/**
* Inserts the classifier in the population. Before that, it looks if there is a classifier in the
* population with the same action and condition (in this case, increments its numerosity). After,
* it checks that the number of micro classifiers is less than the maximum population size. If it
* isn't, it deletes one classifier from the population calling the deleteClassifier function. It
* inserts the classifier in the population and in the action set if it's not null.
*
* @param cl is the classifier that has to be inserted in the population.
* @param ASet Population where the classifier will be inserted.
*/
public void insertInPopulation(Classifier cl, Population ASet) {
boolean found = false;
int i = 0;
while (i < macroClSum && !found) {
if (set[i].equals(cl)) {
set[i].increaseNumerosity(cl.getNumerosity());
microClSum += cl.getNumerosity();
if (ASet != null) {
if (ASet.isThereClassifier(set[i]) >= 0) ASet.microClSum += cl.getNumerosity();
}
found = true;
}
i++;
}
if (!found) {
addClassifier(cl);
}
// Here, the classifier has been added to the population
if (microClSum
> Config.popSize) { // If we have inserted to many classifiers, we have to delete one.
deleteClFromPopulation(ASet);
}
} // end insertInPopulation
@Override
public Map<LookupElement, StringBuilder> getRelevanceStrings() {
final LinkedHashMap<LookupElement, StringBuilder> map =
new LinkedHashMap<LookupElement, StringBuilder>();
for (LookupElement item : myItems) {
map.put(item, new StringBuilder());
}
final MultiMap<CompletionSorterImpl, LookupElement> inputBySorter =
groupItemsBySorter(new ArrayList<LookupElement>(map.keySet()));
if (inputBySorter.size() > 1) {
for (LookupElement element : map.keySet()) {
map.get(element).append(obtainSorter(element)).append(": ");
}
}
for (CompletionSorterImpl sorter : inputBySorter.keySet()) {
final LinkedHashMap<LookupElement, StringBuilder> subMap =
new LinkedHashMap<LookupElement, StringBuilder>();
for (LookupElement element : inputBySorter.get(sorter)) {
subMap.put(element, map.get(element));
}
Classifier<LookupElement> classifier = myClassifiers.get(sorter);
if (classifier != null) {
classifier.describeItems(subMap, createContext(false));
}
}
return map;
}
/**
* Inserts the classifier into the population. Before, it looks if there is a classifier in the
* population that can subsume the new one (in this case, increments its numerosity). After, it
* checks that the number of micro classifiers is less than the maximum population size. If it
* isn't, it deletes one classifier of the population calling the deleteClassifier function. It
* inserts the classifier in the population and in the action set if it's not null.
*
* @param cl is the classifier that has to be inserted in the population.
* @param ASet Population where the classifier will be inserted.
*/
public void insertInPSubsumingCl(Classifier cl, Population ASet) {
int i = 0;
Classifier bestSubsumer = null;
Classifier equalClassifier = null;
// We look for the best subsumer or for an equal classifier.
while (i < macroClSum) {
if (set[i].couldSubsume() && set[i].isMoreGeneral(cl)) {
if (bestSubsumer == null) bestSubsumer = set[i];
else if (set[i].isMoreGeneral(bestSubsumer)) bestSubsumer = set[i];
}
if (set[i].equals(cl)) equalClassifier = set[i];
i++;
}
// If there is a subsumer, its numerosity is increased.
if (bestSubsumer != null) {
bestSubsumer.increaseNumerosity(cl.getNumerosity());
microClSum += cl.getNumerosity();
} else if (equalClassifier != null) {
equalClassifier.increaseNumerosity(cl.getNumerosity());
microClSum += cl.getNumerosity();
} else {
addClassifier(cl);
}
// There's no classifier deletion, independent of if the maximum size
// has been overcomen
} // end insertInPSubsumingCl
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @exception Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
if (instance.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
double[] sums = new double[instance.numClasses()], newProbs;
Classifier curr;
for (int i = 0; i < m_Committee.size(); i++) {
curr = (Classifier) m_Committee.get(i);
newProbs = curr.distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
}
if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
/**
* Deletes one classifier from the population. After that, if the population passed as a parameter
* is not null, it looks for the deleted classifier. If it is in the second population, it will
* delete it too.
*
* @param aSet is the population where the deleted classifier has to be searched.
* @return a Classifier that contains the deleted classifier.
*/
public Classifier deleteClFromPopulation(Population aSet) {
// A classifier has been deleted from the population
Classifier clDeleted = deleteClassifier();
if (aSet
!= null) { // Now, this classifier has to be deleted from the action set (if it exists in).
int pos = aSet.isThereClassifier(clDeleted); // It is searched in the action set.
if (pos >= 0) { // It has to be deleted from the action set too.
aSet.microClSum--;
// If the classifier has 0 numerosity, we remove it from the population.
if (clDeleted.getNumerosity() == 0) { // It has to be completely deleted from action set.
aSet.macroClSum--; // Decrements the number of macroclassifiers
aSet.set[pos] = aSet.set[aSet.macroClSum]; // Moves the last classifier to the deleted one
aSet.set[aSet.macroClSum] = null; // Puts the last classifier to null.
}
}
}
return clDeleted;
} // end deleteClFromPopulation
/** This method applies the action set subsumption */
public void doActionSetSubsumption() {
int i, pos = 0;
Classifier cl = null;
for (i = 0; i < macroClSum; i++) {
if (set[i].couldSubsume()) {
if (cl == null
|| set[i].numberOfDontCareSymbols() > cl.numberOfDontCareSymbols()
|| (set[i].numberOfDontCareSymbols() == cl.numberOfDontCareSymbols()
&& Config.rand() < 0.5)) {
cl = set[i];
pos = i;
}
}
}
if (cl != null) {
for (i = 0; i < macroClSum; i++) {
if (cl != set[i] && cl.isMoreGeneral(set[i])) {
cl.increaseNumerosity(set[i].getNumerosity());
// Now, the classifier has to be removed from the actionSet and the population.
// It's deleted from the action set.
Classifier clDeleted = set[i];
deleteClassifier(i);
// And now, it's deleted from the population
Population p = parentRef;
while (p.parentRef != null) {
p = p.parentRef;
}
pos =
p.isThereClassifier(
clDeleted); // The classifier is searched in the initial population.
if (pos >= 0) p.deleteClassifier(pos);
}
}
}
} // end doActionSetSubsumption
@Override
public void addElement(
Lookup lookup, LookupElement element, LookupElementPresentation presentation) {
StatisticsWeigher.clearBaseStatisticsInfo(element);
final String invariant =
presentation.getItemText()
+ "###"
+ getTailTextOrSpace(presentation)
+ "###"
+ presentation.getTypeText();
element.putUserData(PRESENTATION_INVARIANT, invariant);
CompletionSorterImpl sorter = obtainSorter(element);
Classifier<LookupElement> classifier = myClassifiers.get(sorter);
if (classifier == null) {
myClassifiers.put(sorter, classifier = sorter.buildClassifier(new AlphaClassifier(lookup)));
}
classifier.addElement(element);
super.addElement(lookup, element, presentation);
}
/**
* Returns if the classifier of the class subsumes the classifier passed as a parameter.
*
* @param cl is the subsumed classifier.
* @return a boolean indicating if it subsumes
*/
public boolean doesSubsume(Classifier cl) {
int i;
// First, check if the condition is the same
if (action != cl.getAction()) return false;
// Then, check that is more general
if (parameters.couldSubsume()) {
for (i = 0; i < rep.length; i++) {
if (!rep[i].isMoreGeneral(cl.rep[i])) return false;
}
return true;
}
return false;
} // end doesSubsume
/**
* Adds a classifier in the population.
*
* @param cl is the new classifier to be added.
*/
public void addClassifier(Classifier cl) {
try {
set[macroClSum] = cl;
microClSum += cl.getNumerosity();
macroClSum++;
} catch (Exception e) {
System.out.println(
"Exception in the insertion of a new classifier. The macroClSum is : "
+ macroClSum
+ " and the microClsum: "
+ microClSum);
System.out.println(
"And the maximum number of classifiers in the population is: " + Config.popSize);
e.printStackTrace();
}
} // end addClassifier
/**
* Returns if the classifier of the class is equal to the classifier given as a parameter.
*
* @param cl is a classifier.
* @return a boolean indicating if they are equals.
*/
public boolean equals(Classifier cl) {
int i;
try {
// Checking the action
if (action != cl.getAction()) return false;
// Checking the condition
for (i = 0; i < rep.length; i++) {
if (!rep[i].equals(cl.rep[i])) return false;
}
return true;
} catch (Exception e) {
return false;
}
} // end equals
/**
* Applies crossover. It generates two children.
*
* @param parent1 is the first parent.
* @param parent2 is the second parent
* @param child1 is the first child
* @param child2 is the second child.
*/
public void makeCrossover(
Classifier parent1, Classifier parent2, Classifier child1, Classifier child2) {
int i = 0;
// cross1 is a number between [0.. clLength-1]
int cross1 = (int) (Config.rand() * (double) Config.clLength);
// cross2 is a number between [1..clLenght].
int cross2 = (int) (Config.rand() * (double) Config.clLength) + 1;
if (cross1 > cross2) {
int aux = cross2;
cross2 = cross1;
cross1 = aux;
// In the else-if condition is not necessary to check if (cross2<clLength)
// to increment the point, because cross1 [0..length-1]
} else if (cross1 == cross2) cross2++;
// All the intervals (real representation) or genes (ternary representation) that
// are not in the cross point are crossed.
if (!Config.ternaryRep) {
for (i = cross1 + 1; i < cross2 - 1; i++) {
child2.setAllele(i, parent1);
child1.setAllele(i, parent2);
}
// Now we have to cross the border allele
child1.crossAllele(cross1, parent1, parent2);
child1.crossAllele(cross2 - 1, parent2, parent1);
child2.crossAllele(cross1, parent2, parent1);
child2.crossAllele(cross2 - 1, parent1, parent2);
} else {
for (i = cross1; i < cross2 - 1; i++) {
child2.setAllele(i, parent1);
child1.setAllele(i, parent2);
}
}
} // end makeCrossover
/**
* Build Decorate classifier
*
* @param data the training data to be used for generating the classifier
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifier == null) {
throw new Exception("A base classifier has not been specified!");
}
if (data.checkForStringAttributes()) {
throw new UnsupportedAttributeTypeException("Cannot handle string attributes!");
}
if (data.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
if (m_NumIterations < m_DesiredSize)
throw new Exception("Max number of iterations must be >= desired ensemble size!");
// initialize random number generator
if (m_Seed == -1) m_Random = new Random();
else m_Random = new Random(m_Seed);
int i = 1; // current committee size
int numTrials = 1; // number of Decorate iterations
Instances divData = new Instances(data); // local copy of data - diversity data
divData.deleteWithMissingClass();
Instances artData = null; // artificial data
// compute number of artficial instances to add at each iteration
int artSize = (int) (Math.abs(m_ArtSize) * divData.numInstances());
if (artSize == 0) artSize = 1; // atleast add one random example
computeStats(data); // Compute training data stats for creating artificial examples
// initialize new committee
m_Committee = new Vector();
Classifier newClassifier = m_Classifier;
newClassifier.buildClassifier(divData);
m_Committee.add(newClassifier);
double eComm = computeError(divData); // compute ensemble error
if (m_Debug)
System.out.println(
"Initialize:\tClassifier " + i + " added to ensemble. Ensemble error = " + eComm);
// repeat till desired committee size is reached OR the max number of iterations is exceeded
while (i < m_DesiredSize && numTrials < m_NumIterations) {
// Generate artificial training examples
artData = generateArtificialData(artSize, data);
// Label artificial examples
labelData(artData);
addInstances(divData, artData); // Add new artificial data
// Build new classifier
Classifier tmp[] = Classifier.makeCopies(m_Classifier, 1);
newClassifier = tmp[0];
newClassifier.buildClassifier(divData);
// Remove all the artificial data
removeInstances(divData, artSize);
// Test if the new classifier should be added to the ensemble
m_Committee.add(newClassifier); // add new classifier to current committee
double currError = computeError(divData);
if (currError <= eComm) { // adding the new member did not increase the error
i++;
eComm = currError;
if (m_Debug)
System.out.println(
"Iteration: "
+ (1 + numTrials)
+ "\tClassifier "
+ i
+ " added to ensemble. Ensemble error = "
+ eComm);
} else { // reject the current classifier because it increased the ensemble error
m_Committee.removeElementAt(m_Committee.size() - 1); // pop the last member
}
numTrials++;
}
}
/**
* The core implementation of the search.
*
* @param root The root word to search from. Traditionally, this is the root of the sentence.
* @param candidateFragments The callback for the resulting sentence fragments. This is a
* predicate of a triple of values. The return value of the predicate determines whether we
* should continue searching. The triple is a triple of
* <ol>
* <li>The log probability of the sentence fragment, according to the featurizer and the
* weights
* <li>The features along the path to this fragment. The last element of this is the
* features from the most recent step.
* <li>The sentence fragment. Because it is relatively expensive to compute the resulting
* tree, this is returned as a lazy {@link Supplier}.
* </ol>
*
* @param classifier The classifier for whether an arc should be on the path to a clause split, a
* clause split itself, or neither.
* @param featurizer The featurizer to use. Make sure this matches the weights!
* @param actionSpace The action space we are allowed to take. Each action defines a means of
* splitting a clause on a dependency boundary.
*/
protected void search(
// The root to search from
IndexedWord root,
// The output specs
final Predicate<Triple<Double, List<Counter<String>>, Supplier<SentenceFragment>>>
candidateFragments,
// The learning specs
final Classifier<ClauseSplitter.ClauseClassifierLabel, String> classifier,
Map<String, ? extends List<String>> hardCodedSplits,
final Function<Triple<State, Action, State>, Counter<String>> featurizer,
final Collection<Action> actionSpace,
final int maxTicks) {
// (the fringe)
PriorityQueue<Pair<State, List<Counter<String>>>> fringe = new FixedPrioritiesPriorityQueue<>();
// (avoid duplicate work)
Set<IndexedWord> seenWords = new HashSet<>();
State firstState =
new State(null, null, -9000, null, x -> {}, true); // First state is implicitly "done"
fringe.add(Pair.makePair(firstState, new ArrayList<>(0)), -0.0);
int ticks = 0;
while (!fringe.isEmpty()) {
if (++ticks > maxTicks) {
// System.err.println("WARNING! Timed out on search with " + ticks + " ticks");
return;
}
// Useful variables
double logProbSoFar = fringe.getPriority();
assert logProbSoFar <= 0.0;
Pair<State, List<Counter<String>>> lastStatePair = fringe.removeFirst();
State lastState = lastStatePair.first;
List<Counter<String>> featuresSoFar = lastStatePair.second;
IndexedWord rootWord = lastState.edge == null ? root : lastState.edge.getDependent();
// Register thunk
if (lastState.isDone) {
if (!candidateFragments.test(
Triple.makeTriple(
logProbSoFar,
featuresSoFar,
() -> {
SemanticGraph copy = new SemanticGraph(tree);
lastState
.thunk
.andThen(
x -> {
// Add the extra edges back in, if they don't break the tree-ness of the
// extraction
for (IndexedWord newTreeRoot : x.getRoots()) {
if (newTreeRoot != null) { // what a strange thing to have happen...
for (SemanticGraphEdge extraEdge :
extraEdgesByGovernor.get(newTreeRoot)) {
assert Util.isTree(x);
//noinspection unchecked
addSubtree(
x,
newTreeRoot,
extraEdge.getRelation().toString(),
tree,
extraEdge.getDependent(),
tree.getIncomingEdgesSorted(newTreeRoot));
assert Util.isTree(x);
}
}
}
})
.accept(copy);
return new SentenceFragment(copy, assumedTruth, false);
}))) {
break;
}
}
// Find relevant auxilliary terms
SemanticGraphEdge subjOrNull = null;
SemanticGraphEdge objOrNull = null;
for (SemanticGraphEdge auxEdge : tree.outgoingEdgeIterable(rootWord)) {
String relString = auxEdge.getRelation().toString();
if (relString.contains("obj")) {
objOrNull = auxEdge;
} else if (relString.contains("subj")) {
subjOrNull = auxEdge;
}
}
// Iterate over children
// For each outgoing edge...
for (SemanticGraphEdge outgoingEdge : tree.outgoingEdgeIterable(rootWord)) {
// Prohibit indirect speech verbs from splitting off clauses
// (e.g., 'said', 'think')
// This fires if the governor is an indirect speech verb, and the outgoing edge is a ccomp
if (outgoingEdge.getRelation().toString().equals("ccomp")
&& ((outgoingEdge.getGovernor().lemma() != null
&& INDIRECT_SPEECH_LEMMAS.contains(outgoingEdge.getGovernor().lemma()))
|| INDIRECT_SPEECH_LEMMAS.contains(outgoingEdge.getGovernor().word()))) {
continue;
}
// Get some variables
String outgoingEdgeRelation = outgoingEdge.getRelation().toString();
List<String> forcedArcOrder = hardCodedSplits.get(outgoingEdgeRelation);
if (forcedArcOrder == null && outgoingEdgeRelation.contains(":")) {
forcedArcOrder =
hardCodedSplits.get(
outgoingEdgeRelation.substring(0, outgoingEdgeRelation.indexOf(":")) + ":*");
}
boolean doneForcedArc = false;
// For each action...
for (Action action :
(forcedArcOrder == null ? actionSpace : orderActions(actionSpace, forcedArcOrder))) {
// Check the prerequisite
if (!action.prerequisitesMet(tree, outgoingEdge)) {
continue;
}
if (forcedArcOrder != null && doneForcedArc) {
break;
}
// 1. Compute the child state
Optional<State> candidate =
action.applyTo(tree, lastState, outgoingEdge, subjOrNull, objOrNull);
if (candidate.isPresent()) {
double logProbability;
ClauseClassifierLabel bestLabel;
Counter<String> features =
featurizer.apply(Triple.makeTriple(lastState, action, candidate.get()));
if (forcedArcOrder != null && !doneForcedArc) {
logProbability = 0.0;
bestLabel = ClauseClassifierLabel.CLAUSE_SPLIT;
doneForcedArc = true;
} else if (features.containsKey("__undocumented_junit_no_classifier")) {
logProbability = Double.NEGATIVE_INFINITY;
bestLabel = ClauseClassifierLabel.CLAUSE_INTERM;
} else {
Counter<ClauseClassifierLabel> scores = classifier.scoresOf(new RVFDatum<>(features));
if (scores.size() > 0) {
Counters.logNormalizeInPlace(scores);
}
String rel = outgoingEdge.getRelation().toString();
if ("nsubj".equals(rel) || "dobj".equals(rel)) {
scores.remove(
ClauseClassifierLabel.NOT_A_CLAUSE); // Always at least yield on nsubj and dobj
}
logProbability = Counters.max(scores, Double.NEGATIVE_INFINITY);
bestLabel = Counters.argmax(scores, (x, y) -> 0, ClauseClassifierLabel.CLAUSE_SPLIT);
}
if (bestLabel != ClauseClassifierLabel.NOT_A_CLAUSE) {
Pair<State, List<Counter<String>>> childState =
Pair.makePair(
candidate.get().withIsDone(bestLabel),
new ArrayList<Counter<String>>(featuresSoFar) {
{
add(features);
}
});
// 2. Register the child state
if (!seenWords.contains(childState.first.edge.getDependent())) {
// System.err.println(" pushing " + action.signature() + " with " +
// argmax.first.edge);
fringe.add(childState, logProbability);
}
}
}
}
}
seenWords.add(rootWord);
}
// System.err.println("Search finished in " + ticks + " ticks and " + classifierEvals + "
// classifier evaluations.");
}
@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);
}
}