/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
String[] superOptions;
if (m_InitOptions != null) {
try {
((OptionHandler) m_Classifier).setOptions((String[]) m_InitOptions.clone());
superOptions = super.getOptions();
((OptionHandler) m_Classifier).setOptions((String[]) m_BestClassifierOptions.clone());
} catch (Exception e) {
throw new RuntimeException(
"CVParameterSelection: could not set options " + "in getOptions().");
}
} else {
superOptions = super.getOptions();
}
String[] options = new String[superOptions.length + m_CVParams.size() * 2 + 2];
int current = 0;
for (int i = 0; i < m_CVParams.size(); i++) {
options[current++] = "-P";
options[current++] = "" + getCVParameter(i);
}
options[current++] = "-X";
options[current++] = "" + getNumFolds();
System.arraycopy(superOptions, 0, options, current, superOptions.length);
return options;
}
public weka.core.Instances toWekaInstances() {
// attributes
FastVector wattrs = new FastVector();
Iterator itr = attributes.iterator();
while (itr.hasNext()) {
Attribute attr = (Attribute) itr.next();
wattrs.addElement(attr.toWekaAttribute());
}
// data instances
weka.core.Instances winsts = new weka.core.Instances(name, wattrs, instances.size());
itr = instances.iterator();
while (itr.hasNext()) {
Instance inst = (Instance) itr.next();
Iterator itrval = inst.getValues().iterator();
Iterator itrmis = inst.getMissing().iterator();
double[] vals = new double[wattrs.size()];
for (int i = 0; i < wattrs.size(); i++) {
double val = (Double) itrval.next();
if ((Boolean) itrmis.next()) {
vals[i] = weka.core.Instance.missingValue();
} else {
vals[i] = val;
}
}
weka.core.Instance winst = new weka.core.Instance(1, vals);
winst.setDataset(winsts);
winsts.add(winst);
}
winsts.setClassIndex(this.class_index);
return winsts;
}
/**
* Compute the minimal data description length of the ruleset if the rule in the given position is
* NOT deleted.<br>
* The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
*
* @param index the index of the rule in question
* @param expFPRate expected FP/(FP+FN), used in dataDL calculation
* @param checkErr whether check if error rate >= 0.5
* @return the minDataDL
*/
public double minDataDLIfExists(int index, double expFPRate, boolean checkErr) {
// System.out.println("!!!Enter with: ");
double[] rulesetStat = new double[6]; // Stats of ruleset if rule exists
for (int j = 0; j < m_SimpleStats.size(); j++) {
// Covered stats are cumulative
rulesetStat[0] += ((double[]) m_SimpleStats.elementAt(j))[0];
rulesetStat[2] += ((double[]) m_SimpleStats.elementAt(j))[2];
rulesetStat[4] += ((double[]) m_SimpleStats.elementAt(j))[4];
if (j == m_SimpleStats.size() - 1) { // Last rule
rulesetStat[1] = ((double[]) m_SimpleStats.elementAt(j))[1];
rulesetStat[3] = ((double[]) m_SimpleStats.elementAt(j))[3];
rulesetStat[5] = ((double[]) m_SimpleStats.elementAt(j))[5];
}
}
// Potential
double potential = 0;
for (int k = index + 1; k < m_SimpleStats.size(); k++) {
double[] ruleStat = (double[]) getSimpleStats(k);
double ifDeleted = potential(k, expFPRate, rulesetStat, ruleStat, checkErr);
if (!Double.isNaN(ifDeleted)) potential += ifDeleted;
}
// Data DL of the ruleset without the rule
// Note that ruleset stats has already been updated to reflect deletion
// if any potential
double dataDLWith =
dataDL(expFPRate, rulesetStat[0], rulesetStat[1], rulesetStat[4], rulesetStat[5]);
// System.out.println("!!!with: "+dataDLWith + " |potential: "+
// potential);
return (dataDLWith - potential);
}
/**
* Create the options array to pass to the classifier. The parameter values and positions are
* taken from m_ClassifierOptions and m_CVParams.
*
* @return the options array
*/
protected String[] createOptions() {
String[] options = new String[m_ClassifierOptions.length + 2 * m_CVParams.size()];
int start = 0, end = options.length;
// Add the cross-validation parameters and their values
for (int i = 0; i < m_CVParams.size(); i++) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(i);
double paramValue = cvParam.m_ParamValue;
if (cvParam.m_RoundParam) {
// paramValue = (double)((int) (paramValue + 0.5));
paramValue = Math.rint(paramValue);
}
if (cvParam.m_AddAtEnd) {
options[--end] = "" + Utils.doubleToString(paramValue, 4);
options[--end] = "-" + cvParam.m_ParamChar;
} else {
options[start++] = "-" + cvParam.m_ParamChar;
options[start++] = "" + Utils.doubleToString(paramValue, 4);
}
}
// Add the static parameters
System.arraycopy(m_ClassifierOptions, 0, options, start, m_ClassifierOptions.length);
return options;
}
/**
* Method that finds all large itemsets for the given set of instances.
*
* @param the instances to be used
* @exception Exception if an attribute is numeric
*/
private void findLargeItemSets(int index) throws Exception {
FastVector kMinusOneSets, kSets = new FastVector();
Hashtable hashtable;
int i = 0;
// Find large itemsets
// of length 1
if (index == 1) {
kSets = ItemSet.singletons(m_instances);
ItemSet.upDateCounters(kSets, m_instances);
kSets = ItemSet.deleteItemSets(kSets, m_premiseCount, Integer.MAX_VALUE);
if (kSets.size() == 0) return;
m_Ls.addElement(kSets);
}
// of length > 1
if (index > 1) {
if (m_Ls.size() > 0) kSets = (FastVector) m_Ls.lastElement();
m_Ls.removeAllElements();
i = index - 2;
kMinusOneSets = kSets;
kSets = ItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances());
hashtable = ItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size());
m_hashtables.addElement(hashtable);
kSets = ItemSet.pruneItemSets(kSets, hashtable);
ItemSet.upDateCounters(kSets, m_instances);
kSets = ItemSet.deleteItemSets(kSets, m_premiseCount, Integer.MAX_VALUE);
if (kSets.size() == 0) return;
m_Ls.addElement(kSets);
}
}
/**
* Gets the scheme paramter with the given index.
*
* @param index the index for the parameter
* @return the scheme parameter
*/
public String getCVParameter(int index) {
if (m_CVParams.size() <= index) {
return "";
}
return ((CVParameter) m_CVParams.elementAt(index)).toString();
}
/**
* Get the class distribution predicted by the rule in given position
*
* @param index the position index of the rule
* @return the class distributions
*/
public double[] getDistributions(int index) {
if ((m_Distributions != null) && (index < m_Distributions.size()))
return (double[]) m_Distributions.elementAt(index);
return null;
}
/**
* Remove the last rule in the ruleset as well as it's stats. It might be useful when the last
* rule was added for testing purpose and then the test failed
*/
public void removeLast() {
int last = m_Ruleset.size() - 1;
m_Ruleset.removeElementAt(last);
m_Filtered.removeElementAt(last);
m_SimpleStats.removeElementAt(last);
if (m_Distributions != null) m_Distributions.removeElementAt(last);
}
/**
* Get the data after filtering the given rule
*
* @param index the index of the rule
* @return the data covered and uncovered by the rule
*/
public Instances[] getFiltered(int index) {
if ((m_Filtered != null) && (index < m_Filtered.size()))
return (Instances[]) m_Filtered.elementAt(index);
return null;
}
/**
* Handles the various button clicking type activities.
*
* @param e a value of type 'ActionEvent'
*/
public void actionPerformed(ActionEvent e) {
if (e.getSource() == m_ConfigureBut) {
selectProperty();
} else if (e.getSource() == m_StatusBox) {
// notify any listeners
for (int i = 0; i < m_Listeners.size(); i++) {
ActionListener temp = ((ActionListener) m_Listeners.elementAt(i));
temp.actionPerformed(
new ActionEvent(this, ActionEvent.ACTION_PERFORMED, "Editor status change"));
}
// Toggles whether the custom property is used
if (m_StatusBox.getSelectedIndex() == 0) {
m_Exp.setUsePropertyIterator(false);
m_ConfigureBut.setEnabled(false);
m_ArrayEditor.setEnabled(false);
m_ArrayEditor.setValue(null);
validate();
} else {
if (m_Exp.getPropertyArray() == null) {
selectProperty();
}
if (m_Exp.getPropertyArray() == null) {
m_StatusBox.setSelectedIndex(0);
} else {
m_Exp.setUsePropertyIterator(true);
m_ConfigureBut.setEnabled(true);
m_ArrayEditor.setEnabled(true);
}
validate();
}
}
}
/**
* Set the labels for nominal attribute creation.
*
* @param labelList a comma separated list of labels
* @throws IllegalArgumentException if the labelList was invalid
*/
public void setNominalLabels(String labelList) {
FastVector labels = new FastVector(10);
// Split the labelList up into the vector
int commaLoc;
while ((commaLoc = labelList.indexOf(',')) >= 0) {
String label = labelList.substring(0, commaLoc).trim();
if (!label.equals("")) {
labels.addElement(label);
} else {
throw new IllegalArgumentException(
"Invalid label list at " + labelList.substring(commaLoc));
}
labelList = labelList.substring(commaLoc + 1);
}
String label = labelList.trim();
if (!label.equals("")) {
labels.addElement(label);
}
// If everything is OK, make the type change
m_Labels = labels;
if (labels.size() == 0) {
m_AttributeType = Attribute.NUMERIC;
} else {
m_AttributeType = Attribute.NOMINAL;
}
}
/**
* Finds the best parameter combination. (recursive for each parameter being optimised).
*
* @param depth the index of the parameter to be optimised at this level
* @param trainData the data the search is based on
* @param random a random number generator
* @throws Exception if an error occurs
*/
protected void findParamsByCrossValidation(int depth, Instances trainData, Random random)
throws Exception {
if (depth < m_CVParams.size()) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(depth);
double upper;
switch ((int) (cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
upper = m_NumAttributes;
break;
case 2:
upper = m_TrainFoldSize;
break;
default:
upper = cvParam.m_Upper;
break;
}
double increment = (upper - cvParam.m_Lower) / (cvParam.m_Steps - 1);
for (cvParam.m_ParamValue = cvParam.m_Lower;
cvParam.m_ParamValue <= upper;
cvParam.m_ParamValue += increment) {
findParamsByCrossValidation(depth + 1, trainData, random);
}
} else {
Evaluation evaluation = new Evaluation(trainData);
// Set the classifier options
String[] options = createOptions();
if (m_Debug) {
System.err.print("Setting options for " + m_Classifier.getClass().getName() + ":");
for (int i = 0; i < options.length; i++) {
System.err.print(" " + options[i]);
}
System.err.println("");
}
((OptionHandler) m_Classifier).setOptions(options);
for (int j = 0; j < m_NumFolds; j++) {
// We want to randomize the data the same way for every
// learning scheme.
Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
Instances test = trainData.testCV(m_NumFolds, j);
m_Classifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(m_Classifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println("Cross-validated error rate: " + Utils.doubleToString(error, 6, 4));
}
if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {
m_BestPerformance = error;
m_BestClassifierOptions = createOptions();
}
}
}
/**
* 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;
}
/**
* Calculates the performance stats for the default class and return results as a set of
* Instances. The structure of these Instances is as follows:
*
* <p>
*
* <ul>
* <li><b>True Positives </b>
* <li><b>False Negatives</b>
* <li><b>False Positives</b>
* <li><b>True Negatives</b>
* <li><b>False Positive Rate</b>
* <li><b>True Positive Rate</b>
* <li><b>Precision</b>
* <li><b>Recall</b>
* <li><b>Fallout</b>
* <li><b>Threshold</b> contains the probability threshold that gives rise to the previous
* performance values.
* </ul>
*
* <p>For the definitions of these measures, see TwoClassStats
*
* <p>
*
* @see TwoClassStats
* @param predictions the predictions to base the curve on
* @return datapoints as a set of instances, null if no predictions have been made.
*/
public Instances getCurve(FastVector predictions) {
if (predictions.size() == 0) {
return null;
}
return getCurve(
predictions, ((NominalPrediction) predictions.elementAt(0)).distribution().length - 1);
}
/**
* Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order
* and update all the stats
*
* @param expFPRate expected FP/(FP+FN), used in dataDL calculation
* @param checkErr whether check if error rate >= 0.5
*/
public void reduceDL(double expFPRate, boolean checkErr) {
boolean needUpdate = false;
double[] rulesetStat = new double[6];
for (int j = 0; j < m_SimpleStats.size(); j++) {
// Covered stats are cumulative
rulesetStat[0] += ((double[]) m_SimpleStats.elementAt(j))[0];
rulesetStat[2] += ((double[]) m_SimpleStats.elementAt(j))[2];
rulesetStat[4] += ((double[]) m_SimpleStats.elementAt(j))[4];
if (j == m_SimpleStats.size() - 1) { // Last rule
rulesetStat[1] = ((double[]) m_SimpleStats.elementAt(j))[1];
rulesetStat[3] = ((double[]) m_SimpleStats.elementAt(j))[3];
rulesetStat[5] = ((double[]) m_SimpleStats.elementAt(j))[5];
}
}
// Potential
for (int k = m_SimpleStats.size() - 1; k >= 0; k--) {
double[] ruleStat = (double[]) m_SimpleStats.elementAt(k);
// rulesetStat updated
double ifDeleted = potential(k, expFPRate, rulesetStat, ruleStat, checkErr);
if (!Double.isNaN(ifDeleted)) {
/*System.err.println("!!!deleted ("+k+"): save "+ifDeleted
+" | "+rulesetStat[0]
+" | "+rulesetStat[1]
+" | "+rulesetStat[4]
+" | "+rulesetStat[5]);
*/
if (k == (m_SimpleStats.size() - 1)) removeLast();
else {
m_Ruleset.removeElementAt(k);
needUpdate = true;
}
}
}
if (needUpdate) {
m_Filtered = null;
m_SimpleStats = null;
countData();
}
}
/**
* Adds the statistics encapsulated in the supplied Evaluation object into this one. Does not
* perform any checks for compatibility between the supplied Evaluation object and this one.
*
* @param evaluation the evaluation object to aggregate
*/
public void aggregate(Evaluation evaluation) {
m_Incorrect += evaluation.incorrect();
m_Correct += evaluation.correct();
m_Unclassified += evaluation.unclassified();
m_MissingClass += evaluation.m_MissingClass;
m_WithClass += evaluation.m_WithClass;
if (evaluation.m_ConfusionMatrix != null) {
double[][] newMatrix = evaluation.confusionMatrix();
if (newMatrix != null) {
for (int i = 0; i < m_ConfusionMatrix.length; i++) {
for (int j = 0; j < m_ConfusionMatrix[i].length; j++) {
m_ConfusionMatrix[i][j] += newMatrix[i][j];
}
}
}
}
double[] newClassPriors = evaluation.m_ClassPriors;
if (newClassPriors != null) {
for (int i = 0; i < this.m_ClassPriors.length; i++) {
m_ClassPriors[i] = newClassPriors[i];
}
}
m_ClassPriorsSum = evaluation.m_ClassPriorsSum;
m_TotalCost += evaluation.totalCost();
m_SumErr += evaluation.m_SumErr;
m_SumAbsErr += evaluation.m_SumAbsErr;
m_SumSqrErr += evaluation.m_SumSqrErr;
m_SumClass += evaluation.m_SumClass;
m_SumSqrClass += evaluation.m_SumSqrClass;
m_SumPredicted += evaluation.m_SumPredicted;
m_SumSqrPredicted += evaluation.m_SumSqrPredicted;
m_SumClassPredicted += evaluation.m_SumClassPredicted;
m_SumPriorAbsErr += evaluation.m_SumPriorAbsErr;
m_SumPriorSqrErr += evaluation.m_SumPriorSqrErr;
m_SumKBInfo += evaluation.m_SumKBInfo;
double[] newMarginCounts = evaluation.m_MarginCounts;
if (newMarginCounts != null) {
for (int i = 0; i < m_MarginCounts.length; i++) {
m_MarginCounts[i] += newMarginCounts[i];
}
}
m_SumPriorEntropy += evaluation.m_SumPriorEntropy;
m_SumSchemeEntropy += evaluation.m_SumSchemeEntropy;
m_TotalSizeOfRegions += evaluation.m_TotalSizeOfRegions;
m_TotalCoverage += evaluation.m_TotalCoverage;
FastVector predsToAdd = evaluation.m_Predictions;
if (predsToAdd != null) {
if (m_Predictions == null) {
m_Predictions = new FastVector();
}
for (int i = 0; i < predsToAdd.size(); i++) {
m_Predictions.addElement(predsToAdd.elementAt(i));
}
}
}
/**
* @param predictions the predictions to use
* @param classIndex the class index
* @return the probabilities
*/
private double[] getProbabilities(FastVector predictions, int classIndex) {
// sort by predicted probability of the desired class.
double[] probs = new double[predictions.size()];
for (int i = 0; i < probs.length; i++) {
NominalPrediction pred = (NominalPrediction) predictions.elementAt(i);
probs[i] = pred.distribution()[classIndex];
}
return probs;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
Instances trainData = new Instances(instances);
trainData.deleteWithMissingClass();
if (!(m_Classifier instanceof OptionHandler)) {
throw new IllegalArgumentException("Base classifier should be OptionHandler.");
}
m_InitOptions = ((OptionHandler) m_Classifier).getOptions();
m_BestPerformance = -99;
m_NumAttributes = trainData.numAttributes();
Random random = new Random(m_Seed);
trainData.randomize(random);
m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();
// Check whether there are any parameters to optimize
if (m_CVParams.size() == 0) {
m_Classifier.buildClassifier(trainData);
m_BestClassifierOptions = m_InitOptions;
return;
}
if (trainData.classAttribute().isNominal()) {
trainData.stratify(m_NumFolds);
}
m_BestClassifierOptions = null;
// Set up m_ClassifierOptions -- take getOptions() and remove
// those being optimised.
m_ClassifierOptions = ((OptionHandler) m_Classifier).getOptions();
for (int i = 0; i < m_CVParams.size(); i++) {
Utils.getOption(((CVParameter) m_CVParams.elementAt(i)).m_ParamChar, m_ClassifierOptions);
}
findParamsByCrossValidation(0, trainData, random);
String[] options = (String[]) m_BestClassifierOptions.clone();
((OptionHandler) m_Classifier).setOptions(options);
m_Classifier.buildClassifier(trainData);
}
/**
* Parses a given list of options. Valid options are:
*
* <p>-D <br>
* Turn on debugging output.
*
* <p>-S seed <br>
* Random number seed (default 1).
*
* <p>-B classifierstring <br>
* Classifierstring should contain the full class name of a scheme included for selection followed
* by options to the classifier (required, option should be used once for each classifier).
*
* <p>-X num_folds <br>
* Use cross validation error as the basis for classifier selection. (default 0, is to use error
* on the training data instead)
*
* <p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setDebug(Utils.getFlag('D', options));
String numFoldsString = Utils.getOption('X', options);
if (numFoldsString.length() != 0) {
setNumFolds(Integer.parseInt(numFoldsString));
} else {
setNumFolds(0);
}
String randomString = Utils.getOption('S', options);
if (randomString.length() != 0) {
setSeed(Integer.parseInt(randomString));
} else {
setSeed(1);
}
// Iterate through the schemes
FastVector classifiers = new FastVector();
while (true) {
String classifierString = Utils.getOption('B', options);
if (classifierString.length() == 0) {
break;
}
String[] classifierSpec = Utils.splitOptions(classifierString);
if (classifierSpec.length == 0) {
throw new Exception("Invalid classifier specification string");
}
String classifierName = classifierSpec[0];
classifierSpec[0] = "";
classifiers.addElement(Classifier.forName(classifierName, classifierSpec));
}
if (classifiers.size() <= 1) {
throw new Exception("At least two classifiers must be specified" + " with the -B option.");
} else {
Classifier[] classifiersArray = new Classifier[classifiers.size()];
for (int i = 0; i < classifiersArray.length; i++) {
classifiersArray[i] = (Classifier) classifiers.elementAt(i);
}
setClassifiers(classifiersArray);
}
}
public void testEvaluationMode() throws Exception {
int cind = 0;
for (int i = 0; i < ThresholdSelector.TAGS_EVAL.length; i++) {
((ThresholdSelector) m_Classifier)
.setEvaluationMode(
new SelectedTag(ThresholdSelector.TAGS_EVAL[i].getID(), ThresholdSelector.TAGS_EVAL));
m_Instances.setClassIndex(1);
FastVector result = useClassifier();
assertTrue(result.size() != 0);
}
}
/**
* Get the list of labels for nominal attribute creation.
*
* @return the list of labels for nominal attribute creation
*/
public String getNominalLabels() {
String labelList = "";
for (int i = 0; i < m_Labels.size(); i++) {
if (i == 0) {
labelList = (String) m_Labels.elementAt(i);
} else {
labelList += "," + (String) m_Labels.elementAt(i);
}
}
return labelList;
}
public void testDesignatedClass() throws Exception {
int cind = 0;
for (int i = 0; i < ThresholdSelector.TAGS_OPTIMIZE.length; i++) {
((ThresholdSelector) m_Classifier)
.setDesignatedClass(
new SelectedTag(
ThresholdSelector.TAGS_OPTIMIZE[i].getID(), ThresholdSelector.TAGS_OPTIMIZE));
m_Instances.setClassIndex(1);
FastVector result = useClassifier();
assertTrue(result.size() != 0);
}
}
/** 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);
}
}
}
public void testRangeNone() throws Exception {
int cind = 0;
((ThresholdSelector) m_Classifier)
.setDesignatedClass(
new SelectedTag(ThresholdSelector.OPTIMIZE_0, ThresholdSelector.TAGS_OPTIMIZE));
((ThresholdSelector) m_Classifier)
.setRangeCorrection(
new SelectedTag(ThresholdSelector.RANGE_NONE, ThresholdSelector.TAGS_RANGE));
FastVector result = null;
m_Instances.setClassIndex(1);
result = useClassifier();
assertTrue(result.size() != 0);
double minp = 0;
double maxp = 0;
for (int i = 0; i < result.size(); i++) {
NominalPrediction p = (NominalPrediction) result.elementAt(i);
double prob = p.distribution()[cind];
if ((i == 0) || (prob < minp)) minp = prob;
if ((i == 0) || (prob > maxp)) maxp = prob;
}
assertTrue("Upper limit shouldn't increase", maxp <= 1.0);
assertTrue("Lower limit shouldn'd decrease", minp >= 0.25);
}
public void testNumXValFolds() throws Exception {
try {
((ThresholdSelector) m_Classifier).setNumXValFolds(0);
fail("Expected IllegalArgumentException");
} catch (IllegalArgumentException e) {
// OK
}
int cind = 0;
for (int i = 2; i < 20; i += 2) {
((ThresholdSelector) m_Classifier).setNumXValFolds(i);
m_Instances.setClassIndex(1);
FastVector result = useClassifier();
assertTrue(result.size() != 0);
}
}
/**
* Add a rule to the ruleset and update the stats
*
* @param lastRule the rule to be added
*/
public void addAndUpdate(Rule lastRule) {
if (m_Ruleset == null) m_Ruleset = new FastVector();
m_Ruleset.addElement(lastRule);
Instances data = (m_Filtered == null) ? m_Data : ((Instances[]) m_Filtered.lastElement())[1];
double[] stats = new double[6];
double[] classCounts = new double[m_Data.classAttribute().numValues()];
Instances[] filtered = computeSimpleStats(m_Ruleset.size() - 1, data, stats, classCounts);
if (m_Filtered == null) m_Filtered = new FastVector();
m_Filtered.addElement(filtered);
if (m_SimpleStats == null) m_SimpleStats = new FastVector();
m_SimpleStats.addElement(stats);
if (m_Distributions == null) m_Distributions = new FastVector();
m_Distributions.addElement(classCounts);
}
/**
* Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage,
* true/false positive/negatives of each rule
*/
public void countData() {
if ((m_Filtered != null) || (m_Ruleset == null) || (m_Data == null)) return;
int size = m_Ruleset.size();
m_Filtered = new FastVector(size);
m_SimpleStats = new FastVector(size);
m_Distributions = new FastVector(size);
Instances data = new Instances(m_Data);
for (int i = 0; i < size; i++) {
double[] stats = new double[6]; // 6 statistics parameters
double[] classCounts = new double[m_Data.classAttribute().numValues()];
Instances[] filtered = computeSimpleStats(i, data, stats, classCounts);
m_Filtered.addElement(filtered);
m_SimpleStats.addElement(stats);
m_Distributions.addElement(classCounts);
data = filtered[1]; // Data not covered
}
}
/**
* Static utility function to count the data covered by the rules after the given index in the
* given rules, and then remove them. It returns the data not covered by the successive rules.
*
* @param data the data to be processed
* @param rules the ruleset
* @param index the given index
* @return the data after processing
*/
public static Instances rmCoveredBySuccessives(Instances data, FastVector rules, int index) {
Instances rt = new Instances(data, 0);
for (int i = 0; i < data.numInstances(); i++) {
Instance datum = data.instance(i);
boolean covered = false;
for (int j = index + 1; j < rules.size(); j++) {
Rule rule = (Rule) rules.elementAt(j);
if (rule.covers(datum)) {
covered = true;
break;
}
}
if (!covered) rt.add(datum);
}
return rt;
}
/**
* Returns description of the cross-validated classifier.
*
* @return description of the cross-validated classifier as a string
*/
public String toString() {
if (m_InitOptions == null) return "CVParameterSelection: No model built yet.";
String result =
"Cross-validated Parameter selection.\n"
+ "Classifier: "
+ m_Classifier.getClass().getName()
+ "\n";
try {
for (int i = 0; i < m_CVParams.size(); i++) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(i);
result +=
"Cross-validation Parameter: '-"
+ cvParam.m_ParamChar
+ "'"
+ " ranged from "
+ cvParam.m_Lower
+ " to ";
switch ((int) (cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
result += m_NumAttributes;
break;
case 2:
result += m_TrainFoldSize;
break;
default:
result += cvParam.m_Upper;
break;
}
result += " with " + cvParam.m_Steps + " steps\n";
}
} catch (Exception ex) {
result += ex.getMessage();
}
result +=
"Classifier Options: "
+ Utils.joinOptions(m_BestClassifierOptions)
+ "\n\n"
+ m_Classifier.toString();
return result;
}
/**
* Method that finds all association rules.
*
* @exception Exception if an attribute is numeric
*/
private void findRulesQuickly() throws Exception {
FastVector[] rules;
RuleGeneration currentItemSet;
// Build rules
for (int j = 0; j < m_Ls.size(); j++) {
FastVector currentItemSets = (FastVector) m_Ls.elementAt(j);
Enumeration enumItemSets = currentItemSets.elements();
while (enumItemSets.hasMoreElements()) {
currentItemSet = new RuleGeneration((ItemSet) enumItemSets.nextElement());
m_best =
currentItemSet.generateRules(
m_numRules, m_midPoints, m_priors, m_expectation, m_instances, m_best, m_count);
m_count = currentItemSet.m_count;
if (!m_bestChanged && currentItemSet.m_change) m_bestChanged = true;
// update minimum expected predictive accuracy to get into the n best
if (m_best.size() > 0) m_expectation = ((RuleItem) m_best.first()).accuracy();
else m_expectation = 0;
}
}
}
