xgboost Namespace Reference

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	DMatrix
	Internal data structured used by XGBoost during training. 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...
struct	from_chars_result
struct	GenericParameter
class	GradientBooster
	interface of gradient boosting model. More...
struct	GradientBoosterReg
	Registry entry for tree updater. More...
class	HostDeviceVector
struct	HostDeviceVectorImpl
struct	HostSparsePageView
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
struct	NumericLimits
struct	NumericLimits< float >
struct	NumericLimits< int64_t >
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
struct	to_chars_result
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	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,   DataType::kStr = 5 }
	data type accepted by xgboost interface More...
enum	FeatureType : uint8_t { FeatureType::kNumerical }
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)
to_chars_result	to_chars (char *first, char *last, float value)
to_chars_result	to_chars (char *first, char *last, int64_t value)
from_chars_result	from_chars (const char *buffer, const char *end, float &value)

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

Detailed Description

namespace of xgboost

Implement std::to_chars and std::from_chars for float. Only base 10 with scientific format is supported. The implementation guarantees roundtrip reproducibility.

Copyright (c) by XGBoost Contributors 2019-2020

Copyright (c) by Contributors 2019

Copyright 2019 by XGBoost Contributors

Copyright by Contributors 2017-2019

Copyright 2018 XGBoost contributors

Copyright 2019 XGBoost contributors

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

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
kStr

FeatureType

enum xgboost::FeatureType : uint8_t

strong

Enumerator
kNumerical

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

)

from_chars()

from_chars_result xgboost::from_chars ( const char *  buffer, const char *  end, float &  value  )

inline

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]

template<typename T >

bool xgboost::IsA

(

Json const

j

)

to_chars() [1/2]

to_chars_result xgboost::to_chars ( char *  first, char *  last, float  value  )

inline

to_chars() [2/2]

to_chars_result xgboost::to_chars ( char *  first, char *  last, int64_t  value  )

inline

ToJson()

template<typename Parameter >

Object xgboost::ToJson

(

Parameter const &

param

)

Variable Documentation

kRtEps

constexpr bst_float xgboost::kRtEps = 1e-6f

small eps gap for minimum split decision.