Example usage for edu.stanford.nlp.classify SVMLightClassifier setPlatt

Introduction

In this page you can find the example usage for edu.stanford.nlp.classify SVMLightClassifier setPlatt.

Prototype

public void setPlatt(LinearClassifier<L, L> platt) 

Source Link

Usage

From source file:gr.aueb.cs.nlp.wordtagger.classifier.SVMWindows64Factory.java

License:Open Source License

public SVMLightClassifier<L, F> trainClassifierBasic(GeneralDataset<L, F> dataset) {
    Index<L> labelIndex = dataset.labelIndex();
    Index<F> featureIndex = dataset.featureIndex;
    boolean multiclass = (dataset.numClasses() > 2);
    try {//from ww w  . j  a v  a2 s .  c o m

        // this is the file that the model will be saved to
        File modelFile = File.createTempFile("svm-", ".model");
        if (deleteTempFilesOnExit) {
            modelFile.deleteOnExit();
        }

        // this is the file that the svm light formated dataset
        // will be printed to
        File dataFile = File.createTempFile("svm-", ".data");
        if (deleteTempFilesOnExit) {
            dataFile.deleteOnExit();
        }

        // print the dataset
        PrintWriter pw = new PrintWriter(new FileWriter(dataFile));
        dataset.printSVMLightFormat(pw);
        pw.close();

        // -v 0 makes it not verbose
        // -m 400 gives it a larger cache, for faster training
        String cmd = (multiclass ? svmStructLearn : (useSVMPerf ? svmPerfLearn : svmLightLearn)) + " -v "
                + svmLightVerbosity + " -m 5000 -w 3 -t 0 -g 7 ";

        // set the value of C if we have one specified
        if (C > 0.0)
            cmd = cmd + " -c " + C + " "; // C value
        else if (useSVMPerf)
            cmd = cmd + " -c " + 0.01 + " "; //It's required to specify this parameter for SVM perf

        // Alpha File
        if (useAlphaFile) {
            File newAlphaFile = File.createTempFile("svm-", ".alphas");
            if (deleteTempFilesOnExit) {
                newAlphaFile.deleteOnExit();
            }
            cmd = cmd + " -a " + newAlphaFile.getAbsolutePath();
            if (alphaFile != null) {
                cmd = cmd + " -y " + alphaFile.getAbsolutePath();
            }
            alphaFile = newAlphaFile;
        }

        // File and Model Data
        cmd = cmd + " " + dataFile.getAbsolutePath() + " " + modelFile.getAbsolutePath();

        if (verbose)
            System.err.println("<< " + cmd + " >>");

        /*Process p = Runtime.getRuntime().exec(cmd);
                
        p.waitFor();
                
        if (p.exitValue() != 0) throw new RuntimeException("Error Training SVM Light exit value: " + p.exitValue());
        p.destroy();   */
        SystemUtils.run(new ProcessBuilder(whitespacePattern.split(cmd)), new PrintWriter(System.err),
                new PrintWriter(System.err));

        if (doEval) {
            File predictFile = File.createTempFile("svm-", ".pred");
            if (deleteTempFilesOnExit) {
                predictFile.deleteOnExit();
            }
            String evalCmd = (multiclass ? svmStructClassify
                    : (useSVMPerf ? svmPerfClassify : svmLightClassify)) + " " + dataFile.getAbsolutePath()
                    + " " + modelFile.getAbsolutePath() + " " + predictFile.getAbsolutePath();
            if (verbose)
                System.err.println("<< " + evalCmd + " >>");
            SystemUtils.run(new ProcessBuilder(whitespacePattern.split(evalCmd)), new PrintWriter(System.err),
                    new PrintWriter(System.err));
        }
        // read in the model file
        Pair<Double, ClassicCounter<Integer>> weightsAndThresh = readModel(modelFile, multiclass);
        double threshold = weightsAndThresh.first();
        ClassicCounter<Pair<F, L>> weights = convertWeights(weightsAndThresh.second(), featureIndex, labelIndex,
                multiclass);
        ClassicCounter<L> thresholds = new ClassicCounter<L>();
        if (!multiclass) {
            thresholds.setCount(labelIndex.get(0), -threshold);
            thresholds.setCount(labelIndex.get(1), threshold);
        }
        SVMLightClassifier<L, F> classifier = new SVMLightClassifier<L, F>(weights, thresholds);
        if (doEval) {
            File predictFile = File.createTempFile("svm-", ".pred2");
            if (deleteTempFilesOnExit) {
                predictFile.deleteOnExit();
            }
            PrintWriter pw2 = new PrintWriter(predictFile);
            NumberFormat nf = NumberFormat.getNumberInstance();
            nf.setMaximumFractionDigits(5);
            for (Datum<L, F> datum : dataset) {
                Counter<L> scores = classifier.scoresOf(datum);
                pw2.println(Counters.toString(scores, nf));
            }
            pw2.close();
        }

        if (useSigmoid) {
            if (verbose)
                System.out.print("fitting sigmoid...");
            classifier.setPlatt(fitSigmoid(classifier, dataset));
            if (verbose)
                System.out.println("done");
        }

        return classifier;
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

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