public void internalValidationCalibration(ILCSMetric selfAcc) {
final VotingClassificationStrategy str =
rep
.new VotingClassificationStrategy(
(float) SettingsLoader.getNumericSetting("datasetLabelCardinality", 1));
rep.setClassificationStrategy(str);
final InternalValidation ival = new InternalValidation(this, str, selfAcc);
ival.calibrate(10);
}
public VotingClassificationStrategy proportionalCutCalibration() {
final VotingClassificationStrategy str =
rep
.new VotingClassificationStrategy(
(float) SettingsLoader.getNumericSetting("datasetLabelCardinality", 1));
rep.setClassificationStrategy(str);
str.proportionalCutCalibration(this.instances, rulePopulation);
return str;
}
/**
* Constructor.
*
* @throws IOException
*/
public GMlASLCS2() throws IOException {
inputFile = SettingsLoader.getStringSetting("filename", "");
numberOfLabels = (int) SettingsLoader.getNumericSetting("numberOfLabels", 1);
iterations = (int) SettingsLoader.getNumericSetting("trainIterations", 1000);
populationSize = (int) SettingsLoader.getNumericSetting("populationSize", 1500);
final IGeneticAlgorithmStrategy ga =
new SteadyStateGeneticAlgorithm(
new RouletteWheelSelector(AbstractUpdateStrategy.COMPARISON_MODE_EXPLORATION, true),
new SinglePointCrossover(this),
CROSSOVER_RATE,
new UniformBitMutation(MUTATION_RATE),
THETA_GA,
this);
rep =
new GenericMultiLabelRepresentation(
inputFile,
PRECISION_BITS,
numberOfLabels,
GenericMultiLabelRepresentation.EXACT_MATCH,
LABEL_GENERALIZATION_RATE,
ATTRIBUTE_GENERALIZATION_RATE,
this);
rep.setClassificationStrategy(rep.new BestFitnessClassificationStrategy());
final MlASLCS2UpdateAlgorithm strategy =
new MlASLCS2UpdateAlgorithm(
ASLCS_N, ASLCS_ACC0, ASLCS_EXPERIENCE_THRESHOLD, ga, numberOfLabels, this);
this.setElements(rep, strategy);
rulePopulation =
new ClassifierSet(
new FixedSizeSetWorstFitnessDeletion(
this,
populationSize,
new RouletteWheelSelector(AbstractUpdateStrategy.COMPARISON_MODE_DELETION, true)));
}
public void useBestClassificationMode() {
rep.setClassificationStrategy(rep.new BestFitnessClassificationStrategy());
}