Uses of Class
ml.dmlc.xgboost4j.java.XGBoostError

Packages that use XGBoostError

Package	Description
ml.dmlc.xgboost4j.java

Uses of XGBoostError in ml.dmlc.xgboost4j.java

Methods in ml.dmlc.xgboost4j.java that throw XGBoostError

Modifier and Type	Method	Description
void	Booster.boost(DMatrix dtrain, float[] grad, float[] hess)	Deprecated.
void	Booster.boost(DMatrix dtrain, int iter, float[] grad, float[] hess)	Update with give grad and hess
void	ConfigContext.close()
static void	Communicator.communicatorPrint(String msg)	Print the message via the communicator.
static String[]	XGBoost.crossValidation(DMatrix data, Map<String,Object> params, int round, int nfold, String[] metrics, IObjective obj, IEvaluation eval)	Cross-validation with given parameters.
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter)	Evaluate the Booster model with given dmatrixs.
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter, float[] metricsOut)	Evaluate the Booster model with given dmatrixs.
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, IEvaluation eval)	Evaluate the Booster model given customized Evaluation class
String	Booster.evalSet(DMatrix[] evalMatrixs, String[] evalNames, IEvaluation eval, float[] metricsOut)
final String	Booster.getAttr(String key)	Get attribute value from the Booster based on the key provided.
final Map<String,String>	Booster.getAttrs()	Get attributes stored in the Booster as a Map.
float[]	DMatrix.getBaseMargin()	Get base margin of the DMatrix.
Object	ConfigContext.getConfig(String name)
final String[]	Booster.getFeatureNames()	Get all the feature names from the Booster.
String[]	DMatrix.getFeatureNames()	Get feature names
Map<String,Integer>	Booster.getFeatureScore(String featureMap)	Get importance of each feature
Map<String,Integer>	Booster.getFeatureScore(String[] featureNames)	Get importance of each feature with specified feature names.
final String[]	Booster.getFeatureTypes()	Get feature types from the Booster.
String[]	DMatrix.getFeatureTypes()	Get feature types
int[]	DMatrix.getGroup()	Get group sizes of DMatrix
float[]	DMatrix.getLabel()	get label values
String[]	Booster.getModelDump(String[] featureNames, boolean withStats)	Get the dump of the model as a string array with specified feature names.
String[]	Booster.getModelDump(String[] featureNames, boolean withStats, String format)	Get the dump of the model as a string array with specified feature map, stats, and the specified format.
String[]	Booster.getModelDump(String featureMap, boolean withStats)	Get the dump of the model as a string array
String[]	Booster.getModelDump(String featureMap, boolean withStats, String format)	Get the dump of the model as a string array with specified feature map, stats, and the specified format.
int	Booster.getNumBoostedRound()
long	Booster.getNumFeature()	Get number of model features.
DMatrix.QuantileCut	DMatrix.getQuantileCut()	Get the Quantile Cut.
static int	Communicator.getRank()	get rank of current thread.
Map<String,Double>	Booster.getScore(String[] featureNames, String importanceType)	Get the feature importances for gain or cover (average or total)
Map<String,Double>	Booster.getScore(String featureMap, String importanceType)	Get the feature importances for gain or cover (average or total), with feature names
float[]	DMatrix.getWeight()	get weight of the DMatrix
Map<String,Object>	ITracker.getWorkerArgs()
Map<String,Object>	RabitTracker.getWorkerArgs()	Get environments that can be used to pass to worker.
static int	Communicator.getWorldSize()	get world size of current job.
static void	Communicator.init(Map<String,Object> envs)	Initialize the collective communicator on current working thread.
float[][]	Booster.inplace_predict(float[] data, int nrow, int ncol, float missing)	Perform thread-safe prediction.
float[][]	Booster.inplace_predict(float[] data, int nrow, int ncol, float missing, int[] iteration_range)	Perform thread-safe prediction.
float[][]	Booster.inplace_predict(float[] data, int nrow, int ncol, float missing, int[] iteration_range, Booster.PredictionType predict_type, float[] base_margin)	Perform thread-safe prediction.
Booster	ExternalCheckpointManager.loadCheckpointAsBooster()	Read the checkpoint from the checkpoint path.
static Booster	XGBoost.loadModel(byte[] buffer)	Load a new Booster model from a byte array buffer.
static Booster	XGBoost.loadModel(InputStream in)	Load a new Booster model from a file opened as input stream.
static Booster	XGBoost.loadModel(String modelPath)	load model from modelPath
long	DMatrix.nonMissingNum()	Get the number of non-missing values of DMatrix.
float[][]	Booster.predict(DMatrix data)	Make a prediction with test data in a DMatrix format.
float[][]	Booster.predict(DMatrix data, boolean outputMargin)	Make a prediction with test data in a DMatrix format and output margin.
float[][]	Booster.predict(DMatrix data, boolean outputMargin, int treeLimit)	Advanced predict function with all the options.
float[][]	Booster.predictContrib(DMatrix data, int treeLimit)	Output feature contributions toward predictions of given data
float[][]	Booster.predictLeaf(DMatrix data, int treeLimit)	Predict leaf indices given the data
long	DMatrix.rowNum()	get the row number of DMatrix
void	Booster.saveModel(OutputStream out)	Save the model to file opened as output stream.
void	Booster.saveModel(OutputStream out, String format)	Save the model to file opened as output stream.
void	Booster.saveModel(String modelPath)	Save model to modelPath
final void	Booster.setAttr(String key, String value)	Set an attribute key-value pair to the Booster.
void	Booster.setAttrs(Map<String,String> attrs)	Set multiple attribute key-value pairs to the Booster.
void	DMatrix.setBaseMargin(float[] baseMargin)	Set base margin (initial prediction).
void	DMatrix.setBaseMargin(float[][] baseMargin)	Set base margin (initial prediction).
void	DMatrix.setBaseMargin(Column column)	Set base margin of DMatrix from array interface
void	ConfigContext.setConfig(String key, Object value)	Set one single configuration
void	ConfigContext.setConfigs(Map<String,Object> configs)	Set a bunch of configurations
void	Booster.setFeatureNames(String[] featureNames)	Set feature names to the Booster.
void	DMatrix.setFeatureNames(String[] values)	Set feature names
void	Booster.setFeatureTypes(String[] featureTypes)	Set feature types to the Booster.
void	DMatrix.setFeatureTypes(String[] values)	Set feature types
void	DMatrix.setGroup(int[] group)	Set group sizes of DMatrix (used for ranking)
void	DMatrix.setLabel(float[] labels)	set label of dmatrix
void	DMatrix.setLabel(Column column)	Set label of DMatrix from array interface
final void	Booster.setParam(String key, Object value)	Set parameter to the Booster.
void	Booster.setParams(Map<String,Object> params)	Set parameters to the Booster.
void	DMatrix.setQueryId(int[] qid)	Set query ids (used for ranking)
void	DMatrix.setQueryId(Column column)	Set query id of DMatrix from array interface
void	DMatrix.setWeight(float[] weights)	set weight of each instance
void	DMatrix.setWeight(Column column)	Set weight of DMatrix from array interface
static void	Communicator.shutdown()	Shutdown the communicator in current working thread, equals to finalize.
DMatrix	DMatrix.slice(int[] rowIndex)	Slice the DMatrix and return a new DMatrix that only contains `rowIndex`.
boolean	ITracker.start()
boolean	RabitTracker.start()
void	ITracker.stop()
void	RabitTracker.stop()
byte[]	Booster.toByteArray()	Save model into raw byte array in the UBJSON ("ubj") format.
byte[]	Booster.toByteArray(String format)	Save model into raw byte array.
static Booster	XGBoost.train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRound)	Train a booster given parameters.
static Booster	XGBoost.train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRounds, Booster booster)	Train a booster given parameters.
static Booster	XGBoost.train(DMatrix dtrain, Map<String,Object> params, int round, Map<String,DMatrix> watches, IObjective obj, IEvaluation eval)	Train a booster given parameters.
static Booster	XGBoost.trainAndSaveCheckpoint(DMatrix dtrain, Map<String,Object> params, int numRounds, Map<String,DMatrix> watches, float[][] metrics, IObjective obj, IEvaluation eval, int earlyStoppingRounds, Booster booster, int checkpointInterval, String checkpointPath, org.apache.hadoop.fs.FileSystem fs)
void	Booster.update(DMatrix dtrain, int iter)	Update the booster for one iteration.
void	Booster.update(DMatrix dtrain, int iter, IObjective obj)	Update with customize object functon
void	Booster.update(DMatrix dtrain, IObjective obj)	Deprecated.
void	ExternalCheckpointManager.updateCheckpoint(Booster boosterToCheckpoint)	This method updates the booster checkpoint to the the latest or current version and deleted all the previous versions of the checkpoint.
void	ITracker.waitFor(long taskExecutionTimeout)
void	RabitTracker.waitFor(long timeout)

Constructors in ml.dmlc.xgboost4j.java that throw XGBoostError

Modifier	Constructor	Description
	ConfigContext()
	ConfigContext(Map<String,Object> params)
	DMatrix(float[] data, int nrow, int ncol)	Deprecated. Please specify the missing value explicitly using DMatrix(float[],int,int,float)
	DMatrix(float[] data, int nrow, int ncol, float missing)	create DMatrix from dense matrix
	DMatrix(long[] headers, int[] indices, float[] data, DMatrix.SparseType st)	Deprecated.
	DMatrix(long[] headers, int[] indices, float[] data, DMatrix.SparseType st, int shapeParam)	Create DMatrix from Sparse matrix in CSR/CSC format.
	DMatrix(long[] headers, int[] indices, float[] data, DMatrix.SparseType st, int shapeParam, float missing, int nthread)
	DMatrix(String dataPath)	Create DMatrix by loading libsvm file from dataPath
	DMatrix(Iterator<LabeledPoint> iter, String cacheInfo)	Create DMatrix from iterator.
	DMatrix(Iterator<LabeledPoint> iter, String cacheInfo, float missing)	Create DMatrix from iterator.
	DMatrix(ColumnBatch columnBatch, float missing, int nthread)	Create the normal DMatrix from column array interface
	DMatrix(BigDenseMatrix matrix)	create DMatrix from a BigDenseMatrix
	DMatrix(BigDenseMatrix matrix, float missing)	create DMatrix from dense matrix
	ExternalCheckpointManager(String checkpointPath, org.apache.hadoop.fs.FileSystem fs)	This constructor creates a new Expternal Checkpoint Manager at the specified path in the specified file system.
	RabitTracker(int numWorkers)
	RabitTracker(int numWorkers, String hostIp)
	RabitTracker(int numWorkers, String hostIp, int port, int timeout)