org.apache.spark.ml.regression

Class GBTRegressor

java.lang.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, Logging, Params, Identifiable

public final class GBTRegressor
extends Predictor<Vector,GBTRegressor,GBTRegressionModel>
implements Logging

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GBTRegressor()
GBTRegressor(java.lang.String uid)

Method Summary

Methods

Modifier and Type	Method and Description
GBTRegressor	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<java.lang.String>	featuresCol() Param for features column name.
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getLossType()
java.lang.String	getPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
Param<java.lang.String>	lossType() Loss function which GBT tries to minimize.
Param<java.lang.String>	predictionCol() Param for prediction column name.
GBTRegressor	setCacheNodeIds(boolean value)
GBTRegressor	setCheckpointInterval(int value)
GBTRegressor	setImpurity(java.lang.String value) The impurity setting is ignored for GBT models.
GBTRegressor	setLossType(java.lang.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)
GBTRegressor	setSeed(long value)
GBTRegressor	setStepSize(double value)
GBTRegressor	setSubsamplingRate(double value)
static java.lang.String[]	supportedLossTypes() Accessor for supported loss settings: squared (L2), absolute (L1)
protected GBTRegressionModel	train(DataFrame dataset) Train a model using the given dataset and parameters.
java.lang.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.

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

extractLabeledPoints, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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

fit, fit, fit, fit

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

transformSchema

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

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, validateParams

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

toString

Constructor Detail

GBTRegressor

public GBTRegressor(java.lang.String uid)

GBTRegressor

public GBTRegressor()

Method Detail

supportedLossTypes

public static final java.lang.String[] supportedLossTypes()

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

uid

public java.lang.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)

setImpurity

public GBTRegressor setImpurity(java.lang.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)

lossType

public Param<java.lang.String> lossType()

Loss function which GBT tries to minimize. (case-insensitive) Supported: "squared" (L2) and "absolute" (L1) (default = squared)

Returns:

(undocumented)

setLossType

public GBTRegressor setLossType(java.lang.String value)

getLossType

public java.lang.String getLossType()

train

protected GBTRegressionModel train(DataFrame dataset)

Description copied from class: Predictor

Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

Specified by:

train in class Predictor<Vector,GBTRegressor,GBTRegressionModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

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.

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,GBTRegressor,GBTRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

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<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()