ClassifierErrorsPlotInstances (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.gui.explorer.AbstractPlotInstances
- - weka.gui.explorer.ClassifierErrorsPlotInstances

All Implemented Interfaces:: java.io.Serializable, OptionHandler

Direct Known Subclasses:: ClassifierPerformanceEvaluator.AggregateableClassifierErrorsPlotInstances, ClassifierPerformanceEvaluator.AggregateableClassifierErrorsPlotInstances

public class ClassifierErrorsPlotInstances
extends AbstractPlotInstances

A class for generating plottable visualization errors.

Example usage:

 Instances train = ... // from somewhere
 Instances test = ... // from somewhere
 Classifier cls = ... // from somewhere
 // build classifier
 cls.buildClassifier(train);
 // evaluate classifier and generate plot instances
 ClassifierPlotInstances plotInstances = new ClassifierPlotInstances();
 plotInstances.setClassifier(cls);
 plotInstances.setInstances(train);
 plotInstances.setClassIndex(train.classIndex());
 plotInstances.setUp();
 Evaluation eval = new Evaluation(train);
 for (int i = 0; i < test.numInstances(); i++)
   plotInstances.process(test.instance(i), cls, eval);
 // generate visualization
 VisualizePanel visPanel = new VisualizePanel();
 visPanel.addPlot(plotInstances.getPlotData("plot name"));
 visPanel.setColourIndex(plotInstances.getPlotInstances().classIndex()+1);
 // clean up
 plotInstances.cleanUp();

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected Classifier`	`m_Classifier` the classifier being used.
`protected int`	`m_ClassIndex` the class index.
`protected Evaluation`	`m_Evaluation` the Evaluation object to use.
`protected int`	`m_MaximumPlotSizeNumeric` the maximum plot size for numeric errors.
`protected int`	`m_MinimumPlotSizeNumeric` the minimum plot size for numeric errors.
`protected java.util.ArrayList<java.lang.Integer>`	`m_PlotShapes` for storing the plot shapes.
`protected java.util.ArrayList<java.lang.Object>`	`m_PlotSizes` for storing the plot sizes.
`protected boolean`	`m_pointSizeProportionalToMargin`
`protected boolean`	`m_SaveForVisualization` whether to save the instances for visualization or just evaluate the instance.

Fields inherited from class weka.gui.explorer.AbstractPlotInstances
m_FinishUpCalled, m_Instances, m_PlotInstances

Constructor Summary

Constructors
Constructor and Description

ClassifierErrorsPlotInstances()

Constructors
Constructor and Description
`ClassifierErrorsPlotInstances()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected void`	`addPredictionIntervals()` Adds the prediction intervals as additional attributes at the end.
`protected void`	`check()` Checks whether classifier, class index and evaluation are provided.
`void`	`cleanUp()` For freeing up memory.
`protected PlotData2D`	`createPlotData(java.lang.String name)` Assembles and returns the plot.
`protected void`	`determineFormat()` Sets up the structure for the plot instances.
`protected void`	`finishUp()` Performs optional post-processing.
`Classifier`	`getClassifier()` Returns the currently set classifier.
`int`	`getClassIndex()` Returns the 0-based class index.
`Evaluation`	`getEvaluation()` Returns the Evaluation object in use.
`java.util.ArrayList<java.lang.Integer>`	`getPlotShapes()` Get the vector of plot shapes (see weka.gui.visualize.Plot2D).
`java.util.ArrayList<java.lang.Object>`	`getPlotSizes()` Get the vector of plot sizes (see weka.gui.visualize.Plot2D).
`boolean`	`getPointSizeProportionalToMargin()` Get whether the point size should be proportional to the prediction margin (classification only).
`boolean`	`getSaveForVisualization()` Returns whether the instances are saved for visualization for only evaluation of the prediction is to happen.
`protected void`	`initialize()` Initializes the members.
`void`	`process(Instance toPredict, Classifier classifier, Evaluation eval)` Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info. m_PlotShape for nominal class datasets holds shape types (actual data points have automatic shape type assignment; classifier error data points have box shape type).
`void`	`process(Instances batch, double[][] predictions, Evaluation eval)`
`protected void`	`scaleNumericPredictions()` Scales numeric class predictions into shape sizes for plotting in the visualize panel.
`void`	`setClassifier(Classifier value)` Sets the classifier used for making the predictions.
`void`	`setClassIndex(int index)` Sets the 0-based class index.
`void`	`setEvaluation(Evaluation value)` Sets the Evaluation object to use.
`void`	`setPlotShapes(java.util.ArrayList<java.lang.Integer> plotShapes)` Set the vector of plot shapes to use;
`void`	`setPlotSizes(java.util.ArrayList<java.lang.Object> plotSizes)` Set the vector of plot sizes to use
`void`	`setPointSizeProportionalToMargin(boolean b)` Set whether the point size should be proportional to the prediction margin (classification only).
`void`	`setSaveForVisualization(boolean value)` Sets whether the instances are saved for visualization or only evaluation of the prediction is to happen.

Methods inherited from class weka.gui.explorer.AbstractPlotInstances
canPlot, getInstances, getOptions, getPlotData, getPlotInstances, listOptions, setInstances, setOptions, setUp

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

- Field Detail
  - m_MinimumPlotSizeNumeric
```
protected int m_MinimumPlotSizeNumeric
```
    the minimum plot size for numeric errors.
  - m_MaximumPlotSizeNumeric
```
protected int m_MaximumPlotSizeNumeric
```
    the maximum plot size for numeric errors.
  - m_SaveForVisualization
```
protected boolean m_SaveForVisualization
```
    whether to save the instances for visualization or just evaluate the instance.
  - m_pointSizeProportionalToMargin
```
protected boolean m_pointSizeProportionalToMargin
```
  - m_PlotShapes
```
protected java.util.ArrayList<java.lang.Integer> m_PlotShapes
```
    for storing the plot shapes.
  - m_PlotSizes
```
protected java.util.ArrayList<java.lang.Object> m_PlotSizes
```
    for storing the plot sizes.
  - m_Classifier
```
protected Classifier m_Classifier
```
    the classifier being used.
  - m_ClassIndex
```
protected int m_ClassIndex
```
    the class index.
  - m_Evaluation
```
protected Evaluation m_Evaluation
```
    the Evaluation object to use.
- Constructor Detail
  - ClassifierErrorsPlotInstances
```
public ClassifierErrorsPlotInstances()
```
- Method Detail
  - initialize
```
protected void initialize()
```
    Initializes the members.
    
    Overrides:
    
    initialize in class AbstractPlotInstances
  - getPlotShapes
```
public java.util.ArrayList<java.lang.Integer> getPlotShapes()
```
    Get the vector of plot shapes (see weka.gui.visualize.Plot2D).
    
    Returns:
    
    the vector of plot shapes.
  - getPlotSizes
```
public java.util.ArrayList<java.lang.Object> getPlotSizes()
```
    Get the vector of plot sizes (see weka.gui.visualize.Plot2D).
    
    Returns:
    
    the vector of plot sizes.
  - setPlotShapes
```
public void setPlotShapes(java.util.ArrayList<java.lang.Integer> plotShapes)
```
    Set the vector of plot shapes to use;
    
    Parameters:
    
    plotShapes -
  - setPlotSizes
```
public void setPlotSizes(java.util.ArrayList<java.lang.Object> plotSizes)
```
    Set the vector of plot sizes to use
    
    Parameters:
    
    plotSizes - the plot sizes to use
  - setClassifier
```
public void setClassifier(Classifier value)
```
    Sets the classifier used for making the predictions.
    
    Parameters:
    
    value - the classifier to use
  - getClassifier
```
public Classifier getClassifier()
```
    Returns the currently set classifier.
    
    Returns:
    
    the classifier in use
  - setClassIndex
```
public void setClassIndex(int index)
```
    Sets the 0-based class index.
    
    Parameters:
    
    index - the class index
  - getClassIndex
```
public int getClassIndex()
```
    Returns the 0-based class index.
    
    Returns:
    
    the class index
  - setEvaluation
```
public void setEvaluation(Evaluation value)
```
    Sets the Evaluation object to use.
    
    Parameters:
    
    value - the evaluation to use
  - getEvaluation
```
public Evaluation getEvaluation()
```
    Returns the Evaluation object in use.
    
    Returns:
    
    the evaluation object
  - setSaveForVisualization
```
public void setSaveForVisualization(boolean value)
```
    Sets whether the instances are saved for visualization or only evaluation of the prediction is to happen.
    
    Parameters:
    
    value - if true then the instances will be saved
  - getSaveForVisualization
```
public boolean getSaveForVisualization()
```
    Returns whether the instances are saved for visualization for only evaluation of the prediction is to happen.
    
    Returns:
    
    true if the instances are saved
  - setPointSizeProportionalToMargin
```
public void setPointSizeProportionalToMargin(boolean b)
```
    Set whether the point size should be proportional to the prediction margin (classification only).
    
    Parameters:
    
    b - true if the point size should be proportional to the margin
  - getPointSizeProportionalToMargin
```
public boolean getPointSizeProportionalToMargin()
```
    Get whether the point size should be proportional to the prediction margin (classification only).
    
    Returns:
    
    true if the point size should be proportional to the margin
  - check
```
protected void check()
```
    Checks whether classifier, class index and evaluation are provided.
    
    Overrides:
    
    check in class AbstractPlotInstances
  - determineFormat
```
protected void determineFormat()
```
    Sets up the structure for the plot instances. Sets m_PlotInstances to null if instances are not saved for visualization.
    
    Specified by:
    
    determineFormat in class AbstractPlotInstances
    
    See Also:
    
    getSaveForVisualization()
  - process
```
public void process(Instances batch,
                    double[][] predictions,
                    Evaluation eval)
```
  - process
```
public void process(Instance toPredict,
                    Classifier classifier,
                    Evaluation eval)
```
    Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info. m_PlotShape for nominal class datasets holds shape types (actual data points have automatic shape type assignment; classifier error data points have box shape type). For numeric class datasets, the actual data points are stored in m_PlotInstances and m_PlotSize stores the error (which is later converted to shape size values).
    
    Parameters:
    
    toPredict - the actual data point
    
    classifier - the classifier
    
    eval - the evaluation object to use for evaluating the classifier on the instance to predict
    
    See Also:
    
    m_PlotShapes, m_PlotSizes, AbstractPlotInstances.m_PlotInstances
  - scaleNumericPredictions
```
protected void scaleNumericPredictions()
```
    Scales numeric class predictions into shape sizes for plotting in the visualize panel.
  - addPredictionIntervals
```
protected void addPredictionIntervals()
```
    Adds the prediction intervals as additional attributes at the end. Since classifiers can returns varying number of intervals per instance, the dataset is filled with missing values for non-existing intervals.
  - finishUp
```
protected void finishUp()
```
    Performs optional post-processing.
    
    Overrides:
    
    finishUp in class AbstractPlotInstances
    
    See Also:
    
    scaleNumericPredictions(), addPredictionIntervals()
  - createPlotData
```
protected PlotData2D createPlotData(java.lang.String name)
                             throws java.lang.Exception
```
    Assembles and returns the plot. The relation name of the dataset gets added automatically.
    
    Specified by:
    
    createPlotData in class AbstractPlotInstances
    
    Parameters:
    
    name - the name of the plot
    
    Returns:
    
    the plot or null if plot instances weren't saved for visualization
    
    Throws:
    
    java.lang.Exception - if plot generation fails
  - cleanUp
```
public void cleanUp()
```
    For freeing up memory. Plot data cannot be generated after this call!
    
    Overrides:
    
    cleanUp in class AbstractPlotInstances

Class ClassifierErrorsPlotInstances

Field Summary

Fields inherited from class weka.gui.explorer.AbstractPlotInstances

Constructor Summary

Method Summary

Methods inherited from class weka.gui.explorer.AbstractPlotInstances

Methods inherited from class java.lang.Object

Field Detail

m_MinimumPlotSizeNumeric

m_MaximumPlotSizeNumeric

m_SaveForVisualization

m_pointSizeProportionalToMargin

m_PlotShapes

m_PlotSizes

m_Classifier

m_ClassIndex

m_Evaluation

Constructor Detail

ClassifierErrorsPlotInstances

Method Detail

initialize

getPlotShapes

getPlotSizes

setPlotShapes

setPlotSizes

setClassifier

getClassifier

setClassIndex

getClassIndex

setEvaluation

getEvaluation

setSaveForVisualization

getSaveForVisualization

setPointSizeProportionalToMargin

getPointSizeProportionalToMargin

check

determineFormat

process

process

scaleNumericPredictions

addPredictionIntervals

finishUp

createPlotData

cleanUp