vendor/libxgboost.dylib in xgb-0.5.2 vs vendor/libxgboost.dylib in xgb-0.5.3
- old
+ new
@@ -1,1173 +1,1000 @@
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3T 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 @ NSt3__119basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE N4dmlc5ErrorE N7xgboost6common11DenseColumnIhEE N7xgboost6common6ColumnIhEE N7xgboost6common12SparseColumnIhEE N7xgboost6common11DenseColumnItEE N7xgboost6common6ColumnItEE N7xgboost6common12SparseColumnItEE N7xgboost6common11DenseColumnIjEE N7xgboost6common6ColumnIjEE N7xgboost6common12SparseColumnIjEE N7xgboost6common16PeekableInStreamE N7xgboost6common15FixedSizeStreamE NSt3__114basic_ifstreamIcNS_11char_traitsIcEEEE NSt3__113basic_filebufIcNS_11char_traitsIcEEEE ! N7xgboost10JsonStringE N7xgboost9JsonArrayE N7xgboost10JsonObjectE N7xgboost10JsonNumberE N7xgboost11JsonIntegerE N7xgboost8JsonNullE N7xgboost11JsonBooleanE N7xgboost10JsonReaderE N7xgboost10JsonWriterE A ? _''7 A ? Q D 75 /
+
+ NSt3__120__shared_ptr_emplaceIN7xgboost16HostDeviceVectorIjEENS_9allocatorIS3_EEEE Q K Q O Y@v? L N4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEE Q B .A N7xgboost5ValueE A ? ! 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/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.cc DMatrix/Booster has not been intialized or has already been disposed. Check failed: p_m : Check failed: m Current DMatrix type does not support set data. Check failed: static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0U slice does not support group structure binary saving only supported by SimpleDMatrix Check failed: type >= 1 && type <= 4 XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead. group cache_id == 0 Cache ID is not supported yet Check failed: p_predt json str.size() > 2 str[0] == '{' r w Check failed: p_out text /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h Check failed: at_first_ Cannot reset IteratorAdapter Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columns Number of columns between batches changed from to allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.h XGBoost version not compiled with GPU support. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.h Slicing DMatrix is not supported for Proxy DMatrix. Not implemented. [ ] : %02d:%02d:%02d
+ X X
+ x
+ H G
+ 3
+
+
+
+
+
+
+
+
+
+ ) O O l ( 2 + T ?
+ 4 v -L 2` YQ|B " C C X aW o o @ UK ( i D D 2 + ! B BD ! 5 50u e x false /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.cc Check failed: ec == std::errc() : Unknown global parameters: { , } Check failed: e Check failed: 0 XGBoost distributed mode detected, will split data among workers DMatrix/Booster has not been intialized or has already been disposed. Check failed: p_m Check failed: m Current DMatrix type does not support set data. nthread static_cast<std::shared_ptr<DMatrix>*>(handle) ->get() ->Info() .group_ptr_.size() == 0U slice does not support group structure binary saving only supported by SimpleDMatrix Check failed: type >= 1 && type <= 4 XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead. group Booster has not been intialized or has already been disposed. DMatrix has not been intialized or has already been disposed. iteration_begin iteration_end strict_shape json str.size() > 2 str[0] == '{' r w Check failed: p_out text feature_name feature_type Unknown field for Booster feature info: [ ] : %02d:%02d:%02d
Stack trace:
- [bt] ( ) _Z DMLC_LOG_STACK_TRACE_DEPTH %zu write position exceed fixed buffer size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h read can not have position excceed buffer length /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h fid == static_cast<int>(names_.size()) ( vs. i q int float unknown feature type, use i for indicator and q for quantity [xgboost] Condition %s failed.
- _ptr || _count == 0 NaN 0E0 ;unknown;unknown;0;0;; /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.cc sibling.size() == size parent.size() == size cut.Values().size() > 0U curent_bin_size == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.h bins_type_size == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.h sizeof(BinIdxType) == bins_type_size_ /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.h Check failed: impl_ != nullptr batch_size < offset_vec.size() ibegin + inst.size() == iend Check failed: false /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.cc Size() == other.Size() Size() == other.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc pointer_ <= buffer_.size() Check failed: out file:// :// Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.h Not implemented rb /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc Check failed: ret.ec == std::errc() \ \\ \" \b \f \n \r \t \u%04x String Number Object Array Boolean Integer Object of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first. Unknown construct , around character position: , " " \0
" Unknown escape null Expecting null value "null" ch != -1 cursor_.Pos(): , raw_str_.size(): Expecting digit /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.h beg <= size_ Expecting: " ", got: " EOF" \0" Invalid cast, from HostSketchContainer /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cc columns_size_.size() != 0 count <= total_entries PushRowPage sketches_.size() == info.num_col_ worker_segments.size() >= 1 AllReduce num_cuts.size() == 0 n_columns == sketches_.size() Number of columns differs across workers Check failed: reduced[i].data Check failed: a.data Check failed: a.data && reduced[fid].data cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max() cut_size > cuts->cut_ptrs_.HostVector().back() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.h invalid init parameter Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)) base_rowid < group_ptr.back() Row: is not found in any group. mingap= , maxgap= , wgap= Check failed: size <= sa.size + sb.size bug in combine src.size == 0 this->size == 0 (_count == dynamic_extent) ? (_offset <= size()) : (_offset + _count <= size()) (index_ + n) <= span_->size() index_ < span_->size() index_ != span_->size() Check failed: worker_feature.data() (_idx) < (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 Parameter format for expect but value=' optional, default= , required Invalid Input: ' ', valid values are: ' Required parameter of is not presented value for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default= /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h enum_back_map_.count(value) != 0U Value not found in enum declared map::at: key not found key has already been registered in Enum ( exisit! )
- Enums: ), Out of range value for , value=' pos <= value.length() Some trailing characters could not be parsed: ' Out of range value No conversion could be performed /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h *p == ')' Invalid NAN literal statistic count elapsed /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc Timer for did not get stopped properly. s, calls @ us ======== Monitor: ======== From rank: version /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) . num_row num_col num_nonzero labels group_ptr weights base_margin labels_lower_bound labels_upper_bound feature_names feature_types /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.cc field_cnt == kNumField Wrong number of fields categorical All feature_types must be one of {int, float, i, q, categorical}. major == 1 Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again. Check failed: fi->Read(&num_field) MetaInfo: invalid format num_field >= expected MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.) MetaInfo: the given binary file contains extra fields which will be ignored. this->base_margin_.Size() % this->num_row_ == 0 Incorrect size of base margin vector. label Unknown data type weight Check failed: valid 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 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. feature_type feature_name Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_ Number of columns must be consistent across batches. that.group_ptr_.size() != 0 group_ptr_.size() == weights_.Size() + 1 Size of weights must equal to number of groups when ranking group is used. group_ptr_.back() == num_row_ Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data. weights_.Size() == num_row_ Size of weights must equal to number of rows. labels_.Size() == num_row_ Size of labels must equal to number of rows. labels_lower_bound_.Size() == num_row_ Size of label_lower_bound must equal to number of rows. feature_weigths.Size() == num_col_ Size of feature_weights must equal to number of columns. labels_upper_bound_.Size() == num_row_ Size of label_upper_bound must equal to number of rows. num_nonzero_ <= num_col_ * num_row_ base_margin_.Size() % num_row_ == 0 Size of base margin must be a multiple of number of rows. cache_file.find('#') == std::string::npos Only one `#` is allowed in file path for cache file specification. .r - XGBOOST_TEST_NPART Load part of data parts auto matrix with entries loaded from csv libsvm No format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format= No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csv Encountered parser error:
- .group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from self_offset.size() == other_offset.size() self_data.size(): other_data.size(): beg <= data.size() ptr < offset.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h float32 float64 bool8 int32 int8 int16 int64 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h Unknown data table type. Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == device Data is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: MetaInfo: Invalid format. Check failed: strm->Read(&name) name == expected_name Expected field: , got: Check failed: strm->Read(&type_val) Check failed: type == expected_type Expected field of type: got field type: Check failed: strm->Read(&is_scalar) Check failed: is_scalar Expected field to be a scalar; got a vector Check failed: strm->Read(field) Check failed: !is_scalar to be a vector; got a scalar Check failed: strm->Read(&shape.first) Check failed: strm->Read(&shape.second) shape.second == 1 Number of columns is expected to be 1. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.h Couldn't remove external memory cache file ; you may want to remove it manually .row.page page->Size() == 0 Check failed: qids.empty() || qids.size() == info.num_row_ SparsePageSource Finished writing to cache_shards.size() != 0U raw Cache file exists already; Is there another DMatrix with the same cache prefix? It can be caused by previously used DMatrix that hasn't been collected by language environment garbage collector. Otherwise please remove it manually. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h name_shards.size() == format_shards.size() thread constructor failed SparsePageWriter Finished writing to Unknown format type info.name_shards.size() != 0U Check failed: finfo->Read(&tmagic) tmagic == kMagic invalid format, magic number mismatch Check failed: fi->Read(&format) Invalid page format /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.h Check failed: !producer_sig_processed_.load(std::memory_order_acquire) unique_lock::unlock: not locked Check failed: producer_sig_.load(std::memory_order_acquire) == kProduce Make sure you call BeforeFirst not inconcurrent with Next! Check failed: produce_end_.load(std::memory_order_acquire) Check failed: out_data_ != NULL Calling Value at beginning or end? Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroy Check failed: cell != __null || produce_end_.load(std::memory_order_acquire) Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy No thread Check failed: mutex_.try_lock() Multiple threads attempting to use prefetcher key >= builder_base_row_offset /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required .ellpack.page /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page_source.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPageSource is required /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.cc Check failed: (std::is_same<AdapterT, CSCAdapter>::value) Expecting CSCAdapter Check failed: param != BatchParam{} Batch parameter is not initialized. param.gpu_id >= 0 param.max_bin >= 2 Check failed: in_stream->Read(&tmagic) invalid input file format /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h Check failed: page_ != nullptr /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h Check failed: source_ != nullptr .col.page Writing to in MB/s, written CSCPageSource: Finished writing to Check failed: *out_page == nullptr Check failed: qrecycle_.Pop(out_page) .sorted.col.page SortedCSCPageSource: Finished writing to /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.h Slicing DMatrix is not supported for external memory. Raw binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.cc page->offset.Size() != 0U Invalid SparsePage file fi->Read(dmlc::BeginPtr(data_vec), (page->data).Size() * sizeof(Entry)) == (page->data).Size() * sizeof(Entry) fid + 1 < disk_offset_.size() disk_offset_[fid] > curr_offset fi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i], size_to_read * sizeof(Entry)) == size_to_read * sizeof(Entry) Check failed: page.offset.Size() != 0 && offset_vec[0] == 0 offset_vec.back() == page.data.Size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.cc offset + n_rows + 1 <= in_offset.size() h_offset.size() > 0 n_entries == in_offset.at(offset + n_rows) - in_offset.at(offset) out->Size() != 0 GBLinearTrainParam gblinear Linear booster, implement generalized linear model. updater shotgun Update algorithm for linear model. One of shotgun/coord_descent tolerance Stop if largest weight update is smaller than this number. max_row_perbatch Maximum rows per batch. string /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cc get<String>(in["name"]) == "gblinear" model gpu_coord_descent __ Cannot find argument ' ', Possible Arguments:
+ [bt] ( ) allocator<T>::allocate(size_t n) 'n' exceeds maximum supported size _Z DMLC_LOG_STACK_TRACE_DEPTH %zu /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h Check failed: at_first_ Cannot reset IteratorAdapter Check failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columns Number of columns between batches changed from to /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.h XGBoost version not compiled with GPU support. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.h Slicing DMatrix is not supported for Proxy DMatrix. Not implemented. /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h get<Array const>(jinterface).size() == 1 Column: typestr n_bits == num_rows Shape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting. Check failed: array.find("mask") == array.cend() Masked array is not yet supported. stream version Missing `version' field for array interface Missing `typestr' field for array interface typestr.size() == 3 typestr.front() != '>' shape Missing `shape' field for array interface Missing `data' field for array interface Only version <= 3 of `__cuda_array_interface__' are supported. `typestr' should be of format <endian><type><size of type in bytes>. ( vs. Big endian is not supported. /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.h Invalid cast, from map::at: key not found is not supported. Bit field Boolean Integer Unsigned integer Floating point Complex floating point Timedelta Object String Unicode Other Invalid type code: in `typestr' of input array.
+Please verify the `__cuda_array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html
+Or open an issue. j_shape.size() == 2 Only 1-D and 2-D arrays are supported. shape.first * shape.second == 0 Empty data with non-zero shape. Empty data passed in. j_shape.size() == 1 type_length == 1 mask with bitfield type should be of 1 byte per bitfield. mask with integer type should be of 1 byte per integer. mask must be of integer type or bit field type. strides strides.size() == 1 get<Integer>(strides.at(0)) == type_length Memory should be contigious. j_strides.size() <= 2 Check failed: valid Invalid strides in array. Only dimensional array is valid. Check failed: num_rows == 1 || num_cols == 1 Array should be a vector instead of matrix. missing /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.h Invalid missing value: learner gradient_booster gblinear dart gbtree gbtree_train_param num_parallel_tree Unknown booster: chunksize == groups Unknown prediction type: std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rows write position exceed fixed buffer size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h read can not have position excceed buffer length /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.h fid == static_cast<int>(names_.size()) i q int float unknown feature type, use i for indicator and q for quantity __ Cannot find argument ' ', Possible Arguments:
----------------
- : /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 ntree_limit == 0U GBLinear::Predict ntrees is only valid for gbtree predictor PredictBatchInternal base_margin.size() == nsize * ngroup Inplace predict is not supported by current booster. gblinear does not support prediction of leaf index GBLinear::PredictContribution: ntrees is only valid for gbtree predictor { "bias": [ , ], "weight": [ ] } bias:
+ : get<Integer const>(config["cache_id"]) == 0 Cache ID is not supported yet Check failed: p_predt NaN 0E0 ;unknown;unknown;0;0;; /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.cc sibling.size() == size parent.size() == size cut.Values().size() > 0U curent_bin_size == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.h bins_type_size == kUint32BinsTypeSize /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.h sizeof(BinIdxType) == bins_type_size_ /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.h Check failed: impl_ != nullptr batch_size < offset_vec.size() ibegin + inst.size() == iend Check failed: false /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.cc Size() == other.Size() Size() == other.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc pointer_ <= buffer_.size() Check failed: out file:// :// Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.h Not implemented rb /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc Check failed: ret.ec == std::errc() \ \\ \" \b \f \n \r \t \u%04x Number Array Object of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first. Unknown construct , around character position: , " " \0
" Unknown escape null Expecting null value "null" ch != -1 cursor_.Pos(): raw_str_.size(): Expecting digit beg <= size_ Expecting: " ", got: " EOF" \0" HostSketchContainer /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cc columns_size_.size() != 0 count <= total_entries PushRowPage sketches_.size() == info.num_col_ worker_segments.size() >= 1 AllReduce num_cuts.size() == 0 n_columns == sketches_.size() Number of columns differs across workers Check failed: reduced[i].data Check failed: a.data Check failed: a.data && reduced[fid].data cuts->cut_values_.HostVector().size() <= std::numeric_limits<uint32_t>::max() cut_size > cuts->cut_ptrs_.HostVector().back() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.h invalid init parameter Check failed: nlevel <= std::max(static_cast<size_t>(1), static_cast<size_t>(limit_size * eps)) base_rowid < group_ptr.back() Row: is not found in any group. mingap= , maxgap= , wgap= Check failed: size <= sa.size + sb.size bug in combine src.size == 0 this->size == 0 Check failed: worker_feature.data() /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.cc features.size() > 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.h array.size() == weights.size() aft_loss_distribution normal extreme Choice of distribution for the noise term in Accelerated Failure Time model aft_loss_distribution_scale Scaling factor used to scale the distribution in Accelerated Failure Time model Invalid Parameter format for expect but value=' optional, default= , required Invalid Input: ' ', valid values are: ' Required parameter of is not presented value for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default= /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h enum_back_map_.count(value) != 0U Value not found in enum declared key has already been registered in Enum ( exisit! )
+ Enums: ), Out of range value for , value=' pos <= value.length() Some trailing characters could not be parsed: ' Out of range value No conversion could be performed /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h *p == ')' Invalid NAN literal statistic count elapsed /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc Timer for did not get stopped properly. s, calls @ us ======== Monitor: ======== From rank: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.cc Invaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again. fi->Read(&read[0], verstr.size()) == verstr.size() Check failed: fi->Read(&major) Check failed: fi->Read(&minor) Check failed: fi->Read(&patch) . /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.cc Input data contains `inf` or `nan` num_row num_col num_nonzero labels group_ptr weights base_margin labels_lower_bound labels_upper_bound feature_names feature_types field_cnt == kNumField Wrong number of fields categorical All feature_types must be one of {int, float, i, q, categorical}. major == 1 Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again. Check failed: fi->Read(&num_field) MetaInfo: invalid format num_field >= expected MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.) MetaInfo: the given binary file contains extra fields which will be ignored. this->base_margin_.Size() % this->num_row_ == 0 Incorrect size of base margin vector. label Unknown data type weight Weights must be positive values. qid Check failed: non_dec `qid` must be sorted in non-decreasing order along with data. label_lower_bound label_upper_bound feature_weights Feature weight must be greater than 0. Unknown key for MetaInfo: Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info. size == this->num_col_ Length of must be equal to number of columns. Unknown feature info name: Unknown feature info: this->num_col_ == that.num_col_ Number of columns must be consistent across batches. that.group_ptr_.size() != 0 group_ptr_.size() == weights_.Size() + 1 Size of weights must equal to number of groups when ranking group is used. group_ptr_.back() == num_row_ Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data. weights_.Size() == num_row_ Size of weights must equal to number of rows. labels_.Size() == num_row_ Size of labels must equal to number of rows. labels_lower_bound_.Size() == num_row_ Size of label_lower_bound must equal to number of rows. feature_weigths.Size() == num_col_ Size of feature_weights must equal to number of columns. labels_upper_bound_.Size() == num_row_ Size of label_upper_bound must equal to number of rows. num_nonzero_ <= num_col_ * num_row_ base_margin_.Size() % num_row_ == 0 Size of base margin must be a multiple of number of rows. cache_file.find('#') == std::string::npos Only one `#` is allowed in file path for cache file specification. .r - XGBOOST_TEST_NPART Load part of data parts auto matrix with entries loaded from csv libsvm No format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format= No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csv Encountered parser error:
+ .group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from self_offset.size() == other_offset.size() self_data.size(): other_data.size(): beg <= data.size() ptr < offset.size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h float32 float64 bool8 int32 int8 int16 int64 /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.h Unknown data table type. Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == device Data is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: MetaInfo: Invalid format. Check failed: strm->Read(&name) name == expected_name Expected field: , got: Check failed: strm->Read(&type_val) Check failed: type == expected_type Expected field of type: got field type: Check failed: strm->Read(&is_scalar) Check failed: is_scalar Expected field to be a scalar; got a vector Check failed: strm->Read(field) Check failed: !is_scalar to be a vector; got a scalar Check failed: strm->Read(&shape.first) Check failed: strm->Read(&shape.second) shape.second == 1 Number of columns is expected to be 1. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.h Couldn't remove external memory cache file ; you may want to remove it manually .row.page page->Size() == 0 Check failed: qids.empty() || qids.size() == info.num_row_ SparsePageSource Finished writing to cache_shards.size() != 0U raw Cache file exists already; Is there another DMatrix with the same cache prefix? It can be caused by previously used DMatrix that hasn't been collected by language environment garbage collector. Otherwise please remove it manually. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h name_shards.size() == format_shards.size() thread constructor failed SparsePageWriter Finished writing to Unknown format type info.name_shards.size() != 0U Check failed: finfo->Read(&tmagic) tmagic == kMagic invalid format, magic number mismatch Check failed: fi->Read(&format) Invalid page format /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.h Check failed: !producer_sig_processed_.load(std::memory_order_acquire) unique_lock::unlock: not locked Check failed: producer_sig_.load(std::memory_order_acquire) == kProduce Make sure you call BeforeFirst not inconcurrent with Next! Check failed: produce_end_.load(std::memory_order_acquire) Check failed: out_data_ != NULL Calling Value at beginning or end? Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroy Check failed: cell != __null || produce_end_.load(std::memory_order_acquire) Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy No thread Check failed: mutex_.try_lock() Multiple threads attempting to use prefetcher key >= builder_base_row_offset /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPage is required .ellpack.page /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page_source.cc Internal Error: XGBoost is not compiled with CUDA but EllpackPageSource is required /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.cc Check failed: (std::is_same<AdapterT, CSCAdapter>::value) Expecting CSCAdapter Check failed: param != BatchParam{} Batch parameter is not initialized. param.gpu_id >= 0 param.max_bin >= 2 Check failed: in_stream->Read(&tmagic) invalid input file format /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h Check failed: page_ != nullptr /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h Check failed: source_ != nullptr .col.page Writing to in MB/s, written CSCPageSource: Finished writing to Check failed: *out_page == nullptr Check failed: qrecycle_.Pop(out_page) .sorted.col.page SortedCSCPageSource: Finished writing to /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.h Slicing DMatrix is not supported for external memory. Raw binary data format. /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.cc page->offset.Size() != 0U Invalid SparsePage file fi->Read(dmlc::BeginPtr(data_vec), (page->data).Size() * sizeof(Entry)) == (page->data).Size() * sizeof(Entry) fid + 1 < disk_offset_.size() disk_offset_[fid] > curr_offset fi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i], size_to_read * sizeof(Entry)) == size_to_read * sizeof(Entry) Check failed: page.offset.Size() != 0 && offset_vec[0] == 0 offset_vec.back() == page.data.Size() /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.cc offset + n_rows + 1 <= in_offset.size() h_offset.size() > 0 n_entries == in_offset.at(offset + n_rows) - in_offset.at(offset) out->Size() != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cc layer_begin == 0 Linear booster does not support prediction range. layer_end == 0 GBLinearTrainParam Linear booster, implement generalized linear model. updater shotgun Update algorithm for linear model. One of shotgun/coord_descent tolerance Stop if largest weight update is smaller than this number. max_row_perbatch Maximum rows per batch. string /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h get<String>(in["name"]) == "gblinear" model gpu_coord_descent /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.h fi->Read(¶m_, sizeof(param_)) == sizeof(param_) /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.h Slice is not supported by current booster. DoBoost PredictBatch PredictBatchInternal base_margin.size() == nsize * ngroup Inplace predict is not supported by current booster. gblinear does not support prediction of leaf index { "bias": [ , ], "weight": [ ] } bias:
weight:
gblinear_train_param Check failed: this->updater_ DeprecatedGBLinearModelParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc Unknown gbm type cpu_predictor GBTree /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.cc DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead. Check failed: this->configured_ Check failed: tparam_.GetInitialised() Using updaters: Tree method is automatically selected to be 'approx' for distributed training. Tree method is automatically set to 'approx' since external-memory data matrix is used. Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'. Using tree method: grow_histmaker,prune grow_colmaker,prune Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker. grow_quantile_histmaker grow_gpu_hist Unknown tree_method ( ) detected BoostNewTrees ngroup != 0 in_gpair->Size() % ngroup == 0U must have exactly ngroup * nrow gpairs ups.size() == updaters_.size() Internal Error: mismatched updater sequence.
- Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree() Updater: ` ` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees. Check failed: up->CanModifyTree() can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees. model_.trees.size() < model_.trees_to_update.size() No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training rounds gpair->Size() == p_fmat->Info().num_row_ Mismatching size between number of rows from input data and size of gradient vector. CommitModel gbtree 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 layer_end >= layer_begin step >= 1 Check failed: this->model_.trees_to_update.empty() 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. dart Tree booster, dart. XGBoost version not compiled with OneAPI support. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h tree_begin < tree_end in_it < tree_end num_trees Number of features used for training and prediction. size_leaf_vector Reserved option for vector tree. num_parallel_tree Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. updater_seq Tree updater sequence. update Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model. gpu_predictor oneapi_predictor Predictor algorithm type approx exact Choice of tree construction method. has not been registered in Alias sample_type uniform Different types of sampling algorithm. normalize_type forest Different types of normalization algorithm. rate_drop Fraction of trees to drop during the dropout. Whether at least one tree should always be dropped during the dropout. skip_drop Probability of skipping the dropout during a boosting iteration. learning_rate Learning rate(step size) of update. eta true false 1 0 boolean get<String>(in["name"]) == "dart" weight_drop Check failed: p_dart Check failed: p_dart->weight_drop_.empty() out_preds.size() == n num_group == model_.learner_model_param->num_output_group model_.param.size_leaf_vector == 0 size_leaf_vector is enforced to 0 so far preds.size() == p_fmat->Info().num_row_ * num_group dart_train_param drop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.cc param.num_trees == static_cast<int32_t>(trees.size()) fi->Read(¶m, sizeof(param)) == sizeof(param) GBTree: invalid model file fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_trees param.num_trees == static_cast<int>(trees.size()) gbtree_model_param trees tree_info LearnerModelParamLegacy LearnerTrainParam GenericParameter gpu_id eval_metric num_round CONFIG-offset: Learner /Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.cc Check failed: IsA<Object>(in) learner learner_model_param objective gradient_booster booster attributes num_feature num_class base_score Check failed: !this->need_configuration_ Call Configure before saving model. Configure /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/generic_parameters.h
-n_gpus: n_gpus Check failed: matrix.first Check failed: !matrix.second.ref.expired() num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max()) Unfortunately, XGBoost does not support data matrices with features or greater mparam_.num_feature != 0 0 feature is supplied. Are you using raw Booster interface? multi:softprob num_output_group multi:softmax max_delta_step count:poisson 0.7 _param verbosity
-Parameters: { } might not be used.
+ Specified updaters: Actual updaters: Check failed: !updaters_.front()->CanModifyTree() Updater: ` ` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees. Check failed: up->CanModifyTree() can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees. model_.trees.size() < model_.trees_to_update.size() No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training rounds gpair->Size() == p_fmat->Info().num_row_ Mismatching size between number of rows from input data and size of gradient vector. CommitModel get<String>(in["name"]) == "gbtree" Loading from a raw memory buffer on CPU only machine. Changing predictor to auto. predictor tree_method Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist. specified_updater default process_type Check failed: configured_ Check failed: p_gbtree layer_end > layer_begin step >= 1 Check failed: this->model_.trees_to_update.empty() out_preds->version == 0 Check failed: cpu_predictor_ Data is on CUDA device, but XGBoost is not compiled with CUDA support. GBTreeModelParam GBTreeTrainParam DartTrainParam Tree booster, gradient boosted trees. Tree booster, dart. /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.h tree_begin <= tree_end XGBoost version not compiled with OneAPI support. tparam_.num_parallel_tree != 0 model_.learner_model_param->num_output_group != 0 Unsupported data type for inplace predict. Check failed: success tree_begin == 0 Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead. Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead. Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead. in_it < tree_end num_trees Number of features used for training and prediction. size_leaf_vector Reserved option for vector tree. Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. updater_seq Tree updater sequence. update Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model. gpu_predictor oneapi_predictor Predictor algorithm type approx exact Choice of tree construction method. has not been registered in Alias sample_type uniform Different types of sampling algorithm. normalize_type forest Different types of normalization algorithm. rate_drop Fraction of trees to drop during the dropout. Whether at least one tree should always be dropped during the dropout. skip_drop Probability of skipping the dropout during a boosting iteration. learning_rate Learning rate(step size) of update. eta true 1 boolean get<String>(in["name"]) == "dart" weight_drop Check failed: p_dart Check failed: p_dart->weight_drop_.empty() Check failed: predictor i >= p_out_preds->version p_out_preds->predictions.Size() == predts.predictions.Size() predts.predictions.Size() == out_preds->predictions.Size() dart_train_param drop trees, weight = /Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.cc param.num_trees == static_cast<int32_t>(trees.size()) fi->Read(¶m, sizeof(param)) == sizeof(param) GBTree: invalid model file fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_trees param.num_trees == static_cast<int>(trees.size()) gbtree_model_param trees tree_info GlobalConfiguration verbosity Flag to print out detailed breakdown of runtime. use_rmm Whether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost LearnerModelParamLegacy LearnerTrainParam GenericParameter gpu_id eval_metric num_round CONFIG-offset: Learner /Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.cc Check failed: IsA<Object>(in) learner_model_param objective booster attributes num_feature num_class base_score Check failed: !this->need_configuration_ Call Configure before saving model. Configure /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/generic_parameters.h
+n_gpus: n_gpus Check failed: matrix.first Check failed: !matrix.second.ref.expired() num_col <= static_cast<uint64_t>(std::numeric_limits<unsigned>::max()) Unfortunately, XGBoost does not support data matrices with features or greater mparam_.num_feature != 0 0 feature is supplied. Are you using raw Booster interface? multi:softprob num_output_group multi:softmax max_delta_step count:poisson 0.7 _param Invalid parameter " " contains whitespace.
+Parameters: { ", } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
- UpdateOneIter PredictRaw GetGradient Check failed: tparam_.dsplit != DataSplitMode::kAuto Precondition violated; dsplit cannot be 'auto' in distributed mode Column-wise data split is currently not supported. learner_model_param_.num_feature == p_fmat->Info().num_col_ Number of columns does not match number of features in booster. learner_model_param_.num_feature >= p_fmat->Info().num_col_ Empty dataset at worker: Check failed: gbm_ != nullptr Predict must happen after Load or configuration BoostOneIter EvalOneIter binary:logistic error logloss merror Starting in XGBoost 1.3.0, 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. value margin Unsupported prediction type: bs64 header != "bs64" Base64 format is no longer supported in brick. binf fp.Read(&header[0], 4) == 4U fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_) BoostLearner: wrong model format Check failed: fi->Read(&tparam_.objective) Check failed: fi->Read(&tparam_.booster) SAVED_PARAM_ count_poisson_max_delta_step multi: metrics Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility ; begin_layer >= 0 learner_train_param generic_param Model Config fp.Read(&header[0], header.size()) == serialisation_header_.size() Check failed: header == serialisation_header_
+ UpdateOneIter PredictRaw GetGradient Check failed: tparam_.dsplit != DataSplitMode::kAuto Precondition violated; dsplit cannot be 'auto' in distributed mode Column-wise data split is currently not supported. learner_model_param_.num_feature == p_fmat->Info().num_col_ Number of columns does not match number of features in booster. learner_model_param_.num_feature >= p_fmat->Info().num_col_ Empty dataset at worker: Check failed: gbm_ != nullptr Predict must happen after Load or configuration BoostOneIter EvalOneIter binary:logistic error logloss 1.3.0 binary:logitraw auc 1.4.0 merror Starting in XGBoost , the default evaluation metric used with the objective ' ' was changed from ' ' to ' '. Explicitly set eval_metric if you'd like to restore the old behavior. multiple_predictions <= 1 Perform one kind of prediction at a time. Unsupported prediction type: bs64 header != "bs64" Base64 format is no longer supported in brick. binf fp.Read(&header[0], 4) == 4U fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_) BoostLearner: wrong model format Check failed: fi->Read(&tparam_.objective) Check failed: fi->Read(&tparam_.booster) SAVED_PARAM_ count_poisson_max_delta_step multi: metrics Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility ; begin_layer >= 0 learner_train_param generic_param Model Config fp.Read(&header[0], header.size()) == serialisation_header_.size() Check failed: header == serialisation_header_
If you are loading a serialized model (like pickle in Python) generated by older
XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. There's a simple script for helping
the process. See:
@@ -5826,21 +5563,21 @@
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for reference to the script, and more details about differences between saving model and
serializing.
- fp.Read(&sz, sizeof(sz)) == sizeof(sz) sz > 0 Global bias of the model. Number of features in training data, this parameter will be automatically detected by learner. Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier. int (non-negative) dsplit col row Data split mode for distributed training. disable_default_eval_metric Flag to disable default metric. Set to >0 to disable Gradient booster used for training. reg:squarederror Objective function used for obtaining gradient. Random number seed during training. random_state seed_per_iteration Seed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode. nthread Number of threads to use. n_jobs The primary GPU device ordinal. fail_on_invalid_gpu_id Fail with error when gpu_id is invalid. gpu_page_size GPU page size when running in external memory mode. enable_experimental_json_serialization Enable using JSON for memory serialization (Python Pickle, rabit checkpoints etc.). validate_parameters Enable checking whether parameters are used or not.
+ fp.Read(&sz, sizeof(sz)) == sizeof(sz) sz > 0 Global bias of the model. Number of features in training data, this parameter will be automatically detected by learner. Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier. int (non-negative) dsplit col row Data split mode for distributed training. disable_default_eval_metric Flag to disable default metric. Set to >0 to disable Gradient booster used for training. reg:squarederror Objective function used for obtaining gradient. Random number seed during training. random_state seed_per_iteration Seed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode. Number of threads to use. n_jobs The primary GPU device ordinal. fail_on_invalid_gpu_id Fail with error when gpu_id is invalid. gpu_page_size GPU page size when running in external memory mode. validate_parameters Enable checking whether parameters are used or not.
Deprecated. Single process multi-GPU training is no longer supported.
Please switch to distributed training with one process per GPU.
This can be done using Dask or Spark. See documentation for details. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.cc Unknown linear updater LinearTrainParam Learning rate of each update. reg_lambda L2 regularization on weights. reg_alpha L1 regularization on weights. feature_selector cyclic shuffle thrifty greedy random Feature selection or ordering method. lambda alpha CoordinateParam coord_descent Update linear model according to coordinate descent algorithm. top_k The number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features. linear_train_param coordinate_param CoordinateUpdater /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.h unknown coordinate selector: UpdateFeature Update linear model according to shotgun coordinate descent algorithm. /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.cc Unsupported feature selector for shotgun updater.
- Supported options are: {cyclic, shuffle} ConsoleLoggerParam WARNING: DEBUG: INFO: Flag to print out detailed breakdown of runtime. debug_verbose rmse Rooted mean square error. rmsle Rooted mean square log error. mae Mean absolute error. mape Mean absolute percentage error. mphe Mean Pseudo Huber error. Negative loglikelihood for logistic regression. poisson-nloglik Negative loglikelihood for poisson regression. gamma-deviance Residual deviance for gamma regression. gamma-nloglik Negative log-likelihood for gamma regression. Binary classification error. tweedie-nloglik tweedie-nloglik@rho for tweedie regression. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cu preds.Size() == info.labels_.Size() label and prediction size not match, hint: use merror or mlogloss for multi-class classification %f sscanf(param, "%f", &threshold_) == 1 unable to parse the threshold value for the error metric Check failed: param != nullptr tweedie-nloglik must be in format tweedie-nloglik@rho Check failed: rho_ < 2 && rho_ >= 1 tweedie variance power must be in interval [1, 2) tweedie-nloglik@ /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.cc Unknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builder Multiclass classification error. mlogloss Multiclass negative loglikelihood. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cu preds.Size() == 0 Check failed: preds.Size() % info.labels_.Size() == 0 label and prediction size not match nclass >= 1U mlogloss and merror are only used for multi-class classification, use logloss for binary classification Check failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class) MultiClassEvaluation: label must be in [0, num_class), num_class= but found in label ams AMS metric for higgs. auc Area under curve for both classification and rank. aucpr Area under PR curve for both classification and rank. pre precision@k for rank. ndcg ndcg@k for rank. map map@k for rank. cox-nloglik Negative log partial likelihood of Cox proportioanl hazards model. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cc AMS must be in format ams@k ams@ Check failed: !distributed metric AMS do not support distributed evaluation best-ams-ratio= info.labels_.Size() != 0U label set cannot be empty label size predict size not match gptr.back() == info.labels_.Size() EvalAuc: group structure must match number of prediction dat[1] > 0.0f AUC: the dataset only contains pos or neg samples EvalAucPR: group structure must match number of prediction Check failed: !auc_error AUC-PR: error in calculation AUC-PR: the dataset only contains pos or neg samples dat[0] <= dat[1] AUC-PR: AUC > 1.0 %u[-]? gptr.size() != 0U must specify group when constructing rank file gptr.back() == preds.Size() EvalRank: group structure must match number of prediction Cox metric does not support distributed evaluation aft-nloglik Negative log likelihood of Accelerated Failure Time model. interval-regression-accuracy aft_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cu Unknown probability distribution preds.Size() == info.labels_lower_bound_.Size() preds.Size() == info.labels_upper_bound_.Size() ndata == labels_upper_bound.Size() Check failed: metric_ AFT metric must be configured first, with distribution type and scale survival:aft AFT loss function /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cu info.labels_lower_bound_.Size() == ndata info.labels_upper_bound_.Size() == ndata info.weights_.Size() == ndata Number of weights should be equal to number of data points. Unrecognized distribution /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.h Not part of device code. WITH_CUDA: binary:hinge Hinge loss. Expects labels to be in [0,1f] /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cu labels are not correctly provided preds.size= , label.size= SoftmaxMultiClassParam Softmax for multi-class classification, output class index. Softmax for multi-class classification, output probability distribution. Number of output class in the multi-class classification. softmax_multiclass_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cu Check failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size()) SoftmaxMultiClassObj: label size and pred size does not match.
- label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class). span_ == rhs.span_ Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.cc Unknown objective function: ` `
- LambdaRankParam Pairwise rank objective. LambdaRank with NDCG as objective. LambdaRank with MAP as objective. rank:pairwise rank:ndcg rank:map num_pairsample Number of pair generated for each instance. fix_list_weight Normalize the weight of each list by this value, if equals 0, no effect will happen lambda_rank_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cu Check failed: gptr.size() != 0 && gptr.back() == info.labels_.Size() group structure not consistent with #rows group ponter size: labels size: group pointer back: Computing gradients on CPU. RegLossParam Regression with squared error. Regression with root mean squared logarithmic error. Logistic regression for probability regression task. Regression Pseudo Huber error. Logistic regression for binary classification task. Logistic regression for classification, output score before logistic transformation. reg:linear PoissonRegressionParam Possion regression for count data. survival:cox Cox regression for censored survival data (negative labels are considered censored). reg:gamma Gamma regression for severity data. TweedieRegressionParam reg:tweedie Tweedie regression for insurance data. reg:squaredlogerror reg:logistic reg:pseudohubererror binary:logitraw scale_pos_weight Scale the weight of positive examples by this factor reg_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cu Loss: label must be greater than -1 for rmsle so that log(label + 1) can be valid. label must be in [0,1] for logistic regression /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.h Check failed: base_score > 0.0f && base_score < 1.0f base_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror. Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression. poisson_regression_param PoissonRegression: label must be nonnegative Check failed: last_abs_y <= abs_y CoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed! GammaRegression: label must be nonnegative 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 tree_begin == 0 predts->version >= tree_begin predts->version == 0 output_groups != 0 ntree_limit != 0 beg_version <= end_version delta <= model.trees.size() Check failed: out_preds->Size() == output_groups * dmat->Info().num_row_ || out_preds->Size() == dmat->Info().num_row_ model.learner_model_param->num_output_group != 0 out_preds->Size() == n Ignoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group model.param.size_leaf_vector == 0 Data type is not supported by CPU Predictor. m->NumColumns() == model.learner_model_param->num_feature Number of columns in data must equal to trained model. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h Check failed: type_ != nullptr The any container is empty requested= Check failed: *(type_->ptype_info) == typeid(T) The stored type mismatch stored= ncolumns != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.cc Check failed: container_.find(m) != container_.cend() Check failed: container_.at(m).ref.lock() [Internal error]: DMatrix: has expired. Unknown predictor type unordered_map::at: key not found /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.cc Failed to parse feature interaction constraint:
+ Supported options are: {cyclic, shuffle} WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cc Check failed: !labels.empty() labels.size() == predts.size() n_classes != 0 auc <= 1.0f info.group_ptr_.size() >= 2 Receiver Operating Characteristic Area Under the Curve. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.h Invalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.h info.weights_.Size() == info.group_ptr_.size() - 1 info.group_ptr_.back() == info.labels_.Size() auc <= 1 Total AUC across groups: , valid groups: Dataset contains only positive or negative samples. rmse Rooted mean square error. rmsle Rooted mean square log error. mae Mean absolute error. mape Mean absolute percentage error. mphe Mean Pseudo Huber error. Negative loglikelihood for logistic regression. poisson-nloglik Negative loglikelihood for poisson regression. gamma-deviance Residual deviance for gamma regression. gamma-nloglik Negative log-likelihood for gamma regression. Binary classification error. tweedie-nloglik tweedie-nloglik@rho for tweedie regression. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cu preds.Size() == info.labels_.Size() label and prediction size not match, hint: use merror or mlogloss for multi-class classification %f sscanf(param, "%f", &threshold_) == 1 unable to parse the threshold value for the error metric Check failed: param != nullptr tweedie-nloglik must be in format tweedie-nloglik@rho Check failed: rho_ < 2 && rho_ >= 1 tweedie variance power must be in interval [1, 2) tweedie-nloglik@ /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.cc Unknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builder Multiclass classification error. mlogloss Multiclass negative loglikelihood. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cu preds.Size() == 0 Check failed: preds.Size() % info.labels_.Size() == 0 label and prediction size not match nclass >= 1U mlogloss and merror are only used for multi-class classification, use logloss for binary classification Check failed: label_error >= 0 && label_error < static_cast<int32_t>(n_class) MultiClassEvaluation: label must be in [0, num_class), num_class= but found in label ams AMS metric for higgs. aucpr Area under PR curve for both classification and rank. pre precision@k for rank. ndcg ndcg@k for rank. map map@k for rank. cox-nloglik Negative log partial likelihood of Cox proportioanl hazards model. /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cc AMS must be in format ams@k ams@ Check failed: !distributed metric AMS do not support distributed evaluation best-ams-ratio= info.labels_.Size() != 0U label set cannot be empty label size predict size not match gptr.back() == info.labels_.Size() EvalAucPR: group structure must match number of prediction dat[1] > 0.0f AUC-PR: the dataset only contains pos or neg samples dat[0] <= dat[1] AUC-PR: AUC > 1.0 Check failed: !auc_error AUC-PR: error in calculation %u[-]? gptr.size() != 0U must specify group when constructing rank file gptr.back() == preds.Size() EvalRank: group structure must match number of prediction Cox metric does not support distributed evaluation aft-nloglik Negative log likelihood of Accelerated Failure Time model. interval-regression-accuracy aft_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cu Unknown probability distribution preds.Size() == info.labels_lower_bound_.Size() preds.Size() == info.labels_upper_bound_.Size() ndata == labels_upper_bound.Size() Check failed: metric_ AFT metric must be configured first, with distribution type and scale survival:aft AFT loss function /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cu info.labels_lower_bound_.Size() == ndata info.labels_upper_bound_.Size() == ndata info.weights_.Size() == ndata Number of weights should be equal to number of data points. Unrecognized distribution /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.h Not part of device code. WITH_CUDA: binary:hinge Hinge loss. Expects labels to be in [0,1f] /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cu labels are not correctly provided preds.size= , label.size= SoftmaxMultiClassParam Softmax for multi-class classification, output class index. Softmax for multi-class classification, output probability distribution. Number of output class in the multi-class classification. softmax_multiclass_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cu Check failed: preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size()) SoftmaxMultiClassObj: label size and pred size does not match.
+ label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class). Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.cc Unknown objective function: ` `
+ LambdaRankParam Pairwise rank objective. LambdaRank with NDCG as objective. LambdaRank with MAP as objective. rank:pairwise rank:ndcg rank:map num_pairsample Number of pair generated for each instance. fix_list_weight Normalize the weight of each list by this value, if equals 0, no effect will happen lambda_rank_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cu Check failed: gptr.size() != 0 && gptr.back() == info.labels_.Size() group structure not consistent with #rows group ponter size: labels size: group pointer back: Computing gradients on CPU. RegLossParam Regression with squared error. Regression with root mean squared logarithmic error. Logistic regression for probability regression task. Regression Pseudo Huber error. Logistic regression for binary classification task. Logistic regression for classification, output score before logistic transformation. reg:linear PoissonRegressionParam Possion regression for count data. survival:cox Cox regression for censored survival data (negative labels are considered censored). reg:gamma Gamma regression for severity data. TweedieRegressionParam reg:tweedie Tweedie regression for insurance data. reg:squaredlogerror reg:logistic reg:pseudohubererror scale_pos_weight Scale the weight of positive examples by this factor reg_loss_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cu Loss: label must be greater than -1 for rmsle so that log(label + 1) can be valid. label must be in [0,1] for logistic regression /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.h Check failed: base_score > 0.0f && base_score < 1.0f base_score must be in (0,1) for logistic loss, got: reg:linear is now deprecated in favor of reg:squarederror. Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression. poisson_regression_param PoissonRegression: label must be nonnegative Check failed: last_abs_y <= abs_y CoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed! GammaRegression: label must be positive. tweedie_variance_power Tweedie variance power. Must be between in range [1, 2). tweedie_regression_param TweedieRegression: label must be nonnegative Make predictions using CPU. /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.cc model.learner_model_param->num_output_group != 0 out_preds->Size() == n Ignoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group model.param.size_leaf_vector == 0 size_leaf_vector is enforced to 0 so far m->NumColumns() == model.learner_model_param->num_feature Number of columns in data must equal to trained model. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h Check failed: type_ != nullptr The any container is empty requested= Check failed: *(type_->ptype_info) == typeid(T) The stored type mismatch stored= ncolumns != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.cc Check failed: container_.find(m) != container_.cend() Check failed: container_.at(m).ref.lock() [Internal error]: DMatrix: has expired. Unknown predictor type unordered_map::at: key not found /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.cc Failed to parse feature interaction constraint:
With error:
- newsize != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.cc std::floor(d) == d Found floating point number in interaction constraints Unknown value type for interaction constraint: TreeParam TrainParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc Unknown Model Builder: Dump text representation of tree Dump json representation of tree GraphvizParam dot Dump graphviz representation of tree Check failed: node.IsLeaf() fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam) param.num_nodes != 0 fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size() fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size() static_cast<int>(deleted_nodes_.size()) == param.num_deleted param.num_nodes == static_cast<int>(nodes_.size()) param.num_nodes == static_cast<int>(stats_.size()) param.deprecated_num_roots == 1 categories_segments categories_sizes categories_nodes categories j_end - j_begin != 0 std::numeric_limits<bst_cat_t>::min() != max_cat this->split_types_.size() == param.num_nodes this->GetSplitCategoriesPtr().size() == param.num_nodes tree_param n_nodes != 0 loss_changes loss_changes.size() == n_nodes sum_hessian sum_hessian.size() == n_nodes base_weights base_weights.size() == n_nodes left_children lefts.size() == n_nodes right_children rights.size() == n_nodes parents parents.size() == n_nodes split_indices indices.size() == n_nodes split_conditions conds.size() == n_nodes default_left default_left.size() == n_nodes split_type n_nodes == split_categories_segments_.size() parent != RegTree::kInvalidNodeId static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deleted this->split_categories_segments_.size() == param.num_nodes num_nodes get<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes) this->node_mean_values_.size() > 0U unique_path[i].pweight == 0 Unique path must have zero weight /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h param.num_nodes < std::numeric_limits<int>::max() number of nodes in the tree exceed 2^31 (pos_v.int_pos) < (bits_.size()) Number of features used in tree construction. num_deleted Size of leaf vector, reserved for vector tree min_split_loss Minimum loss reduction required to make a further partition. max_depth Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policy max_leaves Maximum number of leaves; 0 indicates no limit. max_bin if using histogram-based algorithm, maximum number of bins per feature grow_policy depthwise lossguide Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM) min_child_weight Minimum sum of instance weight(hessian) needed in a child. L2 regularization on leaf weight L1 regularization on leaf weight default_direction learn right Default direction choice when encountering a missing value Maximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraint subsample Row subsample ratio of training instance. sampling_method gradient_based Sampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost) colsample_bynode Subsample ratio of columns, resample on each node (split). colsample_bylevel Subsample ratio of columns, resample on each level. colsample_bytree Subsample ratio of columns, resample on each tree construction. sketch_eps EXP Param: Sketch accuracy of approximate algorithm. sketch_ratio EXP Param: Sketch accuracy related parameter of approximate algorithm. cache_opt EXP Param: Cache aware optimization. refresh_leaf Whether the refresh updater needs to update leaf values. monotone_constraints Constraint of variable monotonicity interaction_constraints Constraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more information split_evaluator elastic_net,monotonic The criteria to use for ranking splits sparse_threshold percentage threshold for treating a feature as sparse enable_feature_grouping if >0, enable feature grouping to ameliorate work imbalance among worker threads max_conflict_rate when grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary. max_search_group when grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined. gamma double {nid}:[{fname}] yes={yes},no={no} {nid} {fname} {yes} pos != std::string::npos idx < names_.size() FeatureMap feature index exceed bound {tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing} {tabs} {cond} {left} {right} {missing} ,gain={loss_chg},cover={sum_hess} {loss_chg} {sum_hess} {tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing} Unknown feature map type. {tabs}{nid}:leaf={leaf}{stats} ,cover={cover} {leaf} {stats} {cover} {parent}{stat}
+ newsize != 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.cc std::floor(d) == d Found floating point number in interaction constraints Unknown value type for interaction constraint: TreeParam TrainParam /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc Unknown Model Builder: Dump text representation of tree Dump json representation of tree GraphvizParam dot Dump graphviz representation of tree Check failed: node.IsLeaf() fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam) param.num_nodes != 0 fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size() fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size() static_cast<int>(deleted_nodes_.size()) == param.num_deleted param.num_nodes == static_cast<int>(nodes_.size()) param.num_nodes == static_cast<int>(stats_.size()) param.deprecated_num_roots == 1 categories_segments categories_sizes categories_nodes categories j_end - j_begin != 0 std::numeric_limits<bst_cat_t>::min() != max_cat this->split_types_.size() == param.num_nodes this->GetSplitCategoriesPtr().size() == param.num_nodes tree_param n_nodes != 0 loss_changes loss_changes.size() == n_nodes sum_hessian sum_hessian.size() == n_nodes base_weights base_weights.size() == n_nodes left_children lefts.size() == n_nodes right_children rights.size() == n_nodes parents parents.size() == n_nodes split_indices indices.size() == n_nodes split_conditions conds.size() == n_nodes default_left default_left.size() == n_nodes split_type n_nodes == split_categories_segments_.size() static_cast<bst_node_t>(deleted_nodes_.size()) == param.num_deleted this->split_categories_segments_.size() == param.num_nodes num_nodes get<String>(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes) this->node_mean_values_.size() > 0U unique_path[i].pweight == 0 Unique path must have zero weight /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h param.num_nodes < std::numeric_limits<int>::max() number of nodes in the tree exceed 2^31 Number of features used in tree construction. num_deleted Size of leaf vector, reserved for vector tree min_split_loss Minimum loss reduction required to make a further partition. max_depth Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policy max_leaves Maximum number of leaves; 0 indicates no limit. max_bin if using histogram-based algorithm, maximum number of bins per feature grow_policy depthwise lossguide Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM) min_child_weight Minimum sum of instance weight(hessian) needed in a child. L2 regularization on leaf weight L1 regularization on leaf weight default_direction learn right Default direction choice when encountering a missing value Maximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraint subsample Row subsample ratio of training instance. sampling_method gradient_based Sampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost) colsample_bynode Subsample ratio of columns, resample on each node (split). colsample_bylevel Subsample ratio of columns, resample on each level. colsample_bytree Subsample ratio of columns, resample on each tree construction. sketch_eps EXP Param: Sketch accuracy of approximate algorithm. sketch_ratio EXP Param: Sketch accuracy related parameter of approximate algorithm. cache_opt EXP Param: Cache aware optimization. refresh_leaf Whether the refresh updater needs to update leaf values. monotone_constraints Constraint of variable monotonicity interaction_constraints Constraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more information split_evaluator elastic_net,monotonic The criteria to use for ranking splits sparse_threshold percentage threshold for treating a feature as sparse enable_feature_grouping if >0, enable feature grouping to ameliorate work imbalance among worker threads max_conflict_rate when grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary. max_search_group when grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined. gamma double {nid}:[{fname}] yes={yes},no={no} {nid} {fname} {yes} pos != std::string::npos idx < names_.size() FeatureMap feature index exceed bound {tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing} {tabs} {cond} {left} {right} {missing} ,gain={loss_chg},cover={sum_hess} {loss_chg} {sum_hess} {tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing} Unknown feature map type. {tabs}{nid}:leaf={leaf}{stats} ,cover={cover} {leaf} {stats} {cover} {parent}{stat}
{left}
-{right} {stat} {nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no} {depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} , "gain": {loss_chg}, "cover": {sum_hess} "nodeid": {nid}, "depth": {depth}, "split": {fname}, "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} {{properties} {stat}, "children": [{left}, {right}
+{right} {stat} {nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no} {depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} , "gain": {loss_chg}, "cover": {sum_hess} {{properties} {stat}, "children": [{left}, {right}
{indent}]} {properties} { "nodeid": {nid}, "leaf": {leaf} {stat}} , "cover": {sum_hess} {newline}{indent}{nodes} {newline} yes_color #0000FF Edge color when meets the node condition. #FF0000 Edge color when doesn't meet the node condition. rankdir Passed to graphiz via graph_attr. condition_node_params Conditional node configuration leaf_node_params Leaf node configuration graph_attrs Any other extra attributes for graphviz `graph_attr`. Failed to parse graphviz parameters:
" graph [ {key}="{value}" ]
{key} {value} The following parameters for graphviz are not recognized:
/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.h ch == '{' Error at , Expect '{' but get ' Line , around ^` ` ch == ',' , JSON object expect '}' or ',' ' ch == ':' , Expect ':' but get ' ch == '\"' , Expect '"' but get ' unknown string escape \ , Expect '"' but reach end of line {nid} [ label="{fname}{<}{cond}" {params}]
{<} < {nid} -> {child} [label="{branch}" color="{color}"]
@@ -5848,783 +5585,980 @@
{leaf-value} {parent}
{left}
{right} digraph {
graph [ rankdir={rankdir} ]
{graph_attrs}
-{nodes}} {rankdir} {graph_attrs} /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.cc Unknown tree updater ColMakerTrainParam grow_colmaker Grow tree with parallelization over columns. opt_dense_col EXP Param: speed optimization for dense column. train_param colmaker_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.cc Updater `grow_colmaker` or `exact` tree method doesn't support distributed training. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.h Max leaves and max depth cannot both be unconstrained. this->max_depth <= 31 max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes. Check failed: param_.cache_opt Support for `cache_opt' is removed in 1.0.0 fmat.Info().num_row_ == position_.size() param_.sampling_method == TrainParam::kUniform Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist. ridx < position_.size() ridx exceed bound ridx= pos= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h !common::CheckNAN(mid) grow_local_histmaker Tree constructor that uses approximate histogram construction. grow_histmaker Tree constructor that uses approximate global of histogram construction. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h fi.Read(&this->size, sizeof(this->size)) == sizeof(this->size) fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) check quantile stats, nbig= , n= srcsize= , maxsize= , range= , chunk= Check failed: nbig < n quantile: too many large chunk ] rmin= , rmax= , wmin= , v= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.cc Check failed: param_.max_depth > 0 max_depth must be larger than 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.h node2workindex_[nid] == static_cast<int>(wid) summary_array_.size() == sketchs_.size() offset == -2 this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1 fid * 2 + 1 < fminmax_.size() FeatHelper fid exceed query bound ret > 0U Check failed: tree[nid].IsLeaf() sketch->temp.size < max_size invalid maximum size max_size= , stemp.size INFO: rmax= , sum_total= , naxt_goal= , size= sketch->temp.size <= max_size Finalize: invalid maximum size, max_size= , stemp.size= fid * 2 < fminmax_.size() fv= , hist.size= hist[ ]= , hist.last= istart != hist.size this->qexpand_.size() == 1U this->wspace_.rptr.back() == this->wspace_.cut.size() prune Pruner that prune the tree according to statistics. TreePruner PrunerUpdate /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cc tree pruning end, extra nodes, pruned nodes, max_depth= Check failed: !tree[pid].IsLeaf() Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf() Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf() nid >= 1 Quantile::Builder Update SubtractionTrick UpdatePredictionCache /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc out_preds.size() > 0U Check failed: (param_.max_depth > 0 || param_.max_leaves > 0) max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity. max_depth cannot be 0 (unlimited) when grow_policy is depthwise. min_nbins_per_feature > 0U EvaluateSplits ApplySplit (*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild() upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) InitNewNode BuildLocalHistograms SplitSiblings BuildNodeStats ExpandWithLossGuide CPUHistMakerTrainParam GmatInitialization grow_fast_histmaker (Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram. Grow tree using quantized histogram. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.h elem_of_each_node_.size() == 0U Check failed: e.begin != nullptr n_left + n_right == e.Size() begin + n_left <= e.end begin + n_left + n_right == e.end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/column_matrix.h gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val gmat_bin_size == kUint32BinsTypeSize bins_type_size_ == kUint32BinsTypeSize cpu_hist_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h Attempted to load internal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html single_precision_histogram Use single precision to build histograms. SyncHistograms SyncHistogramsAllreduce /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h begin < end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.h end > begin nid < nodes_ i < first_dimension_.size() i < ranges_.size() AddHistRows row_ptr_[nid] == kMax Check failed: (*p_last_tree_)[nid].IsLeaf() id != kMax cut_ptr[fid] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) cut_ptr[fid + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) local_block_ptr != (BlockInfo*)nullptr nodes == targeted_hists.size() tid < nthreads_ data_ || size_ == 0 grow_fast_histmaker is deprecated, use grow_quantile_histmaker instead. QuantileHistMaker refresh Refresher that refreshes the weight and statistics according to data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.cc batch.Size() < std::numeric_limits<unsigned>::max() Syncher that synchronize the tree in all distributed nodes. NULL worker rabit_reduce_buffer 256MB DMLC_TASK_ID DMLC_ROLE DMLC_NUM_ATTEMPT DMLC_TRACKER_URI DMLC_TRACKER_PORT DMLC_WORKER_CONNECT_RETRY %[^=]=%s mapred_tip_id mapreduce_task_id hadoop_mode is set but cannot find mapred_task_id rabit_task_id rabit_hadoop_mode mapred_task_id %d rabit_num_trial mapred_map_tasks mapreduce_job_maps hadoop_mode is set but cannot find mapred_map_tasks rabit_world_size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.cc Rabit Module currently only work with dmlc worker can only call Init once start Failed to shutdown due to %s print rabit_tracker_uri rabit_tracker_port rabit_tree_reduce_minsize rabit_reduce_ring_mincount rabit_reduce_ring_mincount should be greater than 0 rabit_bootstrap_cache rabit_debug rabit_timeout rabit_timeout_sec rabit_timeout_sec should be non negative second rabit_enable_tcp_no_delay Connect to (failed): [ ]
- Connect Retry connect to ip(retry time ): [ ReConnectLink failure 1 ReConnectLink failure 2 sync::Invalid tracker message, init failure ReConnectLink failure 3 task connected to the tracker ReConnectLink failure 4 must keep rank to same if the node already have one tracker got overwhelemed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specified ReConnectLink failure 5 ReConnectLink failure 6 ReConnectLink failure 7 ReConnectLink failure 8 ReConnectLink failure 9 ReConnectLink failure 10 ReConnectLink failure 12 ReConnectLink failure 13 ReConnectLink failure, link rank inconsistent Override a link that is active ReConnectLink failure 14 ReConnectLink failure 15 ReConnectLink: bad socket tcp no delay is not implemented on non unix platforms cannot find parent in the link cannot find prev ring in the link cannot find next ring in the link failed in ReconnectLink buffer size inconsistent must assign buffer_size Allreduce: size check Allreduce: boundary error Broadcast: root should be smaller than world size need to assume rank structure write ptr boundary check [%d] read_ptr boundary check fail to get host name Socket::Close double close the socket or close without create error during send SendStr %lu%c invalid format for %s invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB} Create cannot obtain address of %s Does not support IPv6 Socket %s Error:%s SendAll RecvAll 0.0.0.0 TryBindHost GetSockError error during send RecvStr Accept SetNonBlock-1 SetNonBlock-2 SetKeepAlive too large type_nbytes=%lu, buffer_size=%lu /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.h Poll timeout Poll ReadToRingBuffer: buffer not allocated ReadToRingBuffer: max_size_read check Allreduce: boundary check AllgatherRing failed Allreduce failed Broadcast failed unknown enum_op unknown data_type Failed to initialize RABIT. Failed to shutdown RABIT worker. Read pickle string not implemented DataType does not support bitwise or operation the rabit has not been initialized cannot initialize reduce handle twice must intialize handle to call AllReduce LibSVMParserParam LibFMParserParam CSVParserParam libfm format File format indexing_mode If >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. File format. label_column Column index (0-based) that will put into label. delimiter Delimiter used in the csv file. weight_column Column index that will put into instance weights. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h Check failed: TryLoadCache() failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.h Check failed: fi->Read(&label) Bad RowBlock format Check failed: fi->Read(&weight) Check failed: fi->Read(&qid) Check failed: fi->Read(&field) Check failed: fi->Read(&index) Check failed: fi->Read(&value) Check failed: fi->Read(&max_field, sizeof(IndexType)) Check failed: fi->Read(&max_index, sizeof(IndexType)) MB read, MB/sec finish reading at %g MB/sec batch.field[i] <= std::numeric_limits<IndexType>::max() field exceed numeric bound of current type batch.index[i] <= std::numeric_limits<IndexType>::max() index exceed numeric bound of current type label.size() + 1 == offset.size() offset.back() == index.size() Check failed: offset.back() == value.size() || value.size() == 0 /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.h finish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc Unknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h param_.format == "libsvm" /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.h chunk.size != 0U qid: Check failed: out->label.size() + 1 == out->offset.size() Check failed: base <= 10 && base >= 2 sign == true /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h cannot call ParseNext /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h param_.format == "libfm" Check failed: out->field.size() == out->index.size() /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.h param_.format == "csv" Check failed: param_.label_column != param_.weight_column || param_.label_column < 0 Must have distinct columns for labels and instance weights Delimiter ' ' is not found in the line. Expected ' ' as the delimiter to separate fields. Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size() hdfs:// viewfs:// /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.cc Please compile with DMLC_USE_HDFS=1 to use hdfs s3:// http:// https:// Please compile with DMLC_USE_S3=1 to use S3 azure:// Please compile with DMLC_USE_AZURE=1 to use Azure unknown filesystem protocol stdin Check failed: part < nsplit invalid input parameter for InputSplit::Create indexed_recordio need to pass index file to use IndexedRecordIO recordio unknown input split type .split .part /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.h name_cache.size() == 1U only one `#` is allowed in file path for cachefile specification Check failed: std::getline(is, kv.first, '=') Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.h Check failed: fp_ != NULL SingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1 InputSplit do not support write /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.h Check failed: nread == sizeof(size) has invalid cache file format Check failed: fi_->Read(p->begin, size) == size BeforeFirst is not supported Check failed: this->InitCachedIter() Failed to initialize CachedIter ResetPartition is not supported in CachedInputSplit /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.cc Check failed: begin != end /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.cc Check failed: fi->Read(&lrec, sizeof(lrec)) != 0 invalid record io format (reinterpret_cast<size_t>(begin) & 3UL) == 0U (reinterpret_cast<size_t>(end) & 3UL) == 0U Check failed: p >= pbegin + 2 Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end Invalid RecordIO Format (reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U (reinterpret_cast<size_t>(chunk->end) & 3UL) == 0U Check failed: chunk->begin <= chunk->end Check failed: cflag == 1U Check failed: p[0] == RecordIOWriter::kMagic /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.cc expanded_list.size() == 1ul IndexedRecordIOSplitter does not support multiple index files /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.cc Check failed: files_[i].size % align_bytes == 0 file do not align by bytes Check failed: offset_end_ >file_offset_[file_ptr_end_] Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is needed files_.size() != 0U Cannot find any files that matches the URI pattern curr= ,begin= ,end= ,fileptr= ,fileoffset= offset[ file offset not calculated correctly C /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.cc Check failed: !IsSymlink(info.path.name) Symlink not supported in TemporaryDirectory Couldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.h lstat(path.c_str(), &sb) == 0 dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.cc LocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory .. stdout wb Check failed: allow_null LocalFileSystem::Open " ": Check failed: std::fwrite(ptr, 1, size, fp_) == size FileStream.Write incomplete Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET) L P XQ ! 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+{nodes}} {rankdir} {graph_attrs} /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.cc Unknown tree updater ColMakerTrainParam grow_colmaker Grow tree with parallelization over columns. opt_dense_col EXP Param: speed optimization for dense column. train_param colmaker_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.cc Updater `grow_colmaker` or `exact` tree method doesn't support distributed training. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.h Max leaves and max depth cannot both be unconstrained. this->max_depth <= 31 max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes. Check failed: param_.cache_opt Support for `cache_opt' is removed in 1.0.0 fmat.Info().num_row_ == position_.size() param_.sampling_method == TrainParam::kUniform Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist. ridx < position_.size() ridx exceed bound ridx= pos= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h grow_local_histmaker Tree constructor that uses approximate histogram construction. grow_histmaker Tree constructor that uses approximate global of histogram construction. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h fi.Read(&this->size, sizeof(this->size)) == sizeof(this->size) fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) check quantile stats, nbig= , n= srcsize= , maxsize= , range= , chunk= Check failed: nbig < n quantile: too many large chunk ] rmin= , rmax= , wmin= , v= /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.cc Check failed: param_.max_depth > 0 max_depth must be larger than 0 /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.h node2workindex_[nid] == static_cast<int>(wid) summary_array_.size() == sketchs_.size() offset == -2 this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1 fid * 2 + 1 < fminmax_.size() FeatHelper fid exceed query bound ret > 0U Check failed: tree[nid].IsLeaf() sketch->temp.size < max_size invalid maximum size max_size= , stemp.size INFO: rmax= , sum_total= , naxt_goal= , size= sketch->temp.size <= max_size Finalize: invalid maximum size, max_size= , stemp.size= fid * 2 < fminmax_.size() fv= , hist.size= hist[ ]= , hist.last= istart != hist.size this->qexpand_.size() == 1U this->wspace_.rptr.back() == this->wspace_.cut.size() prune Pruner that prune the tree according to statistics. TreePruner PrunerUpdate /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cc tree pruning end, extra nodes, pruned nodes, max_depth= Check failed: !tree[pid].IsLeaf() Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf() Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf() nid >= 1 Quantile::Builder Update SubtractionTrick UpdatePredictionCache /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc out_preds.size() > 0U Check failed: (param_.max_depth > 0 || param_.max_leaves > 0) max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity. max_depth cannot be 0 (unlimited) when grow_policy is depthwise. row_indices.size() + unused_rows_.size() == info.num_row_ min_nbins_per_feature > 0U EvaluateSplits ApplySplit (*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild() upper_bound < static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) InitNewNode BuildLocalHistograms SplitSiblings BuildNodeStats ExpandWithLossGuide CPUHistMakerTrainParam GmatInitialization grow_fast_histmaker (Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram. Grow tree using quantized histogram. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.h elem_of_each_node_.size() == 0U Check failed: e.begin != nullptr n_left + n_right == e.Size() begin + n_left <= e.end begin + n_left + n_right == e.end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/column_matrix.h gmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val gmat_bin_size == kUint32BinsTypeSize bins_type_size_ == kUint32BinsTypeSize cpu_hist_train_param /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h Attempted to load internal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html single_precision_histogram Use single precision to build histograms. SyncHistograms SyncHistogramsAllreduce /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h begin < end /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.h end > begin nid < nodes_ i < first_dimension_.size() i < ranges_.size() AddHistRows row_ptr_[nid] == kMax Check failed: (*p_last_tree_)[nid].IsLeaf() id != kMax cut_ptr[fid] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) cut_ptr[fid + 1] <= static_cast<uint32_t>(std::numeric_limits<int32_t>::max()) nodes == targeted_hists.size() tid < nthreads_ grow_fast_histmaker is deprecated, use grow_quantile_histmaker instead. QuantileHistMaker refresh Refresher that refreshes the weight and statistics according to data. /Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.cc batch.Size() < std::numeric_limits<unsigned>::max() Syncher that synchronize the tree in all distributed nodes. NULL worker rabit_reduce_buffer 256MB DMLC_TASK_ID DMLC_ROLE DMLC_NUM_ATTEMPT DMLC_TRACKER_URI DMLC_TRACKER_PORT DMLC_WORKER_CONNECT_RETRY %[^=]=%s mapred_tip_id mapreduce_task_id hadoop_mode is set but cannot find mapred_task_id rabit_task_id rabit_hadoop_mode mapred_task_id %d rabit_num_trial mapred_map_tasks mapreduce_job_maps hadoop_mode is set but cannot find mapred_map_tasks rabit_world_size /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.cc Rabit Module currently only work with dmlc worker can only call Init once start Failed to shutdown due to %s print rabit_tracker_uri rabit_tracker_port rabit_tree_reduce_minsize rabit_reduce_ring_mincount rabit_reduce_ring_mincount should be greater than 0 rabit_bootstrap_cache rabit_debug rabit_timeout rabit_timeout_sec rabit_timeout_sec should be non negative second rabit_enable_tcp_no_delay Connect to (failed): [ ]
+ Connect Retry connect to ip(retry time ): [ ReConnectLink failure 1 ReConnectLink failure 2 sync::Invalid tracker message, init failure ReConnectLink failure 3 task connected to the tracker ReConnectLink failure 4 must keep rank to same if the node already have one tracker got overwhelemed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specified ReConnectLink failure 5 ReConnectLink failure 6 ReConnectLink failure 7 ReConnectLink failure 8 ReConnectLink failure 9 ReConnectLink failure 10 ReConnectLink failure 12 ReConnectLink failure 13 ReConnectLink failure, link rank inconsistent Override a link that is active ReConnectLink failure 14 ReConnectLink failure 15 ReConnectLink: bad socket tcp no delay is not implemented on non unix platforms cannot find parent in the link cannot find prev ring in the link cannot find next ring in the link failed in ReconnectLink buffer size inconsistent must assign buffer_size Allreduce: size check Allreduce: boundary error Broadcast: root should be smaller than world size need to assume rank structure write ptr boundary check [%d] read_ptr boundary check fail to get host name Socket::Close double close the socket or close without create error during send SendStr %lu%c invalid format for %s invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB} Create cannot obtain address of %s Does not support IPv6 Socket %s Error:%s SendAll RecvAll 0.0.0.0 TryBindHost GetSockError error during send RecvStr Accept SetNonBlock-1 SetNonBlock-2 SetKeepAlive too large type_nbytes=%lu, buffer_size=%lu /Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.h Poll timeout Poll ReadToRingBuffer: buffer not allocated ReadToRingBuffer: max_size_read check Allreduce: boundary check AllgatherRing failed Allreduce failed Broadcast failed unknown enum_op unknown data_type Failed to initialize RABIT. Failed to shutdown RABIT worker. Read pickle string not implemented DataType does not support bitwise or operation the rabit has not been initialized cannot initialize reduce handle twice must intialize handle to call AllReduce LibSVMParserParam LibFMParserParam CSVParserParam libfm format File format indexing_mode If >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes. File format. label_column Column index (0-based) that will put into label. delimiter Delimiter used in the csv file. weight_column Column index that will put into instance weights. /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h Check failed: TryLoadCache() failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.h Check failed: fi->Read(&label) Bad RowBlock format Check failed: fi->Read(&weight) Check failed: fi->Read(&qid) Check failed: fi->Read(&field) Check failed: fi->Read(&index) Check failed: fi->Read(&value) Check failed: fi->Read(&max_field, sizeof(IndexType)) Check failed: fi->Read(&max_index, sizeof(IndexType)) MB read, MB/sec finish reading at %g MB/sec label.size() + 1 == offset.size() offset.back() == index.size() Check failed: offset.back() == value.size() || value.size() == 0 /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.h finish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc Unknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h param_.format == "libsvm" /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.h chunk.size != 0U qid: Check failed: out->label.size() + 1 == out->offset.size() Check failed: base <= 10 && base >= 2 sign == true /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h cannot call ParseNext /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h param_.format == "libfm" Check failed: out->field.size() == out->index.size() /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.h param_.format == "csv" Check failed: param_.label_column != param_.weight_column || param_.label_column < 0 Must have distinct columns for labels and instance weights Delimiter ' ' is not found in the line. Expected ' ' as the delimiter to separate fields. Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size() hdfs:// viewfs:// /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.cc Please compile with DMLC_USE_HDFS=1 to use hdfs s3:// http:// https:// Please compile with DMLC_USE_S3=1 to use S3 azure:// Please compile with DMLC_USE_AZURE=1 to use Azure unknown filesystem protocol stdin Check failed: part < nsplit invalid input parameter for InputSplit::Create indexed_recordio need to pass index file to use IndexedRecordIO recordio unknown input split type .split .part /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.h name_cache.size() == 1U only one `#` is allowed in file path for cachefile specification Check failed: std::getline(is, kv.first, '=') Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg name_args.size() == 1U /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.h Check failed: fp_ != NULL SingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1 InputSplit do not support write /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.h Check failed: nread == sizeof(size) has invalid cache file format Check failed: fi_->Read(p->begin, size) == size BeforeFirst is not supported Check failed: this->InitCachedIter() Failed to initialize CachedIter ResetPartition is not supported in CachedInputSplit /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.cc Check failed: begin != end /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.cc Check failed: fi->Read(&lrec, sizeof(lrec)) != 0 invalid record io format (reinterpret_cast<size_t>(begin) & 3UL) == 0U (reinterpret_cast<size_t>(end) & 3UL) == 0U Check failed: p >= pbegin + 2 Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end Invalid RecordIO Format (reinterpret_cast<size_t>(chunk->begin) & 3UL) == 0U (reinterpret_cast<size_t>(chunk->end) & 3UL) == 0U Check failed: chunk->begin <= chunk->end Check failed: cflag == 1U Check failed: p[0] == RecordIOWriter::kMagic /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.cc expanded_list.size() == 1ul IndexedRecordIOSplitter does not support multiple index files /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.cc Check failed: files_[i].size % align_bytes == 0 file do not align by bytes Check failed: offset_end_ >file_offset_[file_ptr_end_] Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is needed files_.size() != 0U Cannot find any files that matches the URI pattern curr= ,begin= ,end= ,fileptr= ,fileoffset= offset[ file offset not calculated correctly C /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.cc Check failed: !IsSymlink(info.path.name) Symlink not supported in TemporaryDirectory Couldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.h lstat(path.c_str(), &sb) == 0 dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.cc LocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory .. stdout wb Check failed: allow_null LocalFileSystem::Open " ": Check failed: std::fwrite(ptr, 1, size, fp_) == size FileStream.Write incomplete Check failed: !std::fseek(fp_, static_cast<long>(pos), SEEK_SET) L P XQ aQ ! 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aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i JsonNulleqERKNS_5ValueE MetaInfo ̸ ixE aSERKNS_5ValueE ݘ4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ˘i ј 5ParseEv 1 4LoadEv 9ParseNullEv 0 ԙ1Parse 2ParseBooleanEv Қ3kMaxNumLengthE ܖ SkipSpacesEv ParseArrayEv StringEv ObjectEv ƚNumberEv ̚ Ї Д Json data tree 4 aSERKS0_ LoadE ݛDumpES0_PNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NS_10StringViewE PNS_10JsonReaderE C 1 9AllReduceEPNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_IiNS9_IiEEEE 8MakeCutsEPNS0_13HistogramCutsE 2ENSt3__16vectorImNS2_9allocatorImEEEEib 1ENSt3__16vectorImNS2_9allocatorImEEEEib 4CalcColumnSizeERKNS_10SparsePageEjm 1 6GatherSketchInfoERKNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_ImNS9_ImEEEESG_PNS3_INS6_5EntryENS9_ISH_EEEE LoadBalanceERKNS_10SparsePageEjm PushRowPageERKNS_10SparsePageERKNS_8MetaInfoE 2 ʡ1 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 5StartERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 4StopERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE Д 21CollectFromOtherRanksEv 15PrintStatisticsERKNSt3__13mapINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4pairImmEENS2_4lessIS9_EENS7_INSA_IKS9_SB_EEEEEE 5PrintEv Е Version DMatrix ʯLearner RegTree 4 6StringERKNSt3__15tupleIJiiiEEE ѭ8kInvalidE LoadE S RKNS_4JsonE ۬PN4dmlc6StreamE a elfEv ŭ veEPN meERKNSt3__15tupleIJiiiEEE S_4JsonE 4dmlc6StreamE ˭ 7xgboost S_16ParserFactoryRegI 4data19SparsePageFormatRegINS1_ Į1 9MetricRegEE3GetEv 1 7CSCPageEEEE3GetEv 0SparsePageEEEE3GetEv 3SortedCSCPageEEEE3GetEv 1EllpackPageEEEE3GetEv į І 6CreateI D 4LoadERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbbS9_m PvS2_FvS2_EFiS2_EEEPS0_T_T0_PT1_PT2_fii NS_4data1 2DenseAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 0CS 6DataTableAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 1FileAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 5IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPS0_PT_fiRKNSt3__112basic_stringIcNSD_11char_traitsIcEENSD_9allocatorIcEEEEm RAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm CAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 4Push 7PushCSCERKS0_ INS_4data ERKS0_ 1 21DataTableAdapterBatchEEEyRKT_fi 7DenseAdapterBatchEEEyRKT_fi 5CS 6FileAdapterBatchEEEyRKT_fi Д RAdapterBatchEEEyRKT_fi CAdapterBatchEEEyRKT_fi 5ClearEv Ƹ1 غ7SetInfoEPKc 6ExtendERKS0_b 9kNumFieldE 1 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 7GetInfoEPKcPyNS_8DataTypeEPPKv 8ValidateEi 0SaveBinaryEPN4dmlc6StreamE ̺4GetFeatureInfoEPKcPNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEE 0LoadBinaryEPN4dmlc6StreamE 4SetFeatureInfoEPKcPS2_y PKvNS_8DataTypeEm RKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE Ж DMatrix14GetThreadLocalEv Learner19AllowLazyCheckPointEv RegTree 2Ev 1Ev 0Ev C D ۾12SetBaseRowIdEm 2E 1E v PNS_7DMatrixERKNS_10BatchParamE Ͼ v ɾPNS_7DMatrixERKNS_10BatchParamE վ 2Ev 1Ev 1 п49__dmlc_registry_file_tag_sparse_page_raw_format__Ev 8DataPool 7 3SimpleDMatrix EllpackPageSourceC SparsePageDMatrix 2EPNS_7DMatrixERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKNS_10BatchParamE 1EPNS_7DMatrixERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKNS_10BatchParamE C 4InfoEv 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 1 22GetSortedColumnBatchesEv 2 1 INS0_1 EPN4dmlc6StreamE 2DenseAdapterEEEPT_fi 0CS 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi 5IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi INS0_1 EPN4dmlc6StreamE 2DenseAdapterEEEPT_fi 0CS 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi 5IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi RAdapterEEEPT_fi CAdapterEEEPT_fi RAdapterEEEPT_fi CAdapterEEEPT_fi м data tree17QuantileHistMaker7BuilderI 1 8DataPool5SliceENSt3__110shared_ptrINS_10SparsePageEEEmmm 3SimpleDMatrix4InfoEv 7SparsePageDMatrix4InfoEv 3GetRowBatchesEv 6GetColumnBatchesEv 7GetEllpackBatchesERKNS_10BatchParamE 5SaveToLocalFileERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 4InfoEv 1 22GetSortedColumnBatchesEv 3GetRowBatchesEv 6GetColumnBatchesEv 7GetEllpackBatchesERKNS_10BatchParamE " # 14SplitWritePageEv 8FinalizeEv # # gbm obj 2FeatureInteractionConstraintHost 3 1 28DeprecatedGBLinearModelParam11__MANAGER__Ev 6GBTree 5__dmlc_registry_file_tag_gblinear__Ev 3__dmlc_registry_file_tag_gbtree__Ev # 8GBLinearTrainParam11__MANAGER__Ev 3GBLinearModel9LoadModelERKNS_4JsonE 6GBTree 4DartTrainParam11__MANAGER__Ev 1GBTreeModel # 1 6GBTree 3GBLinearModel9SaveModelEPNS_4JsonE 1GBTreeModel & & & 8GradientBoosterRegEE3GetEv 6LinearUpdaterRegEE3GetEv 2 4 0TreeGenRegEE3GetEv & & & 9 1 2 7DoBoostEPNS_7DMatrixEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_20PredictionCacheEntryE ConfigureERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE LoadModelERKNS_4JsonE & 7ConfigureUpdatersEv 1 3BoostNewTreesEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixEiPNSt3__16vectorINSA_10unique_ptrINS_7RegTreeENSA_14default_deleteISD_EEEENSA_9allocatorISG_EEEE 0LoadConfigERKNS_4JsonE 2PredictBatchEPNS_7DMatrixEPNS_20PredictionCacheEntryEbj & InitUpdaterERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEE CommitModelEONSt3__16vectorINS3_INS2_10unique_ptrINS_7RegTreeENS2_14default_deleteIS5_EEEENS2_9allocatorIS8_EEEENS9_ISB_EEEEPNS_7DMatrixEPNS_20PredictionCacheEntryE & 2ConfigureWithKnownDataERKNSt3__16vectorINS2_4pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEESA_EENS8_ISB_EEEEPNS_7DMatrixE 6PerformTreeMethodHeuristicEPNS_7DMatrixE & & & ' ' ' hn S I ޅVN 8_N 16_N7xgboost7LearnerD 7xgboost K7xgboost3gbm6GBTree10SaveConfigEPNS_4JsonE 3gbm6GBTree10LoadConfigERKNS_4JsonE 7LearnerD ' 1 9SaveModelEPNS_4JsonE 5SliceEiiiPNS_15GradientBoosterEPb 0SaveConfigEPNS_4JsonE 2GetPredictorEPKNS_16HostDeviceVectorIfEEPNS_7DMatrixE ' ' ' ' ' ( ( ModelParam11__MANAGER__Ev TrainParam11__MANAGER__Ev ( ( ( 4SaveEPN4dmlc6StreamE 9SaveModelEPNS_4JsonE + 4LoadEPN4dmlc6StreamE 9LoadModelERKNS_4JsonE + + , , D 6CreateERKNSt3__16vectorINS1_10shared_ptrINS_7DMatrixEEENS1_9allocatorIS5_EEEE 2Ev 1Ev 0Ev , , 1Ev 0Ev , 1Ev 0Ev , , , Щ, ModelParamC TrainParam11__MANAGER__Ev 2ERKNS_23LearnerModelParamLegacyEf 1ERKNS_23LearnerModelParamLegacyEf , , 3LearnerModelParamLegacy11__MANAGER__Ev 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6DefaultVerbosityEv 5GlobalVerbosityEv 7global_verbosity_E 5 5 2E 1E NS0_12LogVerbosityE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE Т5 NS0_12LogVerbosityE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE 5 Ц5 5 4 3 2 5__ 4__dmlc_registry_file_tag_multiclass_metric__Ev 2__dmlc_registry_file_tag_survival_metric__Ev dmlc_registry_file_tag_elementwise_metric__Ev make_MetricReg_IntervalRegressionAccuracy__E 5 5 7 7 7 MetricGPURegEE3GetEv PredictorRegEE3GetEv 7 7 8__dmlc_registry_file_tag_rank_metric__Ev 5__make_MetricReg_PossionNegLoglik__E 2__make_MetricReg_GammaDeviance__E 1__make_MetricReg_ 3__make_MetricReg_TweedieNLogLik__E 8 : 3 41__dmlc_registry_file_tag_ 22 1 9ListEntry7CmpPredERKS1_S3_ 4__dmlc_registry_file_tag_aft_obj__Ev 6__dmlc_registry_file_tag_hinge_obj__Ev 5__dmlc_registry_file_tag_rank_obj__Ev ; < multiclass_obj__Ev regression_obj__Ev = SoftmaxMultiClassParam11__MANAGER__Ev PoissonRegressionParam11__MANAGER__Ev TweedieRegressionParam11__MANAGER__Ev = ObjFunctionRegEE3GetEv TreeUpdaterRegEE3GetEv > > > 5LambdaRankParam11__MANAGER__Ev 2RegLossParam11__MANAGER__Ev > > @ @ @ @ 40__dmlc_registry_file_tag_cpu_predictor__Ev 9PredValueERKNS_6common4SpanIKNS_5EntryELm18446744073709551615EEERKNSt3__16vectorINS8_10unique_ptrINS_7RegTreeENS8_14default_deleteISB_EEEENS8_9allocatorISE_EEEERKNS9_IiNSF_IiEEEEiPNSB_4FVecEjj D D F 19ClearExpiredEntriesEv 5 9ContainerEv F CacheENSt3__110shared_ptrINS_7DMatrixEEEi EntryEPNS_7DMatrixE F F F 9ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE 6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE F иF 9 5ResetEv ConfigureERKNS_4tree10TrainParamEj SplitImplEijii F F F G G G 1 4 22CPUHistMakerTrainParam11__MANAGER__Ev 39__dmlc_registry_file_tag_updater_sync__Ev 0TrainParam11__MANAGER__Ev 8ColMakerTrainParam11__MANAGER__Ev 7QuantileHistMaker G G 6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_10FeatureMapEb 5MatchERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS1_3mapIS7_S7_NS1_4lessIS7_EENS5_INS1_4pairIS8_S7_EEEEEE G G 9 5EqualERKS0_ ր1 ܀4SaveEPN4dmlc6StreamE 2 8TreeShapERKNS0_4FVecEPfjjPNS_11PathElementEffiijf DumpModelERKNS_10FeatureMapEbNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE ЀSaveModelEPNS_4JsonE ʄ G G 2GetNumLeavesEv 6GetNumSplitNodesEv G G 1 4LoadEPN4dmlc6StreamE 20LoadCategoricalSplitERKNS_4JsonE 9LoadModelERKNS_4JsonE Ą5kRootE 0ExpandNodeEijfbfffffffi 7 8FillNodeMeanValuesEv Є G ExpandCategoricalEijNS_6common4SpanIjLm18446744073709551615EEEbfffffff FillNodeMeanValueEi ք H ЃH H H 0SaveCategoricalSplitEPNS_4JsonE 8CalculateContributionsApproxERKNS0_4FVecEPf ܄2CalculateContributionsERKNS0_4FVecEPfij зH H УI I I I I I I J J L P P 3__dmlc_registry_file_tag_updater_colmaker__Ev 4__dmlc_registry_file_tag_updater_histmaker__Ev 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FindSplitConditionsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_7RegTreeERKNS_6common16GHistIndexMatrixEPNS5_IiNS7_IiEEEE LossGuideERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEE DepthWiseERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEE W W BuildHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS_6common16RowSetCollection4ElemERKNSE_16GHistIndexMatrixERKNSE_21GHistIndexBlockMatrixENSE_4SpanIS8_Lm18446744073709551615EEE LossGuideENS3_11ExpandEntryES4_ W ubtractionTrickENS_6common4SpanINS_6detail20GradientPairInternalIfEELm18446744073709551615EEES9_S9_ etHistRowsAdderEPNS0_13HistRowsAdderIfEE W 1UpdatePredictionCacheEPKNS_7DMatrixEPNS_16HostDeviceVectorIfEE 6SplitContainsMissingValuesENS0_9GradStatsERKNS1_9NodeEntryE 0BuildLocalHistogramsERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSD_9allocatorISH_EEEE ͨ2EvaluateAndApplySplitsERKNS_6common16GHistIndexMatrixERKNS4_12ColumnMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSF_9allocatorISH_EEEE ٨4BuildHistogramsLossGuideENS3_11ExpandEntryERKNS_6common16GHistIndexMatrixERKNS5_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSE_9allocatorISI_EEEE ߨW W W ExpandEntryC ߤInitNewNodeEiRKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE 2Eiiifj 1Eiiifj W W fE11ExpandEntry7IsValidERKNS0_10TrainParamEi dE11ExpandEntry7IsValidERKNS0_10TrainParamEi W W W 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0BuildLocalHistogramsERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSD_9allocatorISH_EEEE 2EvaluateAndApplySplitsERKNS_6common16GHistIndexMatrixERKNS4_12ColumnMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSF_9allocatorISH_EEEE 4BuildHistogramsLossGuideENS3_11ExpandEntryERKNS_6common16GHistIndexMatrixERKNS5_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSE_9allocatorISI_EEEE Y Y Y ExpandEntryC InitNewNodeEiRKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE 2Eiiifj 1Eiiifj Y Y Y Y Y EvaluateSplitsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSC_14HistCollectionIdEERKNS_7RegTreeE BuildNodeStatsERKNS_6common16GHistIndexMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSC_9allocatorISG_EEEE Z Z Z Z Z Z Z Z Z Z Z AddSplitsToTreeERKNS_6common16GHistIndexMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSC_9allocatorISE_EEEESI_ PartitionKernelI Z hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE tEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE jEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE Ѐ[ [ [ hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE tEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE jEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE [ [ [ [ [ [ [ \ ^ _ ` a 1 4InitEiPPc 8FinalizeEv 9 3AllreduceBase 0Allreduce_EPvmmPFvPKvS1_iRKN3MPI8DatatypeEENS0_3mpi8DataTypeENSA_6OpTypeEPFvS1_ES1_ 2ReduceHandle C 4InitEiPPc 1 8S 20TryReduceScatterRingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE 2Ev 1Ev a a a 4ReConnectLinksEPKc 2Tr 6TryAll a hutdownEv 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7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE u u u 1 9 4ResetPartitionEjj 3ReadIndexFileEPNS0_10FileSystemERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE 1 u u u u u ReadChunkEPvPm Next u ChunkEPNS_10InputSplit4BlobE BatchEPNS_10InputSplit4BlobEm u NextBatchExEPNS0_14InputSplitBase5ChunkEm BeforeFirstEv u u u 4 1 D 8StripEndENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEc 9ReadChunkEPvPm 5Chunk InitEPNS0_10FileSystemEPKcmb ReadEPvm u 7InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb 4ResetPartitionEjj 1BeforeFirstEv 3ConvertToURIsERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 6ExtractNextChunkEPNS_10InputSplit4BlobEPNS1_5ChunkE u u u 2Ev 1Ev 0Ev u u u u u v v 4LoadEPS1_m 6AppendEPS1_m v еv v y y 1 4OpenERKNS0_3URIEPKcb 1 3ListDirectoryERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE GetPathInfoERKNS0_3URIE OpenForReadERKNS0_3URIEb z z z z N ZN PF F 7xgboost St3__1 4dmlc 5rabit 4 6 1 9 8JsonNullE 5 7 3 20LearnerConfigurationE ǵ data tree 1 6detail19SingleBatchDataIterINS0_ 2 5IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE 2D 0CS 6 1FileAdapterE 3SimpleDMatrixE 7SparsePageDMatrixE 9SparsePageRawFormatINS_ Ä MatrixProxyE enseAdapterE ̍Ä 2 1 ņ6__ Ӂ 0__ Ā3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE ˄6__end_marked_subexpressionIcEE ҄1__empty_non_own_stateIcEE shared_ptr_ match_char_collateIcNS_12regex_traitsIcEEEE bracket_expressionIcNS_12regex_traitsIcEEEE Ą pointerIPN فemplaceIN7xgboost1 7xgboost S_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 ك7DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoENS_14default_deleteIS5_EENS_9allocatorIS5_EEEE 7BatchIteratorImplINS1_ 0SparsePageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE ߉ 0SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 3SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 1EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE Ą
-4 9basic_ 0 3 8 7 ՟5 ε1__ 6__ 2__match_charIcEE default_deleteIN Ӈbasic_ifstreamIcNS_11char_traitsIcEEEE 7xgboost S_13__empty_stateIcEEEE 1 ψ7DMatrixEEE 4data1 ݑ6common16PartitionBuilderILm2048EE9BlockInfoEEE 7BatchIteratorImplINS1_ 0SparsePageEEE 1 7CSCPageEEEEE 0SparsePageEEEEE ؉3SortedCSCPageEEEEE 1EllpackPageEEEEE ń ń Ƅ DŽ Ȅ Ȅ Ʉ RAdapterE CAdapterE ʄ 1 ъ21DataTableAdapterBatchEEE 5CS 7DenseAdapterBatchEEE Ӎ RAdapterBatchEEE CAdapterBatchEEE ʄ 8DataIterIN 5ErrorE 9parameter1 1 7istream 6 4data 2io 7xgboost S 4data 1 7CSCPageEEE 1 ̌21DataTableAdapterBatchEEE 5CS 7DenseAdapterBatchEEE ڍ6FileAdapterBatchEEE RAdapterBatchEEE CAdapterBatchEEE ō˄ ˄ ˄ ̄ ̄ ̄ ̈́ DataTableAdapterE SparsePageFormatINS_ ˱̈́ ΄ ΄ istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE τ 5 6engine utils1 ʏc_api1 8MemoryBufferStreamE 9MemoryFixSizeBufferE τ Є Є 2D ؐ0CSRAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE MatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ֑enseAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ф 2D 0CSRAdapterEEE MatrixProxyEEE enseAdapterEEE ҄ ҄ ӄ shared_ptrIN7xgboost4data1 ؒ__ Ӣ 2DenseAdapterEEE 0CSRAdapterEEE ӄ Ԅ Ԅ Մ 8kFloatPow5InvSplitE ˓5kFloatPow5SplitE ғ Մ ل ߄ common linear metric MetricE ˽ 1 6ColumnI 1DenseColumnI 2SparseColumnI 6PeekableInStreamE 5FixedSizeStreamE hEE tEE ֕jEE hEE tEE ݕjEE hEE ϕtEE jEE basic_filebufIcNS_11char_traitsIcEEEE Ֆ__empty_stateIcEE 0Json 1 7 5GradientBoosterE г2ConfigurableE 3 StringE ݗObjectE NumberE ReaderE WriterE JsonArrayE LearnerIOE predictor12CPUPredictorE PredictorE Json LearnerImplE ObjFunctionE TreeUpdaterE IntegerE BooleanE 6HostDeviceVectorIjEENS_9allocatorIS3_EEEE 0SparsePageENS_9allocatorIS2_EEEE 0FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI Ԝ4FieldEntryBaseINS0_10FieldEntryI 6FieldAccessEntryE ƶ N mEE ²jEE ͶiEE xEE fEE bEE dEE 7xgboost ٛSt3__1 6common27ProbabilityDistributionTypeEEE 1 0 2 ParamErrorE ͜SeekStreamE InputSplitE fEEfEE iEEiEE ǟjEEjEE ԶxEExEE mEEmEE dEEdEE fEEfEE iEEiEE ΟjEEjEE ۶bEEbEE xEExEE mEEmEE NSt3__1 dEEdEE ValueE ModelE ׳ basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ޞ__ DMatrixE LearnerE RegTreeE E 5InBufE bad_function_callE __ 4ExternalMemoryPrefetcherINS_ 3S 1 Ǡ7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE ThreadedIterIN ѡS RowBlockIterI 7xgboost S 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE copedThreadE ̢erializableE function6__ l_anchorIcEE r_anchorIcEE back_refIcEE funcI baseIF ѥ ZN PF 7xgboost4 ϣ4dmlc data24ExternalMemoryPrefetcherINS2_ tree1 1 7CSCPageEEC1ERKNS3_9CacheInfoEEUl 0SparsePageEEC1ERKNS3_9CacheInfoEEUl 3SortedCSCPageEEC1ERKNS3_9CacheInfoEEUl ® PPS5_E_NS_9allocatorISC_EEFbSB_EEE ʥvE_NS_9allocatorISA_EEFvvEEE b vvEEE PN7xgboost ϯ PPN N7xgboost4tree17QuantileHistMaker7BuilderI 7xgboost 4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI 1 7CSCPageEEEE 0SparsePageEEEE 3SortedCSCPageEEEE 7xgboost4 ˧4dmlc data24ExternalMemoryPrefetcherINS_ tree1 1 7CSCPageEEC1ERKNS0_9CacheInfoEEUl 0SparsePageEEC1ERKNS0_9CacheInfoEEUl 3SortedCSCPageEEC1ERKNS0_9CacheInfoEEUl PPS2_E_ vE_ impleBatchIteratorImplINS_ parseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_ 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1EllpackPageEEE BatchIteratorImplINS_ GraphvizGeneratorE 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1EllpackPageEEE 1 7CSCPageEEES3_EE 0SparsePageEEES3_EE 3SortedCSCPageEEES3_EE 1EllpackPageEEES3_EE ȯ PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ ́ PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ ք 䅅 4data16SparsePageFormatINS2_ 1 ɲ6MetricEPKcEEE 9PredictorEPKNS2_16GenericParameterEEEE 1 Ͱ7CSCPageEEEvEEE 0SparsePageEEEvEEE 3SortedCSCPageEEEvEEE 1 7CSCPageEEE 0SparsePageEEE ı3SortedCSCPageEEE ى 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE Ƌ ˌ ݎ ⏅ ѐ 5GradientBoosterEPKNS2_17LearnerModelParamEEEE 3 1 葅 gbm obj 8GBLinearE ɳ1 6GBTreeE 4DartE ֒ 3GBLinearModelE 1GBTreeModelE 5TreeProcessTypeEEE ´3 ɴ0TreeMethodEEE ו PredictorTypeEEE DataSplitModeEEE ˖ כ basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ ӂ StreamE ParserI ֟ 䡅 LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE ף 1 2 7CoordinateUpdaterE 5FeatureSelectorE 4ShotgunUpdaterE LinearUpdaterE T JsonGeneratorE 1 2 CyclicFeatureSelectorE GreedyFeatureSelectorE ӺRandomFeatureSelectorE ںѤ ShuffleFeatureSelectorE źThriftyFeatureSelectorE ̺ ǥ 1 7Eval 9EvalAucPRE 8Eval 2 3Eval ƻ4Eval 6EvalMultiLogLossE EWiseBaseINS0_ PrecisionE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE 1EvalRow 2EvalRowRMSLEEEE ҽ0EvalRowMAEEEE ٽ4EvalRowLogLossEEE 7EvalGammaDevianceEEE 6EvalGammaNLogLikEEE 8EvalTweedieNLogLikEEE RMSEEEE ĽM Ω APEEEE PHEEEE ɭ 쮅 ᲅ Ӷ M ľRankConfigE atchErrorE ClassBaseINS0_1 4EvalMatchErrorEEE 6EvalMultiLogLossEEE ˸ A ʿMAPE CoxE MSE ڿucE RankE NDCGE Ŀ ߿ Å 0AFTNLogLikDispatcherE 1EvalEWiseSurvivalBaseINS0_ ą 14EvalAFTNLogLikINS_6common 30EvalIntervalRegressionAccuracyEEE 1 20LogisticDistributionEEEEE 8NormalDistributionEEEEE 9ExtremeDistributionEEEEE ą Ņ ƅ Dž ObjFunctionEvEEE TreeUpdaterEvEEE ʅ 6AFTObjE 8HingeObjE 20SoftmaxMultiClassObjE 1 ʅ ʅ ˅ ͅ 3 0RegLossObjINS0_ 7 5GammaRegressionE LambdaRankObjINS0_2 CoxRegressionE 8PairwiseLambdaWeightComputerEEE 4NDCGLambdaWeightComputerEEE 3MAPLambdaWeightComputerEEE υ х ҅ 1 22LogisticClassificationEEE 6 5SquaredLogErrorEEE 8LogisticRegressionEEE 1LogisticRawEEE LinearSquareLossEEE PseudoHuberErrorEEE ԅ Յ օ م څ PoissonRegressionE TweedieRegressionE ܅ ݅ ޅ 6vectorIiNS2_9allocatorIiEEEEEE 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS3_9allocatorIiEEEEEES7_EE 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE extGeneratorE reeGeneratorE 8ColMaker 1 9 2 E 7BuilderE 1 0TreePrunerE 7QuantileHistMaker 6HistSynchronizerI 3 8BatchHistRowsAdderI CQHistMakerE TreeSyncherE HistMakerE BaseMakerE 1CQHistMaker10CreateHistERKNS_6vectorINS2_6detail20GradientPairInternalIfEENS_9allocatorIS8_EEEEPNS2_7DMatrixERKNS5_IjNS9_IjEEEERKNS2_7RegTreeEEUlvE_NS9_ISN_EEFvvEEE 3TreeRefresher6UpdateEPNS2_16HostDeviceVectorINS2_6detail20GradientPairInternalIfEEEEPNS2_7DMatrixERKNS_6vectorIPNS2_7RegTreeENS_9allocatorISF_EEEEEUlvE_NSG_ISL_EEFvvEEE 1CQHistMaker10CreateHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS2_9allocatorIS6_EEEEPNS_7DMatrixERKNS3_IjNS7_IjEEEERKNS_7RegTreeEEUlvE_ 3TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKNSt3__16vectorIPNS_7RegTreeENSA_9allocatorISD_EEEEEUlvE_ 3GlobalProposalHistMakerE 7DistributedHistSynchronizerI 4DistributedHistRowsAdderI 1BatchHistSynchronizerI E 7BuilderI fEE dEE fEE dEE fEE dEE fEE dEE HistRowsAdderI TreeRefresherE fEE dEE fEE dEE fEE dEE bN7xgboost4tree17QuantileHistMaker7BuilderI vvENS_9allocatorIS3_EES2_EE fE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE dE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE fE11ExpandEntryES7_EEE dE11ExpandEntryES7_EEE bN7xgboost4tree17QuantileHistMaker7BuilderI vvE ǀ fE11ExpandEntryES4_E dE11ExpandEntryES4_E bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E π 傆 ڃ 扆 13AllreduceBaseE 7IEngineE 1ReadWrapperE 2WriteWrapperE 1 9CSVParserI 1 2 4T 0ParserImplI DiskRowIterI LibFMParserI j y fEE iEE xEE j y fEE iEE xEE _ t3__16vectorINS_4data17RowBlockContainerI 8RowBlockI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE Ɏ _ t3__16vectorINS_4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE 펆 j y fEEEE iEEEE xEEEE 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ؏ 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEEE j y fEEEEE iEEEEE xEEEEE ߐ 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIjfEEE_ vE_ ޒ BasicRowIterI LibSVMParserI j y fEE iEE xEE fEE iEE xEE fEE iEE xEE ϓ fEEEE iEEEE xEEEE 铆 fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE Ŕ fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIyfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE fEEEEE iEEEEE xEEEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIyfEEE_ vE_ Ŗ fEE iEE xEE ј 噆 PPNS3_17RowBlockContainerIjiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIjiEEE_ vE_ 囆 ҝ ͞ PPNS3_17RowBlockContainerIyiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIyiEEE_ vE_ ֡ ɣ PPNS3_17RowBlockContainerIjxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ؤ ߥ PPNS0_17RowBlockContainerIjxEEE_ vE_ ާ Ϩ 騆 ũ PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIyxEEE_ vE_ ū jfEE yfEE ѭ extParserBaseI hreadedParserI j y fEE iEE xEE j y fEE iEE xEE j y fEE iEE xEE jfEE yfEE jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ⮆ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE jfEC1EPNS0_10ParserImplIjfEEEUl yfEC1EPNS0_10ParserImplIyfEEEUl PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ ݴ fEE iEE xEE fEE iEE xEE fEE iEE xEE ͵ ˶ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ jfEE yfEE 黆 j y fEE iEE xEE fEE iEE xEE ۼ ǽ ⽆ ξ ﾆ ۿ 1 23IndexedRecordIOSplitterE 5 8ThreadedInputSplitE 6 2LineSplitterE Հ4InputSplitBaseE 0FileS SingleFileSplitE LocalFileSystemE Ѕ 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS7_EEFvvEEE 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS2_5ChunkEE_ vE_ Æ Æ Ć CachedInputSplitE RecordIOSplitterE ܀ņ 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE vE_NS_9allocatorIS5_EEFvvEEE ņ 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ vE_ Ɔ dž Ȇ Ȇ Ɇ ʆ ʆ ʆ ʆ ʆ ˆ ˆ end_stateIcEE ́lookaheadIcNS_12regex_traitsIcEEEE alternateIcEE match_anyIcEE ˆ nodeIcEE loopIcEE لˆ ̆ ̆ ͆ owns_one_stateIcEE ̂back_ref_icaseIcNS_12regex_traitsIcEEEE ͆ has_one_stateIcEE word_boundaryIcNS_12regex_traitsIcEEEE ͆ ͆ Ά Ά Ά φ match_char_icaseIcNS_12regex_traitsIcEEEE back_ref_collateIcNS_12regex_traitsIcEEEE φ φ І І І ц ц ц ҆ ҆ owns_two_statesIcEE repeat_one_loopIcEE ҆ ҆ ӆ ӆ ӆ ystemE ɅtreamE ׅ ӆ ӆ Ԇ N ZN бF PF 4dmlc 7xgboost St3__1 5rabit 5ErrorE 8DataIterIN 6 1 Η7istream 9parameter1 4data 2io 7xgboost S 4data 1 7CSCPageEEE 1 Ї21DataTableAdapterBatchEEE 6FileAdapterBatchEEE 5CS 7DenseAdapterBatchEEE ϖ 4 ߈6 ՛5 1 9 8JsonNullE 7 ũ3 20LearnerConfigurationE data tree 1 6detail19SingleBatchDataIterINS0_ 2 ժ 5IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE ʊ2D ъ0CS 6 1FileAdapterE 3SimpleDMatrixE 7SparsePageDMatrixE 9SparsePageRawFormatINS_ ȼ MatrixProxyE enseAdapterE ݖؾ 2 1 6__ 0__ 3__match_any_but_newlineIcEE ď8__begin_marked_subexpressionIcEE ؐ6__end_marked_subexpressionIcEE ߐ1__empty_non_own_stateIcEE shared_ptr_ match_char_collateIcNS_12regex_traitsIcEEEE bracket_expressionIcNS_12regex_traitsIcEEEE ѐ pointerIPN emplaceIN7xgboost1 7xgboost S_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoENS_14default_deleteIS5_EENS_9allocatorIS5_EEEE 7BatchIteratorImplINS1_ 0SparsePageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 А7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 0SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 3SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 1EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE
-9basic_ 5 0 4basic_ifstreamIcNS_11char_traitsIcEEEE 3 8 ʨ7 1__ 6__ 2__match_charIcEE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ Č RAdapterBatchEEE CAdapterBatchEEE Č 1 ĕ21DataTableAdapterBatchEEE 5CS 7DenseAdapterBatchEEE ֖ RAdapterBatchEEE CAdapterBatchEEE Č RAdapterE CAdapterE ȖŌ Ō Ō ƌ ƌ ƌ nj nj Ȍ DataTableAdapterE SparsePageFormatINS_ Ȍ ʌ StreamE ǗParserI ʌ 0 ڗ2 ī SeekStreamE ParamErrorE InputSplitE ʌ 5 6engine utils1 c_api1 9MemoryFixSizeBufferE 8MemoryBufferStreamE ˌ ˌ E 5InBufE ͌ Ό ό 2D 0CSRAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE Ǜ MatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE enseAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ό Ќ shared_ptrIN7xgboost4data1 __ 2DenseAdapterEEE 0CSRAdapterEEE ΛЌ ь ь common linear MetricE metric 6ColumnI 1 hEE tEE jEE ҝь 1DenseColumnI 2SparseColumnI 6PeekableInStreamE 5FixedSizeStreamE hEE tEE ĝjEE ٝҌ hEE tEE ˝jEE Ҍ Ҍ ӌ ӌ ӌ Ԍ Ԍ 7xgboost 5rabit6engine13AllreduceBaseE 4dmlc2io ŋ 6common1 ڞ1 9 Š8JsonNullE 7 4 ʳ3gbm 5FixedSizeStreamE 6PeekableInStreamE Ԍ Ռ Ռ Ռ basic_filebufIcNS_11char_traitsIcEEEE __empty_stateIcEE ، 0Json 1Json ObjectE StringE NumberE ReaderE WriterE ، JsonArrayE PredictorE ٌ ٌ ValueE ModelE ڌ 0Json ϡ1 7 2ConfigurableE 5GradientBoosterE 3 StringE ObjectE ޢNumberE ReaderE WriterE ڌ JsonArrayE עLearnerIOE predictor12CPUPredictorE PredictorE ڌ ڌ ی ی IntegerE BooleanE ی Json ΣLearnerImplE ObjFunctionE TreeUpdaterE IntegerE BooleanE ܌ ܌ ތ ތ ތ ߌ 6HostDeviceVectorIjEENS_9allocatorIS3_EEEE 0SparsePageENS_9allocatorIS2_EEEE 6FieldAccessEntryE 4FieldEntryBaseINS0_10FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI ٦0FieldEntryI iEEiEE ҦfEEfEE NSt3__1 mEEmEE bEEbEE jEEjEE xEExEE dEEdEE iEEiEE fEEfEE mEEmEE jEEjEE xEExEE dEEdEE iEE ֧N ݧfEE èmEE bEE jEE xEE dEE 7xgboost St3__1 6common27ProbabilityDistributionTypeEEE 1 basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ ˏ DMatrixE LearnerE RegTreeE DMatrixE LearnerE RegTreeE bad_function_callE __ 0SparsePageEEE Ϊ3SortedCSCPageEEE 4ExternalMemoryPrefetcherINS_ 3S 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE ThreadedIterIN S ǬRowBlockIterI 7xgboost S 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE copedThreadE erializableE function6__ l_anchorIcEE r_anchorIcEE back_refIcEE ʐ baseIF ƭfuncI b vvEEE PN7xgboost ̺ PPN N7xgboost4tree17QuantileHistMaker7BuilderI 7xgboost ߮4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI 1 7CSCPageEEEE 0SparsePageEEEE 3SortedCSCPageEEEE ZN PF 7xgboost4 ί4dmlc data24ExternalMemoryPrefetcherINS2_ tree1 1 7CSCPageEEC1ERKNS3_9CacheInfoEEUl 0SparsePageEEC1ERKNS3_9CacheInfoEEUl 3SortedCSCPageEEC1ERKNS3_9CacheInfoEEUl ƹ PPS5_E_NS_9allocatorISC_EEFbSB_EEE ɱvE_NS_9allocatorISA_EEFvvEEE 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS_ tree1 1 Ų7CSCPageEEC1ERKNS0_9CacheInfoEEUl 0SparsePageEEC1ERKNS0_9CacheInfoEEUl 3SortedCSCPageEEC1ERKNS0_9CacheInfoEEUl PPS2_E_ vE_ ó data1 tree17QuantileHistMaker 3SimpleDMatrixE 7SparsePageDMatrixE BatchIteratorImplINS_ ۴GraphvizGeneratorE 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1EllpackPageEEE impleBatchIteratorImplINS_ parseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_ 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1EllpackPageEEE 1 7CSCPageEEES3_EE 0SparsePageEEES3_EE 3SortedCSCPageEEES3_EE 1EllpackPageEEES3_EE ź PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ 4data16SparsePageFormatINS2_ 1 6MetricEPKcEEE 9PredictorEPKNS2_16GenericParameterEEEE 1 ʻ7CSCPageEEEvEEE 0SparsePageEEEvEEE 3SortedCSCPageEEEvEEE 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 聍 肍 ȃ ȅ 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE 6vectorIiNS3_9allocatorIiEEEEEES7_EE 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS2_9allocatorIiEEEEEE Љ 5GradientBoosterEPKNS2_17LearnerModelParamEEEE 3 1 Ȍ ، 茍 gbm obj 8GBLinearE 1 6GBTreeE 4DartE 1 6GBTreeE 3GBLinearModelE 1GBTreeModelE ȍ 3GBLinearModelE 1GBTreeModelE 5TreeProcessTypeEEE 3 0TreeMethodEEE PredictorTypeEEE DataSplitModeEEE 蘍 Х 襍 LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE LinearUpdaterE T JsonGeneratorE 1 2 7CoordinateUpdaterE 5FeatureSelectorE 4ShotgunUpdaterE ઍ 1 2 CyclicFeatureSelectorE GreedyFeatureSelectorE RandomFeatureSelectorE ShuffleFeatureSelectorE ThriftyFeatureSelectorE Ȭ 讍 词 ذ 1 7Eval 9EvalAucPRE 8Eval 2 3Eval 4Eval 6EvalMultiLogLossE EWiseBaseINS0_ PrecisionE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE 1EvalRow 2EvalRowRMSLEEEE 0EvalRowMAEEEE 4EvalRowLogLossEEE 7EvalGammaDevianceEEE 6EvalGammaNLogLikEEE 8EvalTweedieNLogLikEEE RMSEEEE M в APEEEE PHEEEE й M RankConfigE ClassBaseINS0_1 atchErrorE 4EvalMatchErrorEEE 6EvalMultiLogLossEEE č č ƍ ƍ A MAPE CoxE MSE ucE ȍ ɍ ˍ ͍ RankE NDCGE ͍ ͍ Ѝ ҍ Ӎ 0AFTNLogLikDispatcherE 1EvalEWiseSurvivalBaseINS0_ Ս 14EvalAFTNLogLikINS_6common 30EvalIntervalRegressionAccuracyEEE 1 20LogisticDistributionEEEEE 8NormalDistributionEEEEE 9ExtremeDistributionEEEEE ֍ ٍ ObjFunctionEvEEE TreeUpdaterEvEEE ڍ ۍ 6AFTObjE 8HingeObjE 20SoftmaxMultiClassObjE 1 ۍ ݍ ߍ 3 0RegLossObjINS0_ 7 5GammaRegressionE LambdaRankObjINS0_2 CoxRegressionE 8PairwiseLambdaWeightComputerEEE 4NDCGLambdaWeightComputerEEE 3MAPLambdaWeightComputerEEE 1 22LogisticClassificationEEE 6 5SquaredLogErrorEEE 8LogisticRegressionEEE 1LogisticRawEEE LinearSquareLossEEE PseudoHuberErrorEEE PoissonRegressionE TweedieRegressionE ȁ Ђ reeGeneratorE extGeneratorE ؆ ؈ ؉ ؊ 8ColMaker 9 1 2 E 7BuilderE BaseMakerE HistMakerE Ѝ 荎 1 0TreePrunerE 7QuantileHistMaker 6HistSynchronizerI 3 8BatchHistRowsAdderI CQHistMakerE TreeSyncherE 1CQHistMaker10CreateHistERKNS_6vectorINS2_6detail20GradientPairInternalIfEENS_9allocatorIS8_EEEEPNS2_7DMatrixERKNS5_IjNS9_IjEEEERKNS2_7RegTreeEEUlvE_NS9_ISN_EEFvvEEE 3TreeRefresher6UpdateEPNS2_16HostDeviceVectorINS2_6detail20GradientPairInternalIfEEEEPNS2_7DMatrixERKNS_6vectorIPNS2_7RegTreeENS_9allocatorISF_EEEEEUlvE_NSG_ISL_EEFvvEEE ؐ 1CQHistMaker10CreateHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS2_9allocatorIS6_EEEEPNS_7DMatrixERKNS3_IjNS7_IjEEEERKNS_7RegTreeEEUlvE_ 3TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKNSt3__16vectorIPNS_7RegTreeENSA_9allocatorISD_EEEEEUlvE_ 3GlobalProposalHistMakerE 7DistributedHistSynchronizerI 4DistributedHistRowsAdderI 1BatchHistSynchronizerI 7BuilderI E fEE dEE Е E 7BuilderI fEE dEE Ж fEE dEE fEE dEE HistRowsAdderI TreeRefresherE fEE dEE ؗ fEE dEE 藎 fEE dEE fEE dEE 蘎 Ț fE11ExpandEntryES7_EEE dE11ExpandEntryES7_EEE bN7xgboost4tree17QuantileHistMaker7BuilderI vvENS_9allocatorIS3_EES2_EE fE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE dE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E 7IEngineE 13AllreduceBaseE Ц 1ReadWrapperE 2WriteWrapperE _ t3__16vectorINS_4data17RowBlockContainerI 8RowBlockI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE Ӆ j y fEEEE iEEEE xEEEE Ȩ j y fEE iEE xEE ب 1 9CSVParserI 1 2 0ParserImplI 4T DiskRowIterI LibFMParserI Â j y fEE iEE xEE j y fEEEE iEEEE xEEEE _ t3__16vectorINS_4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE څ j y fEEEE iEEEE xEEEE Щ 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEEE j y fEEEEE iEEEEE xEEEEE 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE Ъ 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIjfEEE_ vE_ 誎 Ы 諎 BasicRowIterI LibSVMParserI j y fEE iEE xEE fEEEE iEEEE xEEEE fEE iEE xEE fEE iEE xEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE fEEEEE iEEEEE xEEEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIyfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIyfEEE_ vE_ fEE iEE xEE ر 豎 в ಎ г PPNS3_17RowBlockContainerIjiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIjiEEE_ vE_ എ ȵ ȶ PPNS3_17RowBlockContainerIyiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIyiEEE_ vE_ 躎 ໎ ༎ PPNS3_17RowBlockContainerIjxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIjxEEE_ vE_ ؾ ؿ PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE PPNS0_17RowBlockContainerIyxEEE_ vE_ Î j y fEE iEE xEE Ď j y fEE iEE xEE Ď extParserBaseI hreadedParserI j y fEE iEE xEE Ď jfEE yfEE Ď jfEE yfEE Ǝ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE Ȁǎ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE πǎ jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE րȎ jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl ݀ PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE Ȏ jfEC1EPNS0_10ParserImplIjfEEEUl yfEC1EPNS0_10ParserImplIyfEEEUl ܁ PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ Ȏ Ɏ Ɏ fEE iEE ǃxEE ʎ fEE iEE xEE ʎ fEE iEE ՃxEE ʎ ʎ ̎ ͎ ͎ Ύ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE ՁvE_NS_9allocatorIS9_EEFvvEEE Ύ PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ Ύ ώ ώ jfEE ՂyfEE ܂Ў ю j y fEE iEE xEE Ҏ fEE iEE ܃xEE Ҏ ӎ ӎ Ԏ Ԏ ֎ ֎ ֎ ֎ ؎ َ َ َ ێ ێ ێ ێ ގ 1 DŽ23IndexedRecordIOSplitterE 5 8ThreadedInputSplitE ̅6 2LineSplitterE Ԍ4InputSplitBaseE 0FileS Ǒ SingleFileSplitE ŅLocalFileSystemE ގ ߎ ߎ ߎ 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS7_EEFvvEEE 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl ׇ6CachedInputSplit1 PPNS2_5ChunkEE_ vE_ CachedInputSplitE RecordIOSplitterE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE vE_NS_9allocatorIS5_EEFvvEEE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ vE_ 1 23IndexedRecordIOSplitterE 2LineSplitterE ͌6RecordIOSplitterE ی4InputSplitBaseE 0FileSystemE 5LocalFileSystemE nodeIcEE loopIcEE end_stateIcEE lookaheadIcNS_12regex_traitsIcEEEE alternateIcEE match_anyIcEE 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+RabitCheckpoint
+ JsonConfig Model
+RabitCheckpoint
+ pdateOneIter nserializeFromBuffer
+ From D C Matrix ense SR
+UDA
+ Array
+Columnar
+ FromBuffer
+ М Ex WithFeatures WithFeatures Names ZN N 18XGBAPISetLastErrorPKc T StrsRNSt3__113basic_istreamIcNS_11char_traitsIcEEEERNS_6vectorIiNS_9allocatorIiEEEE ʅG 7xgboost 4dmlc ǡK7xgboost ұ 4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_E4NextEvENUlS2_S3_E_8__invokeES2_S3_ 1 20ArrayInterfaceErrors9DimensionEiE3str ܯ 9LogCallbackRegistryC1EvENUlPKcE_8__invokeES2_ 3 7GraphvizGenerator 4dmlc 7xgboost K -5rabit 3any11TypeOnStackINSt3__110shared_ptrIN7xgboost4data1 8RegistryIN ί1 4data1 6 2io 2ArrayAdapterEEEE 5CSRArrayAdapterEEEE 7destroyEPNS0_4DataE 16create_from_dataEPNS0_4DataERKS9_ 7destroyEPNS0_4DataE 16create_from_dataEPNS0_4DataERKS9_
+6 1 )9 8 4 ÜlsERNSt3__113basic_ostreamIcNS0_11char_traitsIcEEEENS_10StringViewE 7 5Entry8CmpValueERKS0_S2_ 3 2 detail1 common linear metric Metric6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE 6 9ToCharsUnsignedImplEPcS1_y 7FromCharFloatImplEPKciPf 4RyuPowLogUtils1 Ф ToCharsFloatImplEfPc ItoaUnsignedImplEPcjy 1 21 8 5Index1 )7Monitor 2G 4AllVisibleGPUsEv 3 5 6 (8LoadSequentialFileENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEb 9HostSketchContainer 1AddCutPointERKNS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerEiPNS0_13HistogramCutsE lobalRandomEv HistBuilderI InitilizeHistByZeroesI GHistIndexBlockMatrix4InitERKNS0_16GHistIndexMatrixERKNS0_12ColumnMatrixERKNS_4tree10TrainParamE ( fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEEmm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEEmm IncrementHistI HistogramCutsC (ColumnSampler9ColSampleENSt3__110shared_ptrINS_16HostDeviceVectorIjEEEEf fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm CopyHistI AFTParam11__MANAGER__Ev Prefetch15kNoPrefetchSizeE fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_mm SubtractionHistI FixedSizeStream fEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_S7_mm dEEvNS0_4SpanINS_6detail20GradientPairInternalIT_EELm18446744073709551615EEES7_S7_mm fE dE ! 9BuildHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS3_9allocatorIS7_EEEENS0_16RowSetCollection4ElemERKNS0_16GHistIndexMatrixENS0_4SpanIS7_Lm18446744073709551615EEEb !1 # 9BuildHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS3_9allocatorIS7_EEEENS0_16RowSetCollection4ElemERKNS0_16GHistIndexMatrixENS0_4SpanINS6_IdEELm18446744073709551615EEEb #1 % 4BuildBlockHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS3_9allocatorIS7_EEEENS0_16RowSetCollection4ElemERKNS0_21GHistIndexBlockMatrixENS0_4SpanIS7_Lm18446744073709551615EEE %6SubtractionTrickENS0_4SpanINS_6detail20GradientPairInternalIfEELm18446744073709551615EEES7_S7_ ' 4BuildBlockHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS3_9allocatorIS7_EEEENS0_16RowSetCollection4ElemERKNS0_21GHistIndexBlockMatrixENS0_4SpanINS6_IdEELm18446744073709551615EEE '6SubtractionTrickENS0_4SpanINS_6detail20GradientPairInternalIdEELm18446744073709551615EEES7_S7_ ' GHistIndexMatrix (PeekableInStream 11ResizeIndexEmb (4InitEPNS_7DMatrixEi ( 2Ev (1Ev (
+ 7GetValueFromUint8EPvm )8GetValueFromUint )
+ 16EPvm )32EPvm )
+
+ 6 *0 5 1 Ӓ9 7Learner 3 4UnwoundPathSumEPKNS_11PathElementEjj HostDeviceVectorI *GenericParameter fE *NS_ 4iE >hE FyE _jE gmE x C +D -aSEOS1_ -1 /8HostSpanEv 24 36 4 2E +1E , mfi ,St16initializer_listIfEi ,RKNSt3__16vectorIfNS2_9allocatorIfEEEEi ,OS1_ - mfi ,St16initializer_listIfEi ,RKNSt3__16vectorIfNS2_9allocatorIfEEEEi ,OS1_ - 2Ev -1Ev - 7xgboost -5rabit6engine13AllreduceBase14ConnectTrackerEv 1 .5Value7TypeStrEv 9JsonArrayeqERKNS_5ValueE 8 6common7Monitor 7 4 3gbm 6HostDeviceVectorI .0 1 fE /NS_ 8iE AhE IyE ajE jmE z 5EmptyEv /4SizeEv /9 /1 08HostSpanEv 2 DeviceIdxEv /SetDeviceEi 4 0 03DevicePointerEv 11HostPointerEv 2 DeviceSpanEv 0HostVectorEv 2 5Const 10 18ConstDevicePointerEv 13 16ConstHostPointerEv 21Host 22 44DeviceCanWriteEv 4 DeviceSpanEv 1HostVectorEv 2 DeviceSpanEv 1HostVectorEv 2 Device 2ConstHostSpanEv 2 PointerEv 2CanReadEv 4 PointerEv 3CanReadEv 4 FillEf 3CopyE 3 RK 3St16initializer_listIfE 3 S1_ 3NSt3__16vectorIfNS2_9allocatorIfEEEE 3
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6detail20GradientPairInternalIfEEE 511FeatureTypeEE N5EntryEE W7RegTree7SegmentEE o C 5D 7aSEOS4_ 81 98HostSpanEv <4 <6 = 2E 61E 6 mS3_i 6St16initializer_listIS3_Ei 7RKNSt3__16vectorIS3_NS5_9allocatorIS3_EEEEi 7OS4_ 7
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C GD HaSEOS1_ I1 I8HostSpanEv L4 M6 N 2E G1E G mhi GSt16initializer_listIhEi HRKNSt3__16vectorIhNS2_9allocatorIhEEEEi HOS1_ I
mhi HSt16initializer_listIhEi HRKNSt3__16vectorIhNS2_9allocatorIhEEEEi HOS1_ I
2Ev H1Ev H
5EmptyEv I4SizeEv I9 I1 J8HostSpanEv L
DeviceIdxEv ISetDeviceEi N
0 J3DevicePointerEv K1HostPointerEv L DeviceSpanEv JHostVectorEv L
5Const K0 K8ConstDevicePointerEv K3 K6ConstHostPointerEv L1Host L2 N4DeviceCanWriteEv N DeviceSpanEv KHostVectorEv L
DeviceSpanEv KHostVectorEv L
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HostCanWriteEv NDeviceAccessEv N
C OD QaSEOS2_ Q1 R8HostSpanEv T4 U6 V 2E O1E P mS1_i PSt16initializer_listIS1_Ei PRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi POS2_ Q
mS1_i PSt16initializer_listIS1_Ei PRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi QOS2_ Q
2Ev Q1Ev Q
5EmptyEv Q4SizeEv Q9 Q1 R8HostSpanEv T
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0 R3DevicePointerEv T1HostPointerEv T DeviceSpanEv RHostVectorEv T
5Const S0 S8ConstDevicePointerEv T3 T6ConstHostPointerEv T1Host U2 V4DeviceCanWriteEv W DeviceSpanEv SHostVectorEv U
DeviceSpanEv THostVectorEv T
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ExtendERKS2_ VResizeEmS1_ W HostCanWriteEv VDeviceAccessEv W Ѓ C WD YaSEOS2_ Y1 Z8HostSpanEv ]4 ]6 ^ 2E W1E X mS1_i XSt16initializer_listIS1_Ei YRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi YOS2_ Y mS1_i YSt16initializer_listIS1_Ei YRKNSt3__16vectorIS1_NS3_9allocatorIS1_EEEEi YOS2_ YЈ 2Ev Y1Ev Y А В 5EmptyEv Z4SizeEv Z9 Z1 [8HostSpanEv ] DeviceIdxEv ZSetDeviceEi _ 0 Z3DevicePointerEv \1HostPointerEv ] DeviceSpanEv [HostVectorEv ] 5Const [0 \8ConstDevicePointerEv \3 \6ConstHostPointerEv ]1Host ]2 _4DeviceCanWriteEv _ DeviceSpanEv \HostVectorEv ] DeviceSpanEv \HostVectorEv ] Device \ConstHostSpanEv ] PointerEv \CanReadEv _ Е Ж PointerEv ]CanReadEv ^ FillES1_ ]CopyE ] RK ^St16initializer_listIS1_E ^ S2_ ^NSt3__16vectorIS1_NS3_9allocatorIS1_EEEE ^ Р ExtendERKS2_ ^ResizeEmS1_ _ HostCanWriteEv _DeviceAccessEv _ C _D aaSEOS1_ a1 b8HostSpanEv e4 e6 f 2E `1E ` myi `St16initializer_listIyEi aRKNSt3__16vectorIyNS2_9allocatorIyEEEEi aOS1_ a myi aSt16initializer_listIyEi aRKNSt3__16vectorIyNS2_9allocatorIyEEEEi aOS1_ a Э а в 2Ev a1Ev a 5EmptyEv b4SizeEv b9 b1 c8HostSpanEv e DeviceIdxEv bSetDeviceEi g 0 c3DevicePointerEv d1HostPointerEv e DeviceSpanEv cHostVectorEv e 5Const d0 d8ConstDevicePointerEv d3 d6ConstHostPointerEv e1Host e2 g4DeviceCanWriteEv g DeviceSpanEv dHostVectorEv e DeviceSpanEv dHostVectorEv eз Device dConstHostSpanEv e PointerEv eCanReadEv g PointerEv eCanReadEv g FillEy fCopyE f RK fSt16initializer_listIyE f S1_ fNSt3__16vectorIyNS2_9allocatorIyEEEE f ExtendERKS1_ gResizeEmy g HostCanWriteEv gDeviceAccessEv g C hD iaSEOS1_ j1 j8HostSpanEv m4 m6 n 2E h1E h mji hSt16initializer_listIjEi iRKNSt3__16vectorIjNS2_9allocatorIjEEEEi iOS1_ i mji iSt16initializer_listIjEi iRKNSt3__16vectorIjNS2_9allocatorIjEEEEi iOS1_ i 2Ev i1Ev i 5EmptyEv j4SizeEv j9 j1 k8HostSpanEv m DeviceIdxEv jSetDeviceEi o 0 k3DevicePointerEv l1HostPointerEv m DeviceSpanEv kHostVectorEv m 5Const l0 l8ConstDevicePointerEv l3 l6ConstHostPointerEv m1Host m2 o4DeviceCanWriteEv o DeviceSpanEv lHostVectorEv m DeviceSpanEv lHostVectorEv m Device mConstHostSpanEv m PointerEv mCanReadEv o PointerEv mCanReadEv o FillEj nCopyE n RK nSt16initializer_listIjE n S1_ nNSt3__16vectorIjNS2_9allocatorIjEEEE n ExtendERKS1_ oResizeEmj o HostCanWriteEv oDeviceAccessEv o C pD raSEOS3_ r1 s8HostSpanEv u4 v6 w 2E p1E q mS2_i qSt16initializer_listIS2_Ei qRKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi qOS3_ r mS2_i qSt16initializer_listIS2_Ei qRKNSt3__16vectorIS2_NS4_9allocatorIS2_EEEEi qOS3_ r 2Ev r1Ev r 5EmptyEv r4SizeEv r9 r1 s8HostSpanEv u DeviceIdxEv sSetDeviceEi x 0 s3DevicePointerEv u1HostPointerEv u DeviceSpanEv sHostVectorEv u 5Const t0 t8ConstDevicePointerEv u3 u6ConstHostPointerEv u1Host u2 w4DeviceCanWriteEv w DeviceSpanEv tHostVectorEv u DeviceSpanEv uHostVectorEv u Device uConstHostSpanEv u PointerEv uCanReadEv w PointerEv vCanReadEv w FillES2_ vCopyE v RK vSt16initializer_listIS2_E w S3_ wNSt3__16vectorIS2_NS4_9allocatorIS2_EEEE w Ѐ ExtendERKS3_ wResizeEmS2_ x HostCanWriteEv wDeviceAccessEv x Ј C xD zaSEOS1_ z1 {8HostSpanEv }4 ~6 2E x1E y mmi ySt16initializer_listImEi zRKNSt3__16vectorImNS2_9allocatorImEEEEi zOS1_ z mmi ySt16initializer_listImEi zRKNSt3__16vectorImNS2_9allocatorImEEEEi zOS1_ z 2Ev z1Ev z 5EmptyEv z4SizeEv z9 {1 {8HostSpanEv } DeviceIdxEv {SetDeviceEi 0 {3DevicePointerEv }1HostPointerEv } DeviceSpanEv {HostVectorEv } 5Const |0 |8ConstDevicePointerEv }3 }6ConstHostPointerEv ~1Host ~2 4DeviceCanWriteEv DeviceSpanEv |HostVectorEv ~К DeviceSpanEv }HostVectorEv ~ Device }ConstHostSpanEv ~ PointerEv }CanReadEv Л М PointerEv ~CanReadEv FillEm ~CopyE ~ RK ~St16initializer_listImE S1_ NSt3__16vectorImNS2_9allocatorImEEEE ExtendERKS1_ ResizeEmm Щ Ъ HostCanWriteEv DeviceAccessEv 4ReadEPvm ̀8PeekReadEPvm Ҁ C 4 8PeekReadEPvm 2EPNS0_16PeekableInStreamE 1EPNS0_16PeekableInStreamE ReadEPvm SeekEm TakeEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE Json SparsePage ExtendPathEPNS_11PathElementEjffi UnwindPathEPNS_11PathElementEjj Writer ӃObject ߇String Number Reader Ԛ 4SaveENS_4JsonE 5VisitEPKNS_ 9JsonArrayE 1 8JsonNullE Ч 0Json 1Json ObjectE ߄NumberE StringE IntegerE BooleanE DummyJsonObjectEv هLoadFeatureTypeERKNSt3__16vectorINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS5_IS7_EEEEPNS1_INS_11FeatureTypeENS5_ISC_EEEE GradientBooster6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterEPKNS_17LearnerModelParamE C ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE 2EO 1EO S0_ NSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE ˊ S0_ ŊNSt3__13mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS_4JsonENS1_4lessIS8_EENS6_INS1_4pairIKS8_S9_EEEEEE ъ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json ؋SparsePage12GetTransposeEi ObjecteqERKNS_5ValueE StringeqERKNS_5ValueE NumbereqERKNS_5ValueE Reader5ErrorENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i JsonArray GPUMetric15CreateGPUMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE predictor Predictor TreeParam11__MANAGER__Ev օ C ixE ƐaSERKNS_5ValueE 4SaveEPNS_10JsonWriterE 2EOS0_ 1EOS0_ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i ixE aSERKNS_5ValueE ǒ4SaveEPNS_10JsonWriterE ͒ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json EllpackPage ObjFunction6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE TreeUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ȏLearnerImpl14kRandSeedMagicE Integer Boolean ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i Json EllpackPage4SizeEv IntegereqERKNS_5ValueE BooleaneqERKNS_5ValueE JsonNull ParseStrERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE MetaInfo ixE aSERKNS_5ValueE 4SaveEPNS_10JsonWriterE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i JsonNulleqERKNS_5ValueE MetaInfo ixE aSERKNS_5ValueE Ț4SaveEPNS_10JsonWriterE Κ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE i 5ParseEv 1 4LoadEv 9ParseNullEv С 0 1Parse 2ParseBooleanEv 3kMaxNumLengthE SkipSpacesEv ParseArrayEv StringEv ObjectEv NumberEv Json ݜdata tree ܅ 4 aSERKS0_ LoadE ȝDumpES0_PNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NS_10StringViewE PNS_10JsonReaderE C ̟1 9AllReduceEPNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_IiNS9_IiEEEE 8MakeCutsEPNS0_13HistogramCutsE 2ENSt3__16vectorImNS2_9allocatorImEEEEib 1ENSt3__16vectorImNS2_9allocatorImEEEEib 4CalcColumnSizeERKNS_10SparsePageEjm 1 6GatherSketchInfoERKNSt3__16vectorINS0_22QuantileSketchTemplateIffNS0_9WQSummaryIffEEE16SummaryContainerENS2_9allocatorIS8_EEEEPNS3_ImNS9_ImEEEESG_PNS3_INS6_5EntryENS9_ISH_EEEE LoadBalanceERKNS_10SparsePageEjm PushRowPageERKNS_10SparsePageERKNS_8MetaInfoE 2 1 ޑ6engine 5c_api9All op7ReducerINS0_ ץ4SerializeReducerFuncImplIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEEEvPKvPviRKN3MPI8DatatypeE 2SerializeReduceClosureIN7xgboost6common22QuantileSketchTemplateIffNS2_10WXQSummaryIffEEE16SummaryContainerEE6InvokeEPv 3 5BitORE SumE M mEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE axE inE mEEvPKvPviRKN3MPI8DatatypeE yEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE ĔcEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE 5StartERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ԫ4StopERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ګ 21CollectFromOtherRanksEv 15PrintStatisticsERKNSt3__13mapINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4pairImmEENS2_4lessIS9_EENS7_INSA_IKS9_SB_EEEEEE 5PrintEv Ы Version DMatrix Learner RegTree Ћ 4 6StringERKNSt3__15tupleIJiiiEEE ¯8kInvalidE LoadE S خ RKNS_4JsonE ̮PN4dmlc6StreamE Ү a elfEv veEPN meERKNSt3__15tupleIJiiiEEE ȯ S_4JsonE 4dmlc6StreamE 7xgboost S_16ParserFactoryRegI 4data19SparsePageFormatRegINS1_ 1 9MetricRegEE3GetEv 1 Ұ7CSCPageEEEE3GetEv 0SparsePageEEEE3GetEv 3SortedCSCPageEEEE3GetEv 1EllpackPageEEEE3GetEv 6CreateI D ٿ4LoadERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbbS9_m PvS2_FvS2_EFiS2_EEEPS0_T_T0_PT1_PT2_fii ڲNS_4data1 2DenseAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 0CS 6DataTableAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ۶1FileAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm 5 RAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ϶CAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ն CSRArrayAdapterEEEPS0_PT_fiRKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEm ոIteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPS0_PT_fiRKNSt3__112basic_stringIcNSD_11char_traitsIcEENSD_9allocatorIcEEEEm ۸ 4Push 7PushCSCERKS0_ INS_4data ERKS0_ 1 ù21DataTableAdapterBatchEEEyRKT_fi պ 7DenseAdapterBatchEEEyRKT_fi 5CS 6FileAdapterBatchEEEyRKT_fi ۺ RAdapterBatchEEEyRKT_fi ɺCAdapterBatchEEEyRKT_fi Ϻ 5ClearEv 1 7SetInfoEPKc 6ExtendERKS0_b 9kNumFieldE 1 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 7GetInfoEPKcPyNS_8DataTypeEPPKv 8ValidateEi 0SaveBinaryEPN4dmlc6StreamE 4GetFeatureInfoEPKcPNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEE 0LoadBinaryEPN4dmlc6StreamE 4SetFeatureInfoEPKcPS2_y PKvNS_8DataTypeEm ݾRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE DMatrix14GetThreadLocalEv ӿLearner19AllowLazyCheckPointEv RegTree 2Ev 1Ev 0Ev Ѐ Ў C D 12SetBaseRowIdEm 2E 1E v PNS_7DMatrixERKNS_10BatchParamE v PNS_7DMatrixERKNS_10BatchParamE 2Ev 1Ev 1 49__dmlc_registry_file_tag_sparse_page_raw_format__Ev 8DataPool 7 3SimpleDMatrix EllpackPageSourceC SparsePageDMatrix 2EPNS_7DMatrixERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKNS_10BatchParamE 1EPNS_7DMatrixERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKNS_10BatchParamE C 4InfoEv 5SliceENS_6common4SpanIKiLm18446744073709551615EEE 1 22GetSortedColumnBatchesEv 2 1 INS0_1 EPN4dmlc6StreamE 2DenseAdapterEEEPT_fi 0CS 5 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi INS0_1 EPN4dmlc6StreamE 2DenseAdapterEEEPT_fi 0CS 5 6DataTableAdapterEEEPT_fi 1FileAdapterEEEPT_fi RAdapterEEEPT_fi CAdapterEEEPT_fi RAdapterEEEPT_fi CAdapterEEEPT_fi CSRArrayAdapterEEEPT_fi IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi CSRArrayAdapterEEEPT_fi IteratorAdapterIPvFiS4_PFiS4_15XGBoostBatchCSRES4_ES5_EEEEPT_fi ! ! ! ! data tree17QuantileHistMaker7BuilderI 1 8DataPool5SliceENSt3__110shared_ptrINS_10SparsePageEEEmmm 3SimpleDMatrix4InfoEv 7SparsePageDMatrix4InfoEv М! ! 3GetRowBatchesEv 6GetColumnBatchesEv 7GetEllpackBatchesERKNS_10BatchParamE 5SaveToLocalFileERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE ! ! ! ! ! ! ! ! 4InfoEv 1 22GetSortedColumnBatchesEv ! ! 3GetRowBatchesEv 6GetColumnBatchesEv 7GetEllpackBatchesERKNS_10BatchParamE ! ! ! 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Є2 4 LinearUpdater6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE ConsoleLogger Tr GraphvizParam11__MANAGER__Ev 4 1 4 6LinearTrainParam11__MANAGER__Ev 5CoordinateParam11__MANAGER__Ev 4 4 5__dmlc_registry_file_tag_updater_coordinate__Ev 2__dmlc_registry_file_tag_updater_shotgun__Ev 4 6 6CustomLogMessage3LogERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 2 0 8TemporaryDirectory15RecursiveDeleteERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6 D 9 1 C 2Ev 1Ev 6 ShouldLogENS0_12LogVerbosityE ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE 6 6 ackerLoggerD eeGenerator 2Ev 1Ev 6 6 6 6DefaultVerbosityEv 5GlobalVerbosityEv 6 6 2E 1E NS0_12LogVerbosityE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE 6 NS0_12LogVerbosityE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS0_12LogVerbosityE 6 6 6 9BinaryAUCERKNSt3__16vectorIfNS1_9allocatorIfEEEES7_S7_ 1 4 3 2 6 3 5GroupRankingAUCENS_6common4SpanIKfLm18446744073709551615EEES4_f 0RankingAUCERKNSt3__16vectorIfNS1_9allocatorIfEEEERKNS_8MetaInfoE 2GPUBinaryAUCENS_6common4SpanIKfLm18446744073709551615EEERKNS_8MetaInfoEiPNSt3__110shared_ptrINS0_14DeviceAUCCacheEEE 9GPUMultiClassAUCOVRENS_6common4SpanIKfLm18446744073709551615EEERKNS_8MetaInfoEiPNSt3__110shared_ptrINS0_14DeviceAUCCacheEEE MultiClassOVRERKNSt3__16vectorIfNS1_9allocatorIfEEEERKNS_8MetaInfoE GPURankingAUCENS_6common4SpanIKfLm18446744073709551615EEERKNS_8MetaInfoEiPNSt3__110shared_ptrINS0_14DeviceAUCCacheEEE 7 7 7 7 7 7 5__ 4__dmlc_registry_file_tag_multiclass_metric__Ev 2__dmlc_registry_file_tag_survival_metric__Ev dmlc_registry_file_tag_elementwise_metric__Ev make_MetricReg_IntervalRegressionAccuracy__E 7 7 9 9 Ѐ: MetricGPURegEE3GetEv PredictorRegEE3GetEv : : 8__dmlc_registry_file_tag_rank_metric__Ev 2__make_MetricReg_ 5__make_MetricReg_PossionNegLoglik__E 1__make_MetricReg_ 3__make_MetricReg_TweedieNLogLik__E : < 3 41__dmlc_registry_file_tag_ 22 1 9ListEntry7CmpPredERKS1_S3_ 4__dmlc_registry_file_tag_aft_obj__Ev 6__dmlc_registry_file_tag_hinge_obj__Ev 5__dmlc_registry_file_tag_rank_obj__Ev > ? multiclass_obj__Ev regression_obj__Ev м? SoftmaxMultiClassParam11__MANAGER__Ev PoissonRegressionParam11__MANAGER__Ev TweedieRegressionParam11__MANAGER__Ev ? ObjFunctionRegEE3GetEv TreeUpdaterRegEE3GetEv Ю@ @ @ 5LambdaRankParam11__MANAGER__Ev 2RegLossParam11__MANAGER__Ev @ ГA B B B B 40__dmlc_registry_file_tag_cpu_predictor__Ev ݁9PredValueERKNS_6common4SpanIKNS_5EntryELm18446744073709551615EEERKNSt3__16vectorINS8_10unique_ptrINS_7RegTreeENS8_14default_deleteISB_EEEENS8_9allocatorISE_EEEERKNS9_IiNSF_IiEEEEiPNSB_4FVecEjj F F I 19ClearExpiredEntriesEv 5 9ContainerEv I CacheENSt3__110shared_ptrINS_7DMatrixEEEi EntryEPNS_7DMatrixE I I I 9ConfigureERKNSt3__16vectorINS1_4pairINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_EENS7_ISA_EEEE ߄6CreateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKNS_16GenericParameterE оI I 9 5ResetEv ConfigureERKNS_4tree10TrainParamEj SplitImplEijii ą I I I J J J 1 4 Ώ22CPUHistMakerTrainParam11__MANAGER__Ev 39__dmlc_registry_file_tag_updater_sync__Ev 0TrainParam11__MANAGER__Ev 8ColMakerTrainParam11__MANAGER__Ev ґ7QuantileHistMaker ֔ J J 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0BuildLocalHistogramsERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSD_9allocatorISH_EEEE 2EvaluateAndApplySplitsERKNS_6common16GHistIndexMatrixERKNS4_12ColumnMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSF_9allocatorISH_EEEE 4BuildHistogramsLossGuideENS3_11ExpandEntryERKNS_6common16GHistIndexMatrixERKNS5_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSE_9allocatorISI_EEEE \ Љ] ] ExpandEntryC InitNewNodeEiRKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE 2Eiiifj 1Eiiifj ] Њ] ] ] ] EvaluateSplitsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSC_14HistCollectionIdEERKNS_7RegTreeE BuildNodeStatsERKNS_6common16GHistIndexMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSC_9allocatorISG_EEEE ] ] ] ] ] ] ] ] ] ] ^ AddSplitsToTreeERKNS_6common16GHistIndexMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSC_9allocatorISE_EEEESI_ PartitionKernelI ^ hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE tEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE jEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE ^ ^ ^ hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE tEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE jEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE ^ ^ ^ ^ ^ ^ ^ _ _ a b c d 1 4InitEiPPc 8FinalizeEv 9 3AllreduceBase 0Allreduce_EPvmmPFvPKvS1_iRKN3MPI8DatatypeEENS0_3mpi8DataTypeENSA_6OpTypeEPFvS1_ES1_ 2ReduceHandle C 4InitEiPPc 1 8S 20TryReduceScatterRingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE 2Ev 1Ev d d d 4ReConnectLinksEPKc 2Tr 6TryAll d hutdownEv etParamEPKcS3_ e e ackerPrintERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE y e e AllreduceEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE BroadcastEPvmi e reduce gatherRingEPvmmmm RingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE TreeEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE e e f f f reduceEPvmNS_6engine3mpi8DataTypeENS3_6OpTypeEPFvS1_ES1_ gatherEPvmmmmi f f I Finalize Get TrackerPrint Broadcast All L CheckPoint VersionNumber nit sDistributed f f R WorldSize ProcessorName ingPrevRank ank f f f f f f f gather reduce f f oadCheckPoint inkTag f f f f f f f f f cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE mEEvPKvPviRKN3MPI8DatatypeE fEEvPKvPviRKN3MPI8DatatypeE dEEvPKvPviRKN3MPI8DatatypeE Ѐg g g g g g g g Лg g g g g cEEvPKvPviRKN3MPI8DatatypeE hEEvPKvPviRKN3MPI8DatatypeE iEEvPKvPviRKN3MPI8DatatypeE jEEvPKvPviRKN3MPI8DatatypeE lEEvPKvPviRKN3MPI8DatatypeE mEEvPKvPviRKN3MPI8DatatypeE g Ьg g g g g g g GetEngineEv AllgatherEPvmmmm g g пg C D 8TypeSizeERKN3MPI8DatatypeE 4InitEPFvPKvPviRKN3MPI8DatatypeEEm 9AllreduceEPvmmPFvS2_ES2_ 2Ev 1Ev g g 2Ev 1Ev g g g g g 7 6LibFMParserParam11__MANAGER__Ev 4CSVParserParam11__MANAGER__Ev 8CreateLibSVMParserI 5CreateCSVParserI LibSVMParserParam11__MANAGER__Ev CreateLibFMParserI g g g RowBlockIterI ThreadedIterINS_2io14InputSplitBase5ChunkEE14NotImplementedEv j y fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g g g g g ParserI Stream6CreateEPKcS2_b j y fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g fE6CreateEPKcjjS3_ iE6CreateEPKcjjS3_ xE6CreateEPKcjjS3_ g h h h h j y fEEE3GetEv iEEE3GetEv xEEE3GetEv аh h h fEEE3GetEv iEEE3GetEv xEEE3GetEv h h h jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj yfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj h h jfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj yfEEPNS_6ParserIT_fEERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERKNS6_3mapISC_SC_NS6_4lessISC_EENSA_INS6_4pairISD_SC_EEEEEEjj h h j y fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj iEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj xEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj h fEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj iEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj xEEPNS_6ParserIT_T0_EERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS7_3mapISD_SD_NS7_4lessISD_EENSB_INS7_4pairISE_SD_EEEEEEjj h h h h h 1 23IndexedRecordIOSplitter 0FileSystem 2LineSplitter1 6RecordIOSplitter1 4InputSplitBase 5LocalFileSystem
11GetInstanceERKNS0_3URIE 22ListDirectoryRecursiveERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE u InputSplit6CreateEPKc SeekStream13CreateForReadEPKcb jjS2_ S2_jjS2_bimb u u u u w 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE w w w 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE w w w 1 9 4ResetPartitionEjj 3ReadIndexFileEPNS0_10FileSystemERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 5SeekRecordBeginEPNS_6StreamE 9FindLastRecordBeginEPKcS3_ 7ExtractNextRecordEPNS_10InputSplit4BlobEPNS0_14InputSplitBase5ChunkE 1 w w w w w ReadChunkEPvPm Next w ChunkEPNS_10InputSplit4BlobE BatchEPNS_10InputSplit4BlobEm w NextBatchExEPNS0_14InputSplitBase5ChunkEm ۀBeforeFirstEv w w w 4 1 D 8StripEndENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEc 9ReadChunkEPvPm 5Chunk InitEPNS0_10FileSystemEPKcmb ReadEPvm x 7InitInputFileInfoERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEb 4ResetPartitionEjj 1BeforeFirstEv 3ConvertToURIsERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 6ExtractNextChunkEPNS_10InputSplit4BlobEPNS1_5ChunkE x x x 2Ev 1Ev ބ0Ev x x x x x x x 4LoadEPS1_m 6AppendEPS1_m x x x { { 1 4OpenERKNS0_3URIEPKcb 1 3ListDirectoryERKNS0_3URIEPNSt3__16vectorINS0_8FileInfoENS5_9allocatorIS7_EEEE GetPathInfoERKNS0_3URIE OpenForReadERKNS0_3URIEb а| | | | N ZN PF F 4dmlc χSt3__1 7xgboost 5rabit ؟ 9parameter1 8DataIterIN 1 5ErrorE 7istream 6 4data 2io 6FieldAccessEntryE 0FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI 4FieldEntryBaseINS0_10FieldEntryI iEE ډyEE N ǫbEE xEE mEE fEE dEE jEE xEExEE yEEyEE dEEdEE iEEiEE mEEmEE fEEfEE jEEjEE xEExEE yEEyEE dEEdEE bEEbEE mEEmEE fEEfEE jEEjEE iEEiEE NSt3__1 1 ȋ2 6__
+8 4 9basic_ ܞ0 3 ȧ7 5 1__ 6__ 2__match_charIcEE ޑ basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ 4 6 1 9 8JsonNullE Ϫ5 7 ì3 20LearnerConfigurationE data Ítree 1 6detail19SingleBatchDataIterINS0_ 2 ح 5 2 0CS 6 1FileAdapterE 3SimpleDMatrixE 7SparsePageDMatrixE 9SparsePageRawFormatINS_ վ IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE CSRArrayAdapterE D ӏArrayAdapterE ʞ MatrixProxyE enseAdapterE 0__ 3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE 6__end_marked_subexpressionIcEE 1__empty_non_own_stateIcEE ے shared_ptr_ match_char_collateIcNS_12regex_traitsIcEEEE בbracket_expressionIcNS_12regex_traitsIcEEEE pointerIPN emplaceIN7xgboost1 7xgboost S_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 1 7DMatrixENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoENS_14default_deleteIS5_EENS_9allocatorIS5_EEEE 7BatchIteratorImplINS1_ 0SparsePageENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ʭ 1 7CSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE ՙ 0SparsePageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 3SortedCSCPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE 1EllpackPageEEENS_14default_deleteIS4_EENS_9allocatorIS4_EEEE default_deleteIN ʗbasic_ifstreamIcNS_11char_traitsIcEEEE 7xgboost S_13__empty_stateIcEEEE 1 Ƙ7DMatrixEEE 4data1 6common16PartitionBuilderILm2048EE9BlockInfoEEE 7BatchIteratorImplINS1_ 0SparsePageEEE ѭ 1 7CSCPageEEEEE ۙ 0SparsePageEEEEE ϙ3SortedCSCPageEEEEE 1EllpackPageEEEEE RAdapterE CAdapterE 1 2 5CS 7 RAdapterBatchEEE ߚCAdapterBatchEEE 7xgboost S 4data 1 Ǯ7CSCPageEEE 1 2 5CS ɛ7 ѝ6FileAdapterBatchEEE RAdapterBatchEEE CAdapterBatchEEE 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE 0CSRArrayAdapterBatchEEE 1DataTableAdapterBatchEEE Ğ DenseAdapterBatchEEE ˝ArrayAdapterBatchEEE О DenseAdapterBatchEEE ArrayAdapterBatchEEE ֞ DataTableAdapterE SparsePageFormatINS_ istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE ҟostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 5 6engine utils1 c_api1 9MemoryFixSizeBufferE 8MemoryBufferStreamE 0 Š2 SeekStreamE ParamErrorE InputSplitE 2 ܡ5CSRArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE DMatrixProxyENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE ܢArrayAdapterENS_14default_deleteIS3_EENS_9allocatorIS3_EEEE 2 5CSRArrayAdapterEEE Ĥ DMatrixProxyEEE ArrayAdapterEEE shared_ptrIN7xgboost4data1 __ 2 5CSRArrayAdapterEEE ʤ0CSRAdapterEEE ArrayAdapterEEE DenseAdapterEEE 8kFloatPow5InvSplitE 5kFloatPow5SplitE Ђ 舀 common ̥linear metric MetricE 1 ߥ6ColumnI ͦ 1DenseColumnI 2SparseColumnI 6PeekableInStreamE 5FixedSizeStreamE hEE ƦtEE jEE hEE tEE jEE hEE tEE jEE ݊ ዀ basic_filebufIcNS_11char_traitsIcEEEE __empty_stateIcEE ، 0Json ר1 7 5GradientBoosterE 2ConfigurableE 3 StringE ObjectE NumberE ReaderE ݪWriterE JsonArrayE ߩLearnerIOE predictor12CPUPredictorE PredictorE ؍ 퍀 Json LearnerImplE ObjFunctionE TreeUpdaterE IntegerE ȪBooleanE ֪ ǎ ߎ 6HostDeviceVectorIjEENS_9allocatorIS3_EEEE 0SparsePageENS_9allocatorIS2_EEEE 7xgboost ߫St3__1 6common27ProbabilityDistributionTypeEEE 1 ValueE ModelE ؔ DMatrixE LearnerE RegTreeE 攀 E 5InBufE Е bad_function_callE í__ Ж 4ExternalMemoryPrefetcherINS_ 3S 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 嗀 0SparsePageEEE 3SortedCSCPageEEE ThreadedIterIN S RowBlockIterI 7xgboost S 1 ʯ7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE ʘ copedThreadE erializableE function6__ l_anchorIcEE ѐr_anchorIcEE ؐback_refIcEE funcI baseIF ZN PF 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS2_ ѱtree1 1 7CSCPageEEC1ERKNS3_9CacheInfoEEUl 0SparsePageEEC1ERKNS3_9CacheInfoEEUl Ҳ3SortedCSCPageEEC1ERKNS3_9CacheInfoEEUl PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE b vvEEE PN7xgboost PPN N7xgboost4tree17QuantileHistMaker7BuilderI 7xgboost 4dmlc S_6vectorIN4dmlc4data17RowBlockContainerI 1 Ѵ7CSCPageEEEE ƻ 0SparsePageEEEE 3SortedCSCPageEEEE 7xgboost4 4dmlc data24ExternalMemoryPrefetcherINS_ ̵tree1 1 7CSCPageEEC1ERKNS0_9CacheInfoEEUl ͻ 0SparsePageEEC1ERKNS0_9CacheInfoEEUl Ͷ3SortedCSCPageEEC1ERKNS0_9CacheInfoEEUl PPS2_E_ vE_ ؚ ߜ impleBatchIteratorImplINS_ parseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_ 1 7CSCPageEEE Ĺ 0SparsePageEEE 3SortedCSCPageEEE ҹ1EllpackPageEEE ԝ BatchIteratorImplINS_ GraphvizGeneratorE 1 7CSCPageEEE ˹ 0SparsePageEEE 3SortedCSCPageEEE ٹ1EllpackPageEEE Ϡ 1 7CSCPageEEES3_EE 0SparsePageEEES3_EE ۺ3SortedCSCPageEEES3_EE 1EllpackPageEEES3_EE ¢ 碀 PPS5_E_NS_9allocatorISC_EEFbSB_EEE vE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ Ӥ ݧ Ȩ PPS5_E_NS_9allocatorISC_EEFbSB_EEE ڼvE_NS_9allocatorISA_EEFvvEEE PPS2_E_ vE_ ɪ נּ 4data16SparsePageFormatINS2_ 1 6MetricEPKcEEE 9PredictorEPKNS2_16GenericParameterEEEE 1 7CSCPageEEEvEEE 0SparsePageEEEvEEE ξ3SortedCSCPageEEEvEEE ӯ 1 7CSCPageEEE 0SparsePageEEE 3SortedCSCPageEEE 1 7CSCPageEEE 0SparsePageEEE ڿ3SortedCSCPageEEE ఀ 貀 Դ 絀 ݷ 5GradientBoosterEPKNS2_17LearnerModelParamEEEE 3 1 gbm obj 8GBLinearE 1 6GBTreeE 4DartE ⹀ 3GBLinearModelE 1GBTreeModelE к ༀ 5TreeProcessTypeEEE 3 0TreeMethodEEE PredictorTypeEEE DataSplitModeEEE 뽀 À À À À basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ À StreamE ParserI Ā Ā ŀ ŀ ŀ ƀ ƀ ǀ 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS2_9allocatorIiEEEEEE ǀ 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEES9_EE 6vectorIiNS3_9allocatorIiEEEEEES7_EE Ȁ ɀ ɀ LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE ˀ 1 2 7CoordinateUpdaterE 5FeatureSelectorE 4ShotgunUpdaterE ˀ LinearUpdaterE T JsonGeneratorE ̀ 1 2 CyclicFeatureSelectorE GreedyFeatureSelectorE RandomFeatureSelectorE ̀ ̀ ShuffleFeatureSelectorE ThriftyFeatureSelectorE ̀ ̀ ̀ ̀ π Ѐ 7Eval 1 9EvalAucPRE 8Eval 2 A MAPE CoxE UCE MSE Ѐ р 3Eval 4Eval 6EvalMultiLogLossE EWiseBaseINS0_ PrecisionE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE 1EvalRow 2EvalRowRMSLEEEE 0EvalRowMAEEEE 4EvalRowLogLossEEE 7EvalGammaDevianceEEE 6EvalGammaNLogLikEEE 8EvalTweedieNLogLikEEE RMSEEEE M Ҁ Ӏ Հ APEEEE PHEEEE ր ׀ ڀ ۀ ܀ ހ ߀ M RankConfigE atchErrorE ClassBaseINS0_1 4EvalMatchErrorEEE 6EvalMultiLogLossEEE RankE NDCGE 0AFTNLogLikDispatcherE 1EvalEWiseSurvivalBaseINS0_ 14EvalAFTNLogLikINS_6common 30EvalIntervalRegressionAccuracyEEE 1 20LogisticDistributionEEEEE 8NormalDistributionEEEEE 9ExtremeDistributionEEEEE ObjFunctionEvEEE TreeUpdaterEvEEE 6AFTObjE 8HingeObjE 20SoftmaxMultiClassObjE 1 3 0RegLossObjINS0_ 7 5GammaRegressionE LambdaRankObjINS0_2 CoxRegressionE 8PairwiseLambdaWeightComputerEEE 4NDCGLambdaWeightComputerEEE 3MAPLambdaWeightComputerEEE 1 22LogisticClassificationEEE 6 5SquaredLogErrorEEE 8LogisticRegressionEEE 1LogisticRawEEE LinearSquareLossEEE PseudoHuberErrorEEE PoissonRegressionE TweedieRegressionE Ӊ 䍁 琁 extGeneratorE reeGeneratorE Γ ˖ 8ColMaker 1 9 2 E 7BuilderE 1 0TreePrunerE 7QuantileHistMaker 6HistSynchronizerI 3 8BatchHistRowsAdderI CQHistMakerE TreeSyncherE 㘁 HistMakerE BaseMakerE 1CQHistMaker10CreateHistERKNS_6vectorINS2_6detail20GradientPairInternalIfEENS_9allocatorIS8_EEEEPNS2_7DMatrixERKNS5_IjNS9_IjEEEERKNS2_7RegTreeEEUlvE_NS9_ISN_EEFvvEEE 3TreeRefresher6UpdateEPNS2_16HostDeviceVectorINS2_6detail20GradientPairInternalIfEEEEPNS2_7DMatrixERKNS_6vectorIPNS2_7RegTreeENS_9allocatorISF_EEEEEUlvE0_NSG_ISL_EEFvvEEE 1CQHistMaker10CreateHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS2_9allocatorIS6_EEEEPNS_7DMatrixERKNS3_IjNS7_IjEEEERKNS_7RegTreeEEUlvE_ 3TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKNSt3__16vectorIPNS_7RegTreeENSA_9allocatorISD_EEEEEUlvE0_ 3GlobalProposalHistMakerE 7DistributedHistSynchronizerI 4DistributedHistRowsAdderI 1BatchHistSynchronizerI E 7BuilderI fEE dEE េ fEE dEE fEE dEE fEE dEE 堁 HistRowsAdderI TreeRefresherE fEE dEE fEE dEE fEE dEE ޡ ڢ ӣ bN7xgboost4tree17QuantileHistMaker7BuilderI vvENS_9allocatorIS3_EES2_EE fE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE dE11ExpandEntryES7_ENS_9allocatorIS9_EES8_EE fE11ExpandEntryES7_EEE dE11ExpandEntryES7_EEE 更 bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E ͥ bN7xgboost4tree17QuantileHistMaker7BuilderI vvE fE11ExpandEntryES4_E dE11ExpandEntryES4_E Ц ԧ ѭ ± 13AllreduceBaseE 7IEngineE 1ReadWrapperE 2WriteWrapperE ײ 1 9CSVParserI 1 2 4T 0ParserImplI DiskRowIterI LibFMParserI j y fEE iEE xEE ѳ j y fEE iEE xEE ﳁ _ t3__16vectorINS_4data17RowBlockContainerI 8RowBlockI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE _ t3__16vectorINS_4data17RowBlockContainerI 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEE j y fEEEE iEEEE xEEEE j y fEEEE iEEEE xEEEE 崁 4data1 2io1 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIjfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE 4data17RowBlockContainerI 2io14InputSplitBase5ChunkEEEE ʉ j y fEEEEE iEEEEE xEEEEE 4data1 2io1 щ 1DiskRowIterI 4ThreadedParserI j y fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIjfEEE_ vE_ 嶁 BasicRowIterI LibSVMParserI j y fEE iEE xEE Ҹ fEE iEE xEE fEE iEE xEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE fEEEE iEEEE xEEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS3_17RowBlockContainerIyfEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE fEEEEE iEEEEE xEEEEE fE12TryLoadCacheEvEUl iE12TryLoadCacheEvEUl xE12TryLoadCacheEvEUl PPNS0_17RowBlockContainerIyfEEE_ vE_ ּ fEE iEE xEE ɾ PPNS3_17RowBlockContainerIjiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ؿ PPNS0_17RowBlockContainerIjiEEE_ vE_ Á Á Á Á ā ā PPNS3_17RowBlockContainerIyiEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ā Ł PPNS0_17RowBlockContainerIyiEEE_ vE_ Ɓ ǁ ǁ ȁ ȁ ȁ Ɂ Ɂ Ɂ PPNS3_17RowBlockContainerIjxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ʁ ˁ PPNS0_17RowBlockContainerIjxEEE_ vE_ ˁ ́ ́ ́ ́ ρ PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE ρ Ё PPNS0_17RowBlockContainerIyxEEE_ vE_ с с ҁ ҁ jfEE yfEE Ӂ extParserBaseI hreadedParserI j y fEE iEE xEE Ӂ j y ˂ fEE iEE xEE Ӂ j y fEE iEE xEE Ӂ jfEE yfEE ԁ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ԁ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ԁ jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE Ձ jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE ׁ jfEC1EPNS0_10ParserImplIjfEEEUl yfEC1EPNS0_10ParserImplIyfEEEUl PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ ׁ ف ځ fEE ĂiEE xEE ̆ځ fEE iEE xEE ӆځ fEE iEE xEE چځ ہ ہ ܁ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ܁ ށ PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ ށ ߁ jfEE yfEE j y Ӆ fEE ̅iEE xEE fEE iEE xEE ņ 1 23IndexedRecordIOSplitterE 5 ڇ8ThreadedInputSplitE 6 Ί2LineSplitterE 4InputSplitBaseE 0FileS SingleFileSplitE LocalFileSystemE 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE ÉvE_NS_9allocatorIS7_EEFvvEEE 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl 6CachedInputSplit1 Ռ PPNS2_5ChunkEE_ vE_ NJ CachedInputSplitE RecordIOSplitterE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE ΌvE_NS_9allocatorIS5_EEFvvEEE ڍ 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ ӍvE_ end_stateIcEE lookaheadIcNS_12regex_traitsIcEEEE alternateIcEE Ԓmatch_anyIcEE nodeIcEE loopIcEE owns_one_stateIcEE back_ref_icaseIcNS_12regex_traitsIcEEEE has_one_stateIcEE ʐword_boundaryIcNS_12regex_traitsIcEEEE ߐ match_char_icaseIcNS_12regex_traitsIcEEEE Бback_ref_collateIcNS_12regex_traitsIcEEEE owns_two_statesIcEE ƒrepeat_one_loopIcEE ͒ ystemE treamE N ZN F PF 4dmlc ד7xgboost St3__1 5rabit 5ErrorE 9parameter1 8DataIterIN 6 1 7istream 4data 2io 6FieldAccessEntryE 4FieldEntryBaseINS0_10FieldEntryI 7FieldEntryNumericINS0_10FieldEntryI 0FieldEntryI ޖ iEEiEE xEExEE jEEjEE yEEyEE ΗfEEfEE dEEdEE bEEbEE NSt3__1 mEEmEE iEEiEE זxEExEE jEEjEE yEEyEE fEEfEE dEEdEE mEEmEE iEE xEE jEE ǗyEE ܗfEE dEE bEE N mEE о 辏 ȿ ࿏ 5 4 6 ϯ1 9 8JsonNullE ӷ7 3 20LearnerConfigurationE ValueE ModelE 1 2 ڟ6__ Ӝ
+9basic_ 5 8 ՛0 4basic_ifstreamIcNS_11char_traitsIcEEEE 3 7 1__ 6__ 2__match_charIcEE ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE Ï basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE Λ__ ݝŏ basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __ ɏ 7xgboost S 4data ՜1 7CSCPageEEE 1 2 6FileAdapterBatchEEE 5CS 7 ɏ data tree 1 ѝ6detail19SingleBatchDataIterINS0_ 2 5 2 0CS 6 Ū1FileAdapterE 3SimpleDMatrixE 7SparsePageDMatrixE 9SparsePageRawFormatINS_ IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EE CSRArrayAdapterE ɏ D ArrayAdapterE MatrixProxyE ӟenseAdapterE ˏ 0__ 3__match_any_but_newlineIcEE 8__begin_marked_subexpressionIcEE 6__end_marked_subexpressionIcEE 1__empty_non_own_stateIcEE shared_ptr_ ءmatch_char_collateIcNS_12regex_traitsIcEEEE bracket_expressionIcNS_12regex_traitsIcEEEE pointerIPN emplaceIN7xgboost1 7xgboost ӢS_13__empty_stateIcEENS_14default_deleteIS2_EENS_9allocatorIS2_EEEE ֝ 1 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12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE 6vectorIiNS2_9allocatorIiEEEEEE 5GradientBoosterEPKNS2_17LearnerModelParamEEEE 3 1 Ȓ ؒ gbm obj 8GBLinearE 1 6GBTreeE 4DartE 1 6GBTreeE 3GBLinearModelE 1GBTreeModelE 3GBLinearModelE 1GBTreeModelE 蓐 5TreeProcessTypeEEE 3 0TreeMethodEEE PredictorTypeEEE DataSplitModeEEE 蝐 LinearUpdaterEvEEE TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEE ஐ LinearUpdaterE T JsonGeneratorE ȯ 1 2 7CoordinateUpdaterE 5FeatureSelectorE 4ShotgunUpdaterE ௐ 1 2 CyclicFeatureSelectorE GreedyFeatureSelectorE RandomFeatureSelectorE ShuffleFeatureSelectorE ThriftyFeatureSelectorE ȱ ز 7Eval 1 9EvalAucPRE 8Eval 2 A MAPE CoxE UCE MSE 3Eval 4Eval 6EvalMultiLogLossE EWiseBaseINS0_ PrecisionE 1 20EvalPoissonNegLogLikEEE 9EvalErrorEEE 1EvalRow 2EvalRowRMSLEEEE 0EvalRowMAEEEE 4EvalRowLogLossEEE 7EvalGammaDevianceEEE 6EvalGammaNLogLikEEE 8EvalTweedieNLogLikEEE RMSEEEE M ػ APEEEE PHEEEE Đ Ɛ ǐ ɐ M RankConfigE 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PPNS3_17RowBlockContainerIyxEEE_NS_9allocatorISA_EEFbS9_EEE vE_NS_9allocatorIS6_EEFvvEEE Ƒ PPNS0_17RowBlockContainerIyxEEE_ vE_ Ǒ Ǒ ȑ ȑ j y fEE iEE xEE ɑ j y ̏ fEE ԉiEE xEE ɑ extParserBaseI hreadedParserI Ċ j y fEE iEE xEE ɑ jfEE yfEE ʑ jfEE ֊yfEE ̑ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ̑ jfEENS1_9allocatorIS5_EEEEEE yfEENS1_9allocatorIS5_EEEEEE ̑ jfEENS_9allocatorIS6_EEEEEEE yfEENS_9allocatorIS6_EEEEEEE ͑ jfEC1EPNS3_10ParserImplIjfEEEUl yfEC1EPNS3_10ParserImplIyfEEEUl PPNS_6vectorINS3_17RowBlockContainerIjfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ͑ jfEC1EPNS0_10ParserImplIjfEEEUl ǎyfEC1EPNS0_10ParserImplIyfEEEUl PPNSt3__16vectorINS0_17RowBlockContainerIjfEENS6_9allocatorIS9_EEEEE_ vE_ Α Α ϑ fEE ŏiEE xEE ͓ϑ fEE iEE xEE ԓϑ fEE iEE xEE ۓБ Б ґ ґ ӑ ӑ PPNS_6vectorINS3_17RowBlockContainerIyfEENS_9allocatorISB_EEEEE_NSC_ISH_EEFbSG_EEE vE_NS_9allocatorIS9_EEFvvEEE ԑ PPNSt3__16vectorINS0_17RowBlockContainerIyfEENS6_9allocatorIS9_EEEEE_ vE_ ԑ Ց Ց jfEE yfEE Ց ֑ j y ے fEE ԒiEE xEE Ɠב fEE iEE xEE ؑ ّ ّ ّ ّ ۑ ۑ ܑ ܑ ޑ ޑ ޑ ޑ 1 23IndexedRecordIOSplitterE 5 8ThreadedInputSplitE 6 ֗2LineSplitterE 4InputSplitBaseE ̜0FileS SingleFileSplitE LocalFileSystemE 8ThreadedInputSplitC1EPNS3_14InputSplitBaseEmEUl 6CachedInputSplit1 PPNS5_5ChunkEE_NS_9allocatorISA_EEFbS9_EEE ҖvE_NS_9allocatorIS7_EEFvvEEE ȗ 8ThreadedInputSplitC1EPNS0_14InputSplitBaseEmEUl 6CachedInputSplit1 ݙ PPNS2_5ChunkEE_ vE_ ϗ CachedInputSplitE RecordIOSplitterE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE PPNS3_14InputSplitBase5ChunkEE_NS_9allocatorIS9_EEFbS8_EEE ֙vE_NS_9allocatorIS5_EEFvvEEE 4InitCachedIterEvEUl 5InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ PPNS0_14InputSplitBase5ChunkEE_ ۚvE_ 1 23IndexedRecordIOSplitterE 2LineSplitterE 6RecordIOSplitterE 4InputSplitBaseE Ŝ0FileSystemE 5LocalFileSystemE nodeIcEE loopIcEE end_stateIcEE ϝlookaheadIcNS_12regex_traitsIcEEEE alternateIcEE match_anyIcEE has_one_stateIcEE word_boundaryIcNS_12regex_traitsIcEEEE owns_one_stateIcEE back_ref_icaseIcNS_12regex_traitsIcEEEE match_char_icaseIcNS_12regex_traitsIcEEEE back_ref_collateIcNS_12regex_traitsIcEEEE owns_two_statesIcEE repeat_one_loopIcEE ystemE treamE Ȁ 1 3any8TypeInfoINSt3__110shared_ptrIN7xgboost4data1 2io15LocalFileSystem11GetInstanceEvE8instance 5LogMessageFatal5Entry11ThreadLocalEvE6result 6ThreadLocalStoreI VZN գRZN7xgboost13TextGenerator9BuildTreeERKNS_7RegTreeEE13kTreeTemplate_ 4dmlc 7xgboost K7xgboost 1 3any8TypeInfoINSt3__110shared_ptrIN7xgboost4data1 2io15LocalFileSystem11GetInstanceEvE8instance 5LogMessageFatal5Entry11ThreadLocalEvE6result 6ThreadLocalStoreI N 16XGBAPIErrorEntryE3GetEvE4inst 7xgboost St3__13mapIPKN7xgboost7 5rabit6engine16ThreadLocalEntryEE3GetEvE4inst ί 19 22XGBAPIThreadLocalEntryEE3GetEvE4inst 6common22RandomThreadLocalEntryEE3GetEvE4inst LogCallbackRegistryEE3GetEvE4inst GlobalConfigurationEE3GetEvE4inst Ȃ N 16XGBAPIErrorEntryE3GetEvE4inst 7xgboost 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__ZNK7xgboost3obj10RegLossObjINS0_18LogisticRegressionEE12ProbToMarginEf __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_18LogisticRegressionEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E10LaunchCUDAILPv0EJS9_SI_SA_SA_SA_EEEvSP_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_18LogisticRegressionEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS9_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSL_ISI_Lm18446744073709551615EEENSL_IKfLm18446744073709551615EEESP_SP_E_E9LaunchCPUIJSA_SJ_SB_SB_SB_EEEvSQ_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..116 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_18LogisticRegressionEE11GetGradientERKNS2_16HostDeviceVectorIfEERKNS2_8MetaInfoEiPNSB_INS2_6detail20GradientPairInternalIfEEEEEUlmNS3_4SpanIfLm18446744073709551615EEENSN_ISK_Lm18446744073709551615EEENSN_IKfLm18446744073709551615EEESR_SR_E_E9LaunchCPUIJSC_SL_SD_SD_SD_EEEvSS_DpPT_EUlmE_JmEEEvT_DpT0_ __ZZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_18LogisticRegressionEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E9LaunchCPUIJS9_SI_SA_SA_SA_EEEvSP_DpPT_ENKUlmE_clEm __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_18LogisticRegressionEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E10LaunchCUDAILPv0EJS9_EEEvSD_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_18LogisticRegressionEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSA_EEEvSE_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..118 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_18LogisticRegressionEE13PredTransformEPNS2_16HostDeviceVectorIfEEEUlmNS3_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSC_EEEvSG_DpPT_EUlmE_JmEEEvT_DpT0_ __ZN7xgboost3obj18LogisticRegression12ProbToMarginEf __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED1Ev __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED0Ev __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEPNS0_6__baseIS9_EE __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7destroyEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE18destroy_deallocateEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEclEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE6targetERKSt9type_info __ZNKSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE11target_typeEv __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEED1Ev __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEED0Ev __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE10LoadConfigERKNS_4JsonE __ZNK7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE10SaveConfigEPNS_4JsonE __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS4_INS_6detail20GradientPairInternalIfEEEE __ZNK7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE17DefaultEvalMetricEv __ZN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEE13PredTransformEPNS_16HostDeviceVectorIfEE __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E10LaunchCUDAILPv0EJS9_SI_SA_SA_SA_EEEvSP_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS9_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSL_ISI_Lm18446744073709551615EEENSL_IKfLm18446744073709551615EEESP_SP_E_E9LaunchCPUIJSA_SJ_SB_SB_SB_EEEvSQ_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..122 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_16PseudoHuberErrorEE11GetGradientERKNS2_16HostDeviceVectorIfEERKNS2_8MetaInfoEiPNSB_INS2_6detail20GradientPairInternalIfEEEEEUlmNS3_4SpanIfLm18446744073709551615EEENSN_ISK_Lm18446744073709551615EEENSN_IKfLm18446744073709551615EEESR_SR_E_E9LaunchCPUIJSC_SL_SD_SD_SD_EEEvSS_DpPT_EUlmE_JmEEEvT_DpT0_ __ZZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_16PseudoHuberErrorEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E9LaunchCPUIJS9_SI_SA_SA_SA_EEEvSP_DpPT_ENKUlmE_clEm __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_16PseudoHuberErrorEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E10LaunchCUDAILPv0EJS9_EEEvSD_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_16PseudoHuberErrorEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSA_EEEvSE_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..124 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_16PseudoHuberErrorEE13PredTransformEPNS2_16HostDeviceVectorIfEEEUlmNS3_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSC_EEEvSG_DpPT_EUlmE_JmEEEvT_DpT0_ __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED1Ev __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED0Ev __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEPNS0_6__baseIS9_EE __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7destroyEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE18destroy_deallocateEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEclEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE6targetERKSt9type_info __ZNKSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE11target_typeEv __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEED1Ev __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEED0Ev __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE10LoadConfigERKNS_4JsonE __ZNK7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE10SaveConfigEPNS_4JsonE __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS4_INS_6detail20GradientPairInternalIfEEEE __ZNK7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE17DefaultEvalMetricEv __ZN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEE13PredTransformEPNS_16HostDeviceVectorIfEE __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_22LogisticClassificationEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E10LaunchCUDAILPv0EJS9_SI_SA_SA_SA_EEEvSP_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_22LogisticClassificationEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS9_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSL_ISI_Lm18446744073709551615EEENSL_IKfLm18446744073709551615EEESP_SP_E_E9LaunchCPUIJSA_SJ_SB_SB_SB_EEEvSQ_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..125 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_22LogisticClassificationEE11GetGradientERKNS2_16HostDeviceVectorIfEERKNS2_8MetaInfoEiPNSB_INS2_6detail20GradientPairInternalIfEEEEEUlmNS3_4SpanIfLm18446744073709551615EEENSN_ISK_Lm18446744073709551615EEENSN_IKfLm18446744073709551615EEESR_SR_E_E9LaunchCPUIJSC_SL_SD_SD_SD_EEEvSS_DpPT_EUlmE_JmEEEvT_DpT0_ __ZZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_22LogisticClassificationEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E9LaunchCPUIJS9_SI_SA_SA_SA_EEEvSP_DpPT_ENKUlmE_clEm __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_22LogisticClassificationEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E10LaunchCUDAILPv0EJS9_EEEvSD_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_22LogisticClassificationEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSA_EEEvSE_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..127 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_22LogisticClassificationEE13PredTransformEPNS2_16HostDeviceVectorIfEEEUlmNS3_4SpanIfLm18446744073709551615EEEE_E9LaunchCPUIJSC_EEEvSG_DpPT_EUlmE_JmEEEvT_DpT0_ __ZNSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED1Ev __ZNSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEED0Ev __ZNKSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7__cloneEPNS0_6__baseIS9_EE __ZNSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE7destroyEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE18destroy_deallocateEv __ZNSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEclEv __ZNKSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE6targetERKSt9type_info __ZNKSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEE11target_typeEv __ZN7xgboost3obj10RegLossObjINS0_11LogisticRawEED1Ev __ZN7xgboost3obj10RegLossObjINS0_11LogisticRawEED0Ev __ZN7xgboost3obj10RegLossObjINS0_11LogisticRawEE10LoadConfigERKNS_4JsonE __ZNK7xgboost3obj10RegLossObjINS0_11LogisticRawEE10SaveConfigEPNS_4JsonE __ZN7xgboost3obj10RegLossObjINS0_11LogisticRawEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS4_INS_6detail20GradientPairInternalIfEEEE __ZNK7xgboost3obj10RegLossObjINS0_11LogisticRawEE17DefaultEvalMetricEv __ZN7xgboost3obj10RegLossObjINS0_11LogisticRawEE13PredTransformEPNS_16HostDeviceVectorIfEE __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_11LogisticRawEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E10LaunchCUDAILPv0EJS9_SI_SA_SA_SA_EEEvSP_DpPT0_ __ZN7xgboost6common11ParallelForImZNKS0_9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS5_11LogisticRawEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS9_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSL_ISI_Lm18446744073709551615EEENSL_IKfLm18446744073709551615EEESP_SP_E_E9LaunchCPUIJSA_SJ_SB_SB_SB_EEEvSQ_DpPT_EUlmE_EEvT_mT0_ _.omp_outlined..128 __ZN4dmlc12OMPException3RunIZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS2_3obj10RegLossObjINS7_11LogisticRawEE11GetGradientERKNS2_16HostDeviceVectorIfEERKNS2_8MetaInfoEiPNSB_INS2_6detail20GradientPairInternalIfEEEEEUlmNS3_4SpanIfLm18446744073709551615EEENSN_ISK_Lm18446744073709551615EEENSN_IKfLm18446744073709551615EEESR_SR_E_E9LaunchCPUIJSC_SL_SD_SD_SD_EEEvSS_DpPT_EUlmE_JmEEEvT_DpT0_ __ZZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_11LogisticRawEE11GetGradientERKNS_16HostDeviceVectorIfEERKNS_8MetaInfoEiPNS8_INS_6detail20GradientPairInternalIfEEEEEUlmNS0_4SpanIfLm18446744073709551615EEENSK_ISH_Lm18446744073709551615EEENSK_IKfLm18446744073709551615EEESO_SO_E_E9LaunchCPUIJS9_SI_SA_SA_SA_EEEvSP_DpPT_ENKUlmE_clEm __ZNK7xgboost6common9TransformILb0EE9EvaluatorIZNS_3obj10RegLossObjINS4_11LogisticRawEE13PredTransformEPNS_16HostDeviceVectorIfEEEUlmNS0_4SpanIfLm18446744073709551615EEEE_E10LaunchCUDAILPv0EJS9_EEEvSD_DpPT0_ 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