| Cxgboost::linalg::detail::AllTag | |
| Cxgboost::linalg::detail::ArrayInterfaceHandler | |
| ►CB1 | |
| Cxgboost::linalg::detail::Conjunction< B1 > | |
| Cxgboost::BatchIterator< T > | |
| Cxgboost::BatchIteratorImpl< T > | |
| Cxgboost::BatchParam | Parameters for constructing histogram index batches |
| Cxgboost::BatchSet< T > | |
| Cxgboost::RegTree::CategoricalSplitMatrix | CSR-like matrix for categorical splits |
| ►Cstd::conditional_t | |
| Cxgboost::linalg::detail::Conjunction< B1, Bn... > | |
| ►Cxgboost::Configurable | |
| Cxgboost::GradientBooster | Interface of gradient boosting model |
| Cxgboost::Learner | Learner class that does training and prediction. This is the user facing module of xgboost training. The Load/Save function corresponds to the model used in python/R |
| Cxgboost::LinearUpdater | Interface of linear updater |
| Cxgboost::Metric | Interface of evaluation metric used to evaluate model performance. This has nothing to do with training, but merely act as evaluation purpose |
| Cxgboost::ObjFunction | The interface of objective function |
| Cxgboost::TreeUpdater | Interface of tree update module, that performs update of a tree |
| Cxgboost::CopyUniquePtr< T > | Helper for defining copyable data structure that contains unique pointers |
| Cxgboost::CopyUniquePtr< xgboost::MultiTargetTree > | |
| Cxgboost::DeviceOrd | A type for device ordinal. The type is packed into 32-bit for efficient use in viewing types like linalg::TensorView |
| Cxgboost::DeviceSym | |
| Cxgboost::DMatrix | Internal data structured used by XGBoost to hold all external data |
| Cxgboost::DMatrixCache< CacheT > | Thread-aware FIFO cache for DMatrix related data |
| ►Cxgboost::DMatrixCache< PredictionCacheEntry > | |
| Cxgboost::PredictionContainer | A container for managed prediction caches |
| Cxgboost::Entry | Element from a sparse vector |
| Cxgboost::ExtMemConfig | |
| Cxgboost::ExtSparsePage | Sparse page for exporting DMatrix. Same as SparsePage, just a different type to prevent being used internally |
| ►Cstd::false_type | |
| ►Cxgboost::common::detail::IsSpanOracle< std::remove_cv_t< T > > | |
| Cxgboost::common::detail::IsSpan< T > | |
| Cxgboost::common::detail::IsSpanOracle< T > | |
| Cxgboost::FeatureMap | Feature map data structure to help text model dump. TODO(tqchen) consider make it even more lightweight |
| ►Cdmlc::FunctionRegEntryBase | |
| Cxgboost::GradientBoosterReg | Registry entry for tree updater |
| Cxgboost::LinearUpdaterReg | Registry entry for linear updater |
| Cxgboost::MetricReg | Registry entry for Metric factory functions. The additional parameter const char* param gives the value after @, can be null. For example, metric map@3, then: param == "3" |
| Cxgboost::ObjFunctionReg | Registry entry for objective factory functions |
| Cxgboost::PredictorReg | Registry entry for predictor |
| Cxgboost::TreeUpdaterReg | Registry entry for tree updater |
| Cxgboost::RegTree::FVec | Dense feature vector that can be taken by RegTree and can be construct from sparse feature vector |
| Cxgboost::GradientPairInt64 | Fixed point representation for high precision gradient pair. Has a different interface so we don't accidentally use it in gain calculations |
| Cxgboost::detail::GradientPairInternal< T > | Implementation of gradient statistics pair. Template specialisation may be used to overload different gradients types e.g. low precision, high precision, integer, floating point |
| Cxgboost::common::detail::Greater< T > | |
| Cdmlc::serializer::Handler< xgboost::Entry > | |
| Cxgboost::DMatrixCache< CacheT >::Hash | |
| ►Cxgboost::IntrusivePtr< T >::Hash | |
| Cstd::hash< xgboost::IntrusivePtr< T > > | |
| Cxgboost::HostDeviceVector< T > | |
| Cxgboost::HostDeviceVector< bst_feature_t > | |
| Cxgboost::HostDeviceVector< bst_float > | |
| Cxgboost::HostDeviceVector< bst_idx_t > | |
| Cxgboost::HostDeviceVector< bst_node_t > | |
| Cxgboost::HostDeviceVector< FeatureType > | |
| Cxgboost::HostDeviceVector< float > | |
| Cxgboost::HostDeviceVector< std::uint8_t > | |
| Cxgboost::HostDeviceVector< xgboost::Entry > | |
| Cxgboost::HostDeviceVectorImpl< T > | |
| Cxgboost::HostSparsePageView | |
| Cxgboost::InitNewThread | |
| ►Cstd::integral_constant | |
| Cxgboost::common::detail::ExtentAsBytesValue< T, Extent > | |
| Cxgboost::common::detail::ExtentValue< Extent, Offset, Count > | |
| Cxgboost::common::detail::IsAllowedElementTypeConversion< From, To > | |
| Cxgboost::common::detail::IsAllowedExtentConversion< From, To > | |
| Cxgboost::IntrusivePtr< T > | Implementation of Intrusive Pointer. A smart pointer that points to an object with an embedded reference counter. The underlying object must implement a friend function IntrusivePtrRefCount() that returns the ref counter (of type IntrusivePtrCell). The intrusive pointer is faster than std::shared_ptr<>: std::shared_ptr<> makes an extra memory allocation for the ref counter whereas the intrusive pointer does not |
| Cxgboost::IntrusivePtr< xgboost::Value > | |
| Cxgboost::IntrusivePtrCell | Helper class for embedding reference counting into client objects. See https://www.boost.org/doc/libs/1_74_0/doc/html/atomic/usage_examples.html for discussions of memory order |
| Cxgboost::linalg::detail::IntTag | |
| Cxgboost::DMatrixCache< CacheT >::Item | |
| Cxgboost::common::IterSpan< It > | A simple custom Span type that uses general iterator instead of pointer |
| Cxgboost::Json | Data structure representing JSON format |
| ►Cxgboost::JsonReader | A json reader, currently error checking and utf-8 is not fully supported |
| Cxgboost::UBJReader | Reader for UBJSON https://ubjson.org/ |
| ►Cxgboost::JsonWriter | |
| Cxgboost::UBJWriter | Writer for UBJSON https://ubjson.org/ |
| Cxgboost::DMatrixCache< CacheT >::Key | |
| Cxgboost::LearnerModelParam | Basic model parameters, used to describe the booster |
| Cxgboost::common::detail::Less< T > | |
| Cxgboost::MetaInfo | Meta information about dataset, always sit in memory |
| ►Cxgboost::Model | |
| Cxgboost::GradientBooster | Interface of gradient boosting model |
| Cxgboost::Learner | Learner class that does training and prediction. This is the user facing module of xgboost training. The Load/Save function corresponds to the model used in python/R |
| Cxgboost::MultiTargetTree | Tree structure for multi-target model |
| Cxgboost::RegTree | Define regression tree to be the most common tree model |
| Cxgboost::MultiTargetTreeView | A view to the @MultiTargetTree suitable for both host and device |
| Cxgboost::RegTree::Node | Tree node |
| Cxgboost::ObjInfo | A struct returned by objective, which determines task at hand. The struct is not used by any algorithm yet, only for future development like categorical split |
| ►Cdmlc::Parameter | |
| ►Cxgboost::XGBoostParameter< Context > | |
| Cxgboost::Context | Runtime context for XGBoost. Contains information like threads and device |
| ►Cxgboost::XGBoostParameter< GlobalConfiguration > | |
| Cxgboost::GlobalConfiguration | |
| Cxgboost::XGBoostParameter< Type > | |
| Crabit::utils::PollHelper | Helper data structure to perform poll |
| Cxgboost::PredictionCacheEntry | Contains pointer to input matrix and associated cached predictions |
| Cxgboost::Predictor | Performs prediction on individual training instances or batches of instances for GBTree. Prediction functions all take a GBTreeModel and a DMatrix as input and output a vector of predictions. The predictor does not modify any state of the model itself |
| Cxgboost::linalg::detail::RangeTag< I > | |
| Cxgboost::collective::Result | An error type that's easier to handle than throwing dmlc exception. We can record and propagate the system error code |
| Cxgboost::collective::detail::ResultImpl | |
| Cxgboost::RTreeNodeStat | Node statistics used in regression tree |
| Cxgboost::RegTree::CategoricalSplitMatrix::Segment | |
| ►Cdmlc::Serializable | |
| Cxgboost::Learner | Learner class that does training and prediction. This is the user facing module of xgboost training. The Load/Save function corresponds to the model used in python/R |
| Cxgboost::collective::SockAddress | Address for TCP socket, can be either IPv4 or IPv6 |
| Cxgboost::collective::SockAddrV4 | |
| Cxgboost::collective::SockAddrV6 | |
| Cxgboost::JsonReader::SourceLocation | |
| Cxgboost::common::Span< T, Extent > | Span class implementation, based on ISO++20 span<T>. The interface should be the same |
| Cxgboost::common::Span< bst_idx_t const > | |
| Cxgboost::common::Span< FeatureType const > | |
| Cxgboost::common::Span< float const > | |
| Cxgboost::common::Span< uint32_t const > | |
| Cxgboost::common::Span< xgboost::Entry const > | |
| Cxgboost::common::Span< xgboost::RegTree::CategoricalSplitMatrix::Segment const > | |
| Cxgboost::common::detail::SpanIterator< SpanType, IsConst > | |
| ►Cxgboost::SparsePage | In-memory storage unit of sparse batch, stored in CSR format |
| Cxgboost::CSCPage | |
| Cxgboost::SortedCSCPage | |
| Cxgboost::StringView | |
| Cxgboost::collective::TCPSocket | TCP socket for simple communication |
| Cxgboost::linalg::Tensor< T, kDim > | A tensor storage. To use it for other functionality like slicing one needs to obtain a view first. This way we can use it on both host and device |
| Cxgboost::linalg::Tensor< float > | |
| Cxgboost::linalg::Tensor< float, 2 > | |
| Cxgboost::linalg::TensorView< T, kDim > | A tensor view with static type and dimension. It implements indexing and slicing |
| Cxgboost::linalg::TensorView< float const > | |
| Cxgboost::TreeParam | Meta parameters of the tree |
| ►Cstd::true_type | |
| Cxgboost::common::detail::IsSpanOracle< Span< T, Extent > > | |
| Cxgboost::linalg::detail::Conjunction<... > | |
| ►Cxgboost::Value | |
| Cxgboost::JsonArray | |
| Cxgboost::JsonBoolean | Describes both true and false |
| Cxgboost::JsonInteger | |
| Cxgboost::JsonNull | |
| Cxgboost::JsonNumber | |
| Cxgboost::JsonObject | |
| Cxgboost::JsonString | |
| Cxgboost::JsonTypedArray< T, kind > | Typed array for Universal Binary JSON |
| CXGBoostBatchCSR | Mini batch used in XGBoost Data Iteration |
