DataModelPipeline (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.datatransform.DataModelPipeline

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor
```
public class DataModelPipeline
extends 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.

Author:

Edward Raff

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

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`DataModelPipeline`	`clone()`
`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`	`regress(DataPoint data)`
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`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
  - 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.
  - 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
  - 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,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    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 Object
  - 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.

Class DataModelPipeline

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

DataModelPipeline

DataModelPipeline

DataModelPipeline

DataModelPipeline

DataModelPipeline

Method Detail

classify

trainC

trainC

supportsWeightedData

regress

train

train

clone

getParameters

getParameter