org.apache.spark.ml.regression

Class GeneralizedLinearRegression

java.lang.Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<FeaturesType,Learner,M>

org.apache.spark.ml.regression.GeneralizedLinearRegression

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable

public class GeneralizedLinearRegression
extends Predictor<FeaturesType,Learner,M>

:: Experimental ::

Fit a Generalized Linear Model (https://en.wikipedia.org/wiki/Generalized_linear_model) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson" and "gamma" as family. Valid link functions for each family is listed below. The first link function of each family is the default one. - "gaussian" -> "identity", "log", "inverse" - "binomial" -> "logit", "probit", "cloglog" - "poisson" -> "log", "identity", "sqrt" - "gamma" -> "inverse", "identity", "log"

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	GeneralizedLinearRegression.Binomial$ Binomial exponential family distribution.
static class	GeneralizedLinearRegression.CLogLog$
static class	GeneralizedLinearRegression.Family$
static class	GeneralizedLinearRegression.Gamma$ Gamma exponential family distribution.
static class	GeneralizedLinearRegression.Gaussian$ Gaussian exponential family distribution.
static class	GeneralizedLinearRegression.Identity$
static class	GeneralizedLinearRegression.Inverse$
static class	GeneralizedLinearRegression.Link$
static class	GeneralizedLinearRegression.Log$
static class	GeneralizedLinearRegression.Logit$
static class	GeneralizedLinearRegression.Poisson$ Poisson exponential family distribution.
static class	GeneralizedLinearRegression.Probit$
static class	GeneralizedLinearRegression.Sqrt$

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
protected static <T> T	$(Param<T> param)
static Params	clear(Param<?> param)
GeneralizedLinearRegression	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
protected static <T extends Params> T	copyValues(T to, ParamMap extra)
protected static <T extends Params> ParamMap	copyValues$default$2()
protected static <T extends Params> T	defaultCopy(ParamMap extra)
static java.lang.String	explainParam(Param<?> param)
static java.lang.String	explainParams()
protected static RDD<LabeledPoint>	extractLabeledPoints(Dataset<?> dataset)
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<java.lang.String>	family()
Param<java.lang.String>	family() Param for the name of family which is a description of the error distribution to be used in the model.
static Param<java.lang.String>	featuresCol()
Param<java.lang.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 BooleanParam	fitIntercept()
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static java.lang.String	getFamily()
java.lang.String	getFamily()
static java.lang.String	getFeaturesCol()
java.lang.String	getFeaturesCol()
static boolean	getFitIntercept()
static java.lang.String	getLabelCol()
java.lang.String	getLabelCol()
static java.lang.String	getLink()
java.lang.String	getLink()
static java.lang.String	getLinkPredictionCol()
java.lang.String	getLinkPredictionCol()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<java.lang.Object>	getParam(java.lang.String paramName)
static java.lang.String	getPredictionCol()
java.lang.String	getPredictionCol()
static double	getRegParam()
static java.lang.String	getSolver()
static double	getTol()
static java.lang.String	getWeightCol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(java.lang.String paramName)
protected static void	initializeLogIfNecessary(boolean isInterpreter)
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
protected static boolean	isTraceEnabled()
static Param<java.lang.String>	labelCol()
Param<java.lang.String>	labelCol() Param for label column name.
static Param<java.lang.String>	link()
Param<java.lang.String>	link() Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.
static Param<java.lang.String>	linkPredictionCol()
Param<java.lang.String>	linkPredictionCol() Param for link prediction (linear predictor) column name.
static GeneralizedLinearRegression	load(java.lang.String path)
protected static org.slf4j.Logger	log()
protected static void	logDebug(scala.Function0<java.lang.String> msg)
protected static void	logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logError(scala.Function0<java.lang.String> msg)
protected static void	logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logInfo(scala.Function0<java.lang.String> msg)
protected static void	logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static java.lang.String	logName()
protected static void	logTrace(scala.Function0<java.lang.String> msg)
protected static void	logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logWarning(scala.Function0<java.lang.String> msg)
protected static void	logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
static IntParam	maxIter()
static Param<?>[]	params()
static Param<java.lang.String>	predictionCol()
Param<java.lang.String>	predictionCol() Param for prediction column name.
static DoubleParam	regParam()
static void	save(java.lang.String path)
static <T> Params	set(Param<T> param, T value)
protected static Params	set(ParamPair<?> paramPair)
protected static Params	set(java.lang.String param, java.lang.Object value)
protected static <T> Params	setDefault(Param<T> param, T value)
protected static Params	setDefault(scala.collection.Seq<ParamPair<?>> paramPairs)
GeneralizedLinearRegression	setFamily(java.lang.String value) Sets the value of param family.
static Learner	setFeaturesCol(java.lang.String value)
GeneralizedLinearRegression	setFitIntercept(boolean value) Sets if we should fit the intercept.
static Learner	setLabelCol(java.lang.String value)
GeneralizedLinearRegression	setLink(java.lang.String value) Sets the value of param link.
GeneralizedLinearRegression	setLinkPredictionCol(java.lang.String value) Sets the link prediction (linear predictor) column name.
GeneralizedLinearRegression	setMaxIter(int value) Sets the maximum number of iterations (applicable for solver "irls").
static Learner	setPredictionCol(java.lang.String value)
GeneralizedLinearRegression	setRegParam(double value) Sets the regularization parameter for L2 regularization.
GeneralizedLinearRegression	setSolver(java.lang.String value) Sets the solver algorithm used for optimization.
GeneralizedLinearRegression	setTol(double value) Sets the convergence tolerance of iterations.
GeneralizedLinearRegression	setWeightCol(java.lang.String value) Sets the value of param weightCol.
static Param<java.lang.String>	solver()
static DoubleParam	tol()
static java.lang.String	toString()
protected GeneralizedLinearRegressionModel	train(Dataset<?> dataset) Train a model using the given dataset and parameters.
static StructType	transformSchema(StructType schema)
protected static StructType	transformSchema(StructType schema, boolean logging)
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
static StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
static void	validateParams()
static Param<java.lang.String>	weightCol()
static MLWriter	write()
MLWriter	write() Returns an MLWriter instance for this ML instance.

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.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, validateParams

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

toString

Constructor Detail

GeneralizedLinearRegression

public GeneralizedLinearRegression(java.lang.String uid)

GeneralizedLinearRegression

public GeneralizedLinearRegression()

Method Detail

load

public static GeneralizedLinearRegression load(java.lang.String path)

toString

public static java.lang.String toString()

params

public static Param<?>[] params()

validateParams

public static void validateParams()

explainParam

public static java.lang.String explainParam(Param<?> param)

explainParams

public static java.lang.String explainParams()

isSet

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

isDefined

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

hasParam

public static boolean hasParam(java.lang.String paramName)

getParam

public static Param<java.lang.Object> getParam(java.lang.String paramName)

set

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

set

protected static final Params set(java.lang.String param,
         java.lang.Object value)

set

protected static final Params set(ParamPair<?> paramPair)

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)

$

protected static final <T> T $(Param<T> param)

setDefault

protected static final <T> Params setDefault(Param<T> param,
                    T value)

setDefault

protected static final Params setDefault(scala.collection.Seq<ParamPair<?>> paramPairs)

getDefault

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

hasDefault

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

defaultCopy

protected static final <T extends Params> T defaultCopy(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

copyValues

protected static <T extends Params> T copyValues(T to,
                              ParamMap extra)

copyValues$default$2

protected static <T extends Params> ParamMap copyValues$default$2()

logName

protected static java.lang.String logName()

log

protected static org.slf4j.Logger log()

logInfo

protected static void logInfo(scala.Function0<java.lang.String> msg)

logDebug

protected static void logDebug(scala.Function0<java.lang.String> msg)

logTrace

protected static void logTrace(scala.Function0<java.lang.String> msg)

logWarning

protected static void logWarning(scala.Function0<java.lang.String> msg)

logError

protected static void logError(scala.Function0<java.lang.String> msg)

logInfo

protected static void logInfo(scala.Function0<java.lang.String> msg,
           java.lang.Throwable throwable)

logDebug

protected static void logDebug(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

logTrace

protected static void logTrace(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

logWarning

protected static void logWarning(scala.Function0<java.lang.String> msg,
              java.lang.Throwable throwable)

logError

protected static void logError(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

isTraceEnabled

protected static boolean isTraceEnabled()

initializeLogIfNecessary

protected static void initializeLogIfNecessary(boolean isInterpreter)

transformSchema

protected static StructType transformSchema(StructType schema,
                         boolean logging)

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<java.lang.String> labelCol()

getLabelCol

public static final java.lang.String getLabelCol()

featuresCol

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

getFeaturesCol

public static final java.lang.String getFeaturesCol()

predictionCol

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

getPredictionCol

public static final java.lang.String getPredictionCol()

setLabelCol

public static Learner setLabelCol(java.lang.String value)

setFeaturesCol

public static Learner setFeaturesCol(java.lang.String value)

setPredictionCol

public static Learner setPredictionCol(java.lang.String value)

fit

public static M fit(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

extractLabeledPoints

protected static RDD<LabeledPoint> extractLabeledPoints(Dataset<?> dataset)

fitIntercept

public static final BooleanParam fitIntercept()

getFitIntercept

public static final boolean getFitIntercept()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

tol

public static final DoubleParam tol()

getTol

public static final double getTol()

regParam

public static final DoubleParam regParam()

getRegParam

public static final double getRegParam()

weightCol

public static final Param<java.lang.String> weightCol()

getWeightCol

public static final java.lang.String getWeightCol()

solver

public static final Param<java.lang.String> solver()

getSolver

public static final java.lang.String getSolver()

family

public static final Param<java.lang.String> family()

getFamily

public static java.lang.String getFamily()

link

public static final Param<java.lang.String> link()

getLink

public static java.lang.String getLink()

linkPredictionCol

public static final Param<java.lang.String> linkPredictionCol()

getLinkPredictionCol

public static java.lang.String getLinkPredictionCol()

validateAndTransformSchema

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

save

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

Throws:

java.io.IOException

write

public static MLWriter write()

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)

setFamily

public GeneralizedLinearRegression setFamily(java.lang.String value)

Sets the value of param family. Default is "gaussian".

Parameters:

value - (undocumented)

Returns:

(undocumented)

setLink

public GeneralizedLinearRegression setLink(java.lang.String value)

Sets the value of param link.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setFitIntercept

public GeneralizedLinearRegression setFitIntercept(boolean value)

Sets if we should fit the intercept. Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxIter

public GeneralizedLinearRegression setMaxIter(int value)

Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public GeneralizedLinearRegression setTol(double value)

Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setRegParam

public GeneralizedLinearRegression setRegParam(double value)

Sets the regularization parameter for L2 regularization. The regularization term is

   0.5 * regParam * L2norm(coefficients)^2
 

Default is 0.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setWeightCol

public GeneralizedLinearRegression setWeightCol(java.lang.String value)

Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is empty, so all instances have weight one.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSolver

public GeneralizedLinearRegression setSolver(java.lang.String value)

Sets the solver algorithm used for optimization. Currently only support "irls" which is also the default solver.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setLinkPredictionCol

public GeneralizedLinearRegression setLinkPredictionCol(java.lang.String value)

Sets the link prediction (linear predictor) column name.

Parameters:

value - (undocumented)

Returns:

(undocumented)

train

protected GeneralizedLinearRegressionModel train(Dataset<?> 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,GeneralizedLinearRegression,GeneralizedLinearRegressionModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public GeneralizedLinearRegression 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,GeneralizedLinearRegression,GeneralizedLinearRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

family

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

Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson" and "gamma". Default is "gaussian".

Returns:

(undocumented)

getFamily

public java.lang.String getFamily()

link

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

Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt".

Returns:

(undocumented)

getLink

public java.lang.String getLink()

linkPredictionCol

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

Param for link prediction (linear predictor) column name. Default is empty, which means we do not output link prediction.

Returns:

(undocumented)

getLinkPredictionCol

public java.lang.String getLinkPredictionCol()

validateAndTransformSchema

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

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

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<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()