xgboost
Namespaces | Classes | Typedefs | Enumerations | Functions | Variables
xgboost Namespace Reference

namespace of xgboost More...

Namespaces

 common
 
 detail
 

Classes

struct  AllVisibleImpl
 
class  BatchIterator
 
class  BatchIteratorImpl
 
class  BatchSet
 
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...
 
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  GPUDistribution
 
class  GPUSet
 
class  GradientBooster
 interface of gradient boosting model. More...
 
struct  GradientBoosterReg
 Registry entry for tree updater. More...
 
class  HostDeviceVector
 
struct  HostDeviceVectorImpl
 
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...
 
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...
 
class  ObjFunction
 interface of objective function More...
 
struct  ObjFunctionReg
 Registry entry for objective factory functions. More...
 
class  Predictor
 Performs prediction on individual training instances or batches of instances for GBTree. The predictor also manages a prediction cache associated with input matrices. If possible, it will use previously calculated predictions instead of calculating new predictions. 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...
 
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...
 
class  RowSet
 A vector-like structure to represent set of rows. But saves the memory when all rows are in the set (common case in xgb) More...
 
struct  RTreeNodeStat
 node statistics used in regression tree More...
 
class  SparsePage
 In-memory storage unit of sparse batch, stored in CSR format. More...
 
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...
 

Typedefs

using bst_uint = uint32_t
 unsigned integer type used in boost, used for feature index and row index. More...
 
using bst_int = int32_t
 
typedef uint64_t bst_ulong
 long integers More...
 
using bst_float = float
 float type, used for storing statistics 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 GradientPairInteger = detail::GradientPairInternal< int64_t >
 High precision gradient statistics pair with integer backed storage. Operators are associative where floating point versions are not associative. More...
 
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...
 

Enumerations

enum  DataType { kFloat32 = 1, kDouble = 2, kUInt32 = 3, kUInt64 = 4 }
 data type accepted by xgboost interface More...
 
enum  GPUAccess { kNone, kRead, kWrite }
 

Functions

GPUAccess operator- (GPUAccess a, GPUAccess b)
 

Variables

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

Detailed Description

namespace of xgboost

Copyright by Contributors 2017

Copyright 2018 XGBoost contributors

Typedef Documentation

◆ bst_float

using xgboost::bst_float = typedef float

float type, used for storing statistics

◆ bst_int

using xgboost::bst_int = typedef int32_t

◆ bst_omp_uint

using xgboost::bst_omp_uint = typedef dmlc::omp_uint

define unsigned int for openmp loop

◆ bst_uint

using xgboost::bst_uint = typedef uint32_t

unsigned integer type used in boost, used for feature index and row index.

◆ bst_ulong

typedef uint64_t xgboost::bst_ulong

long integers

◆ GradientPair

gradient statistics pair usually needed in gradient boosting

◆ GradientPairInteger

High precision gradient statistics pair with integer backed storage. Operators are associative where floating point versions are not associative.

◆ GradientPairPrecise

High precision gradient statistics pair.

◆ omp_ulong

using xgboost::omp_ulong = typedef dmlc::omp_ulong

define unsigned long for openmp loop

Enumeration Type Documentation

◆ DataType

data type accepted by xgboost interface

Enumerator
kFloat32 
kDouble 
kUInt32 
kUInt64 

◆ GPUAccess

Enumerator
kNone 
kRead 
kWrite 

Function Documentation

◆ operator-()

GPUAccess xgboost::operator- ( GPUAccess  a,
GPUAccess  b 
)
inline

Variable Documentation

◆ kRtEps

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

small eps gap for minimum split decision.