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 l1-regularized least squares regression formulation f(weights) = 1/n ||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() Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.

Method Summary

Methods

Modifier and Type	Method and Description
GradientDescent	optimizer() The optimizer to solve the problem.
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

run, run, setFeatureScaling, setIntercept, setValidateData

Methods inherited from class Object

equals, 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

Constructor Detail

RidgeRegressionWithSGD

public RidgeRegressionWithSGD()

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.

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.

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.

optimizer

public GradientDescent optimizer()

Description copied from class: GeneralizedLinearAlgorithm

The optimizer to solve the problem.

Specified by:

optimizer in class GeneralizedLinearAlgorithm<RidgeRegressionModel>