Package org.apache.spark.mllib.classification

Class SVMWithSGD

Object
org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm<SVMModel>
org.apache.spark.mllib.classification.SVMWithSGD
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, scala.Serializable
public class SVMWithSGD extends GeneralizedLinearAlgorithm<SVMModel> implements scala.Serializable
Train a Support Vector Machine (SVM) using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via SVMWithSGD.optimizer.

See Also:
Note:
Labels used in SVM should be {0, 1}.

  • Nested Class Summary

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

    org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Constructor Summary

    Constructors
    Constructor
    Description
    SVMWithSGD()
    Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.

  • Method Summary

    Modifier and Type
    Method
    Description
    GradientDescent
    optimizer()
    The optimizer to solve the problem.
    static SVMModel
    train(RDD<LabeledPoint> input, int numIterations)
    Train a SVM model given an RDD of (label, features) pairs.
    static SVMModel
    train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam)
    Train a SVM model given an RDD of (label, features) pairs.
    static SVMModel
    train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction)
    Train a SVM model given an RDD of (label, features) pairs.
    static SVMModel
    train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction, Vector initialWeights)
    Train a SVM 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 java.lang.Object

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

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

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

  • Constructor Details

    • SVMWithSGD

      public SVMWithSGD()
      Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100, regParam: 0.01, miniBatchFraction: 1.0}.

  • Method Details

    • train

      public static SVMModel train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction, Vector initialWeights)
      Train a SVM 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.
      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.

      Returns:
      (undocumented)
      Note:
      Labels used in SVM should be {0, 1}.

    • train

      public static SVMModel train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam, double miniBatchFraction)
      Train a SVM 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.
      regParam - Regularization parameter.
      miniBatchFraction - Fraction of data to be used per iteration.
      Returns:
      (undocumented)
      Note:
      Labels used in SVM should be {0, 1}

    • train

      public static SVMModel train(RDD<LabeledPoint> input, int numIterations, double stepSize, double regParam)
      Train a SVM 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.
      regParam - Regularization parameter.
      numIterations - Number of iterations of gradient descent to run.
      Returns:
      a SVMModel which has the weights and offset from training.

      Note:
      Labels used in SVM should be {0, 1}

    • train

      public static SVMModel train(RDD<LabeledPoint> input, int numIterations)
      Train a SVM 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 SVMModel which has the weights and offset from training.

      Note:
      Labels used in SVM should be {0, 1}

    • optimizer

      public GradientDescent optimizer()
      Description copied from class: GeneralizedLinearAlgorithm
      The optimizer to solve the problem.

      Specified by:
      optimizer in class GeneralizedLinearAlgorithm<SVMModel>
      Returns:
      (undocumented)