org.apache.spark.ml.regression

Class GBTRegressor

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<Vector,GBTRegressor,GBTRegressionModel>

org.apache.spark.ml.regression.GBTRegressor

All Implemented Interfaces:

java.io.Serializable, Params, DefaultParamsWritable, Identifiable, MLWritable

public class GBTRegressor
extends Predictor<Vector,GBTRegressor,GBTRegressionModel>
implements DefaultParamsWritable

Gradient-Boosted Trees (GBTs) learning algorithm for regression. It supports both continuous and categorical features.

The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

Notes on Gradient Boosting vs. TreeBoost: - This implementation is for Stochastic Gradient Boosting, not for TreeBoost. - Both algorithms learn tree ensembles by minimizing loss functions. - TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not. - When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions. - We expect to implement TreeBoost in the future: [https://issues.apache.org/jira/browse/SPARK-4240]

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GBTRegressor()
GBTRegressor(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
static BooleanParam	cacheNodeIds()
static IntParam	checkpointInterval()
static Params	clear(Param<?> param)
GBTRegressor	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
Param<String>	featuresCol() Param for features column name.
static M	fit(Dataset<?> dataset)
static M	fit(Dataset<?> dataset, ParamMap paramMap)
static scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static <T> scala.Option<T>	get(Param<T> param)
static boolean	getCacheNodeIds()
static int	getCheckpointInterval()
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFeaturesCol()
String	getFeaturesCol()
static String	getImpurity()
static String	getLabelCol()
String	getLabelCol()
static String	getLossType()
static int	getMaxBins()
static int	getMaxDepth()
static int	getMaxIter()
static int	getMaxMemoryInMB()
static double	getMinInfoGain()
static int	getMinInstancesPerNode()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
String	getPredictionCol()
static long	getSeed()
static double	getStepSize()
static double	getSubsamplingRate()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static Param<String>	impurity()
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
Param<String>	labelCol() Param for label column name.
static GBTRegressor	load(String path)
static Param<String>	lossType()
static IntParam	maxBins()
static IntParam	maxDepth()
static IntParam	maxIter()
static IntParam	maxMemoryInMB()
static DoubleParam	minInfoGain()
static IntParam	minInstancesPerNode()
static Param<?>[]	params()
static Param<String>	predictionCol()
Param<String>	predictionCol() Param for prediction column name.
static void	save(String path)
static LongParam	seed()
static <T> Params	set(Param<T> param, T value)
GBTRegressor	setCacheNodeIds(boolean value)
GBTRegressor	setCheckpointInterval(int value) Specifies how often to checkpoint the cached node IDs.
static Learner	setFeaturesCol(String value)
GBTRegressor	setImpurity(String value) The impurity setting is ignored for GBT models.
static Learner	setLabelCol(String value)
GBTRegressor	setLossType(String value)
GBTRegressor	setMaxBins(int value)
GBTRegressor	setMaxDepth(int value)
GBTRegressor	setMaxIter(int value)
GBTRegressor	setMaxMemoryInMB(int value)
GBTRegressor	setMinInfoGain(double value)
GBTRegressor	setMinInstancesPerNode(int value)
static Learner	setPredictionCol(String value)
GBTRegressor	setSeed(long value)
GBTRegressor	setStepSize(double value)
GBTRegressor	setSubsamplingRate(double value)
static DoubleParam	stepSize()
static DoubleParam	subsamplingRate()
static String[]	supportedLossTypes() Accessor for supported loss settings: squared (L2), absolute (L1)
static String	toString()
static StructType	transformSchema(StructType schema)
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
static MLWriter	write()

Methods inherited from class org.apache.spark.ml.Predictor

fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Constructor Detail

GBTRegressor

public GBTRegressor(String uid)

GBTRegressor

public GBTRegressor()

Method Detail

supportedLossTypes

public static final String[] supportedLossTypes()

Accessor for supported loss settings: squared (L2), absolute (L1)

load

public static GBTRegressor load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    scala.collection.Seq<ParamPair<?>> otherParamPairs)

fit

public static M fit(Dataset<?> dataset,
    ParamMap paramMap)

fit

public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                          ParamMap[] paramMaps)

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    ParamPair<?>... otherParamPairs)

labelCol

public static final Param<String> labelCol()

getLabelCol

public static final String getLabelCol()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

setLabelCol

public static Learner setLabelCol(String value)

setFeaturesCol

public static Learner setFeaturesCol(String value)

setPredictionCol

public static Learner setPredictionCol(String value)

fit

public static M fit(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

checkpointInterval

public static final IntParam checkpointInterval()

getCheckpointInterval

public static final int getCheckpointInterval()

seed

public static final LongParam seed()

getSeed

public static final long getSeed()

maxDepth

public static final IntParam maxDepth()

maxBins

public static final IntParam maxBins()

minInstancesPerNode

public static final IntParam minInstancesPerNode()

minInfoGain

public static final DoubleParam minInfoGain()

maxMemoryInMB

public static final IntParam maxMemoryInMB()

cacheNodeIds

public static final BooleanParam cacheNodeIds()

getMaxDepth

public static final int getMaxDepth()

getMaxBins

public static final int getMaxBins()

getMinInstancesPerNode

public static final int getMinInstancesPerNode()

getMinInfoGain

public static final double getMinInfoGain()

getMaxMemoryInMB

public static final int getMaxMemoryInMB()

getCacheNodeIds

public static final boolean getCacheNodeIds()

subsamplingRate

public static final DoubleParam subsamplingRate()

getSubsamplingRate

public static final double getSubsamplingRate()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

stepSize

public static final DoubleParam stepSize()

getStepSize

public static final double getStepSize()

impurity

public static final Param<String> impurity()

getImpurity

public static final String getImpurity()

lossType

public static Param<String> lossType()

getLossType

public static String getLossType()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

write

public static MLWriter write()

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setMaxDepth

public GBTRegressor setMaxDepth(int value)

setMaxBins

public GBTRegressor setMaxBins(int value)

setMinInstancesPerNode

public GBTRegressor setMinInstancesPerNode(int value)

setMinInfoGain

public GBTRegressor setMinInfoGain(double value)

setMaxMemoryInMB

public GBTRegressor setMaxMemoryInMB(int value)

setCacheNodeIds

public GBTRegressor setCacheNodeIds(boolean value)

setCheckpointInterval

public GBTRegressor setCheckpointInterval(int value)

Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in SparkContext. Must be at least 1. (default = 10)

Parameters:

value - (undocumented)

Returns:

(undocumented)

setImpurity

public GBTRegressor setImpurity(String value)

The impurity setting is ignored for GBT models. Individual trees are built using impurity "Variance."

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSubsamplingRate

public GBTRegressor setSubsamplingRate(double value)

setSeed

public GBTRegressor setSeed(long value)

setMaxIter

public GBTRegressor setMaxIter(int value)

setStepSize

public GBTRegressor setStepSize(double value)

setLossType

public GBTRegressor setLossType(String value)

copy

public GBTRegressor copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,GBTRegressor,GBTRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public String getLabelCol()

featuresCol

public Param<String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public String getFeaturesCol()

predictionCol

public Param<String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public String getPredictionCol()