/**
* Returns description of the bias-variance decomposition results.
*
* @return the bias-variance decomposition results as a string
*/
public String toString() {
String result = "\nBias-Variance Decomposition\n";
if (getClassifier() == null) {
return "Invalid setup";
}
result += "\nClassifier : " + getClassifier().getClass().getName();
if (getClassifier() instanceof OptionHandler) {
result += Utils.joinOptions(((OptionHandler) m_Classifier).getOptions());
}
result += "\nData File : " + getDataFileName();
result += "\nClass Index : ";
if (getClassIndex() == 0) {
result += "last";
} else {
result += getClassIndex();
}
result += "\nTraining Pool: " + getTrainPoolSize();
result += "\nIterations : " + getTrainIterations();
result += "\nSeed : " + getSeed();
result += "\nError : " + Utils.doubleToString(getError(), 6, 4);
result += "\nSigma^2 : " + Utils.doubleToString(getSigma(), 6, 4);
result += "\nBias^2 : " + Utils.doubleToString(getBias(), 6, 4);
result += "\nVariance : " + Utils.doubleToString(getVariance(), 6, 4);
return result + "\n";
}
/**
* 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;
}
/**
* Outputs the linear regression model as a string.
*
* @return the model as string
*/
public String toString() {
if (m_TransformedData == null) {
return "Linear Regression: No model built yet.";
}
try {
StringBuffer text = new StringBuffer();
int column = 0;
boolean first = true;
text.append("\nLinear Regression Model\n\n");
text.append(m_TransformedData.classAttribute().name() + " =\n\n");
for (int i = 0; i < m_TransformedData.numAttributes(); i++) {
if ((i != m_ClassIndex) && (m_SelectedAttributes[i])) {
if (!first) text.append(" +\n");
else first = false;
text.append(Utils.doubleToString(m_Coefficients[column], 12, 4) + " * ");
text.append(m_TransformedData.attribute(i).name());
column++;
}
}
text.append(" +\n" + Utils.doubleToString(m_Coefficients[column], 12, 4));
return text.toString();
} catch (Exception e) {
return "Can't print Linear Regression!";
}
}
@Override
public String toString() {
StringBuffer text = new StringBuffer();
text.append("\nsIB\n===\n");
text.append("\nNumber of clusters: " + m_numCluster + "\n");
for (int j = 0; j < m_numCluster; j++) {
text.append(
"\nCluster: "
+ j
+ " Size : "
+ bestT.size(j)
+ " Prior probability: "
+ Utils.doubleToString(bestT.Pt[j], 4)
+ "\n\n");
for (int i = 0; i < m_numAttributes; i++) {
text.append("Attribute: " + m_data.attribute(i).name() + "\n");
text.append(
"Probability given the cluster = "
+ Utils.doubleToString(bestT.Py_t.get(i, j), 4)
+ "\n");
}
}
return text.toString();
}
/**
* Output the current partition
*
* @param insts
* @return a string that describes the partition
*/
@Override
public String toString() {
StringBuffer text = new StringBuffer();
text.append("score (L) : " + Utils.doubleToString(L, 4) + "\n");
text.append("number of changes : " + counter + "\n");
for (int i = 0; i < m_numCluster; i++) {
text.append("\nCluster " + i + "\n");
text.append("size : " + size(i) + "\n");
text.append("prior prob : " + Utils.doubleToString(Pt[i], 4) + "\n");
}
return text.toString();
}
/**
* Prints the condition satisfied by instances in a subset.
*
* @param index of subset
* @param data training set.
*/
@Override
public final String rightSide(int index, Instances data) {
StringBuffer text;
text = new StringBuffer();
if (data.attribute(m_attIndex).isNominal()) {
text.append(" = " + data.attribute(m_attIndex).value(index));
} else if (index == 0) {
text.append(" <= " + Utils.doubleToString(m_splitPoint, 6));
} else {
text.append(" > " + Utils.doubleToString(m_splitPoint, 6));
}
return text.toString();
}
/**
* returns a description of the search as a String
*
* @return a description of the search
*/
public String toString() {
StringBuffer BfString = new StringBuffer();
BfString.append("\tBest first.\n\tStart set: ");
if (m_starting == null) {
BfString.append("no attributes\n");
} else {
BfString.append(startSetToString() + "\n");
}
BfString.append("\tSearch direction: ");
if (m_searchDirection == SELECTION_BACKWARD) {
BfString.append("backward\n");
} else {
if (m_searchDirection == SELECTION_FORWARD) {
BfString.append("forward\n");
} else {
BfString.append("bi-directional\n");
}
}
BfString.append("\tStale search after " + m_maxStale + " node expansions\n");
BfString.append("\tTotal number of subsets evaluated: " + m_totalEvals + "\n");
BfString.append(
"\tMerit of best subset found: "
+ Utils.doubleToString(Math.abs(m_bestMerit), 8, 3)
+ "\n");
return BfString.toString();
}
/** calculates the mean of the given numeric column */
private void calcMean() {
ArffSortedTableModel model;
int i;
double mean;
// no column selected?
if (m_CurrentCol == -1) return;
model = (ArffSortedTableModel) m_TableArff.getModel();
// not numeric?
if (!model.getAttributeAt(m_CurrentCol).isNumeric()) return;
mean = 0;
for (i = 0; i < model.getRowCount(); i++)
mean += model.getInstances().instance(i).value(m_CurrentCol - 1);
mean = mean / model.getRowCount();
// show result
ComponentHelper.showMessageBox(
getParent(),
"Mean for attribute...",
"Mean for attribute '"
+ m_TableArff.getPlainColumnName(m_CurrentCol)
+ "':\n\t"
+ Utils.doubleToString(mean, 3),
JOptionPane.OK_CANCEL_OPTION,
JOptionPane.PLAIN_MESSAGE);
}
/**
* 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();
}
}
}
public String toStringMetric(
int premiseSupport, int consequenceSupport, int totalSupport, int totalTransactions) {
return m_stringVal
+ ":("
+ Utils.doubleToString(
compute(premiseSupport, consequenceSupport, totalSupport, totalTransactions), 2)
+ ")";
}
/**
* Get a bin range string for a specified bin of some attribute's cut points.
*
* @param cutPoints The attribute's cut points; never null.
* @param j The bin number (zero based); never out of range.
* @param precision the precision for the range values
* @return The bin range string.
*/
private static String binRangeString(double[] cutPoints, int j, int precision) {
assert cutPoints != null;
int n = cutPoints.length;
assert 0 <= j && j <= n;
return j == 0
? "" + "(" + "-inf" + "-" + Utils.doubleToString(cutPoints[0], precision) + "]"
: j == n
? "" + "(" + Utils.doubleToString(cutPoints[n - 1], precision) + "-" + "inf" + ")"
: ""
+ "("
+ Utils.doubleToString(cutPoints[j - 1], precision)
+ "-"
+ Utils.doubleToString(cutPoints[j], precision)
+ "]";
}
/**
* returns a description of the search
*
* @return a description of the search as a String
*/
public String toString() {
StringBuffer GAString = new StringBuffer();
GAString.append("\tGenetic search.\n\tStart set: ");
if (m_starting == null) {
GAString.append("no attributes\n");
} else {
GAString.append(startSetToString() + "\n");
}
GAString.append("\tPopulation size: " + m_popSize);
GAString.append("\n\tNumber of generations: " + m_maxGenerations);
GAString.append("\n\tProbability of crossover: " + Utils.doubleToString(m_pCrossover, 6, 3));
GAString.append("\n\tProbability of mutation: " + Utils.doubleToString(m_pMutation, 6, 3));
GAString.append("\n\tReport frequency: " + m_reportFrequency);
GAString.append("\n\tRandom number seed: " + m_seed + "\n");
GAString.append(m_generationReports.toString());
return GAString.toString();
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
@Override
public String toString() {
if (m_Instances == null) {
return "Naive Bayes (simple): No model built yet.";
}
try {
StringBuffer text = new StringBuffer("Naive Bayes (simple)");
int attIndex;
for (int i = 0; i < m_Instances.numClasses(); i++) {
text.append(
"\n\nClass "
+ m_Instances.classAttribute().value(i)
+ ": P(C) = "
+ Utils.doubleToString(m_Priors[i], 10, 8)
+ "\n\n");
Enumeration<Attribute> enumAtts = m_Instances.enumerateAttributes();
attIndex = 0;
while (enumAtts.hasMoreElements()) {
Attribute attribute = enumAtts.nextElement();
text.append("Attribute " + attribute.name() + "\n");
if (attribute.isNominal()) {
for (int j = 0; j < attribute.numValues(); j++) {
text.append(attribute.value(j) + "\t");
}
text.append("\n");
for (int j = 0; j < attribute.numValues(); j++) {
text.append(Utils.doubleToString(m_Counts[i][attIndex][j], 10, 8) + "\t");
}
} else {
text.append("Mean: " + Utils.doubleToString(m_Means[i][attIndex], 10, 8) + "\t");
text.append("Standard Deviation: " + Utils.doubleToString(m_Devs[i][attIndex], 10, 8));
}
text.append("\n\n");
attIndex++;
}
}
return text.toString();
} catch (Exception e) {
return "Can't print Naive Bayes classifier!";
}
}
/**
* Prints out the classifier.
*
* @return a description of the classifier as a string
*/
@Override
public String toString() {
StringBuffer text = new StringBuffer();
text.append("SMOreg\n\n");
if (m_weights != null) {
text.append("weights (not support vectors):\n");
// it's a linear machine
for (int i = 0; i < m_data.numAttributes(); i++) {
if (i != m_classIndex) {
text.append(
(m_weights[i] >= 0 ? " + " : " - ")
+ Utils.doubleToString(Math.abs(m_weights[i]), 12, 4)
+ " * ");
if (m_SVM.getFilterType().getSelectedTag().getID() == SMOreg.FILTER_STANDARDIZE) {
text.append("(standardized) ");
} else if (m_SVM.getFilterType().getSelectedTag().getID() == SMOreg.FILTER_NORMALIZE) {
text.append("(normalized) ");
}
text.append(m_data.attribute(i).name() + "\n");
}
}
} else {
// non linear, print out all supportvectors
text.append("Support vectors:\n");
for (int i = 0; i < m_nInstances; i++) {
if (m_alpha[i] > 0) {
text.append("+" + m_alpha[i] + " * k[" + i + "]\n");
}
if (m_alphaStar[i] > 0) {
text.append("-" + m_alphaStar[i] + " * k[" + i + "]\n");
}
}
}
text.append((m_b <= 0 ? " + " : " - ") + Utils.doubleToString(Math.abs(m_b), 12, 4) + "\n\n");
text.append("\n\nNumber of kernel evaluations: " + m_nEvals);
if (m_nCacheHits >= 0 && m_nEvals > 0) {
double hitRatio = 1 - m_nEvals * 1.0 / (m_nCacheHits + m_nEvals);
text.append(" (" + Utils.doubleToString(hitRatio * 100, 7, 3).trim() + "% cached)");
}
return text.toString();
}
/**
* Returns the test represented by a string in Prolog notation.
*
* @return a string representing the test in Prolog notation
*/
public String toPrologString() {
Attribute att = m_Dataset.attribute(m_AttIndex);
StringBuffer str = new StringBuffer();
String attName = m_Dataset.attribute(m_AttIndex).name();
if (att.isNumeric()) {
str = str.append(attName + " ");
if (m_Not) str = str.append(">= " + Utils.doubleToString(m_Split, 3));
else str = str.append("< " + Utils.doubleToString(m_Split, 3));
} else {
String value = att.value((int) m_Split);
if (value == "false") {
str = str.append("not(" + attName + ")");
} else {
str = str.append(attName);
}
}
return str.toString();
}
/**
* Gives a string representation of the test in Prolog notation, starting from the comparison
* symbol.
*
* @return a string representing the test in Prolog notation
*/
private String testPrologComparisonString() {
Attribute att = m_Dataset.attribute(m_AttIndex);
if (att.isNumeric()) {
return ((m_Not ? ">= " : "< ") + Utils.doubleToString(m_Split, 3));
} else {
if (att.numValues() != 2) return ((m_Not ? "!= " : "= ") + att.value((int) m_Split));
else
return ("= " + (m_Not ? att.value((int) m_Split == 0 ? 1 : 0) : att.value((int) m_Split)));
}
}
/**
* Buildclassifier selects a classifier from the set of classifiers by minimising error on the
* training data.
*
* @param data the training data to be used for generating the boosted classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifiers.length == 0) {
throw new Exception("No base classifiers have been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
newData.randomize(new Random(m_Seed));
if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1))
newData.stratify(m_NumXValFolds);
Instances train = newData; // train on all data by default
Instances test = newData; // test on training data by default
Classifier bestClassifier = null;
int bestIndex = -1;
double bestPerformance = Double.NaN;
int numClassifiers = m_Classifiers.length;
for (int i = 0; i < numClassifiers; i++) {
Classifier currentClassifier = getClassifier(i);
Evaluation evaluation;
if (m_NumXValFolds > 1) {
evaluation = new Evaluation(newData);
for (int j = 0; j < m_NumXValFolds; j++) {
train = newData.trainCV(m_NumXValFolds, j);
test = newData.testCV(m_NumXValFolds, j);
currentClassifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(currentClassifier, test);
}
} else {
currentClassifier.buildClassifier(train);
evaluation = new Evaluation(train);
evaluation.evaluateModel(currentClassifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println(
"Error rate: "
+ Utils.doubleToString(error, 6, 4)
+ " for classifier "
+ currentClassifier.getClass().getName());
}
if ((i == 0) || (error < bestPerformance)) {
bestClassifier = currentClassifier;
bestPerformance = error;
bestIndex = i;
}
}
m_ClassifierIndex = bestIndex;
m_Classifier = bestClassifier;
if (m_NumXValFolds > 1) {
m_Classifier.buildClassifier(newData);
}
}
public String toString() {
if (m_attribute == null) {
return "No model built yet.";
}
StringBuffer text = new StringBuffer();
if (m_attribute == null) {
text.append("Predicting constant " + m_intercept);
} else {
text.append("Linear regression on " + m_attribute.name() + "\n\n");
text.append(Utils.doubleToString(m_slope, 2) + " * " + m_attribute.name());
if (m_intercept > 0) {
text.append(" + " + Utils.doubleToString(m_intercept, 2));
} else {
text.append(" - " + Utils.doubleToString((-m_intercept), 2));
}
}
text.append("\n");
return text.toString();
}
/**
* Determines the output format based on the input format and returns this. In case the output
* format cannot be returned immediately, i.e., immediateOutputFormat() returns false, then this
* method will be called from batchFinished().
*
* @param inputFormat the input format to base the output format on
* @return the output format
* @throws Exception in case the determination goes wrong
* @see #hasImmediateOutputFormat()
* @see #batchFinished()
*/
protected Instances determineOutputFormat(Instances inputFormat) throws Exception {
Instances data;
Instances result;
FastVector atts;
FastVector values;
HashSet hash;
int i;
int n;
boolean isDate;
Instance inst;
Vector sorted;
m_Cols.setUpper(inputFormat.numAttributes() - 1);
data = new Instances(inputFormat);
atts = new FastVector();
for (i = 0; i < data.numAttributes(); i++) {
if (!m_Cols.isInRange(i) || !data.attribute(i).isNumeric()) {
atts.addElement(data.attribute(i));
continue;
}
// date attribute?
isDate = (data.attribute(i).type() == Attribute.DATE);
// determine all available attribtues in dataset
hash = new HashSet();
for (n = 0; n < data.numInstances(); n++) {
inst = data.instance(n);
if (inst.isMissing(i)) continue;
if (isDate) hash.add(inst.stringValue(i));
else hash.add(new Double(inst.value(i)));
}
// sort values
sorted = new Vector();
for (Object o : hash) sorted.add(o);
Collections.sort(sorted);
// create attribute from sorted values
values = new FastVector();
for (Object o : sorted) {
if (isDate) values.addElement(o.toString());
else values.addElement(Utils.doubleToString(((Double) o).doubleValue(), MAX_DECIMALS));
}
atts.addElement(new Attribute(data.attribute(i).name(), values));
}
result = new Instances(inputFormat.relationName(), atts, 0);
result.setClassIndex(inputFormat.classIndex());
return result;
}
public String toXML(
int premiseSupport, int consequenceSupport, int totalSupport, int totalTransactions) {
String result =
"<CRITERE name=\""
+ m_stringVal
+ "\" value=\" "
+ Utils.doubleToString(
compute(premiseSupport, consequenceSupport, totalSupport, totalTransactions), 2)
+ "\"/>";
return result;
}
/**
* returns a description of the search as a String
*
* @return a description of the search
*/
public String toString() {
StringBuffer text = new StringBuffer();
text.append("\tRankSearch :\n");
text.append("\tAttribute evaluator : " + getAttributeEvaluator().getClass().getName() + " ");
if (m_ASEval instanceof OptionHandler) {
String[] evaluatorOptions = new String[0];
evaluatorOptions = ((OptionHandler) m_ASEval).getOptions();
for (int i = 0; i < evaluatorOptions.length; i++) {
text.append(evaluatorOptions[i] + ' ');
}
}
text.append("\n");
text.append("\tAttribute ranking : \n");
int rlength = (int) (Math.log(m_Ranking.length) / Math.log(10) + 1);
for (int i = 0; i < m_Ranking.length; i++) {
text.append(
"\t "
+ Utils.doubleToString((double) (m_Ranking[i] + 1), rlength, 0)
+ " "
+ m_Instances.attribute(m_Ranking[i]).name()
+ '\n');
}
text.append("\tMerit of best subset found : ");
int fieldwidth = 3;
double precision = (m_bestMerit - (int) m_bestMerit);
if (Math.abs(m_bestMerit) > 0) {
fieldwidth = (int) Math.abs((Math.log(Math.abs(m_bestMerit)) / Math.log(10))) + 2;
}
if (Math.abs(precision) > 0) {
precision = Math.abs((Math.log(Math.abs(precision)) / Math.log(10))) + 3;
} else {
precision = 2;
}
text.append(
Utils.doubleToString(Math.abs(m_bestMerit), fieldwidth + (int) precision, (int) precision)
+ "\n");
return text.toString();
}
/**
* generates a report on the current population
*
* @return a report as a String
*/
private String populationReport(int genNum) {
int i;
StringBuffer temp = new StringBuffer();
if (genNum == 0) {
temp.append("\nInitial population\n");
} else {
temp.append("\nGeneration: " + genNum + "\n");
}
temp.append("merit \tscaled \tsubset\n");
for (i = 0; i < m_popSize; i++) {
temp.append(
Utils.doubleToString(Math.abs(m_population[i].getObjective()), 8, 5)
+ "\t"
+ Utils.doubleToString(m_population[i].getFitness(), 8, 5)
+ "\t");
temp.append(printPopMember(m_population[i].getChromosome()) + "\n");
}
return temp.toString();
}
/** Outputs the association rules. */
public String toString() {
StringBuffer text = new StringBuffer();
if (m_allTheRules[0].size() == 0) return "\nNo large itemsets and rules found!\n";
text.append("\nPredictiveApriori\n===================\n\n");
text.append("\nBest rules found:\n\n");
for (int i = 0; i < m_allTheRules[0].size(); i++) {
text.append(
Utils.doubleToString((double) i + 1, (int) (Math.log(m_numRules) / Math.log(10) + 1), 0)
+ ". "
+ ((ItemSet) m_allTheRules[0].elementAt(i)).toString(m_instances)
+ " ==> "
+ ((ItemSet) m_allTheRules[1].elementAt(i)).toString(m_instances)
+ " acc:("
+ Utils.doubleToString(((Double) m_allTheRules[2].elementAt(i)).doubleValue(), 5)
+ ")");
text.append('\n');
}
return text.toString();
}
/**
* determines the "K" for the neighbors from the training set, initializes the labels of the test
* set to "missing" and generates the neighbors for all instances in the test set
*
* @throws Exception if initialization fails
*/
protected void initialize() throws Exception {
int i;
double timeStart;
double timeEnd;
Instances trainingNew;
Instances testNew;
// determine K
if (getVerbose()) System.out.println("\nOriginal KNN = " + m_KNN);
((IBk) m_Classifier).setKNN(m_KNN);
((IBk) m_Classifier).setCrossValidate(true);
m_Classifier.buildClassifier(m_TrainsetNew);
m_Classifier.toString(); // necessary to crossvalidate IBk!
((IBk) m_Classifier).setCrossValidate(false);
m_KNNdetermined = ((IBk) m_Classifier).getKNN();
if (getVerbose()) System.out.println("Determined KNN = " + m_KNNdetermined);
// set class labels in test set to "missing"
for (i = 0; i < m_TestsetNew.numInstances(); i++) m_TestsetNew.instance(i).setClassMissing();
// copy data
trainingNew = new Instances(m_TrainsetNew);
testNew = new Instances(m_TestsetNew);
// filter data
m_Missing.setInputFormat(trainingNew);
trainingNew = Filter.useFilter(trainingNew, m_Missing);
testNew = Filter.useFilter(testNew, m_Missing);
// create the list of neighbors for the instances in the test set
m_NeighborsTestset = new Neighbors[m_TestsetNew.numInstances()];
timeStart = System.currentTimeMillis();
for (i = 0; i < testNew.numInstances(); i++) {
m_NeighborsTestset[i] =
new Neighbors(
testNew.instance(i), m_TestsetNew.instance(i), m_KNNdetermined, trainingNew, testNew);
m_NeighborsTestset[i].setVerbose(getVerbose() || getDebug());
m_NeighborsTestset[i].setUseNaiveSearch(getUseNaiveSearch());
m_NeighborsTestset[i].find();
}
timeEnd = System.currentTimeMillis();
if (getVerbose())
System.out.println(
"Time for finding neighbors: " + Utils.doubleToString((timeEnd - timeStart) / 1000.0, 3));
}
/**
* Optimize the partition
*
* @param tmpT partition to be optimized
* @param input object describing the statistics of the training dataset
* @return the optimized partition
*/
private Partition sIB_OptimizeT(Partition tmpT, Input input) {
boolean done = false;
int change = 0, loopCounter = 0;
if (m_verbose) {
System.out.println("Optimizing...");
System.out.println("-------------");
}
while (!done) {
change = 0;
for (int i = 0; i < m_numInstances; i++) {
int old_t = tmpT.Pt_x[i];
// If the current cluster only has one instance left, leave it.
if (tmpT.size(old_t) == 1) {
if (m_verbose) {
System.out.format("cluster %s has only 1 doc remain\n", old_t);
}
continue;
}
// draw the instance out from its previous cluster
reduce_x(i, old_t, tmpT, input);
// re-cluster the instance
int new_t = clusterInstance(i, input, tmpT);
if (new_t != old_t) {
change++;
updateAssignment(i, new_t, tmpT, input.Px[i], input.Py_x);
}
}
tmpT.counter += change;
if (m_verbose) {
System.out.format("iteration %s , changes : %s\n", loopCounter, change);
}
done = checkConvergence(change, loopCounter);
loopCounter++;
}
// compute the sIB score
tmpT.L = sIB_local_MI(tmpT.Py_t, tmpT.Pt);
if (m_verbose) {
System.out.format("score (L) : %s \n", Utils.doubleToString(tmpT.L, 4));
}
return tmpT;
}
/**
* returns a string representation of the classifier
*
* @return string representation of the classifier
*/
public String toString() {
StringBuffer result;
String classname;
int i;
// only ZeroR model?
if (m_ZeroR != null) {
result = new StringBuffer();
result.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n");
result.append(
this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n");
result.append("Warning: No model could be built, hence ZeroR model is used:\n\n");
result.append(m_ZeroR.toString());
} else {
classname = this.getClass().getName().replaceAll(".*\\.", "");
result = new StringBuffer();
result.append(classname + "\n");
result.append(classname.replaceAll(".", "=") + "\n\n");
if (m_Header == null) {
result.append("No Model built yet.\n");
} else {
if (getInternals()) {
result.append("Mutual information of attributes with class attribute:\n");
for (i = 0; i < m_Header.numAttributes(); i++) {
// skip class
if (i == m_Header.classIndex()) continue;
result.append(
(i + 1)
+ ". "
+ m_Header.attribute(i).name()
+ ": "
+ Utils.doubleToString(m_mutualInformation[i], 6)
+ "\n");
}
} else {
result.append("Model built successfully.\n");
}
}
}
return result.toString();
}
/**
* Processes the given data (may change the provided dataset) and returns the modified version.
* This method is called in batchFinished().
*
* @param instances the data to process
* @return the modified data
* @throws Exception in case the processing goes wrong
* @see #batchFinished()
*/
protected Instances process(Instances instances) throws Exception {
Instances result;
int i;
int n;
double[] values;
String value;
Instance inst;
Instance newInst;
// we need the complete input data!
if (!isFirstBatchDone()) setOutputFormat(determineOutputFormat(getInputFormat()));
result = new Instances(getOutputFormat());
for (i = 0; i < instances.numInstances(); i++) {
inst = instances.instance(i);
values = inst.toDoubleArray();
for (n = 0; n < values.length; n++) {
if (!m_Cols.isInRange(n) || !instances.attribute(n).isNumeric() || inst.isMissing(n))
continue;
// get index of value
if (instances.attribute(n).type() == Attribute.DATE) value = inst.stringValue(n);
else value = Utils.doubleToString(inst.value(n), MAX_DECIMALS);
values[n] = result.attribute(n).indexOfValue(value);
}
// generate new instance
if (inst instanceof SparseInstance) newInst = new SparseInstance(inst.weight(), values);
else newInst = new DenseInstance(inst.weight(), values);
// copy possible string, relational values
newInst.setDataset(getOutputFormat());
copyValues(newInst, false, inst.dataset(), getOutputFormat());
result.add(newInst);
}
return result;
}
/**
* returns a description of the search as a String
*
* @return a description of the search
*/
public String toString() {
StringBuffer BfString = new StringBuffer();
BfString.append("\tAttribute ranking.\n");
if (m_starting != null) {
BfString.append("\tIgnored attributes: ");
BfString.append(startSetToString());
BfString.append("\n");
}
if (m_threshold != -Double.MAX_VALUE) {
BfString.append(
"\tThreshold for discarding attributes: "
+ Utils.doubleToString(m_threshold, 8, 4)
+ "\n");
}
return BfString.toString();
}
/**
* returns a description of the search as a String
*
* @return a description of the search
*/
public String toString() {
StringBuffer LFSString = new StringBuffer();
LFSString.append("\tLinear Forward Selection.\n\tStart set: ");
if (m_starting == null) {
LFSString.append("no attributes\n");
} else {
LFSString.append(startSetToString() + "\n");
}
LFSString.append("\tForward selection method: ");
if (m_forwardSearchMethod == SEARCH_METHOD_FORWARD) {
LFSString.append("forward selection\n");
} else {
LFSString.append("floating forward selection\n");
}
LFSString.append("\tStale search after " + m_maxStale + " node expansions\n");
LFSString.append("\tLinear Forward Selection Type: ");
if (m_linearSelectionType == TYPE_FIXED_SET) {
LFSString.append("fixed-set\n");
} else {
LFSString.append("fixed-width\n");
}
LFSString.append(
"\tNumber of top-ranked attributes that are used: " + m_numUsedAttributes + "\n");
LFSString.append("\tTotal number of subsets evaluated: " + m_totalEvals + "\n");
LFSString.append(
"\tMerit of best subset found: "
+ Utils.doubleToString(Math.abs(m_bestMerit), 8, 3)
+ "\n");
return LFSString.toString();
}
/**
* Returns a string summarising the stats so far.
*
* @return the summary string
*/
public String toString() {
return "Count "
+ Utils.doubleToString(count, 8)
+ '\n'
+ "Min "
+ Utils.doubleToString(min, 8)
+ '\n'
+ "Max "
+ Utils.doubleToString(max, 8)
+ '\n'
+ "Sum "
+ Utils.doubleToString(sum, 8)
+ '\n'
+ "SumSq "
+ Utils.doubleToString(sumSq, 8)
+ '\n'
+ "Mean "
+ Utils.doubleToString(mean, 8)
+ '\n'
+ "StdDev "
+ Utils.doubleToString(stdDev, 8)
+ '\n';
}
