Examples with DecimalFormat java.text.DecimalFormat used on opensource projects

Example 96 with DecimalFormat

use of java.text.DecimalFormat in project CoreNLP by stanfordnlp.

the class SieveCoreferenceSystem method runAndScoreCoref.

public static double runAndScoreCoref(SieveCoreferenceSystem corefSystem, MentionExtractor mentionExtractor, Properties props, String timeStamp) throws Exception {
    // prepare conll output
    PrintWriter writerGold = null;
    PrintWriter writerPredicted = null;
    PrintWriter writerPredictedCoref = null;
    String conllOutputMentionGoldFile = null;
    String conllOutputMentionPredictedFile = null;
    String conllOutputMentionCorefPredictedFile = null;
    String conllMentionEvalFile = null;
    String conllMentionEvalErrFile = null;
    String conllMentionCorefEvalFile = null;
    String conllMentionCorefEvalErrFile = null;
    if (Constants.PRINT_CONLL_OUTPUT || corefSystem.replicateCoNLL) {
        String conllOutput = props.getProperty(Constants.CONLL_OUTPUT_PROP, "conlloutput");
        conllOutputMentionGoldFile = conllOutput + "-" + timeStamp + ".gold.txt";
        conllOutputMentionPredictedFile = conllOutput + "-" + timeStamp + ".predicted.txt";
        conllOutputMentionCorefPredictedFile = conllOutput + "-" + timeStamp + ".coref.predicted.txt";
        conllMentionEvalFile = conllOutput + "-" + timeStamp + ".eval.txt";
        conllMentionEvalErrFile = conllOutput + "-" + timeStamp + ".eval.err.txt";
        conllMentionCorefEvalFile = conllOutput + "-" + timeStamp + ".coref.eval.txt";
        conllMentionCorefEvalErrFile = conllOutput + "-" + timeStamp + ".coref.eval.err.txt";
        logger.info("CONLL MENTION GOLD FILE: " + conllOutputMentionGoldFile);
        logger.info("CONLL MENTION PREDICTED FILE: " + conllOutputMentionPredictedFile);
        logger.info("CONLL MENTION EVAL FILE: " + conllMentionEvalFile);
        if (!Constants.SKIP_COREF) {
            logger.info("CONLL MENTION PREDICTED WITH COREF FILE: " + conllOutputMentionCorefPredictedFile);
            logger.info("CONLL MENTION WITH COREF EVAL FILE: " + conllMentionCorefEvalFile);
        }
        writerGold = new PrintWriter(new FileOutputStream(conllOutputMentionGoldFile));
        writerPredicted = new PrintWriter(new FileOutputStream(conllOutputMentionPredictedFile));
        writerPredictedCoref = new PrintWriter(new FileOutputStream(conllOutputMentionCorefPredictedFile));
    }
    mentionExtractor.resetDocs();
    if (corefSystem.doScore()) {
        corefSystem.initScorers();
    }
    while (true) {
        Document document = mentionExtractor.nextDoc();
        if (document == null)
            break;
        if (!props.containsKey(Constants.MUC_PROP)) {
            printRawDoc(document, true);
            printRawDoc(document, false);
        }
        printDiscourseStructure(document);
        if (corefSystem.doScore()) {
            document.extractGoldCorefClusters();
        }
        if (Constants.PRINT_CONLL_OUTPUT || corefSystem.replicateCoNLL) {
            // Not doing coref - print conll output here
            printConllOutput(document, writerGold, true);
            printConllOutput(document, writerPredicted, false);
        }
        // run mention detection only
        if (Constants.SKIP_COREF) {
            continue;
        }
        // Do Coreference Resolution
        corefSystem.coref(document);
        if (corefSystem.doScore()) {
            //Identifying possible coreferring mentions in the corpus along with any recall/precision errors with gold corpus
            corefSystem.printTopK(logger, document, corefSystem.semantics);
            logger.fine("pairwise score for this doc: ");
            corefSystem.scoreSingleDoc.get(corefSystem.sieves.length - 1).printF1(logger);
            logger.fine("accumulated score: ");
            corefSystem.printF1(true);
            logger.fine("\n");
        }
        if (Constants.PRINT_CONLL_OUTPUT || corefSystem.replicateCoNLL) {
            printConllOutput(document, writerPredictedCoref, false, true);
        }
    }
    double finalScore = 0;
    if (Constants.PRINT_CONLL_OUTPUT || corefSystem.replicateCoNLL) {
        writerGold.close();
        writerPredicted.close();
        writerPredictedCoref.close();
        //if(props.containsKey(Constants.CONLL_SCORER)) {
        if (corefSystem.conllMentionEvalScript != null) {
            //        runConllEval(corefSystem.conllMentionEvalScript, conllOutputMentionGoldFile, conllOutputMentionPredictedFile, conllMentionEvalFile, conllMentionEvalErrFile);
            String summary = getConllEvalSummary(corefSystem.conllMentionEvalScript, conllOutputMentionGoldFile, conllOutputMentionPredictedFile);
            logger.info("\nCONLL EVAL SUMMARY (Before COREF)");
            printScoreSummary(summary, logger, false);
            if (!Constants.SKIP_COREF) {
                //          runConllEval(corefSystem.conllMentionEvalScript, conllOutputMentionGoldFile, conllOutputMentionCorefPredictedFile, conllMentionCorefEvalFile, conllMentionCorefEvalErrFile);
                summary = getConllEvalSummary(corefSystem.conllMentionEvalScript, conllOutputMentionGoldFile, conllOutputMentionCorefPredictedFile);
                logger.info("\nCONLL EVAL SUMMARY (After COREF)");
                printScoreSummary(summary, logger, true);
                printFinalConllScore(summary);
                if (corefSystem.optimizeConllScore) {
                    finalScore = getFinalConllScore(summary, corefSystem.optimizeMetricType, corefSystem.optimizeSubScoreType.toString());
                }
            }
        }
    }
    if (!corefSystem.optimizeConllScore && corefSystem.doScore()) {
        finalScore = corefSystem.getFinalScore(corefSystem.optimizeMetricType, corefSystem.optimizeSubScoreType);
    }
    String scoresFile = props.getProperty(Constants.SCORE_FILE_PROP);
    if (scoresFile != null) {
        PrintWriter pw = IOUtils.getPrintWriter(scoresFile);
        pw.println((new DecimalFormat("#.##")).format(finalScore));
        pw.close();
    }
    if (corefSystem.optimizeSieves) {
        logger.info("Final reported score for sieve optimization " + corefSystem.optimizeScoreType + " : " + finalScore);
    }
    return finalScore;
}

Example 97 with DecimalFormat

use of java.text.DecimalFormat in project CoreNLP by stanfordnlp.

the class CRFClassifier method printLabelValue.

public void printLabelValue(List<IN> document) {
    if (flags.useReverse) {
        Collections.reverse(document);
    }
    NumberFormat nf = new DecimalFormat();
    List<String> classes = new ArrayList<>();
    for (int i = 0; i < classIndex.size(); i++) {
        classes.add(classIndex.get(i));
    }
    String[] columnHeaders = classes.toArray(new String[classes.size()]);
    // log.info("docSize:"+docSize);
    for (int j = 0; j < document.size(); j++) {
        System.out.println("--== " + document.get(j).get(CoreAnnotations.TextAnnotation.class) + " ==--");
        List<String[]> lines = new ArrayList<>();
        List<String> rowHeaders = new ArrayList<>();
        List<String> line = new ArrayList<>();
        for (int p = 0; p < labelIndices.size(); p++) {
            if (j + p >= document.size()) {
                continue;
            }
            CRFDatum<List<String>, CRFLabel> d = makeDatum(document, j + p, featureFactories);
            List<List<String>> features = d.asFeatures();
            for (int k = p, fSize = features.size(); k < fSize; k++) {
                Collection<String> cliqueFeatures = features.get(k);
                for (String feature : cliqueFeatures) {
                    int index = featureIndex.indexOf(feature);
                    if (index >= 0) {
                        // line.add(feature+"["+(-p)+"]");
                        rowHeaders.add(feature + '[' + (-p) + ']');
                        double[] values = new double[labelIndices.get(0).size()];
                        for (CRFLabel label : labelIndices.get(k)) {
                            int[] l = label.getLabel();
                            double v = weights[index][labelIndices.get(k).indexOf(label)];
                            values[l[l.length - 1 - p]] += v;
                        }
                        for (double value : values) {
                            line.add(nf.format(value));
                        }
                        lines.add(line.toArray(new String[line.size()]));
                        line = new ArrayList<>();
                    }
                }
            }
            // lines.add(Collections.<String>emptyList());
            System.out.println(StringUtils.makeTextTable(lines.toArray(new String[lines.size()][0]), rowHeaders.toArray(new String[rowHeaders.size()]), columnHeaders, 0, 1, true));
            System.out.println();
        }
    // log.info(edu.stanford.nlp.util.StringUtils.join(lines,"\n"));
    }
    if (flags.useReverse) {
        Collections.reverse(document);
    }
}

Example 98 with DecimalFormat

use of java.text.DecimalFormat in project CoreNLP by stanfordnlp.

the class CreateClauseDataset method processDirectory.

/**
   * Process all the trees in the given directory. For example, the WSJ section of the Penn Treebank.
   *
   * @param name The name of the directory we are processing.
   * @param directory The directory we are processing.
   * @return A dataset of subject/object pairs in the trees in the directory.
   *         This is a list of sentences, such that each sentence has a collection of pairs of spans.
   *         Each pair of spans is a subject/object span pair that constitutes a valid extraction.
   * @throws IOException
   */
private static List<Pair<CoreMap, Collection<Pair<Span, Span>>>> processDirectory(String name, File directory) throws IOException {
    forceTrack("Processing " + name);
    // Prepare the files to iterate over
    Iterable<File> files = IOUtils.iterFilesRecursive(directory, "mrg");
    Tree tree;
    int numTreesProcessed = 0;
    List<Pair<CoreMap, Collection<Pair<Span, Span>>>> trainingData = new ArrayList<>(1024);
    // Iterate over the files
    for (File file : files) {
        //      log(file);
        TreeReader reader = new PennTreeReader(IOUtils.readerFromFile(file));
        while ((tree = reader.readTree()) != null) {
            try {
                // Prepare the tree
                tree.indexSpans();
                tree.setSpans();
                // Get relevant information from sentence
                List<CoreLabel> tokens = tree.getLeaves().stream().map(leaf -> (CoreLabel) leaf.label()).collect(Collectors.toList());
                SemanticGraph graph = parse(tree);
                Map<Integer, Span> targets = findTraceTargets(tree);
                Map<Integer, Integer> sources = findTraceSources(tree);
                // Create a sentence object
                CoreMap sentence = new ArrayCoreMap(4) {

                    {
                        set(CoreAnnotations.TokensAnnotation.class, tokens);
                        set(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class, graph);
                        set(SemanticGraphCoreAnnotations.EnhancedDependenciesAnnotation.class, graph);
                        set(SemanticGraphCoreAnnotations.EnhancedPlusPlusDependenciesAnnotation.class, graph);
                    }
                };
                natlog.doOneSentence(null, sentence);
                // Generate training data
                Collection<Pair<Span, Span>> trainingDataFromSentence = subjectObjectPairs(graph, tokens, targets, sources);
                trainingData.add(Pair.makePair(sentence, trainingDataFromSentence));
                // Debug print
                numTreesProcessed += 1;
                if (numTreesProcessed % 100 == 0) {
                    log("[" + new DecimalFormat("00000").format(numTreesProcessed) + "] " + countDatums(trainingData) + " known extractions");
                }
            } catch (Throwable t) {
                t.printStackTrace();
            }
        }
    }
    // End
    log("" + numTreesProcessed + " trees processed yielding " + countDatums(trainingData) + " known extractions");
    endTrack("Processing " + name);
    return trainingData;
}

Example 99 with DecimalFormat

use of java.text.DecimalFormat in project CoreNLP by stanfordnlp.

the class StochasticDiffFunctionTester method arrayToFile.

public void arrayToFile(double[] thisArray, String fileName) {
    PrintWriter file = null;
    NumberFormat nf = new DecimalFormat("0.000E0");
    try {
        file = new PrintWriter(new FileOutputStream(fileName), true);
    } catch (IOException e) {
        log.info("Caught IOException outputing List to file: " + e.getMessage());
        System.exit(1);
    }
    for (double element : thisArray) {
        file.print(nf.format(element) + "  ");
    }
    file.close();
}

Example 100 with DecimalFormat

use of java.text.DecimalFormat in project CoreNLP by stanfordnlp.

the class Util method dumpAccuracy.

/**
   * A helper function for dumping the accuracy of the trained classifier.
   *
   * @param classifier The classifier to evaluate.
   * @param dataset The dataset to evaluate the classifier on.
   */
public static void dumpAccuracy(Classifier<ClauseSplitter.ClauseClassifierLabel, String> classifier, GeneralDataset<ClauseSplitter.ClauseClassifierLabel, String> dataset) {
    DecimalFormat df = new DecimalFormat("0.00%");
    log("size:         " + dataset.size());
    log("split count:  " + StreamSupport.stream(dataset.spliterator(), false).filter(x -> x.label() == ClauseSplitter.ClauseClassifierLabel.CLAUSE_SPLIT).collect(Collectors.toList()).size());
    log("interm count: " + StreamSupport.stream(dataset.spliterator(), false).filter(x -> x.label() == ClauseSplitter.ClauseClassifierLabel.CLAUSE_INTERM).collect(Collectors.toList()).size());
    Pair<Double, Double> pr = classifier.evaluatePrecisionAndRecall(dataset, ClauseSplitter.ClauseClassifierLabel.CLAUSE_SPLIT);
    log("p  (split):   " + df.format(pr.first));
    log("r  (split):   " + df.format(pr.second));
    log("f1 (split):   " + df.format(2 * pr.first * pr.second / (pr.first + pr.second)));
    pr = classifier.evaluatePrecisionAndRecall(dataset, ClauseSplitter.ClauseClassifierLabel.CLAUSE_INTERM);
    log("p  (interm):  " + df.format(pr.first));
    log("r  (interm):  " + df.format(pr.second));
    log("f1 (interm):  " + df.format(2 * pr.first * pr.second / (pr.first + pr.second)));
}