Examples with Counter edu.stanford.nlp.stats.Counter used on opensource projects

Example 1 with Counter

use of edu.stanford.nlp.stats.Counter in project CoreNLP by stanfordnlp.

the class FeatureExtractor method getFeatures.

private Counter<String> getFeatures(Document doc, Mention m, Map<Integer, List<Mention>> mentionsByHeadIndex) {
    Counter<String> features = new ClassicCounter<>();
    // type features
    features.incrementCount("mention-type=" + m.mentionType);
    features.incrementCount("gender=" + m.gender);
    features.incrementCount("person-fine=" + m.person);
    features.incrementCount("head-ne-type=" + m.nerString);
    List<String> singletonFeatures = m.getSingletonFeatures(dictionaries);
    for (Map.Entry<Integer, String> e : SINGLETON_FEATURES.entrySet()) {
        if (e.getKey() < singletonFeatures.size()) {
            features.incrementCount(e.getValue() + "=" + singletonFeatures.get(e.getKey()));
        }
    }
    // length and location features
    addNumeric(features, "mention-length", m.spanToString().length());
    addNumeric(features, "mention-words", m.originalSpan.size());
    addNumeric(features, "sentence-words", m.sentenceWords.size());
    features.incrementCount("sentence-words=" + bin(m.sentenceWords.size()));
    features.incrementCount("mention-position", m.mentionNum / (double) doc.predictedMentions.size());
    features.incrementCount("sentence-position", m.sentNum / (double) doc.numSentences);
    // lexical features
    CoreLabel firstWord = firstWord(m);
    CoreLabel lastWord = lastWord(m);
    CoreLabel headWord = headWord(m);
    CoreLabel prevWord = prevWord(m);
    CoreLabel nextWord = nextWord(m);
    CoreLabel prevprevWord = prevprevWord(m);
    CoreLabel nextnextWord = nextnextWord(m);
    String headPOS = getPOS(headWord);
    String firstPOS = getPOS(firstWord);
    String lastPOS = getPOS(lastWord);
    String prevPOS = getPOS(prevWord);
    String nextPOS = getPOS(nextWord);
    String prevprevPOS = getPOS(prevprevWord);
    String nextnextPOS = getPOS(nextnextWord);
    features.incrementCount("first-word=" + wordIndicator(firstWord, firstPOS));
    features.incrementCount("last-word=" + wordIndicator(lastWord, lastPOS));
    features.incrementCount("head-word=" + wordIndicator(headWord, headPOS));
    features.incrementCount("next-word=" + wordIndicator(nextWord, nextPOS));
    features.incrementCount("prev-word=" + wordIndicator(prevWord, prevPOS));
    features.incrementCount("next-bigram=" + wordIndicator(nextWord, nextnextWord, nextPOS + "_" + nextnextPOS));
    features.incrementCount("prev-bigram=" + wordIndicator(prevprevWord, prevWord, prevprevPOS + "_" + prevPOS));
    features.incrementCount("next-pos=" + nextPOS);
    features.incrementCount("prev-pos=" + prevPOS);
    features.incrementCount("first-pos=" + firstPOS);
    features.incrementCount("last-pos=" + lastPOS);
    features.incrementCount("next-pos-bigram=" + nextPOS + "_" + nextnextPOS);
    features.incrementCount("prev-pos-bigram=" + prevprevPOS + "_" + prevPOS);
    addDependencyFeatures(features, "parent", getDependencyParent(m), true);
    addFeature(features, "ends-with-head", m.headIndex == m.endIndex - 1);
    addFeature(features, "is-generic", m.originalSpan.size() == 1 && firstPOS.equals("NNS"));
    // syntax features
    IndexedWord w = m.headIndexedWord;
    String depPath = "";
    int depth = 0;
    while (w != null) {
        SemanticGraphEdge e = getDependencyParent(m, w);
        depth++;
        if (depth <= 3 && e != null) {
            depPath += (depPath.isEmpty() ? "" : "_") + e.getRelation().toString();
            features.incrementCount("dep-path=" + depPath);
            w = e.getSource();
        } else {
            w = null;
        }
    }
    if (useConstituencyParse) {
        int fullEmbeddingLevel = headEmbeddingLevel(m.contextParseTree, m.headIndex);
        int mentionEmbeddingLevel = headEmbeddingLevel(m.mentionSubTree, m.headIndex - m.startIndex);
        if (fullEmbeddingLevel != -1 && mentionEmbeddingLevel != -1) {
            features.incrementCount("mention-embedding-level=" + bin(fullEmbeddingLevel - mentionEmbeddingLevel));
            features.incrementCount("head-embedding-level=" + bin(mentionEmbeddingLevel));
        } else {
            features.incrementCount("undetermined-embedding-level");
        }
        features.incrementCount("num-embedded-nps=" + bin(numEmbeddedNps(m.mentionSubTree)));
        String syntaxPath = "";
        Tree tree = m.contextParseTree;
        Tree head = tree.getLeaves().get(m.headIndex).ancestor(1, tree);
        depth = 0;
        for (Tree node : tree.pathNodeToNode(head, tree)) {
            syntaxPath += node.value() + "-";
            features.incrementCount("syntax-path=" + syntaxPath);
            depth++;
            if (depth >= 4 || node.value().equals("S")) {
                break;
            }
        }
    }
    // mention containment features
    addFeature(features, "contained-in-other-mention", mentionsByHeadIndex.get(m.headIndex).stream().anyMatch(m2 -> m != m2 && m.insideIn(m2)));
    addFeature(features, "contains-other-mention", mentionsByHeadIndex.get(m.headIndex).stream().anyMatch(m2 -> m != m2 && m2.insideIn(m)));
    // features from dcoref rules
    addFeature(features, "bare-plural", m.originalSpan.size() == 1 && headPOS.equals("NNS"));
    addFeature(features, "quantifier-start", dictionaries.quantifiers.contains(firstWord.word().toLowerCase()));
    addFeature(features, "negative-start", firstWord.word().toLowerCase().matches("none|no|nothing|not"));
    addFeature(features, "partitive", RuleBasedCorefMentionFinder.partitiveRule(m, m.sentenceWords, dictionaries));
    addFeature(features, "adjectival-demonym", dictionaries.isAdjectivalDemonym(m.spanToString()));
    if (doc.docType != DocType.ARTICLE && m.person == Person.YOU && nextWord != null && nextWord.word().equalsIgnoreCase("know")) {
        features.incrementCount("generic-you");
    }
    return features;
}

Example 2 with Counter

use of edu.stanford.nlp.stats.Counter in project CoreNLP by stanfordnlp.

the class PairwiseModelTrainer method test.

public static void test(PairwiseModel model, String predictionsName, boolean anaphoricityModel) throws Exception {
    Redwood.log("scoref-train", "Reading compression...");
    Compressor<String> compressor = IOUtils.readObjectFromFile(StatisticalCorefTrainer.compressorFile);
    Redwood.log("scoref-train", "Reading test data...");
    List<DocumentExamples> testDocuments = IOUtils.readObjectFromFile(StatisticalCorefTrainer.extractedFeaturesFile);
    Redwood.log("scoref-train", "Building test set...");
    List<Pair<Example, Map<Integer, CompressedFeatureVector>>> allExamples = anaphoricityModel ? getAnaphoricityExamples(testDocuments) : getExamples(testDocuments);
    Redwood.log("scoref-train", "Testing...");
    PrintWriter writer = new PrintWriter(model.getDefaultOutputPath() + predictionsName);
    Map<Integer, Counter<Pair<Integer, Integer>>> scores = new HashMap<>();
    writeScores(allExamples, compressor, model, writer, scores);
    if (model instanceof MaxMarginMentionRanker) {
        writer.close();
        writer = new PrintWriter(model.getDefaultOutputPath() + predictionsName + "_anaphoricity");
        testDocuments = IOUtils.readObjectFromFile(StatisticalCorefTrainer.extractedFeaturesFile);
        allExamples = getAnaphoricityExamples(testDocuments);
        writeScores(allExamples, compressor, model, writer, scores);
    }
    IOUtils.writeObjectToFile(scores, model.getDefaultOutputPath() + predictionsName + ".ser");
    writer.close();
}

Example 3 with Counter

use of edu.stanford.nlp.stats.Counter in project CoreNLP by stanfordnlp.

the class ScorePhrases method learnNewPhrasesPrivate.

private Counter<CandidatePhrase> learnNewPhrasesPrivate(String label, PatternsForEachToken patternsForEachToken, Counter<E> patternsLearnedThisIter, Counter<E> allSelectedPatterns, Set<CandidatePhrase> alreadyIdentifiedWords, CollectionValuedMap<E, Triple<String, Integer, Integer>> matchedTokensByPat, Counter<CandidatePhrase> scoreForAllWordsThisIteration, TwoDimensionalCounter<CandidatePhrase, E> terms, TwoDimensionalCounter<CandidatePhrase, E> wordsPatExtracted, TwoDimensionalCounter<E, CandidatePhrase> patternsAndWords4Label, String identifier, Set<CandidatePhrase> ignoreWords, boolean computeProcDataFreq) throws IOException, ClassNotFoundException {
    Set<CandidatePhrase> alreadyLabeledWords = new HashSet<>();
    if (constVars.doNotApplyPatterns) {
        // if want to get the stats by the lossy way of just counting without
        // applying the patterns
        ConstantsAndVariables.DataSentsIterator sentsIter = new ConstantsAndVariables.DataSentsIterator(constVars.batchProcessSents);
        while (sentsIter.hasNext()) {
            Pair<Map<String, DataInstance>, File> sentsf = sentsIter.next();
            this.statsWithoutApplyingPatterns(sentsf.first(), patternsForEachToken, patternsLearnedThisIter, wordsPatExtracted);
        }
    } else {
        if (patternsLearnedThisIter.size() > 0) {
            this.applyPats(patternsLearnedThisIter, label, wordsPatExtracted, matchedTokensByPat, alreadyLabeledWords);
        }
    }
    if (computeProcDataFreq) {
        if (!phraseScorer.wordFreqNorm.equals(Normalization.NONE)) {
            Redwood.log(Redwood.DBG, "computing processed freq");
            for (Entry<CandidatePhrase, Double> fq : Data.rawFreq.entrySet()) {
                Double in = fq.getValue();
                if (phraseScorer.wordFreqNorm.equals(Normalization.SQRT))
                    in = Math.sqrt(in);
                else if (phraseScorer.wordFreqNorm.equals(Normalization.LOG))
                    in = 1 + Math.log(in);
                else
                    throw new RuntimeException("can't understand the normalization");
                assert !in.isNaN() : "Why is processed freq nan when rawfreq is " + in;
                Data.processedDataFreq.setCount(fq.getKey(), in);
            }
        } else
            Data.processedDataFreq = Data.rawFreq;
    }
    if (constVars.wordScoring.equals(WordScoring.WEIGHTEDNORM)) {
        for (CandidatePhrase en : wordsPatExtracted.firstKeySet()) {
            if (!constVars.getOtherSemanticClassesWords().contains(en) && (en.getPhraseLemma() == null || !constVars.getOtherSemanticClassesWords().contains(CandidatePhrase.createOrGet(en.getPhraseLemma()))) && !alreadyLabeledWords.contains(en)) {
                terms.addAll(en, wordsPatExtracted.getCounter(en));
            }
        }
        removeKeys(terms, constVars.getStopWords());
        Counter<CandidatePhrase> phraseScores = phraseScorer.scorePhrases(label, terms, wordsPatExtracted, allSelectedPatterns, alreadyIdentifiedWords, false);
        System.out.println("count for word U.S. is " + phraseScores.getCount(CandidatePhrase.createOrGet("U.S.")));
        Set<CandidatePhrase> ignoreWordsAll;
        if (ignoreWords != null && !ignoreWords.isEmpty()) {
            ignoreWordsAll = CollectionUtils.unionAsSet(ignoreWords, constVars.getOtherSemanticClassesWords());
        } else
            ignoreWordsAll = new HashSet<>(constVars.getOtherSemanticClassesWords());
        ignoreWordsAll.addAll(constVars.getSeedLabelDictionary().get(label));
        ignoreWordsAll.addAll(constVars.getLearnedWords(label).keySet());
        System.out.println("ignoreWordsAll contains word U.S. is " + ignoreWordsAll.contains(CandidatePhrase.createOrGet("U.S.")));
        Counter<CandidatePhrase> finalwords = chooseTopWords(phraseScores, terms, phraseScores, ignoreWordsAll, constVars.thresholdWordExtract);
        phraseScorer.printReasonForChoosing(finalwords);
        scoreForAllWordsThisIteration.clear();
        Counters.addInPlace(scoreForAllWordsThisIteration, phraseScores);
        Redwood.log(ConstantsAndVariables.minimaldebug, "\n\n## Selected Words for " + label + " : " + Counters.toSortedString(finalwords, finalwords.size(), "%1$s:%2$.2f", "\t"));
        if (constVars.goldEntities != null) {
            Map<String, Boolean> goldEntities4Label = constVars.goldEntities.get(label);
            if (goldEntities4Label != null) {
                StringBuffer s = new StringBuffer();
                finalwords.keySet().stream().forEach(x -> s.append(x.getPhrase() + (goldEntities4Label.containsKey(x.getPhrase()) ? ":" + goldEntities4Label.get(x.getPhrase()) : ":UKNOWN") + "\n"));
                Redwood.log(ConstantsAndVariables.minimaldebug, "\n\n## Gold labels for selected words for label " + label + " : " + s.toString());
            } else
                Redwood.log(Redwood.DBG, "No gold entities provided for label " + label);
        }
        if (constVars.outDir != null && !constVars.outDir.isEmpty()) {
            String outputdir = constVars.outDir + "/" + identifier + "/" + label;
            IOUtils.ensureDir(new File(outputdir));
            TwoDimensionalCounter<CandidatePhrase, CandidatePhrase> reasonForWords = new TwoDimensionalCounter<>();
            for (CandidatePhrase word : finalwords.keySet()) {
                for (E l : wordsPatExtracted.getCounter(word).keySet()) {
                    for (CandidatePhrase w2 : patternsAndWords4Label.getCounter(l)) {
                        reasonForWords.incrementCount(word, w2);
                    }
                }
            }
            Redwood.log(ConstantsAndVariables.minimaldebug, "Saving output in " + outputdir);
            String filename = outputdir + "/words.json";
            // the json object is an array corresponding to each iteration - of list
            // of objects,
            // each of which is a bean of entity and reasons
            JsonArrayBuilder obj = Json.createArrayBuilder();
            if (writtenInJustification.containsKey(label) && writtenInJustification.get(label)) {
                JsonReader jsonReader = Json.createReader(new BufferedInputStream(new FileInputStream(filename)));
                JsonArray objarr = jsonReader.readArray();
                for (JsonValue o : objarr) obj.add(o);
                jsonReader.close();
            }
            JsonArrayBuilder objThisIter = Json.createArrayBuilder();
            for (CandidatePhrase w : reasonForWords.firstKeySet()) {
                JsonObjectBuilder objinner = Json.createObjectBuilder();
                JsonArrayBuilder l = Json.createArrayBuilder();
                for (CandidatePhrase w2 : reasonForWords.getCounter(w).keySet()) {
                    l.add(w2.getPhrase());
                }
                JsonArrayBuilder pats = Json.createArrayBuilder();
                for (E p : wordsPatExtracted.getCounter(w)) {
                    pats.add(p.toStringSimple());
                }
                objinner.add("reasonwords", l);
                objinner.add("patterns", pats);
                objinner.add("score", finalwords.getCount(w));
                objinner.add("entity", w.getPhrase());
                objThisIter.add(objinner.build());
            }
            obj.add(objThisIter);
            // Redwood.log(ConstantsAndVariables.minimaldebug, channelNameLogger,
            // "Writing justification at " + filename);
            IOUtils.writeStringToFile(StringUtils.normalize(StringUtils.toAscii(obj.build().toString())), filename, "ASCII");
            writtenInJustification.put(label, true);
        }
        if (constVars.justify) {
            Redwood.log(Redwood.DBG, "\nJustification for phrases:\n");
            for (CandidatePhrase word : finalwords.keySet()) {
                Redwood.log(Redwood.DBG, "Phrase " + word + " extracted because of patterns: \t" + Counters.toSortedString(wordsPatExtracted.getCounter(word), wordsPatExtracted.getCounter(word).size(), "%1$s:%2$f", "\n"));
            }
        }
        return finalwords;
    } else if (constVars.wordScoring.equals(WordScoring.BPB)) {
        Counters.addInPlace(terms, wordsPatExtracted);
        Counter<CandidatePhrase> maxPatWeightTerms = new ClassicCounter<>();
        Map<CandidatePhrase, E> wordMaxPat = new HashMap<>();
        for (Entry<CandidatePhrase, ClassicCounter<E>> en : terms.entrySet()) {
            Counter<E> weights = new ClassicCounter<>();
            for (E k : en.getValue().keySet()) weights.setCount(k, patternsLearnedThisIter.getCount(k));
            maxPatWeightTerms.setCount(en.getKey(), Counters.max(weights));
            wordMaxPat.put(en.getKey(), Counters.argmax(weights));
        }
        Counters.removeKeys(maxPatWeightTerms, alreadyIdentifiedWords);
        double maxvalue = Counters.max(maxPatWeightTerms);
        Set<CandidatePhrase> words = Counters.keysAbove(maxPatWeightTerms, maxvalue - 1e-10);
        CandidatePhrase bestw = null;
        if (words.size() > 1) {
            double max = Double.NEGATIVE_INFINITY;
            for (CandidatePhrase w : words) {
                if (terms.getCount(w, wordMaxPat.get(w)) > max) {
                    max = terms.getCount(w, wordMaxPat.get(w));
                    bestw = w;
                }
            }
        } else if (words.size() == 1)
            bestw = words.iterator().next();
        else
            return new ClassicCounter<>();
        Redwood.log(ConstantsAndVariables.minimaldebug, "Selected Words: " + bestw);
        return Counters.asCounter(Arrays.asList(bestw));
    } else
        throw new RuntimeException("wordscoring " + constVars.wordScoring + " not identified");
}

Example 4 with Counter

use of edu.stanford.nlp.stats.Counter in project CoreNLP by stanfordnlp.

the class GetPatternsFromDataMultiClass method setUpConstructor.

@SuppressWarnings("rawtypes")
private void setUpConstructor(Map<String, DataInstance> sents, Map<String, Set<CandidatePhrase>> seedSets, boolean labelUsingSeedSets, Map<String, Class<? extends TypesafeMap.Key<String>>> answerClass, Map<String, Class> generalizeClasses, Map<String, Map<Class, Object>> ignoreClasses) throws IOException, InstantiationException, IllegalAccessException, IllegalArgumentException, InvocationTargetException, NoSuchMethodException, SecurityException, InterruptedException, ExecutionException, ClassNotFoundException {
    Data.sents = sents;
    ArgumentParser.fillOptions(Data.class, props);
    ArgumentParser.fillOptions(ConstantsAndVariables.class, props);
    PatternFactory.setUp(props, PatternFactory.PatternType.valueOf(props.getProperty(Flags.patternType)), seedSets.keySet());
    constVars = new ConstantsAndVariables(props, seedSets, answerClass, generalizeClasses, ignoreClasses);
    if (constVars.writeMatchedTokensFiles && constVars.batchProcessSents) {
        throw new RuntimeException("writeMatchedTokensFiles and batchProcessSents cannot be true at the same time (not implemented; also doesn't make sense to save a large sentences json file)");
    }
    if (constVars.debug < 1) {
        Redwood.hideChannelsEverywhere(ConstantsAndVariables.minimaldebug);
    }
    if (constVars.debug < 2) {
        Redwood.hideChannelsEverywhere(Redwood.DBG);
    }
    constVars.justify = true;
    if (constVars.debug < 3) {
        constVars.justify = false;
    }
    if (constVars.debug < 4) {
        Redwood.hideChannelsEverywhere(ConstantsAndVariables.extremedebug);
    }
    Redwood.log(Redwood.DBG, "Running with debug output");
    Redwood.log(ConstantsAndVariables.extremedebug, "Running with extreme debug output");
    wordsPatExtracted = new HashMap<>();
    for (String label : answerClass.keySet()) {
        wordsPatExtracted.put(label, new TwoDimensionalCounter<>());
    }
    scorePhrases = new ScorePhrases(props, constVars);
    createPats = new CreatePatterns(props, constVars);
    assert !(constVars.doNotApplyPatterns && (PatternFactory.useStopWordsBeforeTerm || PatternFactory.numWordsCompoundMax > 1)) : " Cannot have both doNotApplyPatterns and (useStopWordsBeforeTerm true or numWordsCompound > 1)!";
    if (constVars.invertedIndexDirectory == null) {
        File f = File.createTempFile("inv", "index");
        f.deleteOnExit();
        f.mkdir();
        constVars.invertedIndexDirectory = f.getAbsolutePath();
    }
    Set<String> extremelySmallStopWordsList = CollectionUtils.asSet(".", ",", "in", "on", "of", "a", "the", "an");
    //Function to use to how to add CoreLabels to index
    Function<CoreLabel, Map<String, String>> transformCoreLabelToString = l -> {
        Map<String, String> add = new HashMap<>();
        for (Class gn : constVars.getGeneralizeClasses().values()) {
            Object b = l.get(gn);
            if (b != null && !b.toString().equals(constVars.backgroundSymbol)) {
                add.put(Token.getKeyForClass(gn), b.toString());
            }
        }
        return add;
    };
    boolean createIndex = false;
    if (constVars.loadInvertedIndex)
        constVars.invertedIndex = SentenceIndex.loadIndex(constVars.invertedIndexClass, props, extremelySmallStopWordsList, constVars.invertedIndexDirectory, transformCoreLabelToString);
    else {
        constVars.invertedIndex = SentenceIndex.createIndex(constVars.invertedIndexClass, null, props, extremelySmallStopWordsList, constVars.invertedIndexDirectory, transformCoreLabelToString);
        createIndex = true;
    }
    int totalNumSents = 0;
    boolean computeDataFreq = false;
    if (Data.rawFreq == null) {
        Data.rawFreq = new ClassicCounter<>();
        computeDataFreq = true;
    }
    ConstantsAndVariables.DataSentsIterator iter = new ConstantsAndVariables.DataSentsIterator(constVars.batchProcessSents);
    while (iter.hasNext()) {
        Pair<Map<String, DataInstance>, File> sentsIter = iter.next();
        Map<String, DataInstance> sentsf = sentsIter.first();
        if (constVars.batchProcessSents) {
            for (Entry<String, DataInstance> en : sentsf.entrySet()) {
                Data.sentId2File.put(en.getKey(), sentsIter.second());
            }
        }
        totalNumSents += sentsf.size();
        if (computeDataFreq) {
            Data.computeRawFreqIfNull(sentsf, PatternFactory.numWordsCompoundMax);
        }
        Redwood.log(Redwood.DBG, "Initializing sents size " + sentsf.size() + " sentences, either by labeling with the seed set or just setting the right classes");
        for (String l : constVars.getAnswerClass().keySet()) {
            Redwood.log(Redwood.DBG, "labelUsingSeedSets is " + labelUsingSeedSets + " and seed set size for " + l + " is " + (seedSets == null ? "null" : seedSets.get(l).size()));
            Set<CandidatePhrase> seed = seedSets == null || !labelUsingSeedSets ? new HashSet<>() : (seedSets.containsKey(l) ? seedSets.get(l) : new HashSet<>());
            if (!matchedSeedWords.containsKey(l)) {
                matchedSeedWords.put(l, new ClassicCounter<>());
            }
            Counter<CandidatePhrase> matched = runLabelSeedWords(sentsf, constVars.getAnswerClass().get(l), l, seed, constVars, labelUsingSeedSets);
            System.out.println("matched phrases for " + l + " is " + matched);
            matchedSeedWords.get(l).addAll(matched);
            if (constVars.addIndvWordsFromPhrasesExceptLastAsNeg) {
                Redwood.log(ConstantsAndVariables.minimaldebug, "adding indv words from phrases except last as neg");
                Set<CandidatePhrase> otherseed = new HashSet<>();
                if (labelUsingSeedSets) {
                    for (CandidatePhrase s : seed) {
                        String[] t = s.getPhrase().split("\\s+");
                        for (int i = 0; i < t.length - 1; i++) {
                            if (!seed.contains(t[i])) {
                                otherseed.add(CandidatePhrase.createOrGet(t[i]));
                            }
                        }
                    }
                }
                runLabelSeedWords(sentsf, PatternsAnnotations.OtherSemanticLabel.class, "OTHERSEM", otherseed, constVars, labelUsingSeedSets);
            }
        }
        if (labelUsingSeedSets && constVars.getOtherSemanticClassesWords() != null) {
            String l = "OTHERSEM";
            if (!matchedSeedWords.containsKey(l)) {
                matchedSeedWords.put(l, new ClassicCounter<>());
            }
            matchedSeedWords.get(l).addAll(runLabelSeedWords(sentsf, PatternsAnnotations.OtherSemanticLabel.class, l, constVars.getOtherSemanticClassesWords(), constVars, labelUsingSeedSets));
        }
        if (constVars.removeOverLappingLabelsFromSeed) {
            removeOverLappingLabels(sentsf);
        }
        if (createIndex)
            constVars.invertedIndex.add(sentsf, true);
        if (sentsIter.second().exists()) {
            Redwood.log(Redwood.DBG, "Saving the labeled seed sents (if given the option) to the same file " + sentsIter.second());
            IOUtils.writeObjectToFile(sentsf, sentsIter.second());
        }
    }
    Redwood.log(Redwood.DBG, "Done loading/creating inverted index of tokens and labeling data with total of " + constVars.invertedIndex.size() + " sentences");
    //If the scorer class is LearnFeatWt then individual word class is added as a feature
    if (scorePhrases.phraseScorerClass.equals(ScorePhrasesAverageFeatures.class) && (constVars.usePatternEvalWordClass || constVars.usePhraseEvalWordClass)) {
        if (constVars.externalFeatureWeightsDir == null) {
            File f = File.createTempFile("tempfeat", ".txt");
            f.delete();
            f.deleteOnExit();
            constVars.externalFeatureWeightsDir = f.getAbsolutePath();
        }
        IOUtils.ensureDir(new File(constVars.externalFeatureWeightsDir));
        for (String label : seedSets.keySet()) {
            String externalFeatureWeightsFileLabel = constVars.externalFeatureWeightsDir + "/" + label;
            File f = new File(externalFeatureWeightsFileLabel);
            if (!f.exists()) {
                Redwood.log(Redwood.DBG, "externalweightsfile for the label " + label + " does not exist: learning weights!");
                LearnImportantFeatures lmf = new LearnImportantFeatures();
                ArgumentParser.fillOptions(lmf, props);
                lmf.answerClass = answerClass.get(label);
                lmf.answerLabel = label;
                lmf.setUp();
                lmf.getTopFeatures(new ConstantsAndVariables.DataSentsIterator(constVars.batchProcessSents), constVars.perSelectRand, constVars.perSelectNeg, externalFeatureWeightsFileLabel);
            }
            Counter<Integer> distSimWeightsLabel = new ClassicCounter<>();
            for (String line : IOUtils.readLines(externalFeatureWeightsFileLabel)) {
                String[] t = line.split(":");
                if (!t[0].startsWith("Cluster"))
                    continue;
                String s = t[0].replace("Cluster-", "");
                Integer clusterNum = Integer.parseInt(s);
                distSimWeightsLabel.setCount(clusterNum, Double.parseDouble(t[1]));
            }
            constVars.distSimWeights.put(label, distSimWeightsLabel);
        }
    }
    // computing semantic odds values
    if (constVars.usePatternEvalSemanticOdds || constVars.usePhraseEvalSemanticOdds) {
        Counter<CandidatePhrase> dictOddsWeightsLabel = new ClassicCounter<>();
        Counter<CandidatePhrase> otherSemanticClassFreq = new ClassicCounter<>();
        for (CandidatePhrase s : constVars.getOtherSemanticClassesWords()) {
            for (String s1 : StringUtils.getNgrams(Arrays.asList(s.getPhrase().split("\\s+")), 1, PatternFactory.numWordsCompoundMax)) otherSemanticClassFreq.incrementCount(CandidatePhrase.createOrGet(s1));
        }
        otherSemanticClassFreq = Counters.add(otherSemanticClassFreq, 1.0);
        // otherSemanticClassFreq.setDefaultReturnValue(1.0);
        Map<String, Counter<CandidatePhrase>> labelDictNgram = new HashMap<>();
        for (String label : seedSets.keySet()) {
            Counter<CandidatePhrase> classFreq = new ClassicCounter<>();
            for (CandidatePhrase s : seedSets.get(label)) {
                for (String s1 : StringUtils.getNgrams(Arrays.asList(s.getPhrase().split("\\s+")), 1, PatternFactory.numWordsCompoundMax)) classFreq.incrementCount(CandidatePhrase.createOrGet(s1));
            }
            classFreq = Counters.add(classFreq, 1.0);
            labelDictNgram.put(label, classFreq);
        // classFreq.setDefaultReturnValue(1.0);
        }
        for (String label : seedSets.keySet()) {
            Counter<CandidatePhrase> otherLabelFreq = new ClassicCounter<>();
            for (String label2 : seedSets.keySet()) {
                if (label.equals(label2))
                    continue;
                otherLabelFreq.addAll(labelDictNgram.get(label2));
            }
            otherLabelFreq.addAll(otherSemanticClassFreq);
            dictOddsWeightsLabel = Counters.divisionNonNaN(labelDictNgram.get(label), otherLabelFreq);
            constVars.dictOddsWeights.put(label, dictOddsWeightsLabel);
        }
    }
//Redwood.log(Redwood.DBG, "All options are:" + "\n" + Maps.toString(getAllOptions(), "","","\t","\n"));
}

Example 5 with Counter

use of edu.stanford.nlp.stats.Counter in project CoreNLP by stanfordnlp.

the class ApplyDepPatterns method getMatchedTokensIndex.

private Collection<ExtractedPhrase> getMatchedTokensIndex(SemanticGraph graph, SemgrexPattern pattern, DataInstance sent, String label) {
    //TODO: look at the ignoreCommonTags flag
    ExtractPhraseFromPattern extract = new ExtractPhraseFromPattern(false, PatternFactory.numWordsCompoundMapped.get(label));
    Collection<IntPair> outputIndices = new ArrayList<>();
    boolean findSubTrees = true;
    List<CoreLabel> tokensC = sent.getTokens();
    //TODO: see if you can get rid of this (only used for matchedGraphs)
    List<String> tokens = tokensC.stream().map(x -> x.word()).collect(Collectors.toList());
    List<String> outputPhrases = new ArrayList<>();
    List<ExtractedPhrase> extractedPhrases = new ArrayList<>();
    Function<Pair<IndexedWord, SemanticGraph>, Counter<String>> extractFeatures = new Function<Pair<IndexedWord, SemanticGraph>, Counter<String>>() {

        @Override
        public Counter<String> apply(Pair<IndexedWord, SemanticGraph> indexedWordSemanticGraphPair) {
            //TODO: make features;
            Counter<String> feat = new ClassicCounter<>();
            IndexedWord vertex = indexedWordSemanticGraphPair.first();
            SemanticGraph graph = indexedWordSemanticGraphPair.second();
            List<Pair<GrammaticalRelation, IndexedWord>> pt = graph.parentPairs(vertex);
            for (Pair<GrammaticalRelation, IndexedWord> en : pt) {
                feat.incrementCount("PARENTREL-" + en.first());
            }
            return feat;
        }
    };
    extract.getSemGrexPatternNodes(graph, tokens, outputPhrases, outputIndices, pattern, findSubTrees, extractedPhrases, constVars.matchLowerCaseContext, matchingWordRestriction);
    //System.out.println("extracted phrases are " + extractedPhrases + " and output indices are " + outputIndices);
    return extractedPhrases;
}