org.apache.spark.ml.regression

Class GeneralizedLinearRegression

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, Logging, Params, DefaultParamsWritable, Identifiable, MLWritable

public class GeneralizedLinearRegression
extends Predictor<FeaturesType,Learner,M>
implements DefaultParamsWritable, Logging

:: Experimental ::

Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) 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", "gamma" and "tweedie" 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" - "tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.

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.FamilyAndLink$
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$
static class	GeneralizedLinearRegression.Tweedie$

Constructor Summary

Constructors

Constructor and Description
GeneralizedLinearRegression()
GeneralizedLinearRegression(String uid)

Method Summary

Modifier and Type	Method and Description
static Params	clear(Param<?> param)
GeneralizedLinearRegression	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>	family()
Param<String>	family() Param for the name of family which is a description of the error distribution to be used in the model.
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 BooleanParam	fitIntercept()
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFamily()
String	getFamily()
static String	getFeaturesCol()
String	getFeaturesCol()
static boolean	getFitIntercept()
static String	getLabelCol()
String	getLabelCol()
static String	getLink()
String	getLink()
static double	getLinkPower()
double	getLinkPower()
static String	getLinkPredictionCol()
String	getLinkPredictionCol()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
String	getPredictionCol()
static double	getRegParam()
static String	getSolver()
static double	getTol()
static double	getVariancePower()
double	getVariancePower()
static String	getWeightCol()
static <T> boolean	hasDefault(Param<T> param)
boolean	hasLinkPredictionCol() Checks whether we should output link prediction.
static boolean	hasParam(String paramName)
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
Param<String>	labelCol() Param for label column name.
static Param<String>	link()
Param<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 DoubleParam	linkPower()
DoubleParam	linkPower() Param for the index in the power link function.
static Param<String>	linkPredictionCol()
Param<String>	linkPredictionCol() Param for link prediction (linear predictor) column name.
static GeneralizedLinearRegression	load(String path)
static IntParam	maxIter()
static Param<?>[]	params()
static Param<String>	predictionCol()
Param<String>	predictionCol() Param for prediction column name.
static DoubleParam	regParam()
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
GeneralizedLinearRegression	setFamily(String value) Sets the value of param family.
static Learner	setFeaturesCol(String value)
GeneralizedLinearRegression	setFitIntercept(boolean value) Sets if we should fit the intercept.
static Learner	setLabelCol(String value)
GeneralizedLinearRegression	setLink(String value) Sets the value of param link.
GeneralizedLinearRegression	setLinkPower(double value) Sets the value of param linkPower.
GeneralizedLinearRegression	setLinkPredictionCol(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(String value)
GeneralizedLinearRegression	setRegParam(double value) Sets the regularization parameter for L2 regularization.
GeneralizedLinearRegression	setSolver(String value) Sets the solver algorithm used for optimization.
GeneralizedLinearRegression	setTol(double value) Sets the convergence tolerance of iterations.
GeneralizedLinearRegression	setVariancePower(double value) Sets the value of param variancePower.
GeneralizedLinearRegression	setWeightCol(String value) Sets the value of param weightCol.
static Param<String>	solver()
static DoubleParam	tol()
static String	toString()
static StructType	transformSchema(StructType schema)
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 DoubleParam	variancePower()
DoubleParam	variancePower() Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.
static Param<String>	weightCol()
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.internal.Logging

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

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

GeneralizedLinearRegression

public GeneralizedLinearRegression(String uid)

GeneralizedLinearRegression

public GeneralizedLinearRegression()

Method Detail

load

public static GeneralizedLinearRegression 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)

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<String> weightCol()

getWeightCol

public static final String getWeightCol()

solver

public static final Param<String> solver()

getSolver

public static final String getSolver()

family

public static final Param<String> family()

getFamily

public static String getFamily()

variancePower

public static final DoubleParam variancePower()

getVariancePower

public static double getVariancePower()

link

public static final Param<String> link()

getLink

public static String getLink()

linkPower

public static final DoubleParam linkPower()

getLinkPower

public static double getLinkPower()

linkPredictionCol

public static final Param<String> linkPredictionCol()

getLinkPredictionCol

public static String getLinkPredictionCol()

validateAndTransformSchema

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

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)

setFamily

public GeneralizedLinearRegression setFamily(String value)

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

Parameters:

value - (undocumented)

Returns:

(undocumented)

setVariancePower

public GeneralizedLinearRegression setVariancePower(double value)

Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setLinkPower

public GeneralizedLinearRegression setLinkPower(double value)

Sets the value of param linkPower. Used only when family is "tweedie".

Parameters:

value - (undocumented)

Returns:

(undocumented)

setLink

public GeneralizedLinearRegression setLink(String value)

Sets the value of param link. Used only when family is not "tweedie".

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(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 not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSolver

public GeneralizedLinearRegression setSolver(String value)

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

Parameters:

value - (undocumented)

Returns:

(undocumented)

setLinkPredictionCol

public GeneralizedLinearRegression setLinkPredictionCol(String value)

Sets the link prediction (linear predictor) column name.

Parameters:

value - (undocumented)

Returns:

(undocumented)

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. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,GeneralizedLinearRegression,GeneralizedLinearRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

family

public Param<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", "gamma" and "tweedie". Default is "gaussian".

Returns:

(undocumented)

getFamily

public String getFamily()

variancePower

public DoubleParam variancePower()

Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.

Returns:

(undocumented)

getVariancePower

public double getVariancePower()

link

public Param<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". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.

Returns:

(undocumented)

getLink

public String getLink()

linkPower

public DoubleParam linkPower()

Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.

Returns:

(undocumented)

getLinkPower

public double getLinkPower()

linkPredictionCol

public Param<String> linkPredictionCol()

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

Returns:

(undocumented)

getLinkPredictionCol

public String getLinkPredictionCol()

hasLinkPredictionCol

public boolean hasLinkPredictionCol()

Checks whether we should output link prediction.

validateAndTransformSchema

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

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()