org.apache.spark.mllib.regression

Class LinearRegressionWithSGD

java.lang.Object

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

org.apache.spark.mllib.regression.LinearRegressionWithSGD

All Implemented Interfaces:

java.io.Serializable

Deprecated.

Use ml.regression.LinearRegression or LBFGS. Since 2.0.0.

public class LinearRegressionWithSGD
extends GeneralizedLinearAlgorithm<LinearRegressionModel>
implements scala.Serializable

Train a linear regression model with no regularization using Stochastic Gradient Descent. This solves the least squares regression formulation f(weights) = 1/n ||A weights-y||^2^ (which is the mean squared error). 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.

Constructor Summary

Constructors

Constructor and Description
LinearRegressionWithSGD() Deprecated. Construct a LinearRegression object with default parameters: {stepSize: 1.0, numIterations: 100, miniBatchFraction: 1.0}.

Method Summary

Methods

Modifier and Type	Method and Description
protected static void	addIntercept_$eq(boolean x$1) Deprecated.
protected static boolean	addIntercept() Deprecated.
protected LinearRegressionModel	createModel(Vector weights, double intercept) Deprecated. Create a model given the weights and intercept
protected static Vector	generateInitialWeights(RDD<LabeledPoint> input) Deprecated.
static int	getNumFeatures() Deprecated.
protected static void	initializeLogIfNecessary(boolean isInterpreter) Deprecated.
static boolean	isAddIntercept() Deprecated.
protected static boolean	isTraceEnabled() Deprecated.
protected static org.slf4j.Logger	log() Deprecated.
protected static void	logDebug(scala.Function0<java.lang.String> msg) Deprecated.
protected static void	logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable) Deprecated.
protected static void	logError(scala.Function0<java.lang.String> msg) Deprecated.
protected static void	logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable) Deprecated.
protected static void	logInfo(scala.Function0<java.lang.String> msg) Deprecated.
protected static void	logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable) Deprecated.
protected static java.lang.String	logName() Deprecated.
protected static void	logTrace(scala.Function0<java.lang.String> msg) Deprecated.
protected static void	logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable) Deprecated.
protected static void	logWarning(scala.Function0<java.lang.String> msg) Deprecated.
protected static void	logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable) Deprecated.
protected static void	numFeatures_$eq(int x$1) Deprecated.
protected static int	numFeatures() Deprecated.
protected static void	numOfLinearPredictor_$eq(int x$1) Deprecated.
protected static int	numOfLinearPredictor() Deprecated.
GradientDescent	optimizer() Deprecated. The optimizer to solve the problem.
static M	run(RDD<LabeledPoint> input) Deprecated.
static M	run(RDD<LabeledPoint> input, Vector initialWeights) Deprecated.
static GeneralizedLinearAlgorithm<M>	setIntercept(boolean addIntercept) Deprecated.
static GeneralizedLinearAlgorithm<M>	setValidateData(boolean validateData) Deprecated.
static LinearRegressionModel	train(RDD<LabeledPoint> input, int numIterations) Deprecated. Train a LinearRegression model given an RDD of (label, features) pairs.
static LinearRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize) Deprecated. Train a LinearRegression model given an RDD of (label, features) pairs.
static LinearRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize, double miniBatchFraction) Deprecated. Train a LinearRegression model given an RDD of (label, features) pairs.
static LinearRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize, double miniBatchFraction, Vector initialWeights) Deprecated. Train a Linear Regression model given an RDD of (label, features) pairs.
protected static void	validateData_$eq(boolean x$1) Deprecated.
protected static boolean	validateData() Deprecated.
protected static scala.collection.Seq<scala.Function1<RDD<LabeledPoint>,java.lang.Object>>	validators() Deprecated.

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

addIntercept, generateInitialWeights, getNumFeatures, isAddIntercept, numFeatures, numOfLinearPredictor, run, run, setIntercept, setValidateData, validateData, validators

Methods inherited from class java.lang.Object

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

Constructor Detail

LinearRegressionWithSGD

public LinearRegressionWithSGD()

Deprecated.

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

Method Detail

train

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

Deprecated.

Train a Linear Regression 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

numIterations - Number of iterations of gradient descent to run.

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

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 LinearRegressionModel train(RDD<LabeledPoint> input,
                          int numIterations,
                          double stepSize,
                          double miniBatchFraction)

Deprecated.

Train a LinearRegression 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

numIterations - Number of iterations of gradient descent to run.

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

miniBatchFraction - Fraction of data to be used per iteration.

Returns:

(undocumented)

train

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

Deprecated.

Train a LinearRegression 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

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

numIterations - Number of iterations of gradient descent to run.

Returns:

a LinearRegressionModel which has the weights and offset from training.

train

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

Deprecated.

Train a LinearRegression 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. Each pair describes a row of the data matrix A as well as the corresponding right hand side label y

numIterations - Number of iterations of gradient descent to run.

Returns:

a LinearRegressionModel which has the weights and offset from training.

logName

protected static java.lang.String logName()

Deprecated.

log

protected static org.slf4j.Logger log()

Deprecated.

logInfo

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

Deprecated.

logDebug

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

Deprecated.

logTrace

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

Deprecated.

logWarning

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

Deprecated.

logError

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

Deprecated.

logInfo

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

Deprecated.

logDebug

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

Deprecated.

logTrace

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

Deprecated.

logWarning

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

Deprecated.

logError

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

Deprecated.

isTraceEnabled

protected static boolean isTraceEnabled()

Deprecated.

initializeLogIfNecessary

protected static void initializeLogIfNecessary(boolean isInterpreter)

Deprecated.

validators

protected static scala.collection.Seq<scala.Function1<RDD<LabeledPoint>,java.lang.Object>> validators()

Deprecated.

addIntercept

protected static boolean addIntercept()

Deprecated.

addIntercept_$eq

protected static void addIntercept_$eq(boolean x$1)

Deprecated.

validateData

protected static boolean validateData()

Deprecated.

validateData_$eq

protected static void validateData_$eq(boolean x$1)

Deprecated.

numOfLinearPredictor

protected static int numOfLinearPredictor()

Deprecated.

numOfLinearPredictor_$eq

protected static void numOfLinearPredictor_$eq(int x$1)

Deprecated.

getNumFeatures

public static int getNumFeatures()

Deprecated.

numFeatures

protected static int numFeatures()

Deprecated.

numFeatures_$eq

protected static void numFeatures_$eq(int x$1)

Deprecated.

isAddIntercept

public static boolean isAddIntercept()

Deprecated.

setIntercept

public static GeneralizedLinearAlgorithm<M> setIntercept(boolean addIntercept)

Deprecated.

setValidateData

public static GeneralizedLinearAlgorithm<M> setValidateData(boolean validateData)

Deprecated.

generateInitialWeights

protected static Vector generateInitialWeights(RDD<LabeledPoint> input)

Deprecated.

run

public static M run(RDD<LabeledPoint> input)

Deprecated.

run

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

Deprecated.

optimizer

public GradientDescent optimizer()

Deprecated.

Description copied from class: GeneralizedLinearAlgorithm

The optimizer to solve the problem.

Specified by:

optimizer in class GeneralizedLinearAlgorithm<LinearRegressionModel>

Returns:

(undocumented)

createModel

protected LinearRegressionModel createModel(Vector weights,
                                double intercept)

Deprecated.

Description copied from class: GeneralizedLinearAlgorithm

Create a model given the weights and intercept

Specified by:

createModel in class GeneralizedLinearAlgorithm<LinearRegressionModel>

Parameters:

weights - (undocumented)

intercept - (undocumented)

Returns:

(undocumented)