org.apache.spark.mllib.classification

Class LogisticRegressionWithSGD

Object

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

org.apache.spark.mllib.classification.LogisticRegressionWithSGD

All Implemented Interfaces:

java.io.Serializable, Logging

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.

Using LogisticRegressionWithLBFGS is recommended over this.

See Also:

Serialized Form

Note:

Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem.

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionWithSGD() Deprecated. Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS. 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 LogisticRegressionModel	train(RDD<LabeledPoint> input, int numIterations) Train a logistic regression model given an RDD of (label, features) pairs.
static LogisticRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize) Train a logistic regression model given an RDD of (label, features) pairs.
static LogisticRegressionModel	train(RDD<LabeledPoint> input, int numIterations, double stepSize, double miniBatchFraction) 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) Train a logistic regression 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, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

LogisticRegressionWithSGD

public LogisticRegressionWithSGD()

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

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)

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.

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)

Note:

Labels used in Logistic Regression should be {0, 1}

train

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

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.

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)

Note:

Labels used in Logistic Regression should be {0, 1}

train

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

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.

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.

Note:

Labels used in Logistic Regression should be {0, 1}

train

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

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.

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.

Note:

Labels used in Logistic Regression should be {0, 1}

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<LogisticRegressionModel>

Returns:

(undocumented)