. learner_model_param objective booster attributes num_feature num_class num_target base_score Check failed: !this->need_configuration_ Call Configure before saving model. Configure Check failed: matrix.first Check failed: !matrix.second.ref.expired() num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max()) Unfortunately, XGBoost does not support data matrices with features or greater mparam_.num_feature != 0 0 feature is supplied. Are you using raw Booster interface? multi:softprob num_output_group multi:softmax max_delta_step count:poisson 0.7 Check failed: this->obj_ Check failed: n_targets == t || 1 == t Inconsistent labels. Check failed: n_targets == 1 || n_targets == mparam_.num_target Inconsistent configuration of num_target. Configuration result from input data: , configuration from parameter: _param Invalid parameter " " contains whitespace.
-Parameters: { ", } might not be used.
-
- This could be a false alarm, with some parameters getting used by language bindings but
- then being mistakenly passed down to XGBoost core, or some parameter actually being used
- but getting flagged wrongly here. Please open an issue if you find any such cases.
-
- UpdateOneIter PredictRaw GetGradient Check failed: tparam_.dsplit != DataSplitMode::kAuto Precondition violated; dsplit cannot be 'auto' in distributed mode Column-wise data split is currently not supported. learner_model_param_.num_feature == p_fmat->Info().num_col_ Number of columns does not match number of features in booster. learner_model_param_.num_feature >= p_fmat->Info().num_col_ Empty dataset at worker: Check failed: gbm_ != nullptr Predict must happen after Load or configuration BoostOneIter EvalOneIter binary:logitraw auc logloss 1.4.0 Starting in XGBoost , the default evaluation metric used with the objective ' ' was changed from ' ' to ' '. Explicitly set eval_metric if you'd like to restore the old behavior. multiple_predictions <= 1 Perform one kind of prediction at a time. Unsupported prediction type: bs64 header != "bs64" Base64 format is no longer supported in brick. binf fp.Read(&header[0], 4) == 4U Invalid model format fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_) BoostLearner: wrong model format Check failed: fi->Read(&tparam_.objective) Check failed: fi->Read(&tparam_.booster) SAVED_PARAM_ count_poisson_max_delta_step metrics Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility fixed_size->Read(&buffer[0], size) == size ; this->learner_model_param_.num_feature != 0 begin_layer >= 0 out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_feature out_impl->learner_model_param_.num_feature != 0 best_iteration best_score learner_train_param generic_param
+ Check failed: !model_.weight.empty() Model is not initialized Check failed: trees.empty() gblinear doesn't support number of trees for feature importance. importance_type == "weight" gblinear only has `weight` defined for feature importance. gblinear_train_param Check failed: this->updater_ boosted_rounds DeprecatedGBLinearModelParam deprecated_num_feature deprecated_num_output_group int (non-negative) /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc Unknown gbm type cpu_predictor GBTree /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.cc DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead. Check failed: this->configured_ Check failed: tparam_.GetInitialised() Using updaters: Tree method is automatically selected to be 'approx' for distributed training. Tree method is automatically set to 'approx' since external-memory data matrix is used. Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'. Using tree method: grow_histmaker grow_colmaker,prune Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker. grow_quantile_histmaker grow_gpu_hist Unknown tree_method ( ) detected Check failed: !updaters_.empty() BoostNewTrees ngroup != 0 Current objective doesn't support external memory. in_gpair->Size() % ngroup == 0U must have exactly ngroup * nrow gpairs ups.size() == updaters_.size() Internal Error: mismatched updater sequence.
+ Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree() Updater: ` ` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees. Check failed: up->CanModifyTree() can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees. model_.trees.size() < model_.trees_to_update.size() No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training rounds gpair->Size() == p_fmat->Info().num_row_ Mismatching size between number of rows from input data and size of gradient vector. CommitModel get<String>(in["name"]) == "gbtree" gbtree_train_param Loading from a raw memory buffer on CPU only machine. Changing predictor to auto. predictor tree_method Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist. specified_updater default process_type Check failed: configured_ Check failed: p_gbtree this->model_.learner_model_param->num_feature != 0 layer_trees != 0 layer_end > layer_begin step >= 1 this->model_.trees_to_update.size() == this->model_.trees.size() Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h in_it < tree_end tree_begin <= tree_end out_preds->version == 0 tree_end <= model_.trees.size() Invalid number of trees. Check failed: cpu_predictor_ Data is on CUDA device, but XGBoost is not compiled with CUDA support. XGBoost version not compiled with OneAPI support. GBTreeModelParam num_trees Number of features used for training and prediction. Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. size_leaf_vector Reserved option for vector tree. GBTreeTrainParam updater_seq Tree updater sequence. update Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model. gpu_predictor oneapi_predictor Predictor algorithm type approx exact Choice of tree construction method. has not been registered in Alias DartTrainParam sample_type uniform Different types of sampling algorithm. normalize_type forest Different types of normalization algorithm. rate_drop Fraction of trees to drop during the dropout. Whether at least one tree should always be dropped during the dropout. skip_drop Probability of skipping the dropout during a boosting iteration. learning_rate Learning rate(step size) of update. eta true 1 boolean Tree booster, gradient boosted trees. Tree booster, dart. get<String>(in["name"]) == "dart" weight_drop Check failed: p_dart Check failed: p_dart->weight_drop_.empty() Check failed: predictor i >= p_out_preds->version p_out_preds->predictions.Size() == predts.predictions.Size() Unsupported data type for inplace predict. predts.predictions.Size() == p_out_preds->predictions.Size() Check failed: success Current Predictor: dart_train_param drop trees, weight = model_.param.num_parallel_tree != 0 model_.learner_model_param->num_output_group != 0 tree_begin == 0 Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead. Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead. Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead. gain total_gain cover total_cover Unknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: idx <= total_n_trees Invalid tree index. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.cc param.num_trees == static_cast<int32_t>(trees.size()) fi->Read(¶m, sizeof(param)) == sizeof(param) GBTree: invalid model file fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_trees param.num_trees == static_cast<int>(trees.size()) Check failed: ctx_ trees tree_info GlobalConfiguration verbosity Flag to print out detailed breakdown of runtime. use_rmm Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost /Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.cc Check failed: n_classes <= 1 || n_targets <= 1 Multi-class multi-output is not yet supported. n_classes: , n_targets: Check failed: common::AsConst(base_score_).Data()->HostCanRead() base_score_.Size() == 1 Check failed: base_score_.Data()->HostCanRead() Check failed: base_score_.Data()->DeviceCanRead() base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead() LearnerModelParamLegacy base_score Global bias of the model. num_feature Number of features in training data, this parameter will be automatically detected by learner. num_class Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier. num_target Number of target for multi-target regression. boost_from_average Whether we should calculate the base score from training data. LearnerTrainParam dsplit col row Data split mode for distributed training. disable_default_eval_metric Flag to disable default metric. Set to >0 to disable booster Gradient booster used for training. objective reg:squarederror Objective function used for obtaining gradient. GenericParameter Random number seed during training. random_state seed_per_iteration Seed PRNG determnisticly via iterator number. Number of threads to use. n_jobs The primary GPU device ordinal. fail_on_invalid_gpu_id Fail with error when gpu_id is invalid. validate_parameters Enable checking whether parameters are used or not. eval_metric num_round CONFIG-offset: Learner Check failed: IsA<Object>(in) Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. learner_model_param attributes Check failed: !this->need_configuration_ Call Configure before saving model. Check failed: learner_model_param_.Initialized() learner_model_param_.BaseScore(this->Ctx()).Size() != 0 Check failed: ret.ec == std::errc{} Configure Check failed: matrix.first Check failed: !matrix.second.ref.expired() num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max()) Unfortunately, XGBoost does not support data matrices with features or greater mparam_.num_feature != 0 0 feature is supplied. Are you using raw Booster interface? multi:softprob num_output_group multi:softmax max_delta_step count:poisson 0.7 learner_model_param_.BaseScore(Ctx()).Size() != 0 Check failed: this->obj_ Check failed: n_targets == t || 1 == t Inconsistent labels. Check failed: n_targets == 1 || n_targets == mparam_.num_target Inconsistent configuration of num_target. Configuration result from input data: , configuration from parameter: Check failed: ptr _param Invalid parameter " " contains whitespace.
+Parameters: { ", } are not used.
+ UpdateOneIter PredictRaw GetGradient Check failed: !std::isnan(mparam_.base_score) Check failed: !std::isinf(mparam_.base_score) Check failed: tparam_.dsplit != DataSplitMode::kAuto Precondition violated; dsplit cannot be 'auto' in distributed mode Column-wise data split is currently not supported. learner_model_param_.num_feature == p_fmat->Info().num_col_ Number of columns does not match number of features in booster. learner_model_param_.num_feature >= p_fmat->Info().num_col_ Empty dataset at worker: Check failed: gbm_ != nullptr Predict must happen after Load or configuration BoostOneIter EvalOneIter multiple_predictions <= 1 Perform one kind of prediction at a time. Unsupported prediction type: bs64 header != "bs64" Base64 format is no longer supported in brick. binf fp.Read(&header[0], 4) == 4U Invalid model format fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_) BoostLearner: wrong model format Check failed: fi->Read(&tparam_.objective) Check failed: fi->Read(&tparam_.booster) SAVED_PARAM_ count_poisson_max_delta_step metrics Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility fixed_size->Read(&buffer[0], size) == size data_.Size() == detail::CalcSize(shape_) ; this->learner_model_param_.num_feature != 0 begin_layer >= 0 out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_feature out_impl->learner_model_param_.num_feature != 0 best_iteration best_score learner_train_param generic_param
If you are loading a serialized model (like pickle in Python, RDS in R) generated by
older XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. See:
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for more details about differences between saving model and serializing.
- Model Config Invalid serialization file. fp.Read(&header[0], header.size()) == serialisation_header_.size() Check failed: header == serialisation_header_ fp.Read(&sz, sizeof(sz)) == sizeof(sz) sz > 0 Global bias of the model. Number of features in training data, this parameter will be automatically detected by learner. Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier. Number of target for multi-target regression. int (non-negative) dsplit col row Data split mode for distributed training. disable_default_eval_metric Flag to disable default metric. Set to >0 to disable Gradient booster used for training. reg:squarederror Objective function used for obtaining gradient. Random number seed during training. random_state seed_per_iteration Seed PRNG determnisticly via iterator number. Number of threads to use. n_jobs The primary GPU device ordinal. fail_on_invalid_gpu_id Fail with error when gpu_id is invalid. validate_parameters Enable checking whether parameters are used or not. LinearTrainParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.cc Unknown linear updater Learning rate of each update. reg_lambda L2 regularization on weights. reg_alpha L1 regularization on weights. feature_selector cyclic shuffle thrifty greedy random Feature selection or ordering method. lambda alpha CoordinateParam Update linear model according to coordinate descent algorithm. top_k The number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features. linear_train_param coordinate_param CoordinateUpdater /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.h unknown coordinate selector: /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.h UpdateFeature Update linear model according to shotgun coordinate descent algorithm. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.cc Unsupported feature selector for shotgun updater.
- Supported options are: {cyclic, shuffle} WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cc auc <= 1.0f Receiver Operating Characteristic Area Under the Curve. aucpr Area under PR curve for both classification and rank. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.h labels.Size() != 0 labels.Size() == predts.size() info.weights_.Size() == info.group_ptr_.size() - 1 info.group_ptr_.back() == info.labels.Size() auc <= 1 Total AUC across groups: , valid groups: n_classes != 0 auc <= local_area Dataset is empty, or contains only positive or negative samples. info.group_ptr_.size() >= 2 /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.h Invalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples. labels.Shape(1) == 1 AUC doesn't support multi-target model. PR-AUC supports only binary relevance for learning to rank. rmse Rooted mean square error. rmsle Rooted mean square log error. mae Mean absolute error. mape Mean absolute percentage error. Negative loglikelihood for logistic regression. mphe Mean Pseudo-huber error. poisson-nloglik Negative loglikelihood for poisson regression. gamma-deviance Residual deviance for gamma regression. gamma-nloglik Negative log-likelihood for gamma regression. error Binary classification error. tweedie-nloglik tweedie-nloglik@rho for tweedie regression. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cu preds.Size() == info.labels.Size() label and prediction size not match, hint: use merror or mlogloss for multi-class classification info.labels.Shape(1) != 0 pseduo_huber_param info.labels.Shape(0) == info.num_row_ slope != 0.0 slope for pseudo huber cannot be 0. %f sscanf(param, "%f", &threshold_) == 1 unable to parse the threshold value for the error metric Check failed: param != nullptr tweedie-nloglik must be in format tweedie-nloglik@rho Check failed: rho_ < 2 && rho_ >= 1 tweedie variance power must be in interval [1, 2) tweedie-nloglik@ /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.cc Unknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builder merror Multiclass classification error. mlogloss Multiclass negative loglikelihood. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cu preds.Size() == 0 Check failed: preds.Size() % info.labels.Size() == 0 label and prediction size not match nclass >= 1U mlogloss and merror are only used for multi-class classification, use logloss for binary classification Check failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class) MultiClassEvaluation: label must be in [0, num_class), num_class= but found in label ams AMS metric for higgs. pre precision@k for rank. ndcg ndcg@k for rank. map map@k for rank. cox-nloglik Negative log partial likelihood of Cox proportional hazards model. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cc AMS must be in format ams@k ams@ Check failed: !distributed metric AMS do not support distributed evaluation best-ams-ratio= %u[-]? label size predict size not match gptr.size() != 0U must specify group when constructing rank file gptr.back() == preds.Size() EvalRank: group structure must match number of prediction Check failed: tparam_ Cox metric does not support distributed evaluation aft-nloglik Negative log likelihood of Accelerated Failure Time model. interval-regression-accuracy aft_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cu Unknown probability distribution preds.Size() == info.labels_lower_bound_.Size() preds.Size() == info.labels_upper_bound_.Size() ndata == labels_upper_bound.Size() Check failed: metric_ AFT metric must be configured first, with distribution type and scale survival:aft AFT loss function /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cu info.labels_lower_bound_.Size() == ndata info.labels_upper_bound_.Size() == ndata info.weights_.Size() == ndata Number of weights should be equal to number of data points. Unrecognized distribution /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.h Not part of device code. WITH_CUDA: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.h /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/objective.h multioutput is not supported by current objective function binary:hinge Hinge loss. Expects labels to be in [0,1f] /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cu info.labels.Size() != 0U label set cannot be empty labels are not correctly provided preds.size= , label.size= SoftmaxMultiClassParam Softmax for multi-class classification, output class index. Softmax for multi-class classification, output probability distribution. Number of output class in the multi-class classification. softmax_multiclass_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cu Check failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size()) SoftmaxMultiClassObj: label size and pred size does not match.
- label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class). Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.cc Unknown objective function: ` `
- LambdaRankParam Pairwise rank objective. LambdaRank with NDCG as objective. LambdaRank with MAP as objective. rank:pairwise rank:ndcg rank:map num_pairsample Number of pair generated for each instance. fix_list_weight Normalize the weight of each list by this value, if equals 0, no effect will happen lambda_rank_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cu Check failed: gptr.size() != 0 && gptr.back() == info.labels.Size() group structure not consistent with #rows group ponter size: labels size: group pointer back: Computing gradients on CPU. RegLossParam Regression with squared error. Regression with root mean squared logarithmic error. Logistic regression for probability regression task. Logistic regression for binary classification task. Logistic regression for classification, output score before logistic transformation. reg:linear reg:pseudohubererror Regression Pseudo Huber error. PoissonRegressionParam Poisson regression for count data. survival:cox Cox regression for censored survival data (negative labels are considered censored). reg:gamma Gamma regression for severity data. TweedieRegressionParam reg:tweedie Tweedie regression for insurance data. reg:squaredlogerror reg:logistic binary:logistic scale_pos_weight Scale the weight of positive examples by this factor reg_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cu Invalid shape of labels. info.labels.Size() == preds.Size() info.weights_.Size() == info.num_row_ label must be greater than -1 for rmsle so that log(label + 1) can be valid. label must be in [0,1] for logistic regression /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.h Check failed: base_score > 0.0f && base_score < 1.0f base_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror. /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/common.h Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression. poisson_regression_param PoissonRegression: label must be nonnegative Check failed: last_abs_y <= abs_y CoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed! GammaRegression: label must be positive. tweedie_variance_power Tweedie variance power. Must be between in range [1, 2). tweedie_regression_param TweedieRegression: label must be nonnegative Make predictions using CPU. /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.cc out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group model.param.size_leaf_vector == 0 size_leaf_vector is enforced to 0 so far /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h m->NumColumns() == model.learner_model_param->num_feature Number of columns in data must equal to trained model. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h ncolumns != 0 base_margin.Shape(1) == ngroup /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.cc Check failed: container_.find(m) != container_.cend() Check failed: container_.at(m).ref.lock() [Internal error]: DMatrix: has expired. unordered_map::at: key not found Unknown predictor type model.learner_model_param->num_output_group != 0 Invalid shape of base_margin. Expected: ( ) margin.Shape(0) == n_samples margin.Shape(1) == n_groups /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.cc Failed to parse feature interaction constraint:
+ Model Config Invalid serialization file. fp.Read(&header[0], header.size()) == serialisation_header_.size() Check failed: header == serialisation_header_ fp.Read(&sz, sizeof(sz)) == sizeof(sz) sz > 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.cc Unknown linear updater LinearTrainParam Learning rate of each update. reg_lambda L2 regularization on weights. reg_alpha L1 regularization on weights. feature_selector cyclic shuffle thrifty greedy random Feature selection or ordering method. lambda alpha CoordinateParam top_k The number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features. Update linear model according to coordinate descent algorithm. linear_train_param coordinate_param CoordinateUpdater /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.h unknown coordinate selector: /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.h UpdateFeature Update linear model according to shotgun coordinate descent algorithm. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.cc Unsupported feature selector for shotgun updater.
+ Supported options are: {cyclic, shuffle} WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cc labels.Size() != 0 labels.Size() == predts.size() auc <= 1.0f auc Receiver Operating Characteristic Area Under the Curve. info.weights_.Size() == info.group_ptr_.size() - 1 info.group_ptr_.back() == info.labels.Size() auc <= 1 Total AUC across groups: , valid groups: n_classes != 0 auc <= local_area Dataset is empty, or contains only positive or negative samples. info.group_ptr_.size() >= 2 /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.h Invalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples. labels.Shape(1) == 1 AUC doesn't support multi-target model. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.h aucpr Area under PR curve for both classification and rank. PR-AUC supports only binary relevance for learning to rank. rmse Rooted mean square error. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cu preds.Size() == info.labels.Size() label and prediction size not match, hint: use merror or mlogloss for multi-class classification info.labels.Shape(1) != 0 rmsle Rooted mean square log error. mae Mean absolute error. mape Mean absolute percentage error. logloss Negative loglikelihood for logistic regression. mphe Mean Pseudo-huber error. pseudo_huber_param info.labels.Shape(0) == info.num_row_ slope != 0.0 slope for pseudo huber cannot be 0. poisson-nloglik Negative loglikelihood for poisson regression. gamma-deviance Residual deviance for gamma regression. gamma-nloglik Negative log-likelihood for gamma regression. error Binary classification error. %f sscanf(param, "%f", &threshold_) == 1 unable to parse the threshold value for the error metric tweedie-nloglik tweedie-nloglik@rho for tweedie regression. Check failed: param != nullptr tweedie-nloglik must be in format tweedie-nloglik@rho Check failed: rho_ < 2 && rho_ >= 1 tweedie variance power must be in interval [1, 2) tweedie-nloglik@ /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.cc Unknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builder merror Multiclass classification error. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cu preds.Size() == 0 Check failed: preds.Size() % info.labels.Size() == 0 label and prediction size not match nclass >= 1U mlogloss and merror are only used for multi-class classification, use logloss for binary classification Check failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class) MultiClassEvaluation: label must be in [0, num_class), num_class= but found in label mlogloss Multiclass negative loglikelihood. ams AMS metric for higgs. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cc AMS must be in format ams@k ams@ Check failed: !collective::IsDistributed() metric AMS do not support distributed evaluation best-ams-ratio= pre precision@k for rank. %u[-]? label size predict size not match gptr.size() != 0U must specify group when constructing rank file gptr.back() == preds.Size() EvalRank: group structure must match number of prediction Check failed: tparam_ ndcg ndcg@k for rank. map map@k for rank. cox-nloglik Negative log partial likelihood of Cox proportional hazards model. Cox metric does not support distributed evaluation aft-nloglik Negative log likelihood of Accelerated Failure Time model. aft_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cu Unknown probability distribution preds.Size() == info.labels_lower_bound_.Size() preds.Size() == info.labels_upper_bound_.Size() ndata == labels_upper_bound.Size() Check failed: metric_ AFT metric must be configured first, with distribution type and scale interval-regression-accuracy /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.cc begin_pos <= sorted_pos.size() nptr.size() > 0 n_unique == n_leaf Check failed: !position.empty() h_node_ptr.back() <= info.num_row_ /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.h Check failed: quantiles.empty() || quantiles.size() == n_leaf Check failed: tree[nidx].IsLeaf() /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.h k + 1 < h_node_ptr.size() Check failed: h_row_set.empty() /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/stats.h Check failed: alpha >= 0 && alpha <= 1 k >= 0 survival:aft AFT loss function /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cu info.labels_lower_bound_.Size() == ndata info.labels_upper_bound_.Size() == ndata info.weights_.Size() == ndata Number of weights should be equal to number of data points. Unrecognized distribution /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.h Not part of device code. WITH_CUDA: /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/objective.h multioutput is not supported by current objective function binary:hinge Hinge loss. Expects labels to be in [0,1f] /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cu info.labels.Size() != 0U label set cannot be empty labels are not correctly provided preds.size= , label.size= SoftmaxMultiClassParam Number of output class in the multi-class classification. Softmax for multi-class classification, output class index. softmax_multiclass_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cu Check failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels.Size()) SoftmaxMultiClassObj: label size and pred size does not match.
+ label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class). Softmax for multi-class classification, output probability distribution. Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.cc Unknown objective function: ` `
+ Check failed: base_score LambdaRankParam num_pairsample Number of pair generated for each instance. fix_list_weight Normalize the weight of each list by this value, if equals 0, no effect will happen Pairwise rank objective. rank:pairwise lambda_rank_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cu Check failed: gptr.size() != 0 && gptr.back() == info.labels.Size() group structure not consistent with #rows group ponter size: labels size: group pointer back: Computing gradients on CPU. LambdaRank with NDCG as objective. rank:ndcg LambdaRank with MAP as objective. rank:map RegLossParam scale_pos_weight Scale the weight of positive examples by this factor Regression with squared error. reg_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cu info.labels.Size() == preds.Size() Invalid shape of labels. info.weights_.Size() == info.num_row_ Regression with root mean squared logarithmic error. reg:squaredlogerror label must be greater than -1 for rmsle so that log(label + 1) can be valid. Logistic regression for probability regression task. reg:logistic label must be in [0,1] for logistic regression /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.h Check failed: base_score > 0.0f && base_score < 1.0f base_score must be in (0,1) for logistic loss, got: Logistic regression for binary classification task. binary:logistic Logistic regression for classification, output score before logistic transformation. binary:logitraw reg:linear reg:linear is now deprecated in favor of reg:squarederror. reg:pseudohubererror Regression Pseudo Huber error. /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/common.h PoissonRegressionParam Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression. Poisson regression for count data. poisson_regression_param PoissonRegression: label must be nonnegative survival:cox Cox regression for censored survival data (negative labels are considered censored). Check failed: last_abs_y <= abs_y CoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed! reg:gamma Gamma regression for severity data. GammaRegression: label must be positive. TweedieRegressionParam tweedie_variance_power Tweedie variance power. Must be between in range [1, 2). reg:tweedie Tweedie regression for insurance data. tweedie_regression_param TweedieRegression: label must be nonnegative reg:absoluteerror Mean absoluate error. StringView{get<String const>(in["name"])} == StringView{"reg:absoluteerror"} Sum of weights is close to 0.0, skipping base score estimation. t_v.Shape(1) != 0 Make predictions using CPU. /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.cc out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group model.param.size_leaf_vector == 0 size_leaf_vector is enforced to 0 so far /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h Inplace predict accepts only DMatrixProxy as input. m->NumColumns() == model.learner_model_param->num_feature Number of columns in data must equal to trained model. ncolumns != 0 base_margin.Shape(1) == ngroup /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.cc Check failed: container_.find(m) != container_.cend() Check failed: container_.at(m).ref.lock() [Internal error]: DMatrix: has expired. unordered_map::at: key not found Unknown predictor type model.learner_model_param->num_output_group != 0 Invalid shape of base_margin. Expected: ( ) margin.Shape(0) == n_samples margin.Shape(1) == n_groups /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.cc Failed to parse feature interaction constraint:
With error:
- newsize != 0 CPUHistMakerTrainParam single_precision_histogram Use single precision to build histograms. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.cc std::floor(d) == d Found floating point number in interaction constraints Unknown value type for interaction constraint: TreeParam TrainParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc Unknown Model Builder: { } Dump text representation of tree Dump json representation of tree GraphvizParam dot Dump graphviz representation of tree Check failed: node.IsLeaf() this->split_types_.size() == param.num_nodes this->GetSplitCategoriesPtr().size() == param.num_nodes size != 0 categories_segments categories_sizes categories_nodes categories unique_path[i].pweight == 0 Unique path must have zero weight j_end - j_begin != 0 std::numeric_limits<bst_cat_t>::min() != max_cat /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h param.num_nodes < std::numeric_limits<int>::max() number of nodes in the tree exceed 2^31 fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam) param.num_nodes != 0 fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size() fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size() static_cast<int>(deleted_nodes_.size()) == param.num_deleted param.num_nodes == static_cast<int>(nodes_.size()) param.num_nodes == static_cast<int>(stats_.size()) param.deprecated_num_roots == 1 Check failed: !HasCategoricalSplit() Please use JSON/UBJSON for saving models with categorical splits. loss_changes split_indices static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deleted this->split_categories_segments_.size() == param.num_nodes tree_param n_nodes != 0 loss_changes.size() == n_nodes sum_hessian sum_hessian.size() == n_nodes base_weights base_weights.size() == n_nodes left_children lefts.size() == n_nodes right_children rights.size() == n_nodes parents parents.size() == n_nodes indices.size() == n_nodes split_conditions conds.size() == n_nodes default_left default_left.size() == n_nodes split_type n_nodes == split_categories_segments.size() num_nodes get<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes) mean_values->size() > 0U Number of features used in tree construction. num_deleted Size of leaf vector, reserved for vector tree min_split_loss Minimum loss reduction required to make a further partition. max_depth Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policy max_leaves Maximum number of leaves; 0 indicates no limit. max_bin if using histogram-based algorithm, maximum number of bins per feature grow_policy depthwise lossguide Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM) max_cat_to_onehot Maximum number of categories to use one-hot encoding based split. min_child_weight Minimum sum of instance weight(hessian) needed in a child. L2 regularization on leaf weight L1 regularization on leaf weight Maximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraint subsample Row subsample ratio of training instance. sampling_method gradient_based Sampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost) colsample_bynode Subsample ratio of columns, resample on each node (split). colsample_bylevel Subsample ratio of columns, resample on each level. colsample_bytree Subsample ratio of columns, resample on each tree construction. sketch_eps EXP Param: Sketch accuracy of approximate algorithm. sketch_ratio EXP Param: Sketch accuracy related parameter of approximate algorithm. cache_opt EXP Param: Cache aware optimization. refresh_leaf Whether the refresh updater needs to update leaf values. monotone_constraints Constraint of variable monotonicity interaction_constraints Constraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more information sparse_threshold percentage threshold for treating a feature as sparse gamma double {nid}:[{fname}] yes={yes},no={no} {nid} {fname} {yes} pos != std::string::npos {tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing} {tabs} {cond} {left} {right} {missing} {tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing} ,gain={loss_chg},cover={sum_hess} {loss_chg} {sum_hess} {tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing} Unknown feature map type. Check failed: is_categorical in feature map is numerical but tree node is categorical. Check failed: is_numerical in feature map is categorical but tree node is numerical. {tabs}{nid}:leaf={leaf}{stats} ,cover={cover} {leaf} {stats} {cover} {parent}{stat}
+ newsize != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.cc std::floor(d) == d Found floating point number in interaction constraints Unknown value type for interaction constraint: TreeParam num_nodes Number of features used in tree construction. num_deleted Size of leaf vector, reserved for vector tree TrainParam min_split_loss Minimum loss reduction required to make a further partition. max_depth Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policy max_leaves Maximum number of leaves; 0 indicates no limit. if using histogram-based algorithm, maximum number of bins per feature grow_policy depthwise lossguide Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM) max_cat_to_onehot Maximum number of categories to use one-hot encoding based split. max_cat_threshold Maximum number of categories considered for split. Used only by partition-basedsplits. min_child_weight Minimum sum of instance weight(hessian) needed in a child. L2 regularization on leaf weight L1 regularization on leaf weight Maximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraint subsample Row subsample ratio of training instance. sampling_method gradient_based Sampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost) colsample_bynode Subsample ratio of columns, resample on each node (split). colsample_bylevel Subsample ratio of columns, resample on each level. colsample_bytree Subsample ratio of columns, resample on each tree construction. sketch_ratio EXP Param: Sketch accuracy related parameter of approximate algorithm. cache_opt EXP Param: Cache aware optimization. refresh_leaf Whether the refresh updater needs to update leaf values. monotone_constraints Constraint of variable monotonicity interaction_constraints Constraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more information sparse_threshold percentage threshold for treating a feature as sparse gamma double categories_segments categories_sizes categories_nodes categories /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc j_end - j_begin != 0 std::numeric_limits<bst_cat_t>::min() != max_cat Unknown Model Builder: { } Dump text representation of tree {nid}:[{fname}] yes={yes},no={no} {nid} {fname} {yes} pos != std::string::npos {tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing} {tabs} {cond} {left} {right} {missing} {tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing} ,gain={loss_chg},cover={sum_hess} {loss_chg} {sum_hess} {tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing} Unknown feature map type. Check failed: is_categorical in feature map is numerical but tree node is categorical. Check failed: is_numerical in feature map is categorical but tree node is numerical. {tabs}{nid}:leaf={leaf}{stats} ,cover={cover} {leaf} {stats} {cover} {parent}{stat}
{left}
-{right} {parent} {stat} {nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no} {depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing} ] "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} , "gain": {loss_chg}, "cover": {sum_hess} {{properties} {stat}, "children": [{left}, {right}
-{indent}]} {properties} { "nodeid": {nid}, "leaf": {leaf} {stat}} , "cover": {sum_hess} {newline}{indent}{nodes} {newline} yes_color #0000FF Edge color when meets the node condition. #FF0000 Edge color when doesn't meet the node condition. rankdir Passed to graphiz via graph_attr. condition_node_params Conditional node configuration leaf_node_params Leaf node configuration graph_attrs Any other extra attributes for graphviz `graph_attr`. Failed to parse graphviz parameters:
+{right} {parent} {stat} {nodes} Dump json representation of tree "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} GraphvizParam yes_color #0000FF Edge color when meets the node condition. #FF0000 Edge color when doesn't meet the node condition. rankdir Passed to graphiz via graph_attr. condition_node_params Conditional node configuration leaf_node_params Leaf node configuration graph_attrs Any other extra attributes for graphviz `graph_attr`. dot Dump graphviz representation of tree Failed to parse graphviz parameters:
" graph [ {key}="{value}" ]
{key} {value} The following parameters for graphviz are not recognized:
/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.h ch == '{' Error at , Expect '{' but get ' Line , around ^` ch == ',' , JSON object expect '}' or ',' ' ch == ':' , Expect ':' but get ' ch == '\"' , Expect '"' but get ' unknown string escape \ , Expect '"' but reach end of line {nid} [ label="{fname}:{cond}" {params}]
{nid} -> {child} [label="{branch}" color="{color}"]
no yes , missing {child} {color} {branch} {nid} [ label="{fname}{<}{cond}" {params}]
@@ -5602,1485 +5898,1498 @@
{leaf-value} {parent}
{left}
{right} digraph {
graph [ rankdir={rankdir} ]
{graph_attrs}
-{nodes}} {rankdir} {graph_attrs} /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.cc Unknown tree updater Tree constructor that uses approximate histogram construction for each node. GlobalApproxUpdater train_param hist_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.h Max leaves and max depth cannot both be unconstrained. this->max_depth <= 31 max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.cc Check failed: param.sampling_method != TrainParam::kGradientBased Gradient based sampling is not supported for approx tree method. UpdatePosition EvaluateSplits n_total_bins == page.cut.TotalBins() /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.h elem_of_each_node_.size() == 0U /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h begin < end n_batches_ >= 1 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/hist_util.h row_ptr_[nid] == kMax n_nodes > 0 id != kMax nodes == targeted_hists.size() i < first_dimension_.size() nthreads >= 1 nid < nodes_ tid < nthreads_ i < ranges_.size() end > begin /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/categorical.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.h n_left + n_right == row_set_collection_[nidx].Size() n_left == 0 n_right == 0 n_left + n_right == e.Size() begin + n_left <= e.end begin + n_left + n_right == e.end BuildHistogram /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.h Check failed: !features.empty() cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) UpdatePredictionCache out_preds.Size() == data->Info().num_row_ out_preds.Size() > 0U Check failed: p_last_tree out_preds.DeviceIdx() == GenericParameter::kCpuId part.Size() == n_nodes ColMakerTrainParam grow_colmaker Grow tree with parallelization over columns. opt_dense_col EXP Param: speed optimization for dense column. default_direction learn right Default direction choice when encountering a missing value colmaker_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.cc Updater `grow_colmaker` or `exact` tree method doesn't support distributed training. Updater `grow_colmaker` or `exact` tree method doesn't support external memory training. Check failed: this->ctx_ Check failed: param_.cache_opt Support for `cache_opt' is removed in 1.0.0 param_.max_depth > 0 exact tree method doesn't support unlimited depth. fmat.Info().num_row_ == position_.size() param_.sampling_method == TrainParam::kUniform Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist. ridx < position_.size() ridx exceed bound ridx= pos= grow_local_histmaker Tree constructor that uses approximate histogram construction. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h fi.Read(&this->size, sizeof(this->size)) == sizeof(this->size) fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.cc Check failed: param_.max_depth > 0 max_depth must be larger than 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.h node2workindex_[nid] == static_cast<int>(wid) summary_array_.size() == sketchs_.size() offset == -2 this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1 fid * 2 + 1 < fminmax_.size() FeatHelper fid exceed query bound ret > 0U Check failed: tree[nid].IsLeaf() fid * 2 < fminmax_.size() fv= , hist.size= hist[ ]= , hist.last= istart != hist.size prune Pruner that prune the tree according to statistics. TreePruner PrunerUpdate /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cc tree pruning end, extra nodes, pruned nodes, max_depth= Check failed: !tree[pid].IsLeaf() Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf() Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf() nid >= 1 Quantile::Builder UpdateTree /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc InitSampling ExpandTree upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) (*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild() Grow tree using quantized histogram. row_ptr.size() >= 2 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/hist_util.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h base_rowid == gmat.base_rowid /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.h any_missing == true /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/column_matrix.h sizeof(BinIdxType) == bins_type_size_ any_missing == any_missing_ cpu_hist_train_param Attempted to load internal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../param.h exponent <= mod refresh Refresher that refreshes the weight and statistics according to data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.cc batch.Size() < std::numeric_limits<unsigned>::max() Syncher that synchronize the tree in all distributed nodes. rabit_tracker_uri rabit_tracker_port rabit_task_id DMLC_TRACKER_URI DMLC_TRACKER_PORT DMLC_TASK_ID DMLC_ROLE rabit_world_size rabit_hadoop_mode rabit_tree_reduce_minsize rabit_reduce_ring_mincount rabit_reduce_ring_mincount should be greater than 0 rabit_reduce_buffer DMLC_WORKER_CONNECT_RETRY rabit_bootstrap_cache rabit_debug rabit_timeout rabit_timeout_sec rabit_timeout_sec should be non negative second rabit_enable_tcp_no_delay /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.cc Connect to (failed): [ ]
- Connect Retry connect to ip(retry time ): [ ReConnectLink failure 1 ReConnectLink failure 2 sync::Invalid tracker message, init failure ReConnectLink failure 3 NULL task connected to the tracker ReConnectLink failure 4 must keep rank to same if the node already have one tracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specified ReConnectLink failure 5 ReConnectLink failure 6 ReConnectLink failure 7 ReConnectLink failure 8 ReConnectLink failure 9 ReConnectLink failure 10 ReConnectLink failure 12 ReConnectLink failure 13 ReConnectLink failure, link rank inconsistent Override a link that is active ReConnectLink failure 14 ReConnectLink failure 15 ReConnectLink: bad socket tcp no delay is not implemented on non unix platforms cannot find parent in the link cannot find prev ring in the link cannot find next ring in the link failed in ReconnectLink buffer size inconsistent must assign buffer_size Allreduce: size check Allreduce: boundary error Broadcast: root should be smaller than world size need to assume rank structure write ptr boundary check [%d] read_ptr boundary check worker 256MB DMLC_NUM_ATTEMPT %[^=]=%s mapred_tip_id mapreduce_task_id hadoop_mode is set but cannot find mapred_task_id mapred_task_id %d rabit_num_trial mapred_map_tasks mapreduce_job_maps hadoop_mode is set but cannot find mapred_map_tasks Rabit Module currently only work with dmlc worker can only call Init once start fail to get host name Failed to shutdown due to %s print %lu%c invalid format for %s invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB} Create cannot obtain address of %s Does not support IPv6 Socket %s Error:%s SendAll RecvAll error during send SendStr Socket::Close double close the socket or close without create 0.0.0.0 TryBindHost GetSockError error during send RecvStr Accept SetNonBlock-1 SetNonBlock-2 SetKeepAlive too large type_nbytes=%lu, buffer_size=%lu /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.h Poll timeout Poll ReadToRingBuffer: buffer not allocated ReadToRingBuffer: max_size_read check Allreduce: boundary check AllgatherRing failed Allreduce failed Broadcast failed unknown enum_op unknown data_type DataType does not support bitwise or operation Failed to initialize RABIT. Failed to shutdown RABIT worker. Read pickle string not implemented the rabit has not been initialized cannot initialize reduce handle twice must initialize handle to call AllReduce LibSVMParserParam LibFMParserParam CSVParserParam libfm /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc Unknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h Check failed: TryLoadCache() failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.h Check failed: fi->Read(&label) Bad RowBlock format Check failed: fi->Read(&weight) Check failed: fi->Read(&qid) Check failed: fi->Read(&field) Check failed: fi->Read(&index) Check failed: fi->Read(&value) Check failed: fi->Read(&max_field, sizeof(IndexType)) Check failed: fi->Read(&max_index, sizeof(IndexType)) MB read, MB/sec finish reading at %g MB/sec label.size() + 1 == offset.size() offset.back() == index.size() Check failed: offset.back() == value.size() || value.size() == 0 /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.h finish reading at File format indexing_mode If >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. File format. label_column Column index (0-based) that will put into label. delimiter Delimiter used in the csv file. weight_column Column index that will put into instance weights. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h param_.format == "libsvm" /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.h chunk.size != 0U qid: Check failed: out->label.size() + 1 == out->offset.size() Check failed: base <= 10 && base >= 2 sign == true /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h cannot call ParseNext /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h param_.format == "libfm" Check failed: out->field.size() == out->index.size() /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.h param_.format == "csv" Check failed: param_.label_column != param_.weight_column || param_.label_column < 0 Must have distinct columns for labels and instance weights Delimiter ' ' is not found in the line. Expected ' ' as the delimiter to separate fields. Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size() hdfs:// viewfs:// /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.cc Please compile with DMLC_USE_HDFS=1 to use hdfs s3:// http:// https:// Please compile with DMLC_USE_S3=1 to use S3 azure:// Please compile with DMLC_USE_AZURE=1 to use Azure unknown filesystem protocol stdin Check failed: part < nsplit invalid input parameter for InputSplit::Create indexed_recordio need to pass index file to use IndexedRecordIO recordio unknown input split type .split .part /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.h name_cache.size() == 1U only one `#` is allowed in file path for cachefile specification Check failed: std::getline(is, kv.first, '=') Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.h Check failed: fp_ != NULL SingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1 InputSplit do not support write /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.h Check failed: !producer_sig_processed_.load(std::memory_order_acquire) unique_lock::unlock: not locked Check failed: producer_sig_.load(std::memory_order_acquire) == kProduce Make sure you call BeforeFirst not inconcurrent with Next! Check failed: produce_end_.load(std::memory_order_acquire) Check failed: out_data_ != NULL Calling Value at beginning or end? Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroy Check failed: cell != __null || produce_end_.load(std::memory_order_acquire) Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy No thread /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.h Check failed: nread == sizeof(size) has invalid cache file format Check failed: fi_->Read(p->begin, size) == size BeforeFirst is not supported Check failed: this->InitCachedIter() Failed to initialize CachedIter ResetPartition is not supported in CachedInputSplit /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.cc Check failed: begin != end /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.cc Check failed: fi->Read(&lrec, sizeof(lrec)) != 0 invalid record io format (reinterpret_cast<size_t>(begin) & 3UL) == 0U (reinterpret_cast<size_t>(end) & 3UL) == 0U Check failed: p >= pbegin + 2 Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end Invalid RecordIO Format (reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U (reinterpret_cast<size_t>(chunk->end) & 3UL) == 0U Check failed: chunk->begin <= chunk->end Check failed: cflag == 1U Check failed: p[0] == RecordIOWriter::kMagic /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.cc expanded_list.size() == 1ul IndexedRecordIOSplitter does not support multiple index files /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.cc Check failed: files_[i].size % align_bytes == 0 file do not align by bytes Check failed: offset_end_ >file_offset_[file_ptr_end_] Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is needed files_.size() != 0U Cannot find any files that matches the URI pattern curr= ,begin= ,end= ,fileptr= ,fileoffset= offset[ file offset not calculated correctly C /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.cc Check failed: !IsSymlink(info.path.name) Symlink not supported in TemporaryDirectory Couldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.h lstat(path.c_str(), &sb) == 0 dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.cc LocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: stdout wb Check failed: allow_null LocalFileSystem::Open " ": LocalFileSystem.ListDirectory .. Check failed: std::fwrite(ptr, 1, size, fp_) == size FileStream.Write incomplete Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET) L P XQ QaQX! Q a! Q Q+ 7 Y PB $c h 0
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RK oSt16initializer_listIS1_E p S2_ pNSt3__16vectorIS1_NS3_9allocatorIS1_EEEE p
ExtendERKS2_ pResizeEmS1_ q
HostCanWriteEv pDeviceAccessEv q
DeviceIdxEv qSetDeviceEi t
PointerEv rCanReadEv t
PointerEv rCanReadEv s
FillES1_ rCopyE r
RK sSt16initializer_listIS1_E s S2_ sNSt3__16vectorIS1_NS3_9allocatorIS1_EEEE s
ExtendERKS2_ sResizeEmS1_ t
HostCanWriteEv tDeviceAccessEv t
2E t1E u myi uSt16initializer_listIyEi vRKNSt3__16vectorIyNS2_9allocatorIyEEEEi vOS1_ v
myi vSt16initializer_listIyEi vRKNSt3__16vectorIyNS2_9allocatorIyEEEEi vOS1_ v
2Ev v1Ev v
DeviceIdxEv vSetDeviceEi y
Device wConstHostSpanEv w PointerEv wCanReadEv y
PointerEv xCanReadEv y
FillEy xCopyE x
RK xSt16initializer_listIyE y S1_ xNSt3__16vectorIyNS2_9allocatorIyEEEE x
ExtendERKS1_ yResizeEmy y
HostCanWriteEv yDeviceAccessEv y
2E z1E z mji zSt16initializer_listIjEi {RKNSt3__16vectorIjNS2_9allocatorIjEEEEi {OS1_ { mji {St16initializer_listIjEi {RKNSt3__16vectorIjNS2_9allocatorIjEEEEi {OS1_ { 2Ev {1Ev { DeviceIdxEv |SetDeviceEi Д Device |ConstHostSpanEv } PointerEv |CanReadEv Ж PointerEv }CanReadEv ~ FillEj }CopyE } RK }St16initializer_listIjE ~ S1_ ~NSt3__16vectorIjNS2_9allocatorIjEEEE ~ ExtendERKS1_ ~ResizeEmj HostCanWriteEv DeviceAccessEv Ф 2E 1E mS2_i St16initializer_listIS2_Ei RKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi OS3_ mS2_i St16initializer_listIS2_Ei RKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi OS3_ 2Ev 1Ev Я а DeviceIdxEv SetDeviceEi г Device ConstHostSpanEv PointerEv тCanReadEv д PointerEv CanReadEv DŽ FillES2_ CopyE е RK كSt16initializer_listIS2_E S3_ NSt3__16vectorIS2_NS4_9allocatorIS2_EEEE ExtendERKS3_ ResizeEmS2_ HostCanWriteEv DeviceAccessEv DeviceIdxEv ۅSetDeviceEi PointerEv CanReadEv PointerEv φCanReadEv FillEm CopyE RK St16initializer_listImE Ї S1_ ćNSt3__16vectorImNS2_9allocatorImEEEE ʇ ExtendERKS1_ ResizeEmm ň HostCanWriteEv DeviceAccessEv 8PeekReadEPvm 4ReadEPvm Ɗ C 8PeekReadEPvm ؉4 މ 2EPNS0_16PeekableInStreamE ̉1EPNS0_16PeekableInStreamE ҉ TakeEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ReadEPvm ̊SeekEm Ҋ fLNS_5Value9ValueKindE7EE8GetArrayEv hLNS_5Value9ValueKindE8EE8GetArrayEv iLNS_5Value9ValueKindE9EE8GetArrayEv xLNS_5Value9ValueKindE10EE8GetArrayEv з Json SparsePage ExtendPathEPNS_11PathElementEjffi UnwindPathEPNS_11PathElementEjj Writer ObjectC ǓReader 4SaveENS_4JsonE ō5VisitEPKNS_ ˍ 9JsonArrayE 1 8JsonNullE 4JsonTypedArrayI 0Json 1Json ߏ fLNS_5Value9ValueKindE7EEE hLNS_5Value9ValueKindE8EEE iLNS_5Value9ValueKindE9EEE xLNS_5Value9ValueKindE10EEE ObjectE ӏNumberE ُStringE IntegerE BooleanE DummyJsonObjectEv ͒LoadFeatureTypeERKNSt3__16vectorINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS5_IS7_EEEEPNS1_INS_11FeatureTypeENS5_ISC_EEEE GradientBooster6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterEPKNS_17LearnerModelParamE ً ValueixE Entry8Cmp RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i 2EO ד1EO S0_ NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE S0_ NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE JsonArrayC ɗUBJ МGPUMetric15CreateGPUMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ˲predictor ¹Predictor TreeParam11__MANAGER__Ev 2EOS0_ ߗ1EOS0_ Џ 5ParseEv 1 4LoadEv 9ParseNullEv 0 ֘1Parse 2ParseBooleanEv ؚ3kMaxNumLengthE SkipSpacesEv ParseArrayEv ƚ Json SparsePage1 Reader5ErrorENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE String Object Number Ж StringEv ObjectEv ̚NumberEv Қ Json4 data tree LoadE DumpES0_PNS ś PNS_10JsonReaderE NS_10StringViewEj IJ t3__1 _10JsonWriterE ʲ 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEj Ĝ6vectorIcNS1_9allocatorIcEEEEj ʜ Writer Reader 4SaveENS_4JsonE 5VisitEPKNS_ 1 5ParseEv 9DecodeStrEv 4LoadEv 0ParseArrayEv 1ParseObjectEv Ў 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE JsonArray Predictor18InitOutPredictionsERKNS_8MetaInfoEPNS_16HostDeviceVectorIfEERKNS_3gbm11GBTreeModelE 4SaveEPNS_10JsonWriterE ٟeqERKNS_5ValueE ߟ 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE 4SaveEPNS_10JsonWriterE ѠeqERKNS_5ValueE נ Json EllpackPage4SizeEv Integer Boolean 4SaveEPNS_10JsonWriterE šeqERKNS_5ValueE ˡ JsonNull MetaInfo 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE 4SaveEPNS_10JsonWriterE עeqERKNS_5ValueE ݢ fLNS_5Value9ValueKindE7EE ڣhLNS_5Value9ValueKindE8EE iLNS_5Value9ValueKindE9EE xLNS_5Value9ValueKindE10EE 4S eqERKS1_ aveEPNS_10JsonWriterE izeEv Ы 4S eqERKS1_ ܤ aveEPNS_10JsonWriterE ֤izeEv Ю 4S eqERKS1_ aveEPNS_10JsonWriterE izeEv а 4S eqERKS1_ aveEPNS_10JsonWriterE ޥizeEv ϱв 9JsonArrayE 1 8JsonNullE 4JsonTypedArrayI ¦0Json է1Json fLNS_5Value9ValueKindE7EEE hLNS_5Value9ValueKindE8EEE çiLNS_5Value9ValueKindE9EEE ɧxLNS_5Value9ValueKindE10EEE ϧ ObjectE NumberE StringE IntegerE BooleanE JsonTypedArrayI PrintCatsAsSetERKNSt3__16vectorIiNS0_9allocatorIiEEEE UnwoundPathSumEPKNS_11PathElementEjj fLNS_5Value9ValueKindE7EE hLNS_5Value9ValueKindE8EE iLNS_5Value9ValueKindE9EE xLNS_5Value9ValueKindE10EE ð C ɪ9IsClassOfEPKS1_ 3SetEmf 2E ת1E v m OS3_ v m OS3_ fLNS_5Value9ValueKindE7EE8GetArrayEv جhLNS_5Value9ValueKindE8EE8GetArrayEv iLNS_5Value9ValueKindE9EE8GetArrayEv xLNS_5Value9ValueKindE10EE8GetArrayEv ձ fLNS_5Value9ValueKindE7EE8GetArrayEv hLNS_5Value9ValueKindE8EE8GetArrayEv iLNS_5Value9ValueKindE9EE8GetArrayEv xLNS_5Value9ValueKindE10EE8GetArrayEv ۱ C 9IsClassOfEPKS1_ 3SetEmh 2E 1E ծ v Ϯm OS3_ v m OS3_ C ˯9IsClassOfEPKS1_ 3SetEmi 2E ٯ1E v m OS3_ v m OS3_ Ѐ C 9IsClassOfEPKS1_ ñ3SetEmx ɱ 2E 1E v m OS3_ v m OS3_ Џ ParseStrERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE MetaInfo Г SketchContainerImplINS0_1 HostSketchContainerC 5WQuantileSketchIffEEE ij6WXQuantileSketchIffEEE 8 1 9AllReduceEPNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS5_9allocatorISB_EEEEPNS6_IiNSC_IiEEEE C 21SearchGroupIndFromRowERKNSt3__16vectorIjNS5_9allocatorIjEEEEm UseGroupERKNS_8MetaInfoE MakeCutsEPNS0_13HistogramCutsE ۸ 4CalcColumnSizeERKNS_10SparsePageEjm ͷ1 ӷ6GatherSketchInfoERKNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS5_9allocatorISB_EEEEPNS6_ImNSC_ImEEEESJ_PNS6_INS9_5EntryENSC_ISK_EEEE PushRowPageERKNS_10SparsePageERKNS_8MetaInfoENS0_4SpanIKfLm18446744073709551615EEE ϸLoadBalanceERKNS_10SparsePageEjm ո Д C Ϻ8 9AllReduceEPNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_10WXQSummaryIffEEE16SummaryContainerENS5_9allocatorISB_EEEEPNS6_IiNSC_IiEEEE 1 21SearchGroupIndFromRowERKNSt3__16vectorIjNS5_9allocatorIjEEEEm 2ENSt3__16vectorImNS5_9allocatorImEEEEiNS0_4SpanIKNS_11FeatureTypeELm18446744073709551615EEEbi 1ENSt3__16vectorImNS5_9allocatorImEEEEiNS0_4SpanIKNS_11FeatureTypeELm18446744073709551615EEEbi MakeCutsEPNS0_13HistogramCutsE UseGroupERKNS_8MetaInfoE 2EiRKNS_8MetaInfoENSt3__16vectorImNS5_9allocatorImEEEEbNS0_4SpanIKfLm18446744073709551615EEEi 1EiRKNS_8MetaInfoENSt3__16vectorImNS5_9allocatorImEEEEbNS0_4SpanIKfLm18446744073709551615EEEi 2ENSt3__16vectorImNS5_9allocatorImEEEEiNS0_4SpanIKNS_11FeatureTypeELm18446744073709551615EEEbi 1ENSt3__16vectorImNS5_9allocatorImEEEEiNS0_4SpanIKNS_11FeatureTypeELm18446744073709551615EEEbi Є 2 1 6engine 5c_api9All op7ReducerINS0_ 4SerializeReducerFuncImplIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEEEvPKvPviRKN3MPI8DatatypeE 2SerializeReduceClosureIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEE6InvokeEPv 3 5BitORE SumE M mEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE axE inE mEEvPKvPviRKN3MPI8DatatypeE yEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE 4CalcColumnSizeERKNS_10SparsePageEjm 1 6GatherSketchInfoERKNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_10WXQSummaryIffEEE16SummaryContainerENS5_9allocatorISB_EEEEPNS6_ImNSC_ImEEEESJ_PNS6_INS0_9WQSummaryIffE5EntryENSC_ISM_EEEE LoadBalanceERKNS_10SparsePageEjm PushRowPageERKNS_10SparsePageERKNS_8MetaInfoENS0_4SpanIKfLm18446744073709551615EEE 15PrintStatisticsERKNSt3__13mapINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4pairImmEENS2_4lessIS9_EENS7_INSA_IKS9_SB_EEEEEE 5PrintEv 5StartERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 4StopERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE Version DMatrix Learner RegTree 4 6StringERKNSt3__15tupleIJiiiEEE 8kInvalidE S LoadE elfEv a RKNS_4JsonE PN4dmlc6StreamE Б veEPN meERKNSt3__15tupleIJiiiEEE S_4JsonE 4dmlc6StreamE 6CreateI D 4LoadERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbbS9_ NS_4data PvS2_FvS2_EFiS2_EEEPS0_T_T0_PT1_PT2_fi 1 24RecordBatchesIterAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE 1FileAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE 2 0CS 6DataTableAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE 5 4Push 8SortRowsEi 7PushCSCERKS0_ 11SortIndicesEi INS_4data ERKS0_ 1 2 7 5CS 6FileAdapterBatchEEEyRKT_fi ArrayAdapterBatchEEEyRKT_fi DenseAdapterBatchEEEyRKT_fi 0CSRArrayAdapterBatchEEEyRKT_fi 1DataTableAdapterBatchEEEyRKT_fi 7SetInfoERKNS_16GenericParameterE 1 5ClearEv 6ExtendERKS0_bb 9kNumFieldE NS_10StringViewES4_ PKcPKvNS_8DataTypeEm 5SetInfoFrom 0LoadBinaryEPN4dmlc6StreamE 4SetFeatureInfoEPKcPS2_y CUDAERKNS_16GenericParameterENS_10StringViewENS_4JsonE HostERKNS_16GenericParameterENS_10StringViewENS_4JsonE 2Ev 1Ev 0Ev Tr LinearUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ConsoleLogger GraphvizParam11__MANAGER__Ev yLoadBinaryENSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEb ackerLoggerD ЦeeGenerator6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_10FeatureMapEb 2GetTransposeEii 5IsIndicesSortedEi Ђ 7xgboost S_16ParserFactoryRegI 4data19SparsePageFormatRegINS1_ 1 9MetricRegEE3GetEv Ų 1 7CSCPageEEEE3GetEv 0SparsePageEEEE3GetEv 3SortedCSCPageEEEE3GetEv 1EllpackPageEEEE3GetEv 6GHistIndexMatrixEEEE3GetEv Ы i NSt3__112basic_stringIcNSC_11char_traitsIcEENSC_9allocatorIcEEEE DenseAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE ArrayAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE и RAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE CAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE CSRArrayAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPS0_PT_fiRKNSt3__112basic_stringIcNSD_11char_traitsIcEENSD_9allocatorIcEEEE л RAdapterBatchEEEyRKT_fi CAdapterBatchEEEyRKT_fi 1 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 7GetInfoEPKcPyNS_8DataTypeEPPKv 8ValidateEi 0SaveBinaryEPN4dmlc6StreamE 4GetFeatureInfoEPKcPNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEE ! ! ! ! н! DMatrix14GetThreadLocalEv Learner19AllowLazyCheckPointEv RegTree ! ! 8fileiter 4 23GradientIndexPageSource5FetchEv 1 6detail1 5ResetEPv 4NextEPv Ж" " " " IndexERKS0_S2_ ValueERKS0_S2_ " " # # EllpackPage ObjFunction6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE TreeUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterENS_7ObjInfoE LearnerImpl14kRandSeedMagicE C D 12SetBaseRowIdEm 2E 1E v PNS_7DMatrixERKNS_10BatchParamE # v PNS_7DMatrixERKNS_10BatchParamE # # # 2Ev 1Ev # # # # HistIndexMatrix enericParameter D 9PushBatchERKNS_10SparsePageENS_6common4SpanIKNS_11FeatureTypeELm18446744073709551615EEEmmji 1 4InitE C 2Ev 1Ev # # # 1ResizeIndexEmb 4ReadColumnPageEPN4dmlc10SeekStreamE $ PNS_7DMatrixEidbiNS_6common4SpanIfLm18446744073709551615EEE RKNS_10SparsePageENS_6common4SpanIKNS_11FeatureTypeELm18446744073709551615EEERKNS4_13HistogramCutsEibdi $ 2E 1E v PNS_7DMatrixEidbiNS_6common4SpanIfLm18446744073709551615EEE $ v PNS_7DMatrixEidbiNS_6common4SpanIfLm18446744073709551615EEE $ $ $ & 7GetValueFromUint8EPKhm 8GetValueFromUint & 16EPKhm 32EPKhm & Э& HistIndexMatrix enericParameter7ThreadsEv 9TransposeEv 15WriteColumnPageEPN4dmlc6StreamE & & & 8__dmlc_registry_file_tag_gradient_index_format__Ev 9__dmlc_registry_file_tag_sparse_page_raw_format__Ev & Й' 2DMatrixProxy1 3SimpleDMatrix 7SparsePageDMatrix 2SetArrayDataEPKc 0SetCSRDataEPKcS3_S3_jb ( ( 4InfoEv 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 1 22GetSortedColumnBatchesEv C ( data1 tree17QuantileHistMaker7BuilderI 3SimpleDMatrix4InfoEv 7SparsePageDMatrix4InfoEv ( ( 3GetRowBatchesEv 6Get 7GetEllpackBatchesERKNS_10BatchParamE 5SaveToLocalFileERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ( ColumnBatchesEv GradientIndexERKNS_10BatchParamE ( ( ) Њ) 2 1 INS0_ EPN4dmlc6StreamE 1 24RecordBatchesIterAdapterEEEPT_fi 2 0CS 5 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi DenseAdapterEEEPT_fi ArrayAdapterEEEPT_fi ) INS0_ EPN4dmlc6StreamE 1 24RecordBatchesIterAdapterEEEPT_fi 2 0CS 5 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi DenseAdapterEEEPT_fi ArrayAdapterEEEPT_fi ) ) ) RAdapterEEEPT_fi CAdapterEEEPT_fi ) RAdapterEEEPT_fi CAdapterEEEPT_fi ) CSRArrayAdapterEEEPT_fi IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi ) CSRArrayAdapterEEEPT_fi IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi ) ) ) ) ) ) * * * * * * * + 4InfoEv 2 1 C + 4NSamplesDeviceEPNS0_12DMatrixProxyE 5NFeaturesDeviceEPNS0_12DMatrixProxyE + + 0InitializeSparsePageEv 2GetSortedColumnBatchesEv + 7Get Ձ3GetRowBatchesEv 6Get RowBatchesImplEv EllpackBatchesERKNS_10BatchParamE , , , ColumnBatchesEv ۂGradientIndexERKNS_10BatchParamE , , , , 2EPvS2_PFvS2_EPFiS2_EfiNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE 1EPvS2_PFvS2_EPFiS2_EfiNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE , , . gbm obj 2FeatureInteractionConstraintHost 1 3 2 6GBTree ߋ 6 8GBLinearTrainParam11__MANAGER__Ev 3GBLinearModel9LoadModelERKNS_4JsonE 5GPUCopyGradientEPKNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEjjPS5_ 2CopyGradientEPKNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEijjPS5_ 7GPUDartPredictIncENS_6common4SpanIfLm18446744073709551615EEES3_fmjj 4DartTrainParam11__MANAGER__Ev 1GBTreeModel LinearCheckLayerEj GBTree Ǘ / / 5__dmlc_registry_file_tag_gblinear__Ev 3__dmlc_registry_file_tag_gbtree__Ev / 8DeprecatedGBLinearModelParam11__MANAGER__Ev 4GPUDartInplacePredictIncENS_6common4SpanIfLm18446744073709551615EEES3_fmfjj 1 1 1 6GBTree 3GBLinearModel9SaveModelEPNS_4JsonE 1GBTreeModel 1 8GradientBoosterRegEE3GetEv Ӌ6LinearUpdaterRegEE3GetEv 2 Ѳ4 0TreeGenRegEE3GetEv 1 1 9 1 2 ݒ7DoBoostEPNS_7DMatrixEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_20PredictionCacheEntryE ConfigureERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE LoadModelERKNS_4JsonE 1 7ConfigureUpdatersEv 1 3BoostNewTreesEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixEiPNSt3__16vectorINSA_10unique_ptrINS_7RegTreeENSA_14default_deleteISD_EEEENSA_9allocatorISG_EEEE 0LoadConfigERKNS_4JsonE 2PredictBatchEPNS_7DMatrixEPNS_20PredictionCacheEntryEbjj 2 InitUpdaterERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE גCommitModelEONSt3__16vectorINS3_INS2_10unique_ptrINS_7RegTreeENS2_14default_deleteIS5_EEEENS2_9allocatorIS8_EEEENS9_ISB_EEEEPNS_7DMatrixEPNS_20PredictionCacheEntryE 2 2ConfigureWithKnownDataERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEEPNS_7DMatrixE 6PerformTreeMethodHeuristicEPNS_7DMatrixE 2 2 Ю2 2 2 2 2 hn S I VN 8_N 16_N7xgboost7LearnerD 7xgboost K7xgboost3gbm6GBTree10SaveConfigEPNS_4JsonE 3gbm6GBTree10LoadConfigERKNS_4JsonE 7LearnerD 2 1 9SaveModelEPNS_4JsonE 5SliceEiiiPNS_15GradientBoosterEPb 0SaveConfigEPNS_4JsonE 2GetPredictorEPKNS_16HostDeviceVectorIfEEPNS_7DMatrixE 2 3 3 3 3 3 3 3 ModelParam11__MANAGER__Ev TrainParam11__MANAGER__Ev 3 3 3 3 3 4 4SaveEPN4dmlc6StreamE ۘ9SaveModelEPNS_4JsonE 8 4LoadEPN4dmlc6StreamE 9LoadModelERKNS_4JsonE М8 8 8 GlobalConfiguration11__MANAGER__Ev PredictionContainer ֽ 8 D ƚ6CreateERKNSt3__16vectorINS1_10shared_ptrINS_7DMatrixEEENS1_9allocatorIS5_EEEE 2Ev ݚ1Ev 0Ev 8 8 1Ev 0Ev 8 1Ev 0Ev 8 8 8 8 ModelParamC ՛TrainParam11__MANAGER__Ev 2ERKNS_23LearnerModelParamLegacyEfNS_7ObjInfoE 1ERKNS_23LearnerModelParamLegacyEfNS_7ObjInfoE 8 8 3LearnerModelParamLegacy11__MANAGER__Ev 6ParseInteractionConstraintERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_6vectorINS9_IjNS4_IjEEEENS4_ISB_EEEE 0LearnerConfiguration11kEvalMetricE 8 8 1 ʞC 6kCpuIdE 1__MANAGER__Ev 4ConfigureGpuIdEb 2kDefaultSeedE 8 2Ev 1Ev 9 Ѐ9 9 9 Њ9 9 < 1 џ4 6LinearTrainParam11__MANAGER__Ev 5CoordinateParam11__MANAGER__Ev ? л? ? ? 5__dmlc_registry_file_tag_updater_coordinate__Ev 2__dmlc_registry_file_tag_updater_shotgun__Ev ? B 6CustomLogMessage3LogERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 2 ΅0 ʋ8TemporaryDirectory15RecursiveDeleteERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ڗ ВC D 9 C Ф1 2Ev 1Ev ʤ C ShouldLogENS0_12LogVerbosityE ĤConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE C C 2E ޤ1E ץ NS0_12LogVerbosityE ѥRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE C NS0_12LogVerbosityE ʦRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE C 2Ev 1Ev КC C НC 6DefaultVerbosityEv 5GlobalVerbosityEv C C C C 1 ϧ4 3 2 2BinaryROCAUCENS_6common4SpanIKfLm18446744073709551615EEENS_6linalg10TensorViewIS3_Li1EEENS1_15OptionalWeightsE 3 ì5G ڭ1BinaryPRAUCENS_6common4SpanIKfLm18446744073709551615EEENS_6linalg10TensorViewIS3_Li1EEENS1_15OptionalWeightsE 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+{nodes}} {rankdir} {graph_attrs} Check failed: node.IsLeaf() /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h param.num_nodes < std::numeric_limits<int>::max() number of nodes in the tree exceed 2^31 fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam) param.num_nodes != 0 fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size() fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size() static_cast<int>(deleted_nodes_.size()) == param.num_deleted param.num_nodes == static_cast<int>(nodes_.size()) param.num_nodes == static_cast<int>(stats_.size()) param.deprecated_num_roots == 1 Check failed: !HasCategoricalSplit() Please use JSON/UBJSON for saving models with categorical splits. this->split_types_.size() == param.num_nodes this->GetSplitCategoriesPtr().size() == param.num_nodes size != 0 loss_changes split_indices static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deleted this->split_categories_segments_.size() == param.num_nodes tree_param n_nodes != 0 loss_changes.size() == n_nodes sum_hessian sum_hessian.size() == n_nodes base_weights base_weights.size() == n_nodes left_children lefts.size() == n_nodes right_children rights.size() == n_nodes parents parents.size() == n_nodes indices.size() == n_nodes split_conditions conds.size() == n_nodes default_left default_left.size() == n_nodes split_type n_nodes == split_categories_segments.size() get<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes) mean_values->size() > 0U unique_path[i].pweight == 0 Unique path must have zero weight /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.cc Unknown tree updater Tree constructor that uses approximate histogram construction for each node. GlobalApproxUpdater train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.cc Check failed: param.sampling_method != TrainParam::kGradientBased Gradient based sampling is not supported for approx tree method. UpdatePosition EvaluateSplits n_total_bins == page.cut.TotalBins() /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.h elem_of_each_node_.size() == 0U /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h begin < end n_batches_ >= 1 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../data/../common/hist_util.h row_ptr_[nid] == kMax n_nodes > 0 id != kMax nodes == targeted_hists.size() i < first_dimension_.size() nthreads >= 1 nid < nodes_ tid < nthreads_ i < ranges_.size() end > begin /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.h base_rowid == gmat.base_rowid upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.h any_missing == true (*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild() n_left == 0 n_right == 0 n_left + n_right == e.Size() begin + n_left <= e.end begin + n_left + n_right == e.end BuildHistogram /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.h Check failed: !features.empty() partition > 0 cut_ptr[fidx] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max()) cut_ptr[fidx + 1] <= static_cast<uint32_t>(std::numeric_limits<bst_bin_t>::max()) LeafPartition Check failed: tree[node.node_id].IsLeaf() ptr_offset <= row_set.Data()->size() UpdatePredictionCache out_preds.Size() == data->Info().num_row_ out_preds.Size() > 0U Check failed: p_last_tree out_preds.DeviceIdx() == GenericParameter::kCpuId part.Size() == n_nodes ColMakerTrainParam opt_dense_col EXP Param: speed optimization for dense column. default_direction learn right Default direction choice when encountering a missing value grow_colmaker Grow tree with parallelization over columns. colmaker_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.cc Updater `grow_colmaker` or `exact` tree method doesn't support distributed training. Updater `grow_colmaker` or `exact` tree method doesn't support external memory training. Check failed: this->ctx_ Check failed: param_.cache_opt Support for `cache_opt' is removed in 1.0.0 param_.max_depth > 0 exact tree method doesn't support unlimited depth. fmat.Info().num_row_ == position_.size() param_.sampling_method == TrainParam::kUniform Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist. ridx < position_.size() ridx exceed bound ridx= pos= prune Pruner that prune the tree according to statistics. TreePruner PrunerUpdate /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cc tree pruning end, extra nodes, pruned nodes, max_depth= Check failed: tree[nid].IsLeaf() Check failed: !tree[pid].IsLeaf() Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf() Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf() nid >= 1 Quantile::Builder /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc row_ptr.size() >= 2 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/hist_util.h ExpandTree UpdateTree InitSampling /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h exponent <= mod Grow tree using quantized histogram. refresh Refresher that refreshes the weight and statistics according to data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.cc batch.Size() < std::numeric_limits<unsigned>::max() Syncher that synchronize the tree in all distributed nodes. NULL worker rabit_reduce_buffer 256MB DMLC_TASK_ID DMLC_ROLE DMLC_NUM_ATTEMPT DMLC_TRACKER_URI DMLC_TRACKER_PORT DMLC_WORKER_CONNECT_RETRY %[^=]=%s mapred_tip_id mapreduce_task_id hadoop_mode is set but cannot find mapred_task_id rabit_task_id rabit_hadoop_mode mapred_task_id %d rabit_num_trial mapred_map_tasks mapreduce_job_maps hadoop_mode is set but cannot find mapred_map_tasks rabit_world_size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.cc Rabit Module currently only work with dmlc worker can only call Init once start gethostname(&buf[0], HOST_NAME_MAX) shutdown Failed to shutdown due to %s print rabit_tracker_uri rabit_tracker_port rabit_tree_reduce_minsize rabit_reduce_ring_mincount rabit_reduce_ring_mincount should be greater than 0 rabit_bootstrap_cache rabit_debug rabit_timeout rabit_timeout_sec rabit_timeout_sec should be non negative second rabit_enable_tcp_no_delay %lu%c invalid format for %s invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB} Connecting to (failed): [ ]
+
+Retry connecting to IP(retry time: ): [ tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic) tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic) sync::Invalid tracker message, init failure ReConnectLink failure 3 tracker.Send(xgboost::StringView{task_id}) == task_id.size() task connected to the tracker ReConnectLink failure 4 must keep rank to same if the node already have one tracker got overwhelmed and not able to assign correct rank got new rank ReConnectLink failure 5 ReConnectLink failure 6 ReConnectLink failure 7 ReConnectLink failure 8 ReConnectLink failure 9 ReConnectLink failure 10 ReConnectLink failure 12 ReConnectLink failure 13 ReConnectLink failure, link rank inconsistent Override a link that is active ReConnectLink failure 14 ReConnectLink failure 15 ReConnectLink: bad socket cannot find parent in the link cannot find prev ring in the link cannot find next ring in the link failed in ReconnectLink bind(handle_, handle, sizeof(std::remove_reference_t<decltype(addr.Handle())>)) getsockname(handle_, reinterpret_cast<sockaddr *>(&res_addr), &addrlen) listen(handle_, backlog) getsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast<char *>(&error), &len) accept fcntl setsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast<char *>(&keepalive), sizeof(keepalive)) setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast<char *>(&tcp_no_delay), sizeof(tcp_no_delay)) buffer size inconsistent must assign buffer_size Allreduce: size check Allreduce: boundary error too large type_nbytes=%lu, buffer_size=%lu /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.h Poll timeout Failed to poll. ReadToRingBuffer: buffer not allocated ReadToRingBuffer: max_size_read check Allreduce: boundary check Broadcast: root should be smaller than world size need to assume rank structure write ptr boundary check [%d] read_ptr boundary check AllgatherRing failed Allreduce failed Broadcast failed unknown enum_op unknown data_type DataType does not support bitwise or operation Failed to initialize RABIT. Failed to shutdown RABIT worker. the rabit has not been initialized LibSVMParserParam File format indexing_mode If >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. LibFMParserParam libfm 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. CSVParserParam File format. label_column Column index (0-based) that will put into label. delimiter Delimiter used in the csv file. weight_column Column index that will put into instance weights. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc Unknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h Check failed: TryLoadCache() failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.h Check failed: fi->Read(&label) Bad RowBlock format Check failed: fi->Read(&weight) Check failed: fi->Read(&qid) Check failed: fi->Read(&field) Check failed: fi->Read(&index) Check failed: fi->Read(&value) Check failed: fi->Read(&max_field, sizeof(IndexType)) Check failed: fi->Read(&max_index, sizeof(IndexType)) MB read, MB/sec finish reading at %g MB/sec label.size() + 1 == offset.size() offset.back() == index.size() Check failed: offset.back() == value.size() || value.size() == 0 /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.h finish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h param_.format == "libsvm" /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.h chunk.size != 0U qid: Check failed: out->label.size() + 1 == out->offset.size() Check failed: base <= 10 && base >= 2 sign == true /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h cannot call ParseNext /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h param_.format == "libfm" Check failed: out->field.size() == out->index.size() /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.h param_.format == "csv" Check failed: param_.label_column != param_.weight_column || param_.label_column < 0 Must have distinct columns for labels and instance weights Delimiter ' ' is not found in the line. Expected ' ' as the delimiter to separate fields. Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size() hdfs:// viewfs:// /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.cc Please compile with DMLC_USE_HDFS=1 to use hdfs s3:// http:// https:// Please compile with DMLC_USE_S3=1 to use S3 azure:// Please compile with DMLC_USE_AZURE=1 to use Azure unknown filesystem protocol stdin Check failed: part < nsplit invalid input parameter for InputSplit::Create indexed_recordio need to pass index file to use IndexedRecordIO recordio unknown input split type .split .part /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.h name_cache.size() == 1U only one `#` is allowed in file path for cachefile specification Check failed: std::getline(is, kv.first, '=') Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.h Check failed: fp_ != NULL SingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1 InputSplit do not support write /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.h Check failed: !producer_sig_processed_.load(std::memory_order_acquire) unique_lock::unlock: not locked Check failed: producer_sig_.load(std::memory_order_acquire) == kProduce Make sure you call BeforeFirst not inconcurrent with Next! Check failed: produce_end_.load(std::memory_order_acquire) Check failed: out_data_ != NULL Calling Value at beginning or end? Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroy Check failed: cell != __null || produce_end_.load(std::memory_order_acquire) Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy No thread /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.h Check failed: nread == sizeof(size) has invalid cache file format Check failed: fi_->Read(p->begin, size) == size BeforeFirst is not supported Check failed: this->InitCachedIter() Failed to initialize CachedIter ResetPartition is not supported in CachedInputSplit /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.cc Check failed: begin != end /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.cc Check failed: fi->Read(&lrec, sizeof(lrec)) != 0 invalid record io format (reinterpret_cast<size_t>(begin) & 3UL) == 0U (reinterpret_cast<size_t>(end) & 3UL) == 0U Check failed: p >= pbegin + 2 Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end Invalid RecordIO Format (reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U (reinterpret_cast<size_t>(chunk->end) & 3UL) == 0U Check failed: chunk->begin <= chunk->end Check failed: cflag == 1U Check failed: p[0] == RecordIOWriter::kMagic /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.cc expanded_list.size() == 1ul IndexedRecordIOSplitter does not support multiple index files /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.cc Check failed: files_[i].size % align_bytes == 0 file do not align by bytes Check failed: offset_end_ >file_offset_[file_ptr_end_] Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is needed C files_.size() != 0U Cannot find any files that matches the URI pattern curr= ,begin= ,end= ,fileptr= ,fileoffset= offset[ ]= file offset not calculated correctly /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.cc Check failed: !IsSymlink(info.path.name) Symlink not supported in TemporaryDirectory Couldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.h lstat(path.c_str(), &sb) == 0 dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.cc LocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory .. stdout wb Check failed: allow_null LocalFileSystem::Open " ": Check failed: std::fwrite(ptr, 1, size, fp_) == size FileStream.Write incomplete Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET) L P XQ QaQX! 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P е 1 +2io 93any11TypeOnStackINSt3__110shared_ptrIN7xgboost4data1 >4data1 ?6 O8RegistryIN S 11GetInstanceERKNS0_3URIE /22ListDirectoryRecursiveERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE 0 0FileSystem /2LineSplitter1 14InputSplitBase 45LocalFileSystem 86RecordIOSplitter1 8 Ǟ 5SeekRecordBeginEPNS_6StreamE 17ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE 29FindLastRecordBeginEPKcS3_ 2 Ş Ȟ 1BeforeFirstEv 23ConvertToURIsERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 44ResetPartitionEjj 46ExtractNextChunkEPNS_10InputSplit4BlobEPNS1_5ChunkE 47InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb 4 ཟ 1 24 55Chunk 68StripEndENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEc 69ReadChunkEPvPm 6D 6 InitEPNS0_10FileSystemEPKcmb 5ReadEPvm 6 4LoadEPS1_m 66AppendEPS1_m 6 м Х 0Ev 61Ev 72Ev 7 ຟ ȣ GetPathInfoERKNS0_3URIE 7OpenForReadERKNS0_3URIEb 7 1 73ListDirectoryERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE 8 Σ 1 74OpenERKNS0_3URIEPKcb 8 ݣ ֞ 5SeekRecordBeginEPNS_6StreamE 87ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE 99FindLastRecordBeginEPKcS3_ 9 ˞ ٞ 1 023IndexedRecordIOSplitter = ߞ BeforeFirstEv :NextBatchExEPNS0_14InputSplitBase5ChunkEm : 1 :3ReadIndexFileEPNS0_10FileSystemERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE <4ResetPartitionEjj <5SeekRecordBeginEPNS_6StreamE <7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE =9FindLastRecordBeginEPKcS3_ = 1 :9 = BatchEPNS_10InputSplit4BlobEm =ChunkEPNS_10InputSplit4BlobE = Next =ReadChunkEPvPm > 5 16create_from_dataEPNS0_4DataERKS9_ >7destroyEPNS0_4DataE >5 2ArrayAdapterEEEE >5CSRArrayAdapterEEEE ?5 16create_from_dataEPNS0_4DataERKS9_ ?7destroyEPNS0_4DataE ?5 4CSVParserParam11__MANAGER__Ev ?5CreateCSVParserI D6LibFMParserParam11__MANAGER__Ev H7 J8CreateLibSVMParserI K fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj @iEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj DxEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj D ޛ j @y D fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj DiEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj HxEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj HР Ҏ jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj HyfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj J CreateLibFMParserI HLibSVMParserParam11__MANAGER__Ev K Î ږ jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj KyfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj M Җ fE6CreateEPKcjjS3_ MiE6CreateEPKcjjS3_ NxE6CreateEPKcjjS3_ N Җ Җ j My N Җ fE6CreateEPKcjjS3_ NiE6CreateEPKcjjS3_ NxE6CreateEPKcjjS3_ O Җ Җ ParserI NStream6CreateEPKcS2_b O q 0TreeGenRegEE3GetEv O2 P4 P6LinearUpdaterRegEE3GetEv Q8GradientBoosterRegEE3GetEv Q V MetricGPURegEE3GetEv PPredictorRegEE3GetEv P n ` ObjFunctionRegEE3GetEv PTreeUpdaterRegEE3GetEv Q y L ? 1 O4data19SparsePageFormatRegINS1_ R9MetricRegEE3GetEv S " 0SparsePageEEEE3GetEv Q1EllpackPageEEEE3GetEv R3SortedCSCPageEEEE3GetEv R6GHistIndexMatrixEEEE3GetEv R І# # # 1 Q7CSCPageEEEE3GetEv R # V 7xgboost QS_16ParserFactoryRegI S fEEE3GetEv SiEEE3GetEv SxEEE3GetEv S ڑ j Sy S ඐ fEEE3GetEv SiEEE3GetEv TxEEE3GetEv T 4dmlc .5rabit _7xgboost K O7xgboost14JsonTypedArrayI R7xgboost14JsonTypedArrayI
+cEEvPKvPviRKN3MPI8DatatypeE UdEEvPKvPviRKN3MPI8DatatypeE WfEEvPKvPviRKN3MPI8DatatypeE WhEEvPKvPviRKN3MPI8DatatypeE WiEEvPKvPviRKN3MPI8DatatypeE WjEEvPKvPviRKN3MPI8DatatypeE WlEEvPKvPviRKN3MPI8DatatypeE WmEEvPKvPviRKN3MPI8DatatypeE WxEEvPKvPviRKN3MPI8DatatypeE WyEEvPKvPviRKN3MPI8DatatypeE W
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3 Z5BitORE ^ cEEvPKvPviRKN3MPI8DatatypeE ^hEEvPKvPviRKN3MPI8DatatypeE _iEEvPKvPviRKN3MPI8DatatypeE _jEEvPKvPviRKN3MPI8DatatypeE _lEEvPKvPviRKN3MPI8DatatypeE _mEEvPKvPviRKN3MPI8DatatypeE _ О 2op7ReducerINS0_ ]5c_api9All `6engine e gatherEPvmmmmi `reduceEPvmNS_6engine3mpi8DataTypeENS3_6OpTypeEPFvS1_ES1_ ` Î 0Allreduce_EPvmmPFvPKvS1_iRKN3MPI8DatatypeEENS0_3mpi8DataTypeENSA_6OpTypeEPFvS1_ES1_ `3AllreduceBase d ackerPrintERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ay b AllreduceEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE bBroadcastEPvmi b ҍ 2Tr a4ReConnectLinksEPKc c6TryAll c gatherRingEPvmmmm creduce c RingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE cTreeEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE d 1 c20TryReduceScatterRingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE e4InitEiPPc e8S eC e etParamEPKcS3_ ehutdownEv e 1Ev e2Ev e 1 `4InitEiPPc f8FinalizeEv f9 f AllgatherEPvmmmm fGetEngineEv f y ExtendPathEPNS_11PathElementEjffi fJson iSparsePage nUnwindPathEPNS_11PathElementEjj qcollective s NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIvEENS6_INS1_4pairIKS8_S9_EEEEEE gS0_ h 1EO g2EO h NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIvEENS6_INS1_4pairIKS8_S9_EEEEEE hS0_ i ObjectC hReader kWriter k ParseArrayEv jSkipSpacesEv j 0 j1Parse j2ParseBooleanEv k3kMaxNumLengthE k Ѓ NumberEv jObjectEv kStringEv k 1 j4LoadEv k5ParseEv k9ParseNullEv k И 4SaveENS_4JsonE k5VisitEPKNS_ n NumberE lObjectE lStringE l 0Json l1Json l4JsonTypedArrayI m BooleanE lIntegerE m fLNS_5Value9ValueKindE7EEE mhLNS_5Value9ValueKindE8EEE niLNS_5Value9ValueKindE9EEE nxLNS_5Value9ValueKindE10EEE n 1 l8JsonNullE n9JsonArrayE n * 11SortIndicesEi n4Push o7PushCSCERKS0_ q8SortRowsEi q о* ERKS0_ oINS_4data p$ CAdapterBatchEEEyRKT_fi oRAdapterBatchEEEyRKT_fi o# 5CS o6FileAdapterBatchEEEyRKT_fi p7 pо$ # ArrayAdapterBatchEEEyRKT_fi pDenseAdapterBatchEEEyRKT_fi p# 1 o2 p# 0CSRArrayAdapterBatchEEEyRKT_fi p1DataTableAdapterBatchEEEyRKT_fi q$ * * y
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0 f1 v3 }4 ҅5 چ6 Γ7Learner 8 9 * 12SetBaseRowIdEm uC uD v * PNS_7DMatrixERKNS_10BatchParamE uv u * 1E u2E u * PNS_7DMatrixERKNS_10BatchParamE uv v * * 1Ev v2Ev v * EllpackPage uLearnerImpl14kRandSeedMagicE xObjFunction6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE xTreeUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterENS_7ObjInfoE x ٲ ` y P 5GlobalVerbosityEv x6DefaultVerbosityEv y P 1 x9 yC {D | P ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE yShouldLogENS0_12LogVerbosityE z P P NS0_12LogVerbosityE zRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE { P 1E z2E { P NS0_12LogVerbosityE {RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE | P P 1Ev |2Ev | P ConsoleLogger yGraphvizParam11__MANAGER__Ev ~LinearUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ~Tr ~ t M P 1Ev ~2Ev ~ P ackerLoggerD ~eeGenerator6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_10FeatureMapEb yLoadBinaryENSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEb r ) 3SetEmf 9IsClassOfEPKS1_ ׀C OS3_ ݀m v 1E 2E Щ OS3_ m v fLNS_5Value9ValueKindE7EE hLNS_5Value9ValueKindE8EE iLNS_5Value9ValueKindE9EE xLNS_5Value9ValueKindE10EE ̄ 3SetEmh 9IsClassOfEPKS1_ ڂC OS3_ m v 1E 2E OS3_ m v з ж 3SetEmi 9IsClassOfEPKS1_ C OS3_ m v 1E 2E OS3_ m v 3SetEmx Ƅ9IsClassOfEPKS1_ C OS3_ m v 1E 2E OS3_ m ƅv ̅ JsonTypedArrayI PrintCatsAsSetERKNSt3__16vectorIiNS0_9allocatorIiEEEE ȆUnwoundPathSumEPKNS_11PathElementEjj Ά r y DummyJsonObjectEv ԆGradientBooster6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterEPKNS_17LearnerModelParamE LoadFeatureTypeERKNSt3__16vectorINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS5_IS7_EEEEPNS1_INS_11FeatureTypeENS5_ISC_EEEE ? $ - 1ResizeIndexEmb 4ReadColumnPageEPN4dmlc10SeekStreamE ߉ . 1 23PushAdapterBatchColumnsINS_4data 9PushBatchERKNS_10SparsePageENS_6common4SpanIKNS_11FeatureTypeELm18446744073709551615EEEi ŌC D , 17ArrayAdapterBatchEEEvPKNS_16GenericParameterERKT_fm 2 ŋ* 0CSRArrayAdapterBatchEEEvPKNS_16GenericParameterERKT_fm 2SparsePageAdapterBatchEEEvPKNS_16GenericParameterERKT_fm - - - KNS_16GenericParameterERKNS_8MetaInfoERKNS_11EllpackPageERKNS_10BatchParamE ˌNS_7DMatrixEidbiNS_6common4SpanIfLm18446744073709551615EEE - P ьRKNS_ v о. 10SparsePageENS_6common4SpanIKNS_11FeatureTypeELm18446744073709551615EEERKNS4_13HistogramCutsEibdi 8MetaInfoEONS_6common13HistogramCutsEi - - 1E 2E Ȑ - KNS_16GenericParameterERKNS_8MetaInfoERKNS_11EllpackPageERKNS_10BatchParamE NS_7DMatrixEidbiNS_6common4SpanIfLm18446744073709551615EEE - P RKNS_ v . 10SparsePageENS_6common4SpanIKNS_11FeatureTypeELm18446744073709551615EEERKNS4_13HistogramCutsEibdi ݐ8MetaInfoEONS_6common13HistogramCutsEi - - - 1Ev 2Ev - HistIndexMatrix enericParameter F 1__MANAGER__Ev Œ2kDefaultSeedE 4ConfigureGpuIdEb ز G 1 ˒6kCpuIdE C ٲ F 1Ev 2Ev ȓ G G HostDeviceVectorI ʨPesudoHuberParam11__MANAGER__Ev DeviceSpanEv HostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 4 6 8HostSpanEv ߖC ݗD aSEOS2_ NSt3__16vectorIS1_NS3_9allocatorIS1_EEEE S2_ RK St16initializer_listIS1_E CopyE FillES1_ Й Т ExtendERKS2_ ResizeEmS1_ ٖ OS2_ RKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi ˗St16initializer_listIS1_Ei їmS1_i ח 1E 2E OS2_ RKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi јSt16initializer_listIS1_Ei טmS1_i ݘА 1Ev 2Ev 11FeatureTypeEE 5EntryEE ɚ6detail20GradientPairInternalIfEEE 7RegTree7SegmentEE DeviceSpanEv HostVectorEv л 0 1HostPointerEv 3DevicePointerEv Ú 1 4 6 8HostSpanEv C D aSEOS2_ Ԟ NSt3__16vectorIS1_NS3_9allocatorIS1_EEEE S2_ RK St16initializer_listIS1_E CopyE FillES1_ ExtendERKS2_ ResizeEmS1_ и OS2_ RKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi St16initializer_listIS1_Ei mS1_i 1E 2E Ɲ OS2_ RKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi St16initializer_listIS1_Ei mS1_i г а з 1Ev 2Ev Ξ DeviceSpanEv ڞHostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 4 6 8HostSpanEv C D aSEOS4_ ̣ NSt3__16vectorIS3_NS5_9allocatorIS3_EEEE S4_ RK St16initializer_listIS3_E ܠ CopyE FillES3_ ExtendERKS4_ ResizeEmS3_ OS4_ RKNSt3__16vectorIS3_NS5_9allocatorIS3_EEEEi St16initializer_listIS3_Ei mS3_i 1E 2E OS4_ RKNSt3__16vectorIS3_NS5_9allocatorIS3_EEEEi St16initializer_listIS3_Ei mS3_i Л 1Ev 2Ev ƣ DeviceSpanEv ңHostVectorEv 0 أ1HostPointerEv 3DevicePointerEv 1 4 ڥ6 8HostSpanEv C D aSEOS3_ Ĩ NSt3__16vectorIS2_NS4_9allocatorIS2_EEEE S3_ RK St16initializer_listIS2_E ԥ CopyE FillES2_ л ExtendERKS3_ ResizeEmS2_ OS3_ RKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi St16initializer_listIS2_Ei mS2_i 1E 2E OS3_ RKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi St16initializer_listIS2_Ei mS2_i 1Ev 2Ev NS_ dE fE ®hE iE jE ܼmE yE DeviceSpanEv HostVectorEv 0 1HostPointerEv ة3DevicePointerEv ީ 1 4 6 8HostSpanEv ūC D aSEOS1_ խ NSt3__16vectorIdNS2_9allocatorIdEEEE S1_ Ъ RK St16initializer_listIdE CopyE ֪FillEd Ў ExtendERKS1_ ResizeEmd OS1_ ˫RKNSt3__16vectorIdNS2_9allocatorIdEEEEi St16initializer_listIdEi mdi 1E ѫ2E Ϭ OS1_ ɬRKNSt3__16vectorIdNS2_9allocatorIdEEEEi St16initializer_listIdEi mdi 1Ev 2Ev ϭ DeviceSpanEv ۭHostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 4 ݯ6 8HostSpanEv C D aSEOS1_ NSt3__16vectorIfNS2_9allocatorIfEEEE S1_ RK St16initializer_listIfE ׯ CopyE FillEf ExtendERKS1_ ResizeEmf OS1_ RKNSt3__16vectorIfNS2_9allocatorIfEEEEi St16initializer_listIfEi mfi 1E 2E OS1_ RKNSt3__16vectorIfNS2_9allocatorIfEEEEi St16initializer_listIfEi mfi 1Ev 2Ev DeviceSpanEv HostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 4 6 ܴ8HostSpanEv C D aSEOS1_ NSt3__16vectorIhNS2_9allocatorIhEEEE ճS1_ RK ۳St16initializer_listIhE CopyE FillEh д ExtendERKS1_ ִResizeEmh OS1_ RKNSt3__16vectorIhNS2_9allocatorIhEEEEi St16initializer_listIhEi mhi 1E 2E OS1_ RKNSt3__16vectorIhNS2_9allocatorIhEEEEi St16initializer_listIhEi mhi 1Ev 2Ev DeviceSpanEv HostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 ŷ4 6 8HostSpanEv ߹C պD ٻaSEOS1_ NSt3__16vectorIiNS2_9allocatorIiEEEE S1_ RK St16initializer_listIiE CopyE FillEi ExtendERKS1_ ResizeEmi ٹ OS1_ RKNSt3__16vectorIiNS2_9allocatorIiEEEEi úSt16initializer_listIiEi ɺmii Ϻ 1E 2E OS1_ RKNSt3__16vectorIiNS2_9allocatorIiEEEEi St16initializer_listIiEi ǻmii ͻ 1Ev ӻ2Ev DeviceSpanEv HostVectorEv 0 1HostPointerEv м3DevicePointerEv ּВ 1 4 6 8HostSpanEv C D aSEOS1_ NSt3__16vectorIjNS2_9allocatorIjEEEE S1_ Ƚ RK St16initializer_listIjE CopyE νFillEj ExtendERKS1_ ResizeEmj OS1_ þRKNSt3__16vectorIjNS2_9allocatorIjEEEEi St16initializer_listIjEi mji 1E ɾ2E ǿ OS1_ RKNSt3__16vectorIjNS2_9allocatorIjEEEEi St16initializer_listIjEi mji 1Ev 2Ev DeviceSpanEv HostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 4 6 8HostSpanEv C D aSEOS1_ NSt3__16vectorImNS2_9allocatorImEEEE S1_ RK St16initializer_listImE CopyE FillEm ExtendERKS1_ ResizeEmm OS1_ RKNSt3__16vectorImNS2_9allocatorImEEEEi St16initializer_listImEi mmi 1E 2E OS1_ RKNSt3__16vectorImNS2_9allocatorImEEEEi St16initializer_listImEi mmi 1Ev 2Ev DeviceSpanEv HostVectorEv 0 1HostPointerEv 3DevicePointerEv 1 4 6 8HostSpanEv C D aSEOS1_ NSt3__16vectorIyNS2_9allocatorIyEEEE S1_ RK St16initializer_listIyE CopyE FillEy ExtendERKS1_ ResizeEmy OS1_ RKNSt3__16vectorIyNS2_9allocatorIyEEEEi St16initializer_listIyEi myi 1E 2E OS1_ RKNSt3__16vectorIyNS2_9allocatorIyEEEEi St16initializer_listIyEi myi 1Ev 2Ev F 4CopyERKS0_ C ЯF PKNS_16GenericParameterERKNS_23LearnerModelParamLegacyENS_6linalg6TensorIfLi1EEENS_7ObjInfoE RKNS_23LearnerModelParamLegacyENS_7ObjInfoE F 1E 2E F PKNS_16GenericParameterERKNS_23LearnerModelParamLegacyENS_6linalg6TensorIfLi1EEENS_7ObjInfoE RKNS_23LearnerModelParamLegacyENS_7ObjInfoE ШF ModelParam TrainParam11__MANAGER__Ev F r GetSplitCategoriesERKNS_7RegTreeEi XGBBuildInfoDeviceEPNS_4JsonE Э F GlobalConfiguration11__MANAGER__Ev PredictionContainer n 19ClearExpiredEntriesEv 5 9ContainerEv n CacheENSt3__110shared_ptrINS_7DMatrixEEEi EntryEPNS_7DMatrixE n n 1 t2 3 4 5 ǐ6 7 8 9 0LearnerConfiguration11kEvalMetricE 3LearnerModelParamLegacy11__MANAGER__Ev 6ParseInteractionConstraintERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_6vectorINS9_IjNS4_IjEEEENS4_ISB_EEEE F o o 5ResetEv 9 o ConfigureERKNS_4tree10TrainParamEj SplitImplEijii o 2FeatureInteractionConstraintHost gbm obj E 4LoadEPN4dmlc6StreamE 9LoadModelERKNS_4JsonE E 1GBTreeModel 2CopyGradientEPKNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEijjPS5_ 3GBLinearModel9LoadModelERKNS_4JsonE 4DartTrainParam11__MANAGER__Ev 5GPUCopyGradientEPKNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEjjPS5_ 6 7GPUDartPredictIncENS_6common4SpanIfLm18446744073709551615EEES3_fmjj 8GBLinearTrainParam11__MANAGER__Ev A > B A A 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" 0Ev 1Ev 2Ev " ) DMatrix Learner RegTree Version G 6CreateERKNSt3__16vectorINS1_10shared_ptrINS_7DMatrixEEENS1_9allocatorIS5_EEEE D F 0Ev 1Ev 2Ev F F v 0ExpandNodeEijfbfffffffi 7ExpandCategoricalEijNS_6common4SpanIKjLm18446744073709551615EEEbfffffff v 1 20LoadCategoricalSplitILb 4LoadEPN4dmlc6StreamE 5kRootE 9LoadModelERKNS_4JsonE r 0EEEvRKNS_4JsonE 1EEEvRKNS_4JsonE r v o " PN4dmlc6StreamE RKNS_4JsonE " LoadE S " meERKNSt3__15tupleIJiiiEEE veEPN " 4dmlc6StreamE S_4JsonE " a elfEv " 4 6StringERKNSt3__15tupleIJiiiEEE 8kInvalidE " $ 0LoadBinaryEPN4dmlc6StreamE 4SetFeatureInfoEPKcPS2_y 5SetInfoFrom ( & CUDAERKNS_16GenericParameterENS_10StringViewENS_4JsonE HostERKNS_16GenericParameterENS_10StringViewENS_4JsonE & 1 5ClearEv 6ExtendERKS0_bb 7SetInfoERKNS_16GenericParameterE 9kNumFieldE $ ( & NS_10StringViewES4_ PKcPKvNS_8DataTypeEm и' MetaInfo ParseStrERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE V GPUMetric15CreateGPUMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE JsonArrayC Predictor TreeParam11__MANAGER__Ev UBJ predictor 1EOS0_ 2EOS0_ n 6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE 9ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE n p 0ParseArrayEv 1ParseObjectEv 1 4LoadEv 5ParseEv 9DecodeStrEv Reader Writer 4SaveENS_4JsonE 5VisitEPKNS_ NumberE ObjectE StringE 0Json 1Json 4JsonTypedArrayI У BooleanE IntegerE fLNS_5Value9ValueKindE7EEE hLNS_5Value9ValueKindE8EEE iLNS_5Value9ValueKindE9EEE xLNS_5Value9ValueKindE10EEE 1 8JsonNullE 9JsonArrayE П j 7PredictByAllTreesERKNS_3gbm11GBTreeModelEmmPNSt3__16vectorIfNS5_9allocatorIfEEEEmmRKNS6_INS_7RegTree4FVecENS7_ISC_EEEEmm 8FillNodeMeanValuesEPKNS_7RegTreeE j PNSt3__16vectorIfNS4_9allocatorIfEEEE iPNSt3__16vectorIfNS4_9allocatorIfEEEE j 1 40__dmlc_registry_file_tag_cpu_predictor__Ev 9PredValueERKNS_6common4SpanIKNS_5EntryELm18446744073709551615EEERKNSt3__16vectorINS8_10unique_ptrINS_7RegTreeENS8_14default_deleteISB_EEEENS8_9allocatorISE_EEEERKNS9_IiNSF_IiEEEEiPNSB_4FVecEjj j j 5rabit6engine13AllreduceBase14ConnectTrackerEv 7xgboost ޞR7xgboost14JsonTypedArrayI 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE Number Object Reader5ErrorENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE String 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE Json SparsePage1 ) 2GetTransposeEii 5IsIndicesSortedEi * 0 1 4JsonTypedArrayI 6 7LearnerModelParam9BaseScoreE * EllpackPage4SizeEv Json ObjFunction14InitEstimationERKNS_8MetaInfoEPNS_6linalg6TensorIfLi1EEE 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE Boolean Integer 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE ` aveEPNS_10JsonWriterE izeEv 4S eqERKS1_ fLNS_5Value9ValueKindE7EE hLNS_5Value9ValueKindE8EE iLNS_5Value9ValueKindE9EE xLNS_5Value9ValueKindE10EE aveEPNS_10JsonWriterE izeEv 4S eqERKS1_ aveEPNS_10JsonWriterE izeEv 4S eqERKS1_ aveEPNS_10JsonWriterE izeEv 4S eqERKS1_ . 15WriteColumnPageEPN4dmlc6StreamE 9 . 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HistIndexMatrix enericParameter7ThreadsEv йG G HostDeviceVectorI З DeviceSpanEv HostVectorEv 0 1Host 2 3 4DeviceCanWriteEv 5Const 6ConstHostPointerEv 8ConstDevicePointerEv Х CanReadEv PointerEv DeviceAccessEv HostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv DeviceSpanEv HostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv 9 И DeviceIdxEv SetDeviceEi 11FeatureTypeEE 5EntryEE 6detail20GradientPairInternalIfEEE 7RegTree7SegmentEE DeviceSpanEv HostVectorEv 0 1Host 2 3 4DeviceCanWriteEv 5Const 6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv PointerEv DeviceAccessEv HostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv DeviceSpanEv HostVectorEv м 1 4SizeEv 5EmptyEv 8HostSpanEv 9 к DeviceIdxEv SetDeviceEi Щ DeviceSpanEv HostVectorEv 0 1Host 2 3 4DeviceCanWriteEv 5Const 6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv PointerEv й DeviceAccessEv HostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv DeviceSpanEv HostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv 9 DeviceIdxEv SetDeviceEi DeviceSpanEv HostVectorEv 0 1Host 2 р3 4DeviceCanWriteEv ʁ5Const ց6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv PointerEv ŀ DeviceAccessEv ˀHostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv āй DeviceSpanEv ЁHostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv Ȃ9 Ԃи DeviceIdxEv SetDeviceEi NS_ dE fE hE iE jE mE љyE Ѐ DeviceSpanEv HostVectorEv Ճ 0 1Host ʄ2 3 4DeviceCanWriteEv 5Const 6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv ĄPointerEv DeviceAccessEv HostCanWriteEv В ConstHostSpanEv Device Ʌ CanReadEv ÅPointerEv DeviceSpanEv HostVectorEv 1 ۃ4SizeEv ݆5EmptyEv 8HostSpanEv 9 DeviceIdxEv SetDeviceEi DeviceSpanEv HostVectorEv Ç 0 1Host 2 3 4DeviceCanWriteEv ى5Const 6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv PointerEv Ԉ DeviceAccessEv ڈHostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv Ӊ DeviceSpanEv ߉HostVectorEv 1 ɇ4SizeEv ˊ5EmptyEv ъ8HostSpanEv 9 DeviceIdxEv ݊SetDeviceEi DeviceSpanEv HostVectorEv 0 1Host 2 Ό3 4DeviceCanWriteEv Ǎ5Const Ӎ6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv PointerEv DeviceAccessEv ȌHostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv DeviceSpanEv ͍HostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv Ŏ9 ю DeviceIdxEv ˎSetDeviceEi DeviceSpanEv HostVectorEv 0 1Host 2 3 4DeviceCanWriteEv 5Const 6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv PointerEv DeviceAccessEv HostCanWriteEv ConstHostSpanEv Device CanReadEv PointerEv DeviceSpanEv HostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv 9 DeviceIdxEv SetDeviceEi ߒ DeviceSpanEv HostVectorEv Г 0 1Host 2 3 ܔ4DeviceCanWriteEv 5Const 6ConstHostPointerEv ו8ConstDevicePointerEv ݕ CanReadEv PointerEv DeviceAccessEv HostCanWriteEv Д ConstHostSpanEv ֔Device CanReadEv PointerEv DeviceSpanEv HostVectorEv ѕ 1 4SizeEv 5EmptyEv 8HostSpanEv 9 DeviceIdxEv SetDeviceEi ͖Ф DeviceSpanEv ӖHostVectorEv 0 ٖ1Host 2 3 ʘ4DeviceCanWriteEv 5Const 6ConstHostPointerEv ř8ConstDevicePointerEv ˙ CanReadEv PointerEv DeviceAccessEv HostCanWriteEv ConstHostSpanEv ĘDevice CanReadEv PointerEv DeviceSpanEv HostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv 9 DeviceIdxEv SetDeviceEi DeviceSpanEv HostVectorEv 0 ǚ1Host ޛ2 3 4DeviceCanWriteEv 5Const 6ConstHostPointerEv 8ConstDevicePointerEv CanReadEv ؛PointerEv DeviceAccessEv HostCanWriteEv ConstHostSpanEv Device ݜ CanReadEv לPointerEv DeviceSpanEv HostVectorEv 1 4SizeEv 5EmptyEv 8HostSpanEv 9 DeviceIdxEv SetDeviceEi F PKNS_16GenericParameterE i ؞ F 1 3gbm 4 5Value7TypeStrEv 6common 7 8 9 E 4SaveEPN4dmlc6StreamE 9SaveModelEPNS_4JsonE ڟ E 1GBTreeModel 3GBLinearModel9SaveModelEPNS_4JsonE > 1 6GBTree @ 0SaveConfigEPNS_4JsonE 2GetPredictorEPKNS_16HostDeviceVectorIfEEPNS_7DMatrixE B 1 5SliceEiiiPNS_15GradientBoosterEPb ١9SaveModelEPNS_4JsonE ߡ ? ? 5 3SimpleDMatrix4InfoEv 7SparsePageDMatrix4InfoEv 8 data1 tree17QuantileHistMaker7Builder21UpdatePredictionCacheEPKNS_7DMatrixENS_6linalg10TensorViewIfLi1EEE в 0EEEvRKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS3_9allocatorIS7_EEEENS0_16RowSetCollection4ElemERKNS_16GHistIndexMatrixENS0_4SpanINS6_IdEELm18446744073709551615EEEb 1EEEvRKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS3_9allocatorIS7_EEEENS0_16RowSetCollection4ElemERKNS_16GHistIndexMatrixENS0_4SpanINS6_IdEELm18446744073709551615EEEb
12GHistBuilder9BuildHistILb 7Monitor Ѧ " 15PrintStatisticsERKNSt3__13mapINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4pairImmEENS2_4lessIS9_EENS7_INSA_IKS9_SB_EEEEEE ˦5PrintEv " ) DMatrix14GetThreadLocalEv RegTree v 2GetNumLeavesEv 6GetNumSplitNodesEv ܨ v 1 2 ة4SaveEPN4dmlc6StreamE 5EqualERKS0_ 8TreeShapERKNS0_4FVecEPfijPNS_11PathElementEffiijf 9 ȫ w 0SaveCategoricalSplitEPNS_4JsonE ҩ2CalculateContributionsERKNS0_4FVecEPNSt3__16vectorIfNS4_9allocatorIfEEEEPfij 8CalculateContributionsApproxERKNS0_4FVecEPNSt3__16vectorIfNS4_9allocatorIfEEEEPf y y w v y v DumpModelERKNS_10FeatureMapEbNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE «SaveModelEPNS_4JsonE ì p 4SaveEPNS_10JsonWriterE ɬeqERKNS_5ValueE JsonNull ϬMetaInfo $ 0SaveBinaryEPN4dmlc6StreamE 4GetFeatureInfoEPKcPNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEE ( 1 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 7GetInfoEPKcPyNS_8DataTypeEPPKv 8ValidateEi & ( ( 4SaveEPNS_10JsonWriterE eqERKNS_5ValueE JsonArray ïPredictor18InitOutPredictionsERKNS_8MetaInfoEPNS_16HostDeviceVectorIfEERKNS_3gbm11GBTreeModelE o fLNS_5Value9ValueKindE7EE8GetArrayEv hLNS_5Value9ValueKindE8EE8GetArrayEv iLNS_5Value9ValueKindE9EE8GetArrayEv xLNS_5Value9ValueKindE10EE8GetArrayEv fLNS_5Value9ValueKindE7EE8GetArrayEv hLNS_5Value9ValueKindE8EE8GetArrayEv ԳiLNS_5Value9ValueKindE9EE8GetArrayEv ڳxLNS_5Value9ValueKindE10EE8GetArrayEv fLNS_5Value9ValueKindE7EE8GetArrayEv hLNS_5Value9ValueKindE8EE8GetArrayEv iLNS_5Value9ValueKindE9EE8GetArrayEv xLNS_5Value9ValueKindE10EE8GetArrayEv o ʽ bRKN7xgboost4tree14CPUExpandEntryES3_E vvE Ț F N PF ZN4dmlc InputSplitE ParamErrorE SeekStreamE ¶û ۻ 0 2 ۽ fEE նiEE xEE j ܶy fEE iEE xEE RowBlockIterI S ThreadedIterINS ʹ copedThreadE erializableE ̼ 2io14InputSplitBase5ChunkEEE 4data17RowBlockContainerI ܽ fEEEE ڸiEEEE xEEEE j y fEEEE iEEEE xEEEE ù _ t3__16vectorINS_4data17RowBlockContainerI jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ǺЀ 1 ɶ2io 4data 5ErrorE 6 7istream 8DataIterIN 9parameter1 treamE ystemE خ 0FileS 2LineSplitterE 4InputSplitBaseE 5 6 8ThreadedInputSplitE LocalFileSystemE SingleFileSplitE ּ CachedInputSplitE ݼRecordIOSplitterE 1 û23IndexedRecordIOSplitterE ý fEE ʽiEE xEE j ѽy fEE iEE xEE ȉ ؎ 0ParserImplI 1 2 4T ܽ fEE оiEE xEE j y fEE iEE xEE DiskRowIterI LibFMParserI ݿȃ jfEE ֿyfEE ߽ fEE iEE xEE j y fEE iEE xEE BasicRowIterI LibSVMParserI jfEE yfEE fEE iEE xEE j y fEE iEE xEE extParserBaseI hreadedParserI jfEE yfEE 1 9CSVParserI fEE iEE xEE j y fEE iEE xEE fEE iEE xEE 膾 j y fEE iEE xEE ParserI StreamE ۻ 5InBufE E ݻ ֻ CAdapterBatchEEE RAdapterBatchEEE ӻ 5CS 6FileAdapterBatchEEE 7 ̻ ջ ArrayAdapterBatchEEE DenseAdapterBatchEEE 1 2 Ի 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE ػ 7xgboost4data S 2io14InputSplitBase5ChunkEEE 4data17RowBlockContainerI 8RowBlockI ܽ fEEEE iEEEE xEEEE j y fEEEE iEEEE xEEEE ۽ fEEEE iEEEE xEEEE j y fEEEE iEEEE xEEEE _ t3__16vectorINS ٻ 1_10unique_ptrIN7xgboost4data18ArrowColumnarBatchENS1_14default_deleteIS6_EEEENS1_9allocatorIS9_EEEEEE _4data17RowBlockContainerI jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ü 0TreeMethodEEE 3 5TreeProcessTypeEEE μ DataSplitModeEEE PredictorTypeEEE ¼ 1 6common27ProbabilityDistributionTypeEEE 7xgboost St3__1 ض 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS2_9allocatorIiEEEEEE N bEE dEE fEE iEE jEE mEE xEE yEE ˻ ʻ ʻ Ȼ ɻ Ȼ ɻ 0FieldEntryI 4FieldEntryBaseINS0_10FieldEntryI 6FieldAccessEntryE 7FieldEntryNumericINS0_10FieldEntryI 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE 6vectorIiNS3_9allocatorIiEEEEEES7_EE NSt3__1 bEEbEE dEEdEE fEEfEE iEEiEE jEEjEE mEEmEE xEExEE yEEyEE ˻ ʻ ɻ ǻ Ȼ Ȼ ɻ ǻ ʻ dEEdEE fEEfEE iEEiEE jEEjEE mEEmEE xEExEE yEEyEE ʻ ǻ ɻ ȸ Ȼ ɻ 4dmlc κ5rabit 7xgboost St3__1 ܻ 8MemoryBufferStreamE 9MemoryFixSizeBufferE ۻ 5utils1 6engine ۽ 13AllreduceBaseE 7IEngineE ڽ NumberE ObjectE ReaderE StringE WriterE Json SparsePageE collective1 2CommunicatorE 6NoOpCommunicatorE 7RabitCommunicatorE 0 1 2ConfigurableE 3 4JsonTypedArrayI 5GradientBoosterE 7 BooleanE IntegerE Json LearnerImplE ObjFunctionE TreeUpdaterE Ҽ ǽ ý JsonGeneratorE LinearUpdaterE SortedCSCPageE T ڼ extGeneratorE reeGeneratorE fLNS_5Value9ValueKindE7EEE hLNS_5Value9ValueKindE8EEE iLNS_5Value9ValueKindE9EEE xLNS_5Value9ValueKindE10EEE 0SparsePageEEE 1EllpackPageEEE 3 6GHistIndexMatrixEEE ExtSparsePageEEE SortedCSCPageEEE ؒ 1 7CSCPageEEE 萼 BatchIteratorImplINS_ GraphvizGeneratorE Ž 1 20LearnerConfigurationE 3 4 5 6 7 8JsonNullE 9 Ҽ ɼ 1GBTreeModelE 3GBLinearModelE Ƚ 1 4DartE 6GBTreeE 8GBLinearE ȼ gbm obj 1LogisticRawEEE 5SquaredLogErrorEEE 6LinearSquareLossEEE 8LogisticRegressionEEE إ 1 22LogisticClassificationEEE 0RegLossObjINS0_ 3 5GammaRegressionE 7 б CoxRegressionE LambdaRankObjINS0_2 3MAPLambdaWeightComputerEEE 4NDCGLambdaWeightComputerEEE 8PairwiseLambdaWeightComputerEEE Ȝ 虽 賽 MeanAbsoluteErrorE PoissonRegressionE TweedieRegressionE 1 2 6AFTObjE 8HingeObjE 0SoftmaxMultiClassObjE 1PseudoHuberRegressionE ֻ CAdapterE RAdapterE ӻ 0CS 1FileAdapterE 2 3 5 6 7SparsePageDMatrixE 8 9 ջ ArrayAdapterE D λ MatrixProxyE enseAdapterE CSCPageSourceE SimpleDMatrixE Ի CSRArrayAdapterE IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE PrimitiveColumnI ̻
+aEE dEE fEE hEE iEE jEE sEE tEE xEE yEE ؚ О ػ DataTableAdapterE IterativeDMatrixE SparsePage ౼ 0SparsePageEEE 3SortedCSCPageEEE 6GHistIndexMatrixEEE 1 7CSCPageEEE ȳ FormatINS_ SourceE آ ٻ ArrowColumnarBatchE PageSourceIncMixInINS_ 3SortedCSCPageEEE 6GHistIndexMatrixEEE 1 7CSCPageEEE GHistIndexRawFormatE S ortedCSCPageSourceE parsePageRawFormatINS_ 0SparsePageEEE 3SortedCSCPageEEE 1 7CSCPageEEE س 1 2 6 ़ 0SparsePageEEE 3SortedCSCPageEEE 6GHistIndexMatrixEEE 1 7CSCPageEEE 0SparsePageSourceImplINS_ 3 4RecordBatchesIterAdapterE GradientIndexPageSourceE SimpleBatchIteratorImplINS_ 0SparsePageEEE 1EllpackPageEEE 3 6GHistIndexMatrixEEE ExtSparsePageEEE SortedCSCPageEEE 蒼 1 7CSCPageEEE ٻ ColumnE detail19SingleBatchDataIterINS0_ ֻ CAdapterBatchEEE RAdapterBatchEEE ӻ 5CS 7 ջ ArrayAdapterBatchEEE DenseAdapterBatchEEE 1 2 Ի 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE ػ data tree ҽ 0TreePrunerE 1TreeSyncherE 3TreeRefresherE 7QuantileHistMakerE 9GlobalApproxUpdaterE ٽ ӽ Ƚ 1 8ColMaker н 7BuilderE E н 軼 ModelE ValueE MetricE common linear metric ν hLb1EEE jLb1EEE tLb1EEE ̽ ˽ DenseColumnIterI FixedSizeStreamE 5 6 PeekableInStreamE SparseColumnIterI ͽ hEE jEE tEE ͽ ̽ 1 6ColumnI ͽ hEE jEE tEE ̽ ˽ 4ShotgunUpdaterE 5FeatureSelectorE 7CoordinateUpdaterE ۼ ڼ 1 2 ۼ CyclicFeatureSelectorE GreedyFeatureSelectorE RandomFeatureSelectorE ݼ ݼ 1 2 ۼ ShuffleFeatureSelectorE ThriftyFeatureSelectorE ܼ 0EvalROCAUCE 3Eval 4Eval 5PseudoErrorLossE 6EvalMultiLogLossE 0EvalRowMAEEEE 1EvalRow 2EvalRowRMSLEEEE 4EvalRowLogLossEEE 6EvalGammaNLogLikEEE 7EvalGammaDevianceEEE 8EvalTweedieNLogLikEEE MAPEEEE RMSEEEE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE EWiseBaseINS0_ PrecisionE 4EvalMatchErrorEEE 6EvalMultiLogLossEEE ClassBaseINS0_1 atchErrorE M RankConfigE 1 2 7Eval 8Eval 9EvalPRAUCE 0AFTNLogLikDispatcherE 1EvalEWiseSurvivalBaseINS0_ Љ 8NormalDistributionEEEEE 9ExtremeDistributionEEEEE 1 20LogisticDistributionEEEEE 14EvalAFTNLogLikINS_6common 30EvalIntervalRegressionAccuracyEEE MSE UCINS0_ 10EvalROCAUCEEE 9EvalPRAUCEEE A CoxE MAPE ȃ NDCGE RankE CSCPageE DMatrixE LearnerE RegTreeE ̼ JsonArrayE LearnerIOE PredictorE UBJ predictor12CPUPredictorE ˀҼ Ȼ ReaderE WriterE Ā back_refIcEE Ҁfunction6__ ֈЎ ObjFunctionEvEEE TreeUpdaterEPKNS2_16GenericParameterENS2_7ObjInfoEEEE Ёƽ 1 3 5GradientBoosterEPKNS2_17LearnerModelParamEPKNS2_16GenericParameterEEEE ټ LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE й 1 ׁ4data16SparsePageFormatINS2_ 6MetricEPKcEEE 9PredictorEPKNS2_16GenericParameterEEEE 0SparsePageEEEvEEE 3SortedCSCPageEEEvEEE 6GHistIndexMatrixEEEvEEE ش 1 7CSCPageEEEvEEE PN7xgboost b vvEEE ψɽ N7xgboost4tree14CPUExpandEntryES4_EEE хPPN LJ 2io14InputSplitBase5ChunkEEEE 4data17RowBlockContainerI ݽ fEEEEE цiEEEEE xEEEEE j ؆y fEEEEE iEEEEE xEEEEE 4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE Ȉ baseIF funcI ɽ bRKN7xgboost4tree14CPUExpandEntryES6_ENS_9allocatorIS8_EEFbS4_S4_EEE vvENS_9allocatorIS3_EES2_EE ۉ PF ZN4dmlc PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE vE_NS_9allocatorIS5_EEFvvEEE ܊ 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE Ћ 6CachedInputSplit1 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS7_EEFvvEEE 2io1 4data1 Փݽ PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl ؎xE12TryLoadCacheEvEUl Ǐ PPNS3_17RowBlockContainerIjiEEE_NS_9allocatorISA_EEFbS9_EEE юvE_NS_9allocatorIS6_EEFvvEEE PPNS3_17RowBlockContainerIjxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE j y PPNS3_17RowBlockContainerIyfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIyiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ߒ PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE Γ 1DiskRowIterI 4ThreadedParserI PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl ѕЁ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE ʕvE_NS_9allocatorIS9_EEFvvEEE ɖЂ __ ـshared_ptrIN7xgboost4data1 2ArrayAdapterEEE 5CSRArrayAdapterEEE
+0 Ж1__ 2__match_charIcEE 3 4basic_ifstreamIcNS_11char_traitsIcEEEE 5 ǚ6__ 7 8 9basic_ 謾 alternateIcEE end_stateIcEE lookaheadIcNS_12regex_traitsIcEEEE match_anyIcEE ࠾ __empty_stateIcEE basic_filebufIcNS_11char_traitsIcEEEE has_one_stateIcEE word_boundaryIcNS_12regex_traitsIcEEEE __ basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE » ৾ back_ref_icaseIcNS_12regex_traitsIcEEEE owns_one_stateIcEE ܛ owns_two_statesIcEE repeat_one_loopIcEE __ bad_function_callE back_ref_collateIcNS_12regex_traitsIcEEEE Ɯmatch_char_icaseIcNS_12regex_traitsIcEEEE __ ͜basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ǻ istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 1 2 6__ ɹ bracket_expressionIcNS_12regex_traitsIcEEEE l_anchor_multilineIcEE match_char_collateIcNS_12regex_traitsIcEEEE Ơr_anchor_multilineIcEE ͠shared_ptr_ ϧ Ц 裾 0SparsePageENS_9allocatorIS2_EEEE Ԡ3 6 ؉ ExtSparsePageENS_9allocatorIS2_EEEE SortedCSCPageENS_9allocatorIS2_EEEE GHistIndexMatrixENS_9allocatorIS2_EEEE HostDeviceVectorIjEENS_9allocatorIS3_EEEE ʢ 1 ۠4data1 6common13ColumnSamplerENS_9allocatorIS3_EEEE 7CSCPageENS_9allocatorIS2_EEEE ȧ 3CSCPageSourceENS_9allocatorIS3_EEEE 5PrimitiveColumnI ڤ6SparsePageSourceENS_9allocatorIS3_EEEE 9SortedCSCPageSourceENS_9allocatorIS3_EEEE Д
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__ZGVZN7xgboost9TreeParam11__MANAGER__EvE4inst __ZZN7xgboost4tree10TrainParam11__MANAGER__EvE4inst __ZGVZN7xgboost4tree10TrainParam11__MANAGER__EvE4inst __ZN7xgboostL33__make_TreeGenReg_TextGenerator__E __ZN7xgboostL33__make_TreeGenReg_JsonGenerator__E __ZZN7xgboost13GraphvizParam11__MANAGER__EvE4inst __ZGVZN7xgboost13GraphvizParam11__MANAGER__EvE4inst __ZN7xgboostL37__make_TreeGenReg_GraphvizGenerator__E __ZZN4dmlc8RegistryIN7xgboost10TreeGenRegEE3GetEvE4inst __ZGVZN4dmlc8RegistryIN7xgboost10TreeGenRegEE3GetEvE4inst __ZZN4dmlc8RegistryIN7xgboost14TreeUpdaterRegEE3GetEvE4inst __ZGVZN4dmlc8RegistryIN7xgboost14TreeUpdaterRegEE3GetEvE4inst __ZN7xgboost4treeL39__make_TreeUpdaterReg_GlobalHistMaker__E __ZZN7xgboost4tree18ColMakerTrainParam11__MANAGER__EvE4inst __ZGVZN7xgboost4tree18ColMakerTrainParam11__MANAGER__EvE4inst __ZN7xgboost4treeL32__make_TreeUpdaterReg_ColMaker__E __ZN7xgboost4treeL38__make_TreeUpdaterReg_LocalHistMaker__E __ZN7xgboost4treeL34__make_TreeUpdaterReg_TreePruner__E 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__ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner19FindSplitConditionsERKNSt3__16vectorINS0_14CPUExpandEntryENS2_9allocatorIS4_EEEERKNS_7RegTreeERKNS_16GHistIndexMatrixEPNS3_IiNS5_IiEEEE __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common16PartitionBuilderILm2048EE4InitIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS4_14CPUExpandEntryENSG_9allocatorISI_EEEEPKNS_7RegTreeEEUlmE0_EEvmmT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost4tree20CommonRowPartitioner17AddSplitsToRowSetERKNSt3__16vectorINS0_14CPUExpandEntryENS2_9allocatorIS4_EEEEPKNS_7RegTreeE __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZNSt3__16vectorINS_4pairImmEENS_9allocatorIS2_EEE8__appendEm __ZNKSt3__113__vector_baseINS_4pairImmEENS_9allocatorIS2_EEE20__throw_length_errorEv __ZNSt3__16vectorINS_10shared_ptrIN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoEEENS_9allocatorIS7_EEE8__appendEm __ZNKSt3__113__vector_baseINS_10shared_ptrIN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoEEENS_9allocatorIS7_EEE20__throw_length_errorEv __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZNSt3__120__shared_ptr_pointerIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoENS_10shared_ptrIS5_E27__shared_ptr_default_deleteIS5_S5_EENS_9allocatorIS5_EEE16__on_zero_sharedEv __ZNKSt3__120__shared_ptr_pointerIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoENS_10shared_ptrIS5_E27__shared_ptr_default_deleteIS5_S5_EENS_9allocatorIS5_EEE13__get_deleterERKSt9type_info __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb1ENS0_15DenseColumnIterItLb1EEERZNS2_9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb1ENS0_15DenseColumnIterItLb1EEERZNS2_9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb1ENS0_16SparseColumnIterItEERZNS2_9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb1ENS0_16SparseColumnIterItEERZNS2_9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZNKSt3__113__vector_baseINS_10shared_ptrIN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoEEENS_9allocatorIS7_EEE20__throw_out_of_rangeEv __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmENKUlT_E_clImEEDaSP_ __ZN7xgboost6common16RowSetCollection8AddSplitEjjjmm __ZN4dmlc14LogCheckFormatIPmPKmEENSt3__110unique_ptrINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS4_14default_deleteISB_EEEERKT_RKT0_ __ZNSt3__16vectorIN7xgboost6common16RowSetCollection4ElemENS_9allocatorIS4_EEE8__appendEmRKS4_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb1ENS0_15DenseColumnIterIjLb1EEERZNS2_9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb1ENS0_15DenseColumnIterIjLb1EEERZNS2_9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb1ENS0_16SparseColumnIterIjEERZNS2_9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb1ENS0_16SparseColumnIterIjEERZNS2_9PartitionIjLb1ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIhLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb0ENS0_16SparseColumnIterIhEERZNS2_9PartitionIhLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb0ENS0_16SparseColumnIterIhEERZNS2_9PartitionIhLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionItLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb0ENS0_16SparseColumnIterItEERZNS2_9PartitionItLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb0ENS0_16SparseColumnIterItEERZNS2_9PartitionItLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIjLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb1ELb0ENS0_16SparseColumnIterIjEERZNS2_9PartitionIjLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost6common16PartitionBuilderILm2048EE15PartitionKernelILb0ELb0ENS0_16SparseColumnIterIjEERZNS2_9PartitionIjLb0ELb1EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS7_9allocatorISA_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_T0_E_EENS7_4pairImmEEPT1_NS0_4SpanISQ_Lm18446744073709551615EEENS10_ImLm18446744073709551615EEES12_mOT2_ __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionILb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionILb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIhLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionItLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionItLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb1ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIjLb1ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeE __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIhLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIhLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIhLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionItLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionItLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionItLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionItLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m __ZZN7xgboost4tree20CommonRowPartitioner14UpdatePositionIjLb0ELb0EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS_6common12ColumnMatrixERKNSt3__16vectorINS0_14CPUExpandEntryENSD_9allocatorISF_EEEEPKNS_7RegTreeEENKUlmNS9_7Range1dEE_clEmSO_ __ZN7xgboost6common16PartitionBuilderILm2048EE9PartitionIjLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS4_9allocatorIS7_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKm __ZN7xgboost6common16PartitionBuilderILm2048EE20PartitionRangeKernelIZNS2_9PartitionIjLb0ELb0EEEvmRKNSt3__16vectorINS_4tree14CPUExpandEntryENS5_9allocatorIS8_EEEENS0_7Range1dEiRKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_7RegTreeEPKmEUlT_E0_EENS5_4pairImmEENS0_4SpanISO_Lm18446744073709551615EEENSU_ImLm18446744073709551615EEESW_SQ_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common13ParallelFor2dIZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS2_14CPUExpandEntryENSE_9allocatorISG_EEEEPKNS_7RegTreeEEUlmNS0_7Range1dEE0_EEvRKNS0_14BlockedSpace2dEiT_ __ZN7xgboost6common14BlockedSpace2dC2IZNS_4tree20CommonRowPartitioner14UpdatePositionIhLb1ELb1EEEvPKNS_16GenericParameterERKNS_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNSt3__16vectorINS3_14CPUExpandEntryENSF_9allocatorISH_EEEEPKNS_7RegTreeEEUlmE_EEmT_m 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__ZN4dmlc4data14TextParserBaseIyiED2Ev.cold.1 __ZN4dmlc4data14TextParserBaseIyiE8FillDataEPNSt3__16vectorINS0_17RowBlockContainerIyiEENS3_9allocatorIS6_EEEE.cold.1 __ZN4dmlc4data14TextParserBaseIyiE8FillDataEPNSt3__16vectorINS0_17RowBlockContainerIyiEENS3_9allocatorIS6_EEEE.cold.2 __ZN4dmlc4data14TextParserBaseIjxED2Ev.cold.1 __ZN4dmlc4data14TextParserBaseIjxE8FillDataEPNSt3__16vectorINS0_17RowBlockContainerIjxEENS3_9allocatorIS6_EEEE.cold.1 __ZN4dmlc4data14TextParserBaseIjxE8FillDataEPNSt3__16vectorINS0_17RowBlockContainerIjxEENS3_9allocatorIS6_EEEE.cold.2 __ZN4dmlc4data14TextParserBaseIyxED2Ev.cold.1 __ZN4dmlc4data14TextParserBaseIyxE8FillDataEPNSt3__16vectorINS0_17RowBlockContainerIyxEENS3_9allocatorIS6_EEEE.cold.1 __ZN4dmlc4data14TextParserBaseIyxE8FillDataEPNSt3__16vectorINS0_17RowBlockContainerIyxEENS3_9allocatorIS6_EEEE.cold.2 __ZN4dmlc2io18ThreadedInputSplitC2EPNS0_14InputSplitBaseEm.cold.1 __ZN4dmlc2io18ThreadedInputSplitC2EPNS0_14InputSplitBaseEm.cold.2 __ZNSt3__16threadC2IRZN4dmlc12ThreadedIterINS2_2io14InputSplitBase5ChunkEE4InitENS_8functionIFbPPS6_EEENS8_IFvvEEEEUlvE_JEvEEOT_DpOT0_.cold.1 __ZNSt3__16threadC2IRZN4dmlc12ThreadedIterINS2_2io14InputSplitBase5ChunkEE4InitENS_8functionIFbPPS6_EEENS8_IFvvEEEEUlvE_JEvEEOT_DpOT0_.cold.2 __ZNSt3__1L14__thread_proxyINS_5tupleIJNS_10unique_ptrINS_15__thread_structENS_14default_deleteIS3_EEEEZN4dmlc12ThreadedIterINS7_2io14InputSplitBase5ChunkEE4InitENS_8functionIFbPPSB_EEENSD_IFvvEEEEUlvE_EEEEEPvSM_.cold.1 __ZN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEED2Ev.cold.1 __ZN4dmlc2io16CachedInputSplit14InitCachedIterEv.cold.1 __ZN4dmlc2io16CachedInputSplit14InitCachedIterEv.cold.2 __ZN4dmlc2io16CachedInputSplit15InitPreprocIterEv.cold.1 __ZN4dmlc2io16CachedInputSplit15InitPreprocIterEv.cold.2 __ZN4dmlc10SeekStream13CreateForReadEPKcb.cold.1 __ZN4dmlc6Stream6CreateEPKcS2_b.cold.1 __ZN4dmlc2io23IndexedRecordIOSplitter13ReadIndexFileEPNS0_10FileSystemERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE.cold.1 __ZN4dmlc2io14InputSplitBase17InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb.cold.1 __ZN4dmlc2io14InputSplitBase17InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb.cold.2 __ZN4dmlc5SplitERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEc.cold.1 __ZNSt3__111basic_regexIcNS_12regex_traitsIcEEE21__start_matching_listEb.cold.1 __ZN4dmlc18TemporaryDirectory9IsSymlinkERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE.cold.1 GCC_except_table1 GCC_except_table3 GCC_except_table6 GCC_except_table9 GCC_except_table10 GCC_except_table12 GCC_except_table13 GCC_except_table25 GCC_except_table27 GCC_except_table28 GCC_except_table29 GCC_except_table30 GCC_except_table37 GCC_except_table41 GCC_except_table42 GCC_except_table44 GCC_except_table45 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