jsat.datatransform

Class DataModelPipeline

java.lang.Object

jsat.datatransform.DataModelPipeline

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Parameterized, Regressor

public class DataModelPipeline
extends java.lang.Object
implements Classifier, Regressor, Parameterized

A Data Model Pipeline combines several data transforms and a base Classifier or Regressor into a unified object for performing classification and Regression with. This is useful for certain transforms for which their behavior is more tightly coupled with the model being used. In addition this allows a way for easily turning the parameters for a transform along with those of the predictor.
When using the Data Model Pipeline, the transforms that are apart of the pipeline should not be added to the model evaluators - as this will cause the transforms to be applied multiple times.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DataModelPipeline(Classifier baseClassifier, DataTransform... transforms) Creates a new Data Model Pipeline from the given transform factories and base classifier
DataModelPipeline(Classifier baseClassifier, DataTransformProcess dtp) Creates a new Data Model Pipeline from the given transform process and base classifier
DataModelPipeline(DataModelPipeline toCopy) Copy constructor
DataModelPipeline(Regressor baseRegressor, DataTransform... transforms) Creates a new Data Model Pipeline from the given transform factories and base classifier
DataModelPipeline(Regressor baseRegressor, DataTransformProcess dtp) Creates a new Data Model Pipeline from the given transform process and base regressor

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
DataModelPipeline	clone()
Parameter	getParameter(java.lang.String paramName) Returns the parameter with the given name.
java.util.List<Parameter>	getParameters() Returns the list of parameters that can be altered for this learner.
double	regress(DataPoint data)
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(RegressionDataSet dataSet, boolean parallel)

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.regression.Regressor

train

Constructor Detail

DataModelPipeline

public DataModelPipeline(Classifier baseClassifier,
                         DataTransformProcess dtp)

Creates a new Data Model Pipeline from the given transform process and base classifier

Parameters:

dtp - the data transforms to apply

baseClassifier - the classifier to learn with

DataModelPipeline

public DataModelPipeline(Classifier baseClassifier,
                         DataTransform... transforms)

Creates a new Data Model Pipeline from the given transform factories and base classifier

Parameters:

transforms - the data transforms to apply

baseClassifier - the classifier to learn with

DataModelPipeline

public DataModelPipeline(Regressor baseRegressor,
                         DataTransformProcess dtp)

Creates a new Data Model Pipeline from the given transform process and base regressor

Parameters:

dtp - the data transforms to apply

baseRegressor - the regressor to learn with

DataModelPipeline

public DataModelPipeline(Regressor baseRegressor,
                         DataTransform... transforms)

Creates a new Data Model Pipeline from the given transform factories and base classifier

Parameters:

transforms - the data transforms to apply

baseRegressor - the regressor to learn with

DataModelPipeline

public DataModelPipeline(DataModelPipeline toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

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.

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.

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

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  boolean parallel)

Specified by:

train in interface Regressor

clone

public DataModelPipeline clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class java.lang.Object

getParameters

public java.util.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(java.lang.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.