org.tribuo.classification.xgboost

Class XGBoostClassificationTrainer

java.lang.Object

org.tribuo.common.xgboost.XGBoostTrainer<Label>

org.tribuo.classification.xgboost.XGBoostClassificationTrainer

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<Label>, WeightedExamples

public final class XGBoostClassificationTrainer
extends XGBoostTrainer<Label>

A Trainer which wraps the XGBoost training procedure.

This only exposes a few of XGBoost's training parameters.

It uses pthreads outside of the JVM to parallelise the computation.

See:

 Chen T, Guestrin C.
 "XGBoost: A Scalable Tree Boosting System"
 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
 

and for the original algorithm:

 Friedman JH.
 "Greedy Function Approximation: a Gradient Boosting Machine"
 Annals of statistics, 2001.
 

Note: XGBoost requires a native library, on macOS this library requires libomp (which can be installed via homebrew), on Windows this native library must be compiled into a jar as it's not contained in the official XGBoost binary on Maven Central.

Nested Class Summary

Nested classes/interfaces inherited from class org.tribuo.common.xgboost.XGBoostTrainer

XGBoostTrainer.BoosterType, XGBoostTrainer.DMatrixTuple<T extends Output<T>>, XGBoostTrainer.XGBoostTrainerProvenance

Field Summary

Fields inherited from class org.tribuo.common.xgboost.XGBoostTrainer

numTrees, parameters, trainInvocationCounter

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	XGBoostClassificationTrainer() For olcut.
	XGBoostClassificationTrainer(int numTrees)
	XGBoostClassificationTrainer(int numTrees, double eta, double gamma, int maxDepth, double minChildWeight, double subsample, double featureSubsample, double lambda, double alpha, int nThread, boolean silent, long seed) Create an XGBoost trainer.
	XGBoostClassificationTrainer(int numTrees, int numThreads, boolean silent)
	XGBoostClassificationTrainer(int numTrees, Map<String,Object> parameters) This gives direct access to the XGBoost parameter map.

Method Summary

Modifier and Type	Method and Description
TrainerProvenance	getProvenance()
void	postConfig() Used by the OLCUT configuration system, and should not be called by external code.
XGBoostModel<Label>	train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a predictive model using the examples in the given data set.

Methods inherited from class org.tribuo.common.xgboost.XGBoostTrainer

convertDataset, convertDataset, convertExample, convertExample, convertExamples, convertExamples, convertSingleExample, convertSparseVector, convertSparseVectors, createModel, getInvocationCount, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.tribuo.Trainer

train

Constructor Detail

XGBoostClassificationTrainer

public XGBoostClassificationTrainer(int numTrees)

XGBoostClassificationTrainer

public XGBoostClassificationTrainer(int numTrees,
                                    int numThreads,
                                    boolean silent)

XGBoostClassificationTrainer

public XGBoostClassificationTrainer(int numTrees,
                                    double eta,
                                    double gamma,
                                    int maxDepth,
                                    double minChildWeight,
                                    double subsample,
                                    double featureSubsample,
                                    double lambda,
                                    double alpha,
                                    int nThread,
                                    boolean silent,
                                    long seed)

Create an XGBoost trainer.

Parameters:

numTrees - Number of trees to boost.

eta - Step size shrinkage parameter (default 0.3, range [0,1]).

gamma - Minimum loss reduction to make a split (default 0, range [0,inf]).

maxDepth - Maximum tree depth (default 6, range [1,inf]).

minChildWeight - Minimum sum of instance weights needed in a leaf (default 1, range [0, inf]).

subsample - Subsample size for each tree (default 1, range (0,1]).

featureSubsample - Subsample features for each tree (default 1, range (0,1]).

lambda - L2 regularization term on weights (default 1).

alpha - L1 regularization term on weights (default 0).

nThread - Number of threads to use (default 4).

silent - Silence the training output text.

seed - RNG seed.

XGBoostClassificationTrainer

public XGBoostClassificationTrainer(int numTrees,
                                    Map<String,Object> parameters)

This gives direct access to the XGBoost parameter map.

It lets you pick things that we haven't exposed like dropout trees, binary classification etc.

This sidesteps the validation that Tribuo provides for the hyperparameters, and so can produce unexpected results.

Parameters:

numTrees - Number of trees to boost.

parameters - A map from string to object, where object can be Number or String.

XGBoostClassificationTrainer

protected XGBoostClassificationTrainer()

For olcut.

Method Detail

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

Overrides:

postConfig in class XGBoostTrainer<Label>

train

public XGBoostModel<Label> train(Dataset<Label> examples,
                                 Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Description copied from interface: Trainer

Trains a predictive model using the examples in the given data set.

Parameters:

examples - the data set containing the examples.

runProvenance - Training run specific provenance (e.g., fold number).

Returns:

a predictive model that can be used to generate predictions for new examples.

getProvenance

public TrainerProvenance getProvenance()