org.tribuo.classification.xgboost

Class XGBoostOptions

java.lang.Object

org.tribuo.classification.xgboost.XGBoostOptions

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Options, ClassificationOptions<XGBoostClassificationTrainer>

public class XGBoostOptions
extends Object
implements ClassificationOptions<XGBoostClassificationTrainer>

CLI options for training an XGBoost classifier.

Field Summary

Fields

Modifier and Type	Field and Description
float	xbgAlpha
XGBoostTrainer.BoosterType	xgbBoosterType
int	xgbEnsembleSize
float	xgbEta
float	xgbGamma
float	xgbLambda
XGBoostTrainer.LoggingVerbosity	xgbLogLevel
int	xgbMaxDepth
float	xgbMinWeight
int	xgbNumThreads
boolean	xgbQuiet
float	xgbSubsample
float	xgbSubsampleFeatures
XGBoostTrainer.TreeMethod	xgbTreeMethod

Fields inherited from interface com.oracle.labs.mlrg.olcut.config.Options

header

Constructor Summary

Constructors

Constructor and Description
XGBoostOptions()

Method Summary

Modifier and Type	Method and Description
XGBoostClassificationTrainer	getTrainer() Constructs the trainer based on the provided arguments.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Options

formatUsage, generateTypeDescription, getAllOptions, getEnumConstantString, getOptionFields, getOptions, getOptionsDescription, getOptionUsage, getOptionUsage, getOptionUsage, getUsage

Field Detail

xgbBoosterType

@Option(longName="xgb-booster-type",
        usage="Weak learning algorithm.")
public XGBoostTrainer.BoosterType xgbBoosterType

xgbTreeMethod

@Option(longName="xgb-tree-method",
        usage="Tree building algorithm.")
public XGBoostTrainer.TreeMethod xgbTreeMethod

xgbEnsembleSize

@Option(longName="xgb-ensemble-size",
        usage="Number of trees in the ensemble.")
public int xgbEnsembleSize

xbgAlpha

@Option(longName="xgb-alpha",
        usage="L1 regularization term for weights.")
public float xbgAlpha

xgbMinWeight

@Option(longName="xgb-min-weight",
        usage="Minimum sum of instance weights needed in a leaf (range [0,Infinity]).")
public float xgbMinWeight

xgbMaxDepth

@Option(longName="xgb-max-depth",
        usage="Max tree depth (range (0,Integer.MAX_VALUE]).")
public int xgbMaxDepth

xgbEta

@Option(longName="xgb-eta",
        usage="Step size shrinkage parameter (range [0,1]).")
public float xgbEta

xgbSubsampleFeatures

@Option(longName="xgb-subsample-features",
        usage="Subsample features for each tree (range (0,1]).")
public float xgbSubsampleFeatures

xgbGamma

@Option(longName="xgb-gamma",
        usage="Minimum loss reduction to make a split (range [0,Infinity]).")
public float xgbGamma

xgbLambda

@Option(longName="xgb-lambda",
        usage="L2 regularization term for weights.")
public float xgbLambda

xgbQuiet

@Option(longName="xgb-quiet",
        usage="Deprecated, use xgb-loglevel.")
public boolean xgbQuiet

xgbLogLevel

@Option(longName="xgb-loglevel",
        usage="Make the XGBoost training procedure quiet.")
public XGBoostTrainer.LoggingVerbosity xgbLogLevel

xgbSubsample

@Option(longName="xgb-subsample",
        usage="Subsample size for each tree (range (0,1]).")
public float xgbSubsample

xgbNumThreads

@Option(longName="xgb-num-threads",
        usage="Number of threads to use (range (1, num hw threads)). The default of 0 means use all hw threads.")
public int xgbNumThreads

Constructor Detail

XGBoostOptions

public XGBoostOptions()

Method Detail

getTrainer

public XGBoostClassificationTrainer getTrainer()

Description copied from interface: ClassificationOptions

Constructs the trainer based on the provided arguments.

Specified by:

getTrainer in interface ClassificationOptions<XGBoostClassificationTrainer>

Returns:

The trainer.