/**
* Returns a list of featured thresholded by minPrecision and sorted by their frequency of
* occurrence. precision in this case, is defined as the frequency of majority label over total
* frequency for that feature.
*
* @return list of high precision features.
*/
private List<F> getHighPrecisionFeatures(
GeneralDataset<L, F> dataset, double minPrecision, int maxNumFeatures) {
int[][] feature2label = new int[dataset.numFeatures()][dataset.numClasses()];
for (int f = 0; f < dataset.numFeatures(); f++) Arrays.fill(feature2label[f], 0);
int[][] data = dataset.data;
int[] labels = dataset.labels;
for (int d = 0; d < data.length; d++) {
int label = labels[d];
// System.out.println("datum id:"+d+" label id: "+label);
if (data[d] != null) {
// System.out.println(" number of features:"+data[d].length);
for (int n = 0; n < data[d].length; n++) {
feature2label[data[d][n]][label]++;
}
}
}
Counter<F> feature2freq = new ClassicCounter<F>();
for (int f = 0; f < dataset.numFeatures(); f++) {
int maxF = ArrayMath.max(feature2label[f]);
int total = ArrayMath.sum(feature2label[f]);
double precision = ((double) maxF) / total;
F feature = dataset.featureIndex.get(f);
if (precision >= minPrecision) {
feature2freq.incrementCount(feature, total);
}
}
if (feature2freq.size() > maxNumFeatures) {
Counters.retainTop(feature2freq, maxNumFeatures);
}
// for(F feature : feature2freq.keySet())
// System.out.println(feature+" "+feature2freq.getCount(feature));
// System.exit(0);
return Counters.toSortedList(feature2freq);
}
/**
* TODO(gabor) JavaDoc
*
* @param tokens
* @param span
* @return
*/
public static String guessNER(List<CoreLabel> tokens, Span span) {
Counter<String> nerGuesses = new ClassicCounter<>();
for (int i : span) {
nerGuesses.incrementCount(tokens.get(i).ner());
}
nerGuesses.remove("O");
nerGuesses.remove(null);
if (nerGuesses.size() > 0 && Counters.max(nerGuesses) >= span.size() / 2) {
return Counters.argmax(nerGuesses);
} else {
return "O";
}
}
/** Run some sanity checks on the training statistics, to make sure they look valid. */
public void validate() {
for (Map<SentenceKey, EnsembleStatistics> map : impl) {
for (EnsembleStatistics stats : map.values()) {
for (SentenceStatistics component : stats.statisticsForClassifiers) {
assert !Counters.isUniformDistribution(component.relationDistribution, 1e-5);
Counters.normalize(
component.relationDistribution); // TODO(gabor) this shouldn't be necessary
assert (Math.abs(component.relationDistribution.totalCount() - 1.0)) < 1e-5;
}
assert (Math.abs(stats.mean().relationDistribution.totalCount() - 1.0)) < 1e-5;
assert !Counters.isUniformDistribution(stats.mean().relationDistribution, 1e-5);
}
}
}
public static void main(String[] args) throws Exception {
CompareType compareType = CompareType.MAX_COOR_ABS;
if (args.length == 0) {
usage();
System.exit(-1);
}
if ("-cosine".equals(args[0])) {
compareType = CompareType.COSINE;
args = (String[]) Arrays.copyOfRange(args, 1, args.length);
} else if ("-sse".equals(args[0])) {
compareType = CompareType.SUM_SQUARE_ERROR;
args = (String[]) Arrays.copyOfRange(args, 1, args.length);
}
if (args.length != 2) {
usage();
System.exit(-1);
}
Set<String> allWeights = new HashSet<String>();
Counter<String> wts1 = IOTools.readWeights(args[0]);
Counter<String> wts2 = IOTools.readWeights(args[1]);
allWeights.addAll(wts1.keySet());
allWeights.addAll(wts2.keySet());
if (compareType == CompareType.MAX_COOR_ABS) {
double maxDiff = Double.NEGATIVE_INFINITY;
Counters.multiplyInPlace(wts1, 1.0 / Counters.L1Norm(wts1));
Counters.multiplyInPlace(wts2, 1.0 / Counters.L1Norm(wts2));
for (String wt : allWeights) {
double absDiff = Math.abs(wts1.getCount(wt) - wts2.getCount(wt));
if (absDiff > maxDiff) maxDiff = absDiff;
}
System.out.println(maxDiff);
} else if (compareType == CompareType.COSINE) {
double dotProd = Counters.cosine(wts1, wts2);
System.out.println(dotProd);
} else if (compareType == CompareType.SUM_SQUARE_ERROR) {
double sse = 0;
for (String wt : allWeights) {
double diff = wts1.getCount(wt) - wts2.getCount(wt);
sse += diff * diff;
}
System.out.println(sse);
}
}
private static <T> void display(ClassicCounter<T> c, PrintWriter pw) {
List<T> cats = new ArrayList<>(c.keySet());
Collections.sort(cats, Counters.toComparatorDescending(c));
for (T ob : cats) {
pw.println(ob + " " + c.getCount(ob));
}
}
/**
* Returns a list of all modes in the Collection. (If the Collection has multiple items with the
* highest frequency, all of them will be returned.)
*/
public static <T> Set<T> modes(Collection<T> values) {
Counter<T> counter = new ClassicCounter<T>(values);
List<Double> sortedCounts = CollectionUtils.sorted(counter.values());
Double highestCount = sortedCounts.get(sortedCounts.size() - 1);
Counters.retainAbove(counter, highestCount);
return counter.keySet();
}
@Deprecated
protected void semanticSimilarity(
Counter<String> features, String prefix, Sentence str1, Sentence str2) {
Counter<String> v1 =
new ClassicCounter<>(
str1.lemmas().stream().map(String::toLowerCase).collect(Collectors.toList()));
Counter<String> v2 = new ClassicCounter<>(str2.lemmas());
// Remove any stopwords.
for (String word : stopwords) {
v1.remove(word);
v2.remove(word);
}
// take inner product.
double sim =
Counters.dotProduct(v1, v2) / (Counters.saferL2Norm(v1) * Counters.saferL2Norm(v2));
features.incrementCount(
prefix + "semantic-similarity", 2 * sim - 1); // to make it between 0 and 1.
}
@Override
public L classOf(Datum<L, F> example) {
Counter<L> scores = scoresOf(example);
if (scores != null) {
return Counters.argmax(scores);
} else {
return defaultLabel;
}
}
public static List<String> generateDict(List<String> str, int cutOff) {
Counter<String> freq = new IntCounter<>();
for (String aStr : str) freq.incrementCount(aStr);
List<String> keys = Counters.toSortedList(freq, false);
List<String> dict = new ArrayList<>();
for (String word : keys) {
if (freq.getCount(word) >= cutOff) dict.add(word);
}
return dict;
}
private static <T> void display(ClassicCounter<T> c, int num, PrintWriter pw) {
List<T> rules = new ArrayList<>(c.keySet());
Collections.sort(rules, Counters.toComparatorDescending(c));
int rSize = rules.size();
if (num > rSize) {
num = rSize;
}
for (int i = 0; i < num; i++) {
pw.println(rules.get(i) + " " + c.getCount(rules.get(i)));
}
}
public ClassicCounter<L> scoresOf(RVFDatum<L, F> example) {
ClassicCounter<L> scores = new ClassicCounter<>();
Counters.addInPlace(scores, priors);
if (addZeroValued) {
Counters.addInPlace(scores, priorZero);
}
for (L l : labels) {
double score = 0.0;
Counter<F> features = example.asFeaturesCounter();
for (F f : features.keySet()) {
int value = (int) features.getCount(f);
score += weight(l, f, Integer.valueOf(value));
if (addZeroValued) {
score -= weight(l, f, zero);
}
}
scores.incrementCount(l, score);
}
return scores;
}
public SentenceStatistics mean() {
double sumConfidence = 0;
int countWithConfidence = 0;
Counter<String> avePredictions =
new ClassicCounter<>(MapFactory.<String, MutableDouble>linkedHashMapFactory());
// Sum
for (SentenceStatistics stat : this.statisticsForClassifiers) {
for (Double confidence : stat.confidence) {
sumConfidence += confidence;
countWithConfidence += 1;
}
assert Math.abs(stat.relationDistribution.totalCount() - 1.0) < 1e-5;
for (Map.Entry<String, Double> entry : stat.relationDistribution.entrySet()) {
assert entry.getValue() >= 0.0;
assert entry.getValue() == stat.relationDistribution.getCount(entry.getKey());
avePredictions.incrementCount(entry.getKey(), entry.getValue());
assert stat.relationDistribution.getCount(entry.getKey())
== stat.relationDistribution.getCount(entry.getKey());
}
}
// Normalize
double aveConfidence = sumConfidence / ((double) countWithConfidence);
// Return
if (this.statisticsForClassifiers.size() > 1) {
Counters.divideInPlace(avePredictions, (double) this.statisticsForClassifiers.size());
}
if (Math.abs(avePredictions.totalCount() - 1.0) > 1e-5) {
throw new IllegalStateException("Mean relation distribution is not a distribution!");
}
assert this.statisticsForClassifiers.size() > 1
|| this.statisticsForClassifiers.size() == 0
|| Counters.equals(
avePredictions,
statisticsForClassifiers.iterator().next().relationDistribution,
1e-5);
return countWithConfidence > 0
? new SentenceStatistics(avePredictions, aveConfidence)
: new SentenceStatistics(avePredictions);
}
String probsToString() {
List<Pair<String, Double>> sorted =
Counters.toDescendingMagnitudeSortedListWithCounts(typeProbabilities);
StringBuffer os = new StringBuffer();
os.append("{");
boolean first = true;
for (Pair<String, Double> lv : sorted) {
if (!first) os.append("; ");
os.append(lv.first + ", " + lv.second);
first = false;
}
os.append("}");
return os.toString();
}
public static Set<String> featureWhiteList(FlatNBestList nbest, int minSegmentCount) {
List<List<ScoredFeaturizedTranslation<IString, String>>> nbestlists = nbest.nbestLists();
Counter<String> featureSegmentCounts = new ClassicCounter<String>();
for (List<ScoredFeaturizedTranslation<IString, String>> nbestlist : nbestlists) {
Set<String> segmentFeatureSet = new HashSet<String>();
for (ScoredFeaturizedTranslation<IString, String> trans : nbestlist) {
for (FeatureValue<String> feature : trans.features) {
segmentFeatureSet.add(feature.name);
}
}
for (String featureName : segmentFeatureSet) {
featureSegmentCounts.incrementCount(featureName);
}
}
return Counters.keysAbove(featureSegmentCounts, minSegmentCount - 1);
}
/**
* Update an existing feature whitelist according to nbestlists. Then return the features that
* appear more than minSegmentCount times.
*
* @param featureWhitelist
* @param nbestlists
* @param minSegmentCount
* @return features that appear more than minSegmentCount times
*/
public static Set<String> updatefeatureWhiteList(
Counter<String> featureWhitelist,
List<List<RichTranslation<IString, String>>> nbestlists,
int minSegmentCount) {
for (List<RichTranslation<IString, String>> nbestlist : nbestlists) {
Set<String> segmentFeatureSet = new HashSet<String>(1000);
for (RichTranslation<IString, String> trans : nbestlist) {
for (FeatureValue<String> feature : trans.features) {
if (!segmentFeatureSet.contains(feature.name)) {
segmentFeatureSet.add(feature.name);
featureWhitelist.incrementCount(feature.name);
}
}
}
}
return Counters.keysAbove(featureWhitelist, minSegmentCount - 1);
}
/**
* Returns true if it's worth saving/printing this object This happens in two cases: 1. The type
* of the object is not nilLabel 2. The type of the object is nilLabel but the second ranked label
* is within the given beam (0 -- 100) of the first choice
*
* @param beam
* @param nilLabel
*/
public boolean printableObject(double beam, String nilLabel) {
if (typeProbabilities == null) {
return false;
}
List<Pair<String, Double>> sorted =
Counters.toDescendingMagnitudeSortedListWithCounts(typeProbabilities);
// first choice not nil
if (sorted.size() > 0 && !sorted.get(0).first.equals(nilLabel)) {
return true;
}
// first choice is nil, but second is within beam
if (sorted.size() > 1
&& sorted.get(0).first.equals(nilLabel)
&& beam > 0
&& 100.0 * (sorted.get(0).second - sorted.get(1).second) < beam) {
return true;
}
return false;
}
public double averageKLFromMean() {
Counter<String> mean = this.mean().relationDistribution;
double sumKL = 0;
for (SentenceStatistics stats : this.statisticsForClassifiers) {
double kl = Counters.klDivergence(stats.relationDistribution, mean);
if (kl < 0.0 && kl > -1e-12) {
kl = 0.0;
} // floating point error.
assert kl >= 0.0;
sumKL += kl;
}
double val = sumKL / ((double) this.statisticsForClassifiers.size());
if (Double.isInfinite(val) || Double.isNaN(val) || val < 0.0) {
throw new AssertionError("Invalid average KL value: " + val);
}
assert val >= 0.0; // KL lower bound
assert this.statisticsForClassifiers.size() > 1 || val < 1e-5;
if (val < 1e-10) {
val = 0.0;
} // floating point error
return val;
}
public L classOf(RVFDatum<L, F> example) {
Counter<L> scores = scoresOf(example);
return Counters.argmax(scores);
}
public List<String> selectKeys(ActiveLearningSelectionCriterion criterion) {
Counter<String> weights = uncertainty(criterion);
return Counters.toSortedList(weights);
}
/**
* 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 Counter<E> score() {
Counter<E> currentPatternWeights4Label = new ClassicCounter<>();
Counter<E> pos_i = new ClassicCounter<>();
Counter<E> neg_i = new ClassicCounter<>();
Counter<E> unlab_i = new ClassicCounter<>();
for (Entry<E, ClassicCounter<CandidatePhrase>> en : negPatternsandWords4Label.entrySet()) {
neg_i.setCount(en.getKey(), en.getValue().size());
}
for (Entry<E, ClassicCounter<CandidatePhrase>> en :
unLabeledPatternsandWords4Label.entrySet()) {
unlab_i.setCount(en.getKey(), en.getValue().size());
}
for (Entry<E, ClassicCounter<CandidatePhrase>> en : patternsandWords4Label.entrySet()) {
pos_i.setCount(en.getKey(), en.getValue().size());
}
Counter<E> all_i = Counters.add(pos_i, neg_i);
all_i.addAll(unlab_i);
// for (Entry<Integer, ClassicCounter<String>> en : allPatternsandWords4Label
// .entrySet()) {
// all_i.setCount(en.getKey(), en.getValue().size());
// }
Counter<E> posneg_i = Counters.add(pos_i, neg_i);
Counter<E> logFi = new ClassicCounter<>(pos_i);
Counters.logInPlace(logFi);
if (patternScoring.equals(PatternScoring.RlogF)) {
currentPatternWeights4Label = Counters.product(Counters.division(pos_i, all_i), logFi);
} else if (patternScoring.equals(PatternScoring.RlogFPosNeg)) {
Redwood.log("extremePatDebug", "computing rlogfposneg");
currentPatternWeights4Label = Counters.product(Counters.division(pos_i, posneg_i), logFi);
} else if (patternScoring.equals(PatternScoring.RlogFUnlabNeg)) {
Redwood.log("extremePatDebug", "computing rlogfunlabeg");
currentPatternWeights4Label =
Counters.product(Counters.division(pos_i, Counters.add(neg_i, unlab_i)), logFi);
} else if (patternScoring.equals(PatternScoring.RlogFNeg)) {
Redwood.log("extremePatDebug", "computing rlogfneg");
currentPatternWeights4Label = Counters.product(Counters.division(pos_i, neg_i), logFi);
} else if (patternScoring.equals(PatternScoring.YanGarber02)) {
Counter<E> acc = Counters.division(pos_i, Counters.add(pos_i, neg_i));
double thetaPrecision = 0.8;
Counters.retainAbove(acc, thetaPrecision);
Counter<E> conf = Counters.product(Counters.division(pos_i, all_i), logFi);
for (E p : acc.keySet()) {
currentPatternWeights4Label.setCount(p, conf.getCount(p));
}
} else if (patternScoring.equals(PatternScoring.LinICML03)) {
Counter<E> acc = Counters.division(pos_i, Counters.add(pos_i, neg_i));
double thetaPrecision = 0.8;
Counters.retainAbove(acc, thetaPrecision);
Counter<E> conf =
Counters.product(
Counters.division(Counters.add(pos_i, Counters.scale(neg_i, -1)), all_i), logFi);
for (E p : acc.keySet()) {
currentPatternWeights4Label.setCount(p, conf.getCount(p));
}
} else {
throw new RuntimeException("not implemented " + patternScoring + " . check spelling!");
}
return currentPatternWeights4Label;
}
public List<Pair<String, Double>> selectWeightedKeysWithSampling(
ActiveLearningSelectionCriterion criterion, int numSamples, int seed) {
List<Pair<String, Double>> result = new ArrayList<>();
forceTrack("Sampling Keys");
log("" + numSamples + " to collect");
// Get uncertainty
forceTrack("Computing Uncertainties");
Counter<String> weightCounter = uncertainty(criterion);
assert weightCounter.equals(uncertainty(criterion));
endTrack("Computing Uncertainties");
// Compute some statistics
startTrack("Uncertainty Histogram");
// log(new Histogram(weightCounter, 50).toString()); // removed to make the release easier
// (Histogram isn't in CoreNLP)
endTrack("Uncertainty Histogram");
double totalCount = weightCounter.totalCount();
Random random = new Random(seed);
// Flatten counter
List<String> keys = new LinkedList<>();
List<Double> weights = new LinkedList<>();
List<String> zeroUncertaintyKeys = new LinkedList<>();
for (Pair<String, Double> elem :
Counters.toSortedListWithCounts(
weightCounter,
(o1, o2) -> {
int value = o1.compareTo(o2);
if (value == 0) {
return o1.first.compareTo(o2.first);
} else {
return value;
}
})) {
if (elem.second != 0.0
|| weightCounter.totalCount() == 0.0
|| weightCounter.size() <= numSamples) { // ignore 0 probability weights
keys.add(elem.first);
weights.add(elem.second);
} else {
zeroUncertaintyKeys.add(elem.first);
}
}
// Error check
if (Utils.assertionsEnabled()) {
for (Double elem : weights) {
if (!(elem >= 0 && !Double.isInfinite(elem) && !Double.isNaN(elem))) {
throw new IllegalArgumentException("Invalid weight: " + elem);
}
}
}
// Sample
SAMPLE_ITER:
for (int i = 1; i <= numSamples; ++i) { // For each sample
if (i % 1000 == 0) {
// Debug log
log("sampled " + (i / 1000) + "k keys");
// Recompute total count to mitigate floating point errors
totalCount = 0.0;
for (double val : weights) {
totalCount += val;
}
}
if (weights.size() == 0) {
continue;
}
assert totalCount >= 0.0;
assert weights.size() == keys.size();
double target = random.nextDouble() * totalCount;
Iterator<String> keyIter = keys.iterator();
Iterator<Double> weightIter = weights.iterator();
double runningTotal = 0.0;
while (keyIter.hasNext()) { // For each candidate
String key = keyIter.next();
double weight = weightIter.next();
runningTotal += weight;
if (target <= runningTotal) { // Select that sample
result.add(Pair.makePair(key, weight));
keyIter.remove();
weightIter.remove();
totalCount -= weight;
continue SAMPLE_ITER; // continue sampling
}
}
// We should get here only if the keys list is empty
warn(
"No more uncertain samples left to draw from! (target="
+ target
+ " totalCount="
+ totalCount
+ " size="
+ keys.size());
assert keys.size() == 0;
if (zeroUncertaintyKeys.size() > 0) {
result.add(Pair.makePair(zeroUncertaintyKeys.remove(0), 0.0));
} else {
break;
}
}
endTrack("Sampling Keys");
return result;
}
/** Print some statistics about this lexicon. */
public void printLexStats() {
System.out.println("BaseLexicon statistics");
System.out.println("unknownLevel is " + getUnknownWordModel().getUnknownLevel());
// System.out.println("Rules size: " + rules.size());
System.out.println("Sum of rulesWithWord: " + numRules());
System.out.println("Tags size: " + tags.size());
int wsize = words.size();
System.out.println("Words size: " + wsize);
// System.out.println("Unseen Sigs size: " + sigs.size() +
// " [number of unknown equivalence classes]");
System.out.println(
"rulesWithWord length: "
+ rulesWithWord.length
+ " [should be sum of words + unknown sigs]");
int[] lengths = new int[STATS_BINS];
ArrayList<String>[] wArr = new ArrayList[STATS_BINS];
for (int j = 0; j < STATS_BINS; j++) {
wArr[j] = new ArrayList<String>();
}
for (int i = 0; i < rulesWithWord.length; i++) {
int num = rulesWithWord[i].size();
if (num > STATS_BINS - 1) {
num = STATS_BINS - 1;
}
lengths[num]++;
if (wsize <= 20 || num >= STATS_BINS / 2) {
wArr[num].add(wordIndex.get(i));
}
}
System.out.println("Stats on how many taggings for how many words");
for (int j = 0; j < STATS_BINS; j++) {
System.out.print(j + " taggings: " + lengths[j] + " words ");
if (wsize <= 20 || j >= STATS_BINS / 2) {
System.out.print(wArr[j]);
}
System.out.println();
}
NumberFormat nf = NumberFormat.getNumberInstance();
nf.setMaximumFractionDigits(0);
System.out.println("Unseen counter: " + Counters.toString(uwModel.unSeenCounter(), nf));
if (wsize < 50 && tags.size() < 10) {
nf.setMaximumFractionDigits(3);
StringWriter sw = new StringWriter();
PrintWriter pw = new PrintWriter(sw);
pw.println("Tagging probabilities log P(word|tag)");
for (int t = 0; t < tags.size(); t++) {
pw.print('\t');
pw.print(tagIndex.get(t));
}
pw.println();
for (int w = 0; w < wsize; w++) {
pw.print(wordIndex.get(w));
pw.print('\t');
for (int t = 0; t < tags.size(); t++) {
IntTaggedWord iTW = new IntTaggedWord(w, t);
pw.print(nf.format(score(iTW, 1, wordIndex.get(w))));
if (t == tags.size() - 1) {
pw.println();
} else pw.print('\t');
}
}
pw.close();
System.out.println(sw.toString());
}
}
public List<Pair<String, Double>> selectWeightedKeys(ActiveLearningSelectionCriterion criterion) {
Counter<String> weights = uncertainty(criterion);
return Counters.toSortedListWithCounts(weights);
}
