org.apache.spark.mllib.classification

Class LogisticRegressionWithSGD

java.lang.Object

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

org.apache.spark.mllib.classification.LogisticRegressionWithSGD

All Implemented Interfaces:

java.io.Serializable

Deprecated.

Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS. Since 2.0.0.

public class LogisticRegressionWithSGD
extends GeneralizedLinearAlgorithm<LogisticRegressionModel>
implements scala.Serializable

Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via LogisticRegressionWithSGD.optimizer. NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem. Using LogisticRegressionWithLBFGS is recommended over this.

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionWithSGD() Deprecated. Construct a LogisticRegression object with default parameters: {stepSize: 1.0, numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.

Method Summary

Methods

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

Methods inherited from class java.lang.Object

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

Constructor Detail

LogisticRegressionWithSGD

public LogisticRegressionWithSGD()

Deprecated.

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

Method Detail

train

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

Deprecated.

Train a logistic 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 the gradient. The weights used in gradient descent are initialized using the initial weights provided. NOTE: Labels used in Logistic Regression should be {0, 1}

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.

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

Deprecated.

Train a logistic 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 the gradient. NOTE: Labels used in Logistic Regression should be {0, 1}

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.

miniBatchFraction - Fraction of data to be used per iteration.

Returns:

(undocumented)

train

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

Deprecated.

Train a logistic regression 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 update the gradient in each iteration. NOTE: Labels used in Logistic Regression should be {0, 1}

Parameters:

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

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

numIterations - Number of iterations of gradient descent to run.

Returns:

a LogisticRegressionModel which has the weights and offset from training.

train

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

Deprecated.

Train a logistic regression 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 update the gradient in each iteration. NOTE: Labels used in Logistic Regression should be {0, 1}

Parameters:

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

numIterations - Number of iterations of gradient descent to run.

Returns:

a LogisticRegressionModel 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.

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<LogisticRegressionModel>

Returns:

(undocumented)

validators

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

Deprecated.

Overrides:

validators in class GeneralizedLinearAlgorithm<LogisticRegressionModel>

createModel

protected LogisticRegressionModel 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<LogisticRegressionModel>

Parameters:

weights - (undocumented)

intercept - (undocumented)

Returns:

(undocumented)