Documentation of 'jsat.classifiers.svm.DCDs' Java class
DCDs
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jsat.classifiers.svm

Class DCDs

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier, WarmClassifier, Parameterized, Regressor, WarmRegressor, SimpleWeightVectorModel, SingleWeightVectorModel

    public class DCDs
extends java.lang.Object
implements BinaryScoreClassifier, Regressor, Parameterized, SingleWeightVectorModel, WarmClassifier, WarmRegressor
    Implements Dual Coordinate Descent with shrinking (DCDs) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression. NOTE: While this implementation makes use of the dual formulation only the linear kernel is ever used. The algorithm also uses the primal representation and uses the explicit formulation of w in training and classification. As such, the support vectors found are not necessary once training is complete - and will be discarded.

    DCDs man be warm started by other DCDs models trained on the same data set.

    See:
    • Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S., & Sundararajan, S. (2008). A Dual Coordinate Descent Method for Large-scale Linear SVM. Proceedings of the 25th international conference on Machine learning - ICML ’08 (pp. 408–415). New York, New York, USA: ACM Press. doi:10.1145/1390156.1390208
    • Ho, C.-H., & Lin, C.-J. (2012). Large-scale Linear Support Vector Regression. Journal of Machine Learning Research, 13, 3323–3348. Retrieved from here
    See Also:
    DCD, Serialized Form

    • Constructor Summary

      Constructors 
      Constructor and Description
      DCDs()
      Creates a new DCDL2 SVM object
      DCDs(int maxIterations, boolean useL1)
      Creates a new DCD SVM object
      DCDs(int maxIterations, double tolerance, double C, boolean useL1)
      Creates a new DCD SVM object

    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method and Description
      CategoricalResults classify(DataPoint data)
      Performs classification on the given data point.
      DCDs clone() 
      double getBias()
      Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
      double getBias(int index)
      Returns the bias term used with the weight vector for the given class index.
      double getC()
      Returns the penalty parameter for misclassifications.
      double getEps()
      Returns the epsilon insensitivity parameter used in regression problems.
      int getMaxIterations()
      Returns the maximum number of allowed training epochs
      Vec getRawWeight()
      Returns the only weight vector used for the model
      Vec getRawWeight(int index)
      Returns the raw weight vector associated with the given class index.
      double getScore(DataPoint dp)
      Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
      double getTolerance()
      Returns the tolerance value used to terminate early
      static Distribution guessC(DataSet d)
      Guess the distribution to use for the regularization term C in a SVM.
      boolean isUseBias()
      Returns true if an implicit bias term is in use, or false if not.
      boolean isUseL1()
      Returns true if the L1 form is in use
      int numWeightsVecs()
      Returns the number of weight vectors that can be returned.
      double regress(DataPoint data) 
      void setC(double C)
      Sets the penalty parameter for misclassifications.
      void setEps(double eps)
      Sets the eps used in the epsilon insensitive loss function used when performing regression.
      void setMaxIterations(int maxIterations)
      Sets the maximum number of iterations allowed through the whole training set.
      void setTolerance(double tolerance)
      Sets the tolerance for the stopping condition when training, a small value near zero allows training to stop early when little to no additional convergence is possible.
      void setUseBias(boolean useBias)
      Sets whether or not an implicit bias term should be added to the inputs.
      void setUseL1(boolean useL1)
      Determines whether or not to use the L1 or L2 SVM
      boolean supportsWeightedData()
      Indicates whether the model knows how to train using weighted data points.
      void train(ClassificationDataSet dataSet)
      Trains the classifier and constructs a model for classification using the given data set.
      void train(ClassificationDataSet dataSet, boolean parallel)
      Trains the classifier and constructs a model for classification using the given data set.
      void train(ClassificationDataSet dataSet, Classifier warmSolution)
      Trains the classifier and constructs a model for classification using the given data set.
      void train(ClassificationDataSet dataSet, Classifier warmSolution, boolean parallel)
      Trains the classifier and constructs a model for classification using the given data set.
      void train(RegressionDataSet dataSet) 
      void train(RegressionDataSet dataSet, boolean parallel) 
      void train(RegressionDataSet dataSet, Regressor warmSolution)
      Trains the regressor and constructs a model for regression using the given data set.
      void train(RegressionDataSet dataSet, Regressor warmSolution, boolean parallel)
      Trains the regressor and constructs a model for regression using the given data set.
      boolean warmFromSameDataOnly()
      Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from.

      • Methods inherited from class java.lang.Object

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

    • Constructor Detail

      • DCDs

        public DCDs()
        Creates a new DCDL2 SVM object

      • DCDs

        public DCDs(int maxIterations,
            boolean useL1)
        Creates a new DCD SVM object
        Parameters:
        maxIterations - the maximum number of training iterations
        useL1 - whether or not to use L1 or L2 form

      • DCDs

        public DCDs(int maxIterations,
            double tolerance,
            double C,
            boolean useL1)
        Creates a new DCD SVM object
        Parameters:
        maxIterations - the maximum number of training iterations
        tolerance - the tolerance value for early stopping
        C - the misclassification penalty
        useL1 - whether or not to use L1 or L2 form

    • Method Detail

      • setC

        public void setC(double C)
        Sets the penalty parameter for misclassifications. The recommended value is 1, and values larger than 4 are not normally needed according to the original paper.
        Parameters:
        C - the penalty parameter in (0, Inf)

      • getC

        public double getC()
        Returns the penalty parameter for misclassifications.
        Returns:
        the penalty parameter for misclassifications.

      • setEps

        public void setEps(double eps)
        Sets the eps used in the epsilon insensitive loss function used when performing regression. Errors in the output that less than eps during training are treated as correct.
        This parameter has no impact on classification problems.
        Parameters:
        eps - the non-negative value to use as the error tolerance in regression

      • getEps

        public double getEps()
        Returns the epsilon insensitivity parameter used in regression problems.
        Returns:
        the epsilon insensitivity parameter used in regression problems.

      • setTolerance

        public void setTolerance(double tolerance)
        Sets the tolerance for the stopping condition when training, a small value near zero allows training to stop early when little to no additional convergence is possible.
        Parameters:
        tolerance - the tolerance value to use to stop early

      • getTolerance

        public double getTolerance()
        Returns the tolerance value used to terminate early
        Returns:
        the tolerance value used to terminate early

      • setUseL1

        public void setUseL1(boolean useL1)
        Determines whether or not to use the L1 or L2 SVM
        Parameters:
        useL1 - true to use the L1 form, false to use the L2 form.

      • isUseL1

        public boolean isUseL1()
        Returns true if the L1 form is in use
        Returns:
        true if the L1 form is in use

      • setMaxIterations

        public void setMaxIterations(int maxIterations)
        Sets the maximum number of iterations allowed through the whole training set.
        Parameters:
        maxIterations - the maximum number of training epochs

      • getMaxIterations

        public int getMaxIterations()
        Returns the maximum number of allowed training epochs
        Returns:
        the maximum number of allowed training epochs

      • setUseBias

        public void setUseBias(boolean useBias)
        Sets whether or not an implicit bias term should be added to the inputs.
        Parameters:
        useBias - true to add an implicit bias term to inputs, false to use the input data as provided.

      • isUseBias

        public boolean isUseBias()
        Returns true if an implicit bias term is in use, or false if not.
        Returns:
        true if an implicit bias term is in use, or false if not.

      • getBias

        public double getBias()
        Description copied from interface: SingleWeightVectorModel
        Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
        Specified by:
        getBias in interface SingleWeightVectorModel
        Returns:
        the bias term for the model

      • getRawWeight

        public Vec getRawWeight(int index)
        Description copied from interface: SimpleWeightVectorModel
        Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
        Do not alter the returned weight vector, as it will change the model's values.

        If a regression problem, only index = 0 should be used
        Specified by:
        getRawWeight in interface SimpleWeightVectorModel
        Parameters:
        index - the class index to get the weight vector for
        Returns:
        the weight vector used for the specified class

      • getBias

        public double getBias(int index)
        Description copied from interface: SimpleWeightVectorModel
        Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.

        If a regression problem, only index = 0 should be used
        Specified by:
        getBias in interface SimpleWeightVectorModel
        Parameters:
        index - the class index to get the weight vector for
        Returns:
        the bias term for the specified class

      • numWeightsVecs

        public int numWeightsVecs()
        Description copied from interface: SimpleWeightVectorModel
        Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).
        Specified by:
        numWeightsVecs in interface SimpleWeightVectorModel
        Returns:
        the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.

      • classify

        public CategoricalResults classify(DataPoint data)
        Description copied from interface: Classifier
        Performs classification on the given data point.
        Specified by:
        classify in interface Classifier
        Parameters:
        data - the data point to classify
        Returns:
        the results of the classification.

      • getScore

        public double getScore(DataPoint dp)
        Description copied from interface: BinaryScoreClassifier
        Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
        Specified by:
        getScore in interface BinaryScoreClassifier
        Parameters:
        dp - the data point to predict the class label of
        Returns:
        the score for the given data point

      • train

        public void train(ClassificationDataSet dataSet,
                  boolean parallel)
        Description copied from interface: Classifier
        Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
        Specified by:
        train in interface Classifier
        Parameters:
        dataSet - the data set to train on
        parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

      • train

        public void train(ClassificationDataSet dataSet)
        Description copied from interface: Classifier
        Trains the classifier and constructs a model for classification using the given data set.
        Specified by:
        train in interface Classifier
        Parameters:
        dataSet - the data set to train on

      • train

        public void train(ClassificationDataSet dataSet,
                  Classifier warmSolution,
                  boolean parallel)
        Description copied from interface: WarmClassifier
        Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
        Specified by:
        train in interface WarmClassifier
        Parameters:
        dataSet - the data set to train on
        warmSolution - the solution to use to warm start this model
        parallel - true if the training should be done using multiple-cores, false for single threaded.

      • train

        public void train(ClassificationDataSet dataSet,
                  Classifier warmSolution)
        Description copied from interface: WarmClassifier
        Trains the classifier and constructs a model for classification using the given data set.
        Specified by:
        train in interface WarmClassifier
        Parameters:
        dataSet - the data set to train on
        warmSolution - the solution to use to warm start this model

      • supportsWeightedData

        public boolean supportsWeightedData()
        Description copied from interface: Classifier
        Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
        Specified by:
        supportsWeightedData in interface Classifier
        Specified by:
        supportsWeightedData in interface Regressor
        Returns:
        true if the model supports weighted data, false otherwise

      • warmFromSameDataOnly

        public boolean warmFromSameDataOnly()
        Description copied from interface: WarmClassifier
        Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from. If this is the case true will be returned. The behavior for training on a different data set when this is defined is undefined. It may cause an error, or it may cause the algorithm to take longer or reach a worse solution.
        When true, it is important that the data set be unaltered - this includes mutating the values stored or re-arranging the data points within the data set.
        Specified by:
        warmFromSameDataOnly in interface WarmClassifier
        Specified by:
        warmFromSameDataOnly in interface WarmRegressor
        Returns:
        true if the algorithm can only be warm started from the model trained on the exact same data set.

      • train

        public void train(RegressionDataSet dataSet,
                  Regressor warmSolution,
                  boolean parallel)
        Description copied from interface: WarmRegressor
        Trains the regressor and constructs a model for regression using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
        Specified by:
        train in interface WarmRegressor
        Parameters:
        dataSet - the data set to train on
        warmSolution - the solution to use to warm start this model
        parallel - true if the training should be done using multiple-cores, false for single threaded.

      • train

        public void train(RegressionDataSet dataSet,
                  Regressor warmSolution)
        Description copied from interface: WarmRegressor
        Trains the regressor and constructs a model for regression using the given data set.
        Specified by:
        train in interface WarmRegressor
        Parameters:
        dataSet - the data set to train on
        warmSolution - the solution to use to warm start this model

      • guessC

        public static Distribution guessC(DataSet d)
        Guess the distribution to use for the regularization term C in a SVM.
        Parameters:
        d - the data set to get the guess for
        Returns:
        the guess for the C parameter in the SVM
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