/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* ClassifierErrorsPlotInstances.java
* Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
*/
package weka.gui.explorer;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.IntervalEstimator;
import weka.classifiers.evaluation.NumericPrediction;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;
import weka.gui.visualize.Plot2D;
import weka.gui.visualize.PlotData2D;
/**
* A class for generating plottable visualization errors.
* <p/>
* Example usage:
* <pre>
* 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();
* </pre>
*
* @author fracpete (fracpete at waikato dot ac dot nz)
* @version $Revision: 6904 $
*/
public class ClassifierErrorsPlotInstances
extends AbstractPlotInstances {
/** for serialization. */
private static final long serialVersionUID = -3941976365792013279L;
/** the minimum plot size for numeric errors. */
protected int m_MinimumPlotSizeNumeric;
/** the maximum plot size for numeric errors. */
protected int m_MaximumPlotSizeNumeric;
/** whether to save the instances for visualization or just evaluate the
* instance. */
protected boolean m_SaveForVisualization;
/** for storing the plot shapes. */
protected FastVector m_PlotShapes;
/** for storing the plot sizes. */
protected FastVector m_PlotSizes;
/** the classifier being used. */
protected Classifier m_Classifier;
/** the class index. */
protected int m_ClassIndex;
/** the Evaluation object to use. */
protected Evaluation m_Evaluation;
/**
* Initializes the members.
*/
protected void initialize() {
super.initialize();
m_PlotShapes = new FastVector();
m_PlotSizes = new FastVector();
m_Classifier = null;
m_ClassIndex = -1;
m_Evaluation = null;
m_SaveForVisualization = true;
m_MinimumPlotSizeNumeric = ExplorerDefaults.getClassifierErrorsMinimumPlotSizeNumeric();
m_MaximumPlotSizeNumeric = ExplorerDefaults.getClassifierErrorsMaximumPlotSizeNumeric();
}
/**
* Sets the classifier used for making the predictions.
*
* @param value the classifier to use
*/
public void setClassifier(Classifier value) {
m_Classifier = value;
}
/**
* Returns the currently set classifier.
*
* @return the classifier in use
*/
public Classifier getClassifier() {
return m_Classifier;
}
/**
* Sets the 0-based class index.
*
* @param index the class index
*/
public void setClassIndex(int index) {
m_ClassIndex = index;
}
/**
* Returns the 0-based class index.
*
* @return the class index
*/
public int getClassIndex() {
return m_ClassIndex;
}
/**
* Sets the Evaluation object to use.
*
* @param value the evaluation to use
*/
public void setEvaluation(Evaluation value) {
m_Evaluation = value;
}
/**
* Returns the Evaluation object in use.
*
* @return the evaluation object
*/
public Evaluation getEvaluation() {
return m_Evaluation;
}
/**
* Sets whether the instances are saved for visualization or only evaluation
* of the prediction is to happen.
*
* @param value if true then the instances will be saved
*/
public void setSaveForVisualization(boolean value) {
m_SaveForVisualization = value;
}
/**
* Returns whether the instances are saved for visualization for only
* evaluation of the prediction is to happen.
*
* @return true if the instances are saved
*/
public boolean getSaveForVisualization() {
return m_SaveForVisualization;
}
/**
* Checks whether classifier, class index and evaluation are provided.
*/
protected void check() {
super.check();
if (m_Classifier == null)
throw new IllegalStateException("No classifier set!");
if (m_ClassIndex == -1)
throw new IllegalStateException("No class index set!");
if (m_Evaluation == null)
throw new IllegalStateException("No evaluation set");
}
/**
* Sets up the structure for the plot instances. Sets m_PlotInstances to null
* if instances are not saved for visualization.
*
* @see #getSaveForVisualization()
*/
protected void determineFormat() {
FastVector hv;
Attribute predictedClass;
Attribute classAt;
FastVector attVals;
int i;
if (!m_SaveForVisualization) {
m_PlotInstances = null;
return;
}
hv = new FastVector();
classAt = m_Instances.attribute(m_ClassIndex);
if (classAt.isNominal()) {
attVals = new FastVector();
for (i = 0; i < classAt.numValues(); i++)
attVals.addElement(classAt.value(i));
predictedClass = new Attribute("predicted" + classAt.name(), attVals);
}
else {
predictedClass = new Attribute("predicted" + classAt.name());
}
for (i = 0; i < m_Instances.numAttributes(); i++) {
if (i == m_Instances.classIndex())
hv.addElement(predictedClass);
hv.addElement(m_Instances.attribute(i).copy());
}
m_PlotInstances = new Instances(
m_Instances.relationName() + "_predicted", hv, m_Instances.numInstances());
m_PlotInstances.setClassIndex(m_ClassIndex + 1);
}
/**
* 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).
*
* @param toPredict the actual data point
* @param classifier the classifier
* @param eval the evaluation object to use for evaluating the classifier on
* the instance to predict
* @see #m_PlotShapes
* @see #m_PlotSizes
* @see #m_PlotInstances
*/
public void process(Instance toPredict, Classifier classifier, Evaluation eval) {
double pred;
double[] values;
int i;
try {
pred = eval.evaluateModelOnceAndRecordPrediction(classifier, toPredict);
if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
toPredict = ((weka.classifiers.misc.InputMappedClassifier)classifier).
constructMappedInstance(toPredict);
}
if (!m_SaveForVisualization)
return;
if (m_PlotInstances != null) {
values = new double[m_PlotInstances.numAttributes()];
for (i = 0; i < m_PlotInstances.numAttributes(); i++) {
if (i < toPredict.classIndex()) {
values[i] = toPredict.value(i);
}
else if (i == toPredict.classIndex()) {
values[i] = pred;
values[i+1] = toPredict.value(i);
i++;
}
else {
values[i] = toPredict.value(i-1);
}
}
m_PlotInstances.add(new DenseInstance(1.0, values));
if (toPredict.classAttribute().isNominal()) {
if (toPredict.isMissing(toPredict.classIndex()) || Utils.isMissingValue(pred)) {
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
}
else if (pred != toPredict.classValue()) {
// set to default error point shape
m_PlotShapes.addElement(new Integer(Plot2D.ERROR_SHAPE));
}
else {
// otherwise set to constant (automatically assigned) point shape
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
}
m_PlotSizes.addElement(new Integer(Plot2D.DEFAULT_SHAPE_SIZE));
}
else {
// store the error (to be converted to a point size later)
Double errd = null;
if (!toPredict.isMissing(toPredict.classIndex()) && !Utils.isMissingValue(pred)) {
errd = new Double(pred - toPredict.classValue());
m_PlotShapes.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
}
else {
// missing shape if actual class not present or prediction is missing
m_PlotShapes.addElement(new Integer(Plot2D.MISSING_SHAPE));
}
m_PlotSizes.addElement(errd);
}
}
}
catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Scales numeric class predictions into shape sizes for plotting
* in the visualize panel.
*/
protected void scaleNumericPredictions() {
double maxErr;
double minErr;
double err;
int i;
Double errd;
double temp;
maxErr = Double.NEGATIVE_INFINITY;
minErr = Double.POSITIVE_INFINITY;
// find min/max errors
for (i = 0; i < m_PlotSizes.size(); i++) {
errd = (Double) m_PlotSizes.elementAt(i);
if (errd != null) {
err = Math.abs(errd.doubleValue());
if (err < minErr)
minErr = err;
if (err > maxErr)
maxErr = err;
}
}
// scale errors
for (i = 0; i < m_PlotSizes.size(); i++) {
errd = (Double) m_PlotSizes.elementAt(i);
if (errd != null) {
err = Math.abs(errd.doubleValue());
if (maxErr - minErr > 0) {
temp = (((err - minErr) / (maxErr - minErr)) * (m_MaximumPlotSizeNumeric - m_MinimumPlotSizeNumeric + 1));
m_PlotSizes.setElementAt(new Integer((int) temp) + m_MinimumPlotSizeNumeric, i);
}
else {
m_PlotSizes.setElementAt(new Integer(m_MinimumPlotSizeNumeric), i);
}
}
else {
m_PlotSizes.setElementAt(new Integer(m_MinimumPlotSizeNumeric), i);
}
}
}
/**
* 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.
*/
protected void addPredictionIntervals() {
int maxNum;
int num;
int i;
int n;
FastVector preds;
FastVector atts;
Instances data;
Instance inst;
Instance newInst;
double[] values;
double[][] predInt;
// determine the maximum number of intervals
maxNum = 0;
preds = m_Evaluation.predictions();
for (i = 0; i < preds.size(); i++) {
num = ((NumericPrediction) preds.elementAt(i)).predictionIntervals().length;
if (num > maxNum)
maxNum = num;
}
// create new header
atts = new FastVector();
for (i = 0; i < m_PlotInstances.numAttributes(); i++)
atts.addElement(m_PlotInstances.attribute(i));
for (i = 0; i < maxNum; i++) {
atts.addElement(new Attribute("predictionInterval_" + (i+1) + "-lowerBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i+1) + "-upperBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i+1) + "-width"));
}
data = new Instances(m_PlotInstances.relationName(), atts, m_PlotInstances.numInstances());
data.setClassIndex(m_PlotInstances.classIndex());
// update data
for (i = 0; i < m_PlotInstances.numInstances(); i++) {
inst = m_PlotInstances.instance(i);
// copy old values
values = new double[data.numAttributes()];
System.arraycopy(inst.toDoubleArray(), 0, values, 0, inst.numAttributes());
// add interval data
predInt = ((NumericPrediction) preds.elementAt(i)).predictionIntervals();
for (n = 0; n < maxNum; n++) {
if (n < predInt.length){
values[m_PlotInstances.numAttributes() + n*3 + 0] = predInt[n][0];
values[m_PlotInstances.numAttributes() + n*3 + 1] = predInt[n][1];
values[m_PlotInstances.numAttributes() + n*3 + 2] = predInt[n][1] - predInt[n][0];
}
else {
values[m_PlotInstances.numAttributes() + n*3 + 0] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n*3 + 1] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n*3 + 2] = Utils.missingValue();
}
}
// create new Instance
newInst = new DenseInstance(inst.weight(), values);
data.add(newInst);
}
m_PlotInstances = data;
}
/**
* Performs optional post-processing.
*
* @see #scaleNumericPredictions()
* @see #addPredictionIntervals()
*/
protected void finishUp() {
super.finishUp();
if (!m_SaveForVisualization)
return;
if (m_Instances.attribute(m_ClassIndex).isNumeric()) {
scaleNumericPredictions();
if (m_Classifier instanceof IntervalEstimator)
addPredictionIntervals();
}
}
/**
* Assembles and returns the plot. The relation name of the dataset gets
* added automatically.
*
* @param name the name of the plot
* @return the plot or null if plot instances weren't saved for visualization
* @throws Exception if plot generation fails
*/
protected PlotData2D createPlotData(String name) throws Exception {
PlotData2D result;
if (!m_SaveForVisualization)
return null;
result = new PlotData2D(m_PlotInstances);
result.setShapeSize(m_PlotSizes);
result.setShapeType(m_PlotShapes);
result.setPlotName(name + " (" + m_Instances.relationName() + ")");
// result.addInstanceNumberAttribute();
return result;
}
/**
* For freeing up memory. Plot data cannot be generated after this call!
*/
public void cleanUp() {
super.cleanUp();
m_Classifier = null;
m_PlotShapes = null;
m_PlotSizes = null;
m_Evaluation = null;
}
}