namespace of xgboost More...

Namespaces
	common

	detail

	gbm

Classes
class	BatchIterator

class	BatchIteratorImpl

struct	BatchParam
	Parameters for constructing batches. More...

class	BatchSet

struct	BitFieldContainer
	A non-owning type with auxiliary methods defined for manipulating bits. More...

struct	Configurable

class	CSCPage

class	DataSource
	This is data structure that user can pass to DMatrix::Create to create a DMatrix for training, user can create this data structure for customized Data Loading on single machine. More...

class	DMatrix
	Internal data structured used by XGBoost during training. There are two ways to create a customized DMatrix that reads in user defined-format. More...

class	EllpackPage
	A page stored in ELLPACK format. More...

struct	Entry
	Element from a sparse vector. More...

class	FeatureMap
	Feature map data structure to help text model dump. TODO(tqchen) consider make it even more lightweight. More...

class	FixedPrecisionStreamContainer

struct	GenericParameter

class	GradientBooster
	interface of gradient boosting model. More...

struct	GradientBoosterReg
	Registry entry for tree updater. More...

class	HostDeviceVector

struct	HostDeviceVectorImpl

class	Json
	Data structure representing JSON format. More...

class	JsonArray

class	JsonBoolean
	Describes both true and false. More...

class	JsonInteger

class	JsonNull

class	JsonNumber

class	JsonObject

class	JsonReader

class	JsonString

class	JsonWriter

struct	LBitsPolicy

class	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. More...

struct	LearnerModelParam

class	LinearUpdater
	interface of linear updater More...

struct	LinearUpdaterReg
	Registry entry for linear updater. More...

class	MetaInfo
	Meta information about dataset, always sit in memory. More...

class	Metric
	interface of evaluation metric used to evaluate model performance. This has nothing to do with training, but merely act as evaluation purpose. More...

struct	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". More...

struct	Model

class	ObjFunction
	interface of objective function More...

struct	ObjFunctionReg
	Registry entry for objective factory functions. More...

struct	PredictionCacheEntry
	Contains pointer to input matrix and associated cached predictions. More...

class	PredictionContainer

class	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. More...

struct	PredictorReg
	Registry entry for predictor. More...

struct	RBitsPolicy

class	RegTree
	define regression tree to be the most common tree model. This is the data structure used in xgboost's major tree models. More...

struct	RTreeNodeStat
	node statistics used in regression tree More...

class	SortedCSCPage

class	SparsePage
	In-memory storage unit of sparse batch, stored in CSR format. More...

struct	StringView

class	TrainingObserver

struct	TreeParam
	meta parameters of the tree More...

class	TreeUpdater
	interface of tree update module, that performs update of a tree. More...

struct	TreeUpdaterReg
	Registry entry for tree updater. More...

class	Value

struct	Version

struct	XGBAPIThreadLocalEntry
	entry to to easily hold returning information More...

struct	XGBoostParameter

Typedefs
using	bst_uint = uint32_t
	unsigned integer type used for feature index. More...

using	bst_int = int32_t
	integer type. More...

using	bst_ulong = uint64_t
	unsigned long integers More...

using	bst_float = float
	float type, used for storing statistics More...

using	bst_feature_t = uint32_t
	Type for data column (feature) index. More...

using	bst_row_t = std::size_t
	Type for data row index. More...

using	bst_node_t = int32_t
	Type for tree node index. More...

using	bst_group_t = uint32_t
	Type for ranking group index. More...

using	GradientPair = detail::GradientPairInternal< float >
	gradient statistics pair usually needed in gradient boosting More...

using	GradientPairPrecise = detail::GradientPairInternal< double >
	High precision gradient statistics pair. More...

using	Args = std::vector< std::pair< std::string, std::string > >

using	omp_ulong = dmlc::omp_ulong
	define unsigned long for openmp loop More...

using	bst_omp_uint = dmlc::omp_uint
	define unsigned int for openmp loop More...

using	XGBoostVersionT = int32_t
	Type used for representing version number in binary form. More...

using	Object = JsonObject

using	Array = JsonArray

using	Number = JsonNumber

using	Integer = JsonInteger

using	Boolean = JsonBoolean

using	String = JsonString

using	Null = JsonNull

using	FixedPrecisionStream = FixedPrecisionStreamContainer< std::allocator< char > >

using	LBitField64 = BitFieldContainer< uint64_t, LBitsPolicy< uint64_t > >

using	RBitField8 = BitFieldContainer< uint8_t, RBitsPolicy< unsigned char > >

Enumerations
enum	DataType : uint8_t { DataType::kFloat32 = 1, DataType::kDouble = 2, DataType::kUInt32 = 3, DataType::kUInt64 = 4 }
	data type accepted by xgboost interface More...

enum	GPUAccess { kNone, kRead, kWrite }
	Controls data access from the GPU. More...

Functions
template<typename T >
bool	IsA (Value const *value)

template<typename T , typename U >
T *	Cast (U *value)

template<typename T >
bool	IsA (Json const j)

template<typename T , typename U >
auto	get (U &json) -> decltype(detail::GetImpl(*Cast< T >(&json.GetValue())))&
	Get Json value. More...

template<typename Parameter >
Object	ToJson (Parameter const &param)

template<typename Parameter >
void	FromJson (Json const &obj, Parameter *param)

Variables
constexpr bst_float	kRtEps = 1e-6f
	small eps gap for minimum split decision. More...

Detailed Description

namespace of xgboost

Copyright (c) by Contributors 2019

Copyright by Contributors 2017-2019

Typedef Documentation

◆ Args

using xgboost::Args = typedef std::vector<std::pair<std::string, std::string> >

◆ Array

using xgboost::Array = typedef JsonArray

◆ Boolean

using xgboost::Boolean = typedef JsonBoolean

◆ bst_feature_t

using xgboost::bst_feature_t = typedef uint32_t

Type for data column (feature) index.

◆ bst_float

using xgboost::bst_float = typedef float

float type, used for storing statistics

◆ bst_group_t

using xgboost::bst_group_t = typedef uint32_t

Type for ranking group index.

◆ bst_int

using xgboost::bst_int = typedef int32_t

integer type.

◆ bst_node_t

using xgboost::bst_node_t = typedef int32_t

Type for tree node index.

◆ bst_omp_uint

using xgboost::bst_omp_uint = typedef dmlc::omp_uint

define unsigned int for openmp loop

◆ bst_row_t

using xgboost::bst_row_t = typedef std::size_t

Type for data row index.

Be careful `std::size_t' is implementation-defined. Meaning that the binary representation of DMatrix might not be portable across platform. Booster model should be portable as parameters are floating points.

◆ bst_uint

using xgboost::bst_uint = typedef uint32_t

unsigned integer type used for feature index.

◆ bst_ulong

using xgboost::bst_ulong = typedef uint64_t

unsigned long integers

◆ FixedPrecisionStream

using xgboost::FixedPrecisionStream = typedef FixedPrecisionStreamContainer<std::allocator<char> >

◆ GradientPair

using xgboost::GradientPair = typedef detail::GradientPairInternal<float>

gradient statistics pair usually needed in gradient boosting

◆ GradientPairPrecise

using xgboost::GradientPairPrecise = typedef detail::GradientPairInternal<double>

High precision gradient statistics pair.

◆ Integer

using xgboost::Integer = typedef JsonInteger

◆ LBitField64

using xgboost::LBitField64 = typedef BitFieldContainer<uint64_t, LBitsPolicy<uint64_t> >

◆ Null

using xgboost::Null = typedef JsonNull

◆ Number

using xgboost::Number = typedef JsonNumber

◆ Object

using xgboost::Object = typedef JsonObject

◆ omp_ulong

using xgboost::omp_ulong = typedef dmlc::omp_ulong

define unsigned long for openmp loop

◆ RBitField8

using xgboost::RBitField8 = typedef BitFieldContainer<uint8_t, RBitsPolicy<unsigned char> >

◆ String

using xgboost::String = typedef JsonString

◆ XGBoostVersionT

using xgboost::XGBoostVersionT = typedef int32_t

Type used for representing version number in binary form.

Enumeration Type Documentation

◆ DataType

enum xgboost::DataType : uint8_t

strong

data type accepted by xgboost interface

Enumerator
kFloat32
kDouble
kUInt32
kUInt64

◆ GPUAccess

enum xgboost::GPUAccess

Controls data access from the GPU.

Since a HostDeviceVector can have data on both the host and device, access control needs to be maintained to keep the data consistent.

There are 3 scenarios supported:

Data is being manipulated on device. GPU has write access, host doesn't have access.
Data is read-only on both the host and device.
Data is being manipulated on the host. Host has write access, device doesn't have access.

Enumerator
kNone
kRead
kWrite

Function Documentation

◆ Cast()

template<typename T , typename U >

T* xgboost::Cast ( U * value )

◆ FromJson()

template<typename Parameter >

void xgboost::FromJson	(	Json const &	obj,
		Parameter *	param
	)

◆ get()

template<typename T , typename U >

auto xgboost::get ( U & json ) -> decltype(detail::GetImpl(*Cast<T>(&json.GetValue())))&

Get Json value.

Template Parameters

T	One of the Json value type.

Parameters

json

Returns: Value contained in Json object of type T.

◆ IsA() [1/2]

template<typename T >

bool xgboost::IsA ( Value const * value )

◆ IsA() [2/2]