public void printCurrentList() {
PriorityQueue<Node> pq = new PriorityQueue<Node>(nodes);
AStarNode p;
System.out.print("\n[");
while ((p = (AStarNode) pq.poll()) != null) {
System.out.print("\n");
p.printNode();
}
System.out.println("]");
}
public TellerManager(ExecutorService e, CustomerLine customers, int adjustmentPeriod) {
exec = e;
this.customers = customers;
this.adjustmentPeriod = adjustmentPeriod;
// Start with a single teller:
Teller teller = new Teller(customers);
exec.execute(teller);
workingTellers.add(teller);
}
public void adjustTellerNumber() {
// This is actually a control system. By adjusting
// the numbers, you can reveal stability issues in
// the control mechanism.
// If line is too long, add another teller:
if (customers.size() / workingTellers.size() > 2) {
// If tellers are on break or doing
// another job, bring one back:
if (tellersDoingOtherThings.size() > 0) {
Teller teller = tellersDoingOtherThings.remove();
teller.serveCustomerLine();
workingTellers.offer(teller);
return;
}
// Else create (hire) a new teller
Teller teller = new Teller(customers);
exec.execute(teller);
workingTellers.add(teller);
return;
}
// If line is short enough, remove a teller:
if (workingTellers.size() > 1 && customers.size() / workingTellers.size() < 2)
reassignOneTeller();
// If there is no line, we only need one teller:
if (customers.size() == 0) while (workingTellers.size() > 1) reassignOneTeller();
}
// Give a teller a different job or a break:
private void reassignOneTeller() {
Teller teller = workingTellers.poll();
teller.doSomethingElse();
tellersDoingOtherThings.offer(teller);
}
@Override
public void trainC(final ClassificationDataSet dataSet, final ExecutorService threadPool) {
final PriorityQueue<ClassificationModelEvaluation> bestModels =
new PriorityQueue<ClassificationModelEvaluation>(
folds,
new Comparator<ClassificationModelEvaluation>() {
@Override
public int compare(
ClassificationModelEvaluation t, ClassificationModelEvaluation t1) {
double v0 = t.getScoreStats(classificationTargetScore).getMean();
double v1 = t1.getScoreStats(classificationTargetScore).getMean();
int order = classificationTargetScore.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<Classifier> paramsToEval = new ArrayList<Classifier>();
while (true) {
setParameters(setTo);
paramsToEval.add(baseClassifier.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<ClassificationDataSet> preFolded;
/** Pre-combine our training combinations so that any caching can be re-used */
final List<ClassificationDataSet> trainCombinations;
if (reuseSameCVFolds) {
preFolded = dataSet.cvSet(folds);
trainCombinations = new ArrayList<ClassificationDataSet>(preFolded.size());
for (int i = 0; i < preFolded.size(); i++)
trainCombinations.add(ClassificationDataSet.comineAllBut(preFolded, i));
} else {
preFolded = null;
trainCombinations = null;
}
boolean considerWarm = useWarmStarts && baseClassifier instanceof WarmClassifier;
/**
* 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
&& (!((WarmClassifier) baseClassifier).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<Classifier> subSet = paramsToEval.subList(startPos, startPos + stepSize);
modelService.submit(
new Runnable() {
@Override
public void run() {
Classifier[] prevModels = null;
for (Classifier c : subSet) {
ClassificationModelEvaluation cme =
trainModelsInParallel
? new ClassificationModelEvaluation(c, dataSet)
: new ClassificationModelEvaluation(c, dataSet, threadPool);
cme.setKeepModels(true); // we need these to do warm starts!
cme.setWarmModels(prevModels);
cme.addScorer(classificationTargetScore.clone());
if (reuseSameCVFolds) cme.evaluateCrossValidation(preFolded, trainCombinations);
else cme.evaluateCrossValidation(folds);
prevModels = cme.getKeptModels();
synchronized (bestModels) {
bestModels.add(cme);
}
}
latch.countDown();
}
});
}
} else // regular CV, train a new model from scratch at every step
{
latch = new CountDownLatch(paramsToEval.size());
for (final Classifier toTrain : paramsToEval) {
modelService.submit(
new Runnable() {
@Override
public void run() {
ClassificationModelEvaluation cme =
trainModelsInParallel
? new ClassificationModelEvaluation(toTrain, dataSet)
: new ClassificationModelEvaluation(toTrain, dataSet, threadPool);
cme.addScorer(classificationTargetScore.clone());
if (reuseSameCVFolds) cme.evaluateCrossValidation(preFolded, trainCombinations);
else cme.evaluateCrossValidation(folds);
synchronized (bestModels) {
bestModels.add(cme);
}
latch.countDown();
}
});
}
}
// now wait for everyone to finish
try {
latch.await();
// Now we know the best classifier, we need to train one on the whole data set.
Classifier bestClassifier =
bestModels.peek().getClassifier(); // Just re-train it on the whole set
if (trainFinalModel) {
// try and warm start the final model if we can
if (useWarmStarts
&& bestClassifier instanceof WarmClassifier
&& !((WarmClassifier) bestClassifier)
.warmFromSameDataOnly()) // last line here needed to make sure we can do this warm
// train
{
WarmClassifier wc = (WarmClassifier) bestClassifier;
if (threadPool instanceof FakeExecutor) wc.trainC(dataSet, wc.clone());
else wc.trainC(dataSet, wc.clone(), threadPool);
} else {
if (threadPool instanceof FakeExecutor) bestClassifier.trainC(dataSet);
else bestClassifier.trainC(dataSet, threadPool);
}
}
trainedClassifier = bestClassifier;
} catch (InterruptedException ex) {
Logger.getLogger(GridSearch.class.getName()).log(Level.SEVERE, null, ex);
}
}