public Model learn(ExampleSet exampleSet) throws OperatorException {
double value = 0.0;
double[] confidences = null;
int method = getParameterAsInt(PARAMETER_METHOD);
Attribute label = exampleSet.getAttributes().getLabel();
if ((label.isNominal()) && ((method == MEDIAN) || (method == AVERAGE))) {
logWarning(
"Cannot use method '" + METHODS[method] + "' for nominal labels: changing to 'mode'!");
method = MODE;
} else if ((!label.isNominal()) && (method == MODE)) {
logWarning(
"Cannot use method '"
+ METHODS[method]
+ "' for numerical labels: changing to 'average'!");
method = AVERAGE;
}
switch (method) {
case MEDIAN:
double[] labels = new double[exampleSet.size()];
Iterator<Example> r = exampleSet.iterator();
int counter = 0;
while (r.hasNext()) {
Example example = r.next();
labels[counter++] = example.getValue(example.getAttributes().getLabel());
}
java.util.Arrays.sort(labels);
value = labels[exampleSet.size() / 2];
break;
case AVERAGE:
exampleSet.recalculateAttributeStatistics(label);
value = exampleSet.getStatistics(label, Statistics.AVERAGE);
break;
case MODE:
exampleSet.recalculateAttributeStatistics(label);
value = exampleSet.getStatistics(label, Statistics.MODE);
confidences = new double[label.getMapping().size()];
for (int i = 0; i < confidences.length; i++) {
confidences[i] =
exampleSet.getStatistics(label, Statistics.COUNT, label.getMapping().mapIndex(i))
/ exampleSet.size();
}
break;
case CONSTANT:
value = getParameterAsDouble(PARAMETER_CONSTANT);
break;
case ATTRIBUTE:
return new AttributeDefaultModel(
exampleSet, getParameterAsString(PARAMETER_ATTRIBUTE_NAME));
default:
// cannot happen
throw new OperatorException("DefaultLearner: Unknown default method '" + method + "'!");
}
log(
"Default value is '"
+ (label.isNominal() ? label.getMapping().mapIndex((int) value) : value + "")
+ "'.");
return new DefaultModel(exampleSet, value, confidences);
}
private RuleModel createNumericalRuleModel(ExampleSet trainingSet, Attribute attribute) {
RuleModel model = new RuleModel(trainingSet);
// split by best attribute
int oldSize = -1;
while ((trainingSet.size() > 0) && (trainingSet.size() != oldSize)) {
ExampleSet exampleSet = (ExampleSet) trainingSet.clone();
Split bestSplit = splitter.getBestSplit(exampleSet, attribute, null);
double bestSplitValue = bestSplit.getSplitPoint();
if (!Double.isNaN(bestSplitValue)) {
SplittedExampleSet splittedSet =
SplittedExampleSet.splitByAttribute(exampleSet, attribute, bestSplitValue);
Attribute label = splittedSet.getAttributes().getLabel();
splittedSet.selectSingleSubset(0);
SplitCondition condition = new LessEqualsSplitCondition(attribute, bestSplitValue);
splittedSet.recalculateAttributeStatistics(label);
int labelValue = (int) splittedSet.getStatistics(label, Statistics.MODE);
String labelName = label.getMapping().mapIndex(labelValue);
Rule rule = new Rule(labelName, condition);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues())
frequencies[counter++] = (int) splittedSet.getStatistics(label, Statistics.COUNT, value);
rule.setFrequencies(frequencies);
model.addRule(rule);
oldSize = trainingSet.size();
trainingSet = rule.removeCovered(trainingSet);
} else {
break;
}
}
// add default rule if some examples were not yet covered
if (trainingSet.size() > 0) {
Attribute label = trainingSet.getAttributes().getLabel();
trainingSet.recalculateAttributeStatistics(label);
int index = (int) trainingSet.getStatistics(label, Statistics.MODE);
String defaultLabel = label.getMapping().mapIndex(index);
Rule defaultRule = new Rule(defaultLabel);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues())
frequencies[counter++] = (int) (trainingSet.getStatistics(label, Statistics.COUNT, value));
defaultRule.setFrequencies(frequencies);
model.addRule(defaultRule);
}
return model;
}
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
Attribute label = exampleSet.getAttributes().getLabel();
RuleModel ruleModel = new RuleModel(exampleSet);
double pureness = getParameterAsDouble(PARAMETER_PURENESS);
TermDetermination termDetermination = new TermDetermination(new AccuracyCriterion(), 0.5d);
ExampleSet trainingSet = (ExampleSet) exampleSet.clone();
for (String labelName : label.getMapping().getValues()) {
trainingSet.recalculateAttributeStatistics(label);
int oldSize = -1;
while (trainingSet.size() > 0
&& trainingSet.size() != oldSize
&& trainingSet.getStatistics(label, Statistics.COUNT, labelName) > 0) {
Rule rule = new Rule(labelName);
ExampleSet oldTrainingSet = (ExampleSet) trainingSet.clone();
// grow rule
int growOldSize = -1;
ExampleSet growSet = (ExampleSet) trainingSet.clone();
while (growSet.size() > 0
&& growSet.size() != growOldSize
&& !rule.isPure(growSet, pureness)
&& growSet.getAttributes().size() > 0) {
SplitCondition term = termDetermination.getBestTerm(growSet, labelName);
if (term == null) {
break;
}
rule.addTerm(term);
Attribute splitAttribute = growSet.getAttributes().get(term.getAttributeName());
growSet.getAttributes().remove(splitAttribute);
growOldSize = growSet.size();
growSet = rule.getCovered(growSet);
}
// add rule if not empty
if (rule.getTerms().size() > 0) {
growSet = rule.getCovered(trainingSet);
growSet.recalculateAttributeStatistics(label);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues()) {
frequencies[counter++] = (int) growSet.getStatistics(label, Statistics.COUNT, value);
}
rule.setFrequencies(frequencies);
ruleModel.addRule(rule);
oldSize = trainingSet.size();
trainingSet = rule.removeCovered(oldTrainingSet);
} else {
break; // no other terms found for this class --> next class
}
trainingSet.recalculateAttributeStatistics(label);
}
checkForStop();
}
// training set not empty? add default rule
if (trainingSet.size() > 0) {
trainingSet.recalculateAttributeStatistics(label);
int index = (int) trainingSet.getStatistics(label, Statistics.MODE);
String defaultLabel = label.getMapping().mapIndex(index);
Rule defaultRule = new Rule(defaultLabel);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues()) {
frequencies[counter++] = (int) trainingSet.getStatistics(label, Statistics.COUNT, value);
}
defaultRule.setFrequencies(frequencies);
ruleModel.addRule(defaultRule);
}
return ruleModel;
}
@Override
public void doWork() throws OperatorException {
// sanity checks
ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);
// checking preconditions
Attribute label = exampleSet.getAttributes().getLabel();
if (label == null) {
throw new UserError(this, 105);
}
if (!label.isNominal()) {
throw new UserError(this, 101, label, "threshold finding");
}
exampleSet.recalculateAttributeStatistics(label);
NominalMapping mapping = label.getMapping();
if (mapping.size() != 2) {
throw new UserError(
this,
118,
new Object[] {label, Integer.valueOf(mapping.getValues().size()), Integer.valueOf(2)});
}
if (exampleSet.getAttributes().getPredictedLabel() == null) {
throw new UserError(this, 107);
}
boolean useExplictLabels = getParameterAsBoolean(PARAMETER_DEFINE_LABELS);
double secondCost = getParameterAsDouble(PARAMETER_MISCLASSIFICATION_COSTS_SECOND);
double firstCost = getParameterAsDouble(PARAMETER_MISCLASSIFICATION_COSTS_FIRST);
if (useExplictLabels) {
String firstLabel = getParameterAsString(PARAMETER_FIRST_LABEL);
String secondLabel = getParameterAsString(PARAMETER_SECOND_LABEL);
if (mapping.getIndex(firstLabel) == -1) {
throw new UserError(this, 143, firstLabel, label.getName());
}
if (mapping.getIndex(secondLabel) == -1) {
throw new UserError(this, 143, secondLabel, label.getName());
}
// if explicit order differs from order in data: internally swap costs.
if (mapping.getIndex(firstLabel) > mapping.getIndex(secondLabel)) {
double temp = firstCost;
firstCost = secondCost;
secondCost = temp;
}
}
// check whether the confidence attributes are available
if (exampleSet.getAttributes().getConfidence(mapping.getPositiveString()) == null) {
throw new UserError(
this, 113, Attributes.CONFIDENCE_NAME + "_" + mapping.getPositiveString());
}
if (exampleSet.getAttributes().getConfidence(mapping.getNegativeString()) == null) {
throw new UserError(
this, 113, Attributes.CONFIDENCE_NAME + "_" + mapping.getNegativeString());
}
// create ROC data
ROCDataGenerator rocDataGenerator = new ROCDataGenerator(firstCost, secondCost);
ROCData rocData =
rocDataGenerator.createROCData(
exampleSet,
getParameterAsBoolean(PARAMETER_USE_EXAMPLE_WEIGHTS),
ROCBias.getROCBiasParameter(this));
// create plotter
if (getParameterAsBoolean(PARAMETER_SHOW_ROC_PLOT)) {
rocDataGenerator.createROCPlotDialog(rocData, true, true);
}
// create and return output
exampleSetOutput.deliver(exampleSet);
thresholdOutput.deliver(
new Threshold(
rocDataGenerator.getBestThreshold(),
mapping.getNegativeString(),
mapping.getPositiveString()));
}
