@Test
public void testClone() {
System.out.println("clone");
for (boolean useCatFeatures : new boolean[] {true, false}) {
RandomForest instance = new RandomForest();
ClassificationDataSet t1 = FixedProblems.getSimpleKClassLinear(100, 3);
ClassificationDataSet t2 = FixedProblems.getSimpleKClassLinear(100, 6);
if (useCatFeatures) {
t1.applyTransform(new NumericalToHistogram(t1));
t2.applyTransform(new NumericalToHistogram(t2));
}
instance = instance.clone();
instance.trainC(t1);
RandomForest result = instance.clone();
for (int i = 0; i < t1.getSampleSize(); i++)
assertEquals(t1.getDataPointCategory(i), result.classify(t1.getDataPoint(i)).mostLikely());
result.trainC(t2);
for (int i = 0; i < t1.getSampleSize(); i++)
assertEquals(
t1.getDataPointCategory(i), instance.classify(t1.getDataPoint(i)).mostLikely());
for (int i = 0; i < t2.getSampleSize(); i++)
assertEquals(t2.getDataPointCategory(i), result.classify(t2.getDataPoint(i)).mostLikely());
}
}
@Override
public void trainC(ClassificationDataSet dataSet, ExecutorService threadPool) {
final int models = baseClassifiers.size();
final int C = dataSet.getClassSize();
weightsPerModel = C == 2 ? 1 : C;
ClassificationDataSet metaSet =
new ClassificationDataSet(
weightsPerModel * models, new CategoricalData[0], dataSet.getPredicting());
List<ClassificationDataSet> dataFolds = dataSet.cvSet(folds);
// iterate in the order of the folds so we get the right dataum weights
for (ClassificationDataSet cds : dataFolds)
for (int i = 0; i < cds.getSampleSize(); i++)
metaSet.addDataPoint(
new DenseVector(weightsPerModel * models),
cds.getDataPointCategory(i),
cds.getDataPoint(i).getWeight());
// create the meta training set
for (int c = 0; c < baseClassifiers.size(); c++) {
Classifier cl = baseClassifiers.get(c);
int pos = 0;
for (int f = 0; f < dataFolds.size(); f++) {
ClassificationDataSet train = ClassificationDataSet.comineAllBut(dataFolds, f);
ClassificationDataSet test = dataFolds.get(f);
if (threadPool == null) cl.trainC(train);
else cl.trainC(train, threadPool);
for (int i = 0;
i < test.getSampleSize();
i++) // evaluate and mark each point in the held out fold.
{
CategoricalResults pred = cl.classify(test.getDataPoint(i));
if (C == 2)
metaSet.getDataPoint(pos).getNumericalValues().set(c, pred.getProb(0) * 2 - 1);
else {
Vec toSet = metaSet.getDataPoint(pos).getNumericalValues();
for (int j = weightsPerModel * c; j < weightsPerModel * (c + 1); j++)
toSet.set(j, pred.getProb(j - weightsPerModel * c));
}
pos++;
}
}
}
// train the meta model
if (threadPool == null) aggregatingClassifier.trainC(metaSet);
else aggregatingClassifier.trainC(metaSet, threadPool);
// train the final classifiers, unless folds=1. In that case they are already trained
if (folds != 1) {
for (Classifier cl : baseClassifiers)
if (threadPool == null) cl.trainC(dataSet);
else cl.trainC(dataSet, threadPool);
}
}
public void trainC(ClassificationDataSet dataSet, ExecutorService threadPool) {
if (dataSet.getClassSize() != 2)
throw new FailedToFitException(
"Logistic Regression works only in the case of two classes, and can not handle "
+ dataSet.getClassSize()
+ " classes");
RegressionDataSet rds =
new RegressionDataSet(dataSet.getNumNumericalVars(), dataSet.getCategories());
for (int i = 0; i < dataSet.getSampleSize(); i++) {
// getDataPointCategory will return either 0 or 1, so it works perfectly
rds.addDataPoint(dataSet.getDataPoint(i), (double) dataSet.getDataPointCategory(i));
}
train(rds, threadPool);
}
