org.apache.spark.mllib.regression

Class RidgeRegressionWithSGD

Object

org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<RidgeRegressionModel>

org.apache.spark.mllib.regression.RidgeRegressionWithSGD

All Implemented Interfaces:

java.io.Serializable, Logging

public class RidgeRegressionWithSGD
extends GeneralizedLinearAlgorithm<RidgeRegressionModel>
implements scala.Serializable

Train a regression model with L2-regularization using Stochastic Gradient Descent. This solves the l2-regularized least squares regression formulation f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^ Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with its corresponding right hand side label y. See also the documentation for the precise formulation.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RidgeRegressionWithSGD() Deprecated. Use ml.regression.LinearRegression with elasticNetParam = 0.0. Note the default regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression. Since 2.0.0.

Method Summary

Modifier and Type	Method and Description
static int	getNumFeatures()
static boolean	isAddIntercept()
GradientDescent	optimizer() The optimizer to solve the problem.
static M	run(RDD<LabeledPoint> input)
static M	run(RDD<LabeledPoint> input, Vector initialWeights)
static GeneralizedLinearAlgorithm<M>	setIntercept(boolean addIntercept)
static GeneralizedLinearAlgorithm<M>	setValidateData(boolean validateData)
static RidgeRegressionModel	train(RDD<LabeledPoint> input, int numIterations) Train a RidgeRegression model given an RDD of (label, features) pairs.
static RidgeRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam) Train a RidgeRegression model given an RDD of (label, features) pairs.
static RidgeRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction) Train a RidgeRegression model given an RDD of (label, features) pairs.
static RidgeRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction, Vector initialWeights) Train a RidgeRegression model given an RDD of (label, features) pairs.

Methods inherited from class org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm

getNumFeatures, isAddIntercept, run, run, setIntercept, setValidateData

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

Constructor Detail

RidgeRegressionWithSGD

public RidgeRegressionWithSGD()

Deprecated. Use ml.regression.LinearRegression with elasticNetParam = 0.0. Note the default regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression. Since 2.0.0.

Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.

Method Detail

train

public static RidgeRegressionModel train(RDD<LabeledPoint> input,
                                         int numIterations,
                                         double stepSize,
                                         double regParam,
                                         double miniBatchFraction,
                                         Vector initialWeights)

Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient. The weights used in gradient descent are initialized using the initial weights provided.

Parameters:

input - RDD of (label, array of features) pairs.

numIterations - Number of iterations of gradient descent to run.

stepSize - Step size to be used for each iteration of gradient descent.

regParam - Regularization parameter.

miniBatchFraction - Fraction of data to be used per iteration.

initialWeights - Initial set of weights to be used. Array should be equal in size to the number of features in the data.

Returns:

(undocumented)

train

public static RidgeRegressionModel train(RDD<LabeledPoint> input,
                                         int numIterations,
                                         double stepSize,
                                         double regParam,
                                         double miniBatchFraction)

Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient.

Parameters:

input - RDD of (label, array of features) pairs.

numIterations - Number of iterations of gradient descent to run.

stepSize - Step size to be used for each iteration of gradient descent.

regParam - Regularization parameter.

miniBatchFraction - Fraction of data to be used per iteration.

Returns:

(undocumented)

train

public static RidgeRegressionModel train(RDD<LabeledPoint> input,
                                         int numIterations,
                                         double stepSize,
                                         double regParam)

Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using the specified step size. We use the entire data set to compute the true gradient in each iteration.

Parameters:

input - RDD of (label, array of features) pairs.

stepSize - Step size to be used for each iteration of Gradient Descent.

regParam - Regularization parameter.

numIterations - Number of iterations of gradient descent to run.

Returns:

a RidgeRegressionModel which has the weights and offset from training.

train

public static RidgeRegressionModel train(RDD<LabeledPoint> input,
                                         int numIterations)

Train a RidgeRegression model given an RDD of (label, features) pairs. We run a fixed number of iterations of gradient descent using a step size of 1.0. We use the entire data set to compute the true gradient in each iteration.

Parameters:

input - RDD of (label, array of features) pairs.

numIterations - Number of iterations of gradient descent to run.

Returns:

a RidgeRegressionModel which has the weights and offset from training.

getNumFeatures

public static int getNumFeatures()

isAddIntercept

public static boolean isAddIntercept()

setIntercept

public static GeneralizedLinearAlgorithm<M> setIntercept(boolean addIntercept)

setValidateData

public static GeneralizedLinearAlgorithm<M> setValidateData(boolean validateData)

run

public static M run(RDD<LabeledPoint> input)

run

public static M run(RDD<LabeledPoint> input,
                    Vector initialWeights)

optimizer

public GradientDescent optimizer()

Description copied from class: GeneralizedLinearAlgorithm

The optimizer to solve the problem.

Specified by:

optimizer in class GeneralizedLinearAlgorithm<RidgeRegressionModel>

Returns:

(undocumented)