org.apache.spark.mllib.classification

Class StreamingLogisticRegressionWithSGD

Object

org.apache.spark.mllib.regression.StreamingLinearAlgorithm<LogisticRegressionModel,LogisticRegressionWithSGD>

org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging

public class StreamingLogisticRegressionWithSGD
extends StreamingLinearAlgorithm<LogisticRegressionModel,LogisticRegressionWithSGD>
implements scala.Serializable

Train or predict a logistic regression model on streaming data. Training uses Stochastic Gradient Descent to update the model based on each new batch of incoming data from a DStream (see LogisticRegressionWithSGD for model equation)

Each batch of data is assumed to be an RDD of LabeledPoints. The number of data points per batch can vary, but the number of features must be constant. An initial weight vector must be provided.

Use a builder pattern to construct a streaming logistic regression analysis in an application, like:

  val model = new StreamingLogisticRegressionWithSGD()
    .setStepSize(0.5)
    .setNumIterations(10)
    .setInitialWeights(Vectors.dense(...))
    .trainOn(DStream)
 

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
StreamingLogisticRegressionWithSGD() Construct a StreamingLogisticRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.

Method Summary

Modifier and Type	Method and Description
StreamingLogisticRegressionWithSGD	setInitialWeights(Vector initialWeights) Set the initial weights.
StreamingLogisticRegressionWithSGD	setMiniBatchFraction(double miniBatchFraction) Set the fraction of each batch to use for updates.
StreamingLogisticRegressionWithSGD	setNumIterations(int numIterations) Set the number of iterations of gradient descent to run per update.
StreamingLogisticRegressionWithSGD	setRegParam(double regParam) Set the regularization parameter.
StreamingLogisticRegressionWithSGD	setStepSize(double stepSize) Set the step size for gradient descent.

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

latestModel, predictOn, predictOn, predictOnValues, predictOnValues, trainOn, trainOn

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

StreamingLogisticRegressionWithSGD

public StreamingLogisticRegressionWithSGD()

Construct a StreamingLogisticRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}. Initial weights must be set before using trainOn or predictOn (see StreamingLinearAlgorithm)

Method Detail

setInitialWeights

public StreamingLogisticRegressionWithSGD setInitialWeights(Vector initialWeights)

Set the initial weights. Default: [0.0, 0.0].

setMiniBatchFraction

public StreamingLogisticRegressionWithSGD setMiniBatchFraction(double miniBatchFraction)

Set the fraction of each batch to use for updates. Default: 1.0.

setNumIterations

public StreamingLogisticRegressionWithSGD setNumIterations(int numIterations)

Set the number of iterations of gradient descent to run per update. Default: 50.

setRegParam

public StreamingLogisticRegressionWithSGD setRegParam(double regParam)

Set the regularization parameter. Default: 0.0.

setStepSize

public StreamingLogisticRegressionWithSGD setStepSize(double stepSize)

Set the step size for gradient descent. Default: 0.1.