Example usage for weka.classifiers.evaluation Evaluation toClassDetailsString

Introduction

In this page you can find the example usage for weka.classifiers.evaluation Evaluation toClassDetailsString.

Prototype

public String toClassDetailsString() throws Exception 

Source Link

Document

Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.

Usage

From source file:gov.va.chir.tagline.TagLineEvaluator.java

License:Open Source License

public void evaluate(final ClassifierType type, final String... options) throws Exception {
    Classifier model = null;// ww w.j a  va2s  . com

    if (type == null) {
        throw new IllegalArgumentException("Classifier type must be specified");
    }

    if (type.equals(ClassifierType.J48)) {
        model = new J48();
    } else if (type.equals(ClassifierType.LMT)) {
        model = new LMT();
    } else if (type.equals(ClassifierType.RandomForest)) {
        model = new RandomForest();
    } else if (type.equals(ClassifierType.SVM)) {
        model = new LibSVM();
    } else {
        throw new IllegalArgumentException(String.format("Classifier type not supported (%s)", type));
    }

    if (model != null) {
        // Set classifier options
        if (options != null && options.length > 0) {
            if (model instanceof AbstractClassifier) {
                ((AbstractClassifier) model).setOptions(options);
            }
        }

        fc.setClassifier(model);

        final Attribute attrDocId = instances.attribute(DatasetUtil.DOC_ID);

        if (attrDocId == null) {
            throw new IllegalStateException(String.format("%s attribute must exist", DatasetUtil.DOC_ID));
        }
        final List<Set<Object>> foldDocIds = getFoldDocIds(attrDocId);

        final RemoveWithValues rmv = new RemoveWithValues();

        // RemoveWithValues filter is not zero-based!
        rmv.setAttributeIndex(String.valueOf(attrDocId.index() + 1));
        rmv.setModifyHeader(false);

        final Evaluation eval = new Evaluation(instances);

        // Perform cross-validation
        for (int i = 0; i < numFolds; i++) {
            rmv.setNominalIndicesArr(getAttributeIndexValues(attrDocId, foldDocIds.get(i)));

            rmv.setInvertSelection(false);
            rmv.setInputFormat(instances); // Must be called AFTER all options
            final Instances train = Filter.useFilter(instances, rmv);

            rmv.setInvertSelection(true);
            rmv.setInputFormat(instances); // Must be called AFTER all options
            final Instances test = Filter.useFilter(instances, rmv);

            fc.buildClassifier(train);
            eval.evaluateModel(fc, test);
        }

        evaluationSummary = String.format("%s%s%s%s%s", eval.toSummaryString(),
                System.getProperty("line.separator"), eval.toMatrixString(),
                System.getProperty("line.separator"), eval.toClassDetailsString());
    }
}

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