CPM (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.svm.extended.CPM

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, Parameterized
```
public class CPM
extends Object
implements BinaryScoreClassifier, Classifier, Parameterized
```
This class implements the Convex Polytope Machine (CPM), which is an extension of the Linear SVM. It is a binary classifier that has training time proportionate to the linear case, but can obtain accuracies closer to that of a kernelized SVM.
Similar to the AMM classifier, CPM uses multiple linear hyper-planes to create a non-linear classifier. Increasing the number of hyper-planes increases training/prediction time, but also increases the amount of non-linearity the model can tolerate.

While the CPM implements the BinaryScoreClassifier interface, the CPM decision algorithm does not completely lend itself to producing a natural score in this manner. For this reason you may observe unusual behavior from the CPM if you rely on this interface, compared with other approaches.
See: Kantchelian, A., Tschantz, M. C., Huang, L., Bartlett, P. L., Joseph, A. D., & Tygar, J. D. (2014). Large-margin Convex Polytope Machine. In Proceedings of the 27th International Conference on Neural Information Processing Systems (pp. 3248–3256). Cambridge, MA, USA: MIT Press. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`CPM()` Creates a new CPM classifier, with default parameters that should work well for most cases.
`CPM(CPM toCopy)` Copy constructor
`CPM(double lambda)` Creates a new CPM classifier
`CPM(double lambda, int K)` Creates a new CPM classifier
`CPM(double lambda, int K, double entropyThreshold)` Creates a new CPM classifier
`CPM(double lambda, int K, double entropyThreshold, int epochs)` Creates a new CPM classifier
`CPM(int K)` Creates a new CPM classifier

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.
`CPM`	`clone()`
`double`	`getEntropyThreshold()`
`int`	`getEpochs()` Returns the number of epochs used for training
`int`	`getK()`
`double`	`getLambda()`
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`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.
`static Distribution`	`guessEntropyThreshold(DataSet d)` Provides a distribution of reasonable values for the `setEntropyThreshold(double)` parameter
`static Distribution`	`guessLambda(DataSet d)` Provides a distribution of reasonable values for the `λ` parameter
`void`	`setEntropyThreshold(double entropyThreshold)` Sets the entropy threshold used for training.
`void`	`setEpochs(int epochs)` Sets the number of whole iterations through the training set that will be performed for training
`void`	`setK(int K)` Sets the number of hyper planes to use when training.
`void`	`setLambda(double lambda)` Sets the regularization parameter λ to use.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - CPM
```
public CPM()
```
    Creates a new CPM classifier, with default parameters that should work well for most cases.
  - CPM
```
public CPM(int K)
```
    Creates a new CPM classifier
    
    Parameters:
    
    K - the number of hyper-planes to learn with.
  - CPM
```
public CPM(double lambda)
```
    Creates a new CPM classifier
    
    Parameters:
    
    lambda - the regularization parameter
  - CPM
```
public CPM(double lambda,
           int K)
```
    Creates a new CPM classifier
    
    Parameters:
    
    lambda - the regularization parameter
    
    K - the number of hyper-planes to learn with.
  - CPM
```
public CPM(double lambda,
           int K,
           double entropyThreshold)
```
    Creates a new CPM classifier
    
    Parameters:
    
    lambda - the regularization parameter
    
    K - the number of hyper-planes to learn with.
    
    entropyThreshold - the parameter that encourages non-linearity to be exploited
  - CPM
```
public CPM(double lambda,
           int K,
           double entropyThreshold,
           int epochs)
```
    Creates a new CPM classifier
    
    Parameters:
    
    lambda - the regularization parameter
    
    K - the number of hyper-planes to learn with.
    
    entropyThreshold - the parameter that encourages non-linearity to be exploited
    
    epochs - the number of training iterations
  - CPM
```
public CPM(CPM toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setEntropyThreshold
```
public void setEntropyThreshold(double entropyThreshold)
```
    Sets the entropy threshold used for training. It ensures a diversity of hyper-planes are used, where larger values encourage using more of the hyper planes.
    
    This method is adjusted from the paper's definition so that the input can be any non-negative value. It is recommended to try values in the range of [0, 10]
    
    Parameters:
    
    entropyThreshold - the non-negative parameter for hyper-plane diversity
  - getEntropyThreshold
```
public double getEntropyThreshold()
```
    Returns:
    
    the non-negative parameter for hyper-plane diversity
  - setLambda
```
public void setLambda(double lambda)
```
    Sets the regularization parameter λ to use. Larger values penalize model complexity. This value is adjusted from the form in the original paper so that you do not need to consider the number of epochs explicitly. The effective regularization will be divided by the total number of training updates.
    
    Parameters:
    
    lambda - the regularization parameter value to use, the recommended range range is (0, 10⁴]
  - getLambda
```
public double getLambda()
```
    Returns:
    
    the regularization parameter value
  - setK
```
public void setK(int K)
```
    Sets the number of hyper planes to use when training. A normal linear model is equivalent to using only 1 hyper plane. The more hyper planes used, the more modeling capacity the algorithm has, but the slower it will run.
    
    Parameters:
    
    K - the number of hyper planes to use.
  - getK
```
public int getK()
```
    Returns:
    
    the number of hyper planes to use.
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of whole iterations through the training set that will be performed for training
    
    Parameters:
    
    epochs - the number of whole iterations through the data set
  - getEpochs
```
public int getEpochs()
```
    Returns the number of epochs used for training
    
    Returns:
    
    the number of epochs used for training
  - 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
  - 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
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    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:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - clone
```
public CPM clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - guessLambda
```
public static Distribution guessLambda(DataSet d)
```
    Provides a distribution of reasonable values for the λ parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the distribution to search this parameter from
  - guessEntropyThreshold
```
public static Distribution guessEntropyThreshold(DataSet d)
```
    Provides a distribution of reasonable values for the setEntropyThreshold(double) parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the distribution to search this parameter from

Class CPM

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

CPM

CPM

CPM

CPM

CPM

CPM

CPM

Method Detail

setEntropyThreshold

getEntropyThreshold

setLambda

getLambda

setK

getK

setEpochs

getEpochs

classify

getScore

supportsWeightedData

getParameters

getParameter

trainC

trainC

clone

guessLambda

guessEntropyThreshold