@Test
public void testTrainC_RegressionDataSet() {
System.out.println("train");
for (boolean useCatFeatures : new boolean[] {true, false}) {
RandomForest instance = new RandomForest();
RegressionDataSet train = FixedProblems.getLinearRegression(1000, new XORWOW(), coefs);
RegressionDataSet test = FixedProblems.getLinearRegression(100, new XORWOW(), coefs);
RegressionModelEvaluation rme = new RegressionModelEvaluation(instance, train);
if (useCatFeatures)
rme.setDataTransformProcess(
new DataTransformProcess(
new NumericalToHistogram.NumericalToHistogramTransformFactory()));
rme.evaluateTestSet(test);
assertTrue(rme.getMeanError() <= test.getTargetValues().mean() * 2.5);
}
}
@Override
public void train(RegressionDataSet dataSet, ExecutorService threadPool) {
final int models = baseRegressors.size();
weightsPerModel = 1;
RegressionDataSet metaSet = new RegressionDataSet(models, new CategoricalData[0]);
List<RegressionDataSet> dataFolds = dataSet.cvSet(folds);
// iterate in the order of the folds so we get the right dataum weights
for (RegressionDataSet rds : dataFolds)
for (int i = 0; i < rds.getSampleSize(); i++)
metaSet.addDataPoint(
new DataPoint(
new DenseVector(weightsPerModel * models), rds.getDataPoint(i).getWeight()),
rds.getTargetValue(i));
// create the meta training set
for (int c = 0; c < baseRegressors.size(); c++) {
Regressor reg = baseRegressors.get(c);
int pos = 0;
for (int f = 0; f < dataFolds.size(); f++) {
RegressionDataSet train = RegressionDataSet.comineAllBut(dataFolds, f);
RegressionDataSet test = dataFolds.get(f);
if (threadPool == null) reg.train(train);
else reg.train(train, threadPool);
for (int i = 0;
i < test.getSampleSize();
i++) // evaluate and mark each point in the held out fold.
{
double pred = reg.regress(test.getDataPoint(i));
metaSet.getDataPoint(pos++).getNumericalValues().set(c, pred);
}
}
}
// train the meta model
if (threadPool == null) aggregatingRegressor.train(metaSet);
else aggregatingRegressor.train(metaSet, threadPool);
// train the final classifiers, unless folds=1. In that case they are already trained
if (folds != 1) {
for (Regressor reg : baseRegressors)
if (threadPool == null) reg.train(dataSet);
else reg.train(dataSet, threadPool);
}
}
@Override
public void train(final RegressionDataSet dataSet, final ExecutorService threadPool) {
final PriorityQueue<RegressionModelEvaluation> bestModels =
new PriorityQueue<RegressionModelEvaluation>(
folds,
new Comparator<RegressionModelEvaluation>() {
@Override
public int compare(RegressionModelEvaluation t, RegressionModelEvaluation t1) {
double v0 = t.getScoreStats(regressionTargetScore).getMean();
double v1 = t1.getScoreStats(regressionTargetScore).getMean();
int order = regressionTargetScore.lowerIsBetter() ? 1 : -1;
return order * Double.compare(v0, v1);
}
});
/**
* Use this to keep track of which parameter we are altering. Index correspondence to the
* parameter, and its value corresponds to which value has been used. Increment and carry counts
* to iterate over all possible combinations.
*/
int[] setTo = new int[searchParams.size()];
/**
* Each model is set to have different combination of parameters. We then train each model to
* determine the best one.
*/
final List<Regressor> paramsToEval = new ArrayList<Regressor>();
while (true) {
setParameters(setTo);
paramsToEval.add(baseRegressor.clone());
if (incrementCombination(setTo)) break;
}
/*
* This is the Executor used for training the models in parallel. If we
* are not supposed to do that, it will be an executor that executes
* them sequentually.
*/
final ExecutorService modelService;
if (trainModelsInParallel) modelService = threadPool;
else modelService = new FakeExecutor();
final CountDownLatch latch; // used for stopping in both cases
// if we are doing our CV splits ahead of time, get them done now
final List<RegressionDataSet> preFolded;
/** Pre-combine our training combinations so that any caching can be re-used */
final List<RegressionDataSet> trainCombinations;
if (reuseSameCVFolds) {
preFolded = dataSet.cvSet(folds);
trainCombinations = new ArrayList<RegressionDataSet>(preFolded.size());
for (int i = 0; i < preFolded.size(); i++)
trainCombinations.add(RegressionDataSet.comineAllBut(preFolded, i));
} else {
preFolded = null;
trainCombinations = null;
}
boolean considerWarm = useWarmStarts && baseRegressor instanceof WarmRegressor;
/**
* make sure we don't do a warm start if its only supported when trained on the same data but we
* aren't reuse-ing the same CV splits So we get the truth table
*
* <p>a | b | (a&&b)||¬a T | T | T T | F | F F | T | T F | F | T
*
* <p>where a = warmFromSameDataOnly and b = reuseSameSplit So we can instead use ¬ a || b
*/
if (considerWarm
&& (!((WarmRegressor) baseRegressor).warmFromSameDataOnly() || reuseSameCVFolds)) {
/* we want all of the first parameter (which is the warm paramter,
* taken care of for us) values done in a group. So We can get this
* by just dividing up the larger list into sub lists, each sub list
* is adjacent in the original and is the number of parameter values
* we wanted to try
*/
int stepSize = searchValues.get(0).size();
int totalJobs = paramsToEval.size() / stepSize;
latch = new CountDownLatch(totalJobs);
for (int startPos = 0; startPos < paramsToEval.size(); startPos += stepSize) {
final List<Regressor> subSet = paramsToEval.subList(startPos, startPos + stepSize);
modelService.submit(
new Runnable() {
@Override
public void run() {
Regressor[] prevModels = null;
for (Regressor r : subSet) {
RegressionModelEvaluation rme =
trainModelsInParallel
? new RegressionModelEvaluation(r, dataSet)
: new RegressionModelEvaluation(r, dataSet, threadPool);
rme.setKeepModels(true); // we need these to do warm starts!
rme.setWarmModels(prevModels);
rme.addScorer(regressionTargetScore.clone());
if (reuseSameCVFolds) rme.evaluateCrossValidation(preFolded, trainCombinations);
else rme.evaluateCrossValidation(folds);
prevModels = rme.getKeptModels();
synchronized (bestModels) {
bestModels.add(rme);
}
}
latch.countDown();
}
});
}
} else // regular CV, train a new model from scratch at every step
{
latch = new CountDownLatch(paramsToEval.size());
for (final Regressor toTrain : paramsToEval) {
modelService.submit(
new Runnable() {
@Override
public void run() {
RegressionModelEvaluation rme =
trainModelsInParallel
? new RegressionModelEvaluation(toTrain, dataSet)
: new RegressionModelEvaluation(toTrain, dataSet, threadPool);
rme.addScorer(regressionTargetScore.clone());
if (reuseSameCVFolds) rme.evaluateCrossValidation(preFolded, trainCombinations);
else rme.evaluateCrossValidation(folds);
synchronized (bestModels) {
bestModels.add(rme);
}
latch.countDown();
}
});
}
}
try {
latch.await();
// Now we know the best classifier, we need to train one on the whole data set.
Regressor bestRegressor =
bestModels.peek().getRegressor(); // Just re-train it on the whole set
if (trainFinalModel) {
// try and warm start the final model if we can
if (useWarmStarts
&& bestRegressor instanceof WarmRegressor
&& !((WarmRegressor) bestRegressor)
.warmFromSameDataOnly()) // last line here needed to make sure we can do this warm
// train
{
WarmRegressor wr = (WarmRegressor) bestRegressor;
if (threadPool instanceof FakeExecutor) wr.train(dataSet, wr.clone());
else wr.train(dataSet, wr.clone(), threadPool);
} else {
if (threadPool instanceof FakeExecutor) bestRegressor.train(dataSet);
else bestRegressor.train(dataSet, threadPool);
}
}
trainedRegressor = bestRegressor;
} catch (InterruptedException ex) {
Logger.getLogger(GridSearch.class.getName()).log(Level.SEVERE, null, ex);
}
}