Example usage for weka.classifiers AbstractClassifier makeCopies

List of usage examples for weka.classifiers AbstractClassifier makeCopies

Introduction

In this page you can find the example usage for weka.classifiers AbstractClassifier makeCopies.

Prototype

public static Classifier[] makeCopies(Classifier model, int num) throws Exception 

Source Link

Document

Creates a given number of deep copies of the given classifier using serialization.

Usage

From source file:mulan.classifier.transformation.TwoStageClassifierChainArchitecture.java

License:Open Source License

@Override
protected void buildInternal(MultiLabelInstances trainingSet) throws Exception {
    // Virtual label models
    debug("Building calibration label models");
    virtualLabelModels = new BinaryRelevance(getBaseClassifier());
    virtualLabelModels.setDebug(getDebug());
    virtualLabelModels.build(trainingSet);

    //Generate the chain: Test the same dataset
    MultiLabelInstances tempTrainingSet = GenerateChain(trainingSet);

    labelIndices = tempTrainingSet.getLabelIndices();
    featureIndices = tempTrainingSet.getFeatureIndices();

    // One-vs-one models
    numModels = ((numLabels) * (numLabels - 1)) / 2;
    oneVsOneModels = AbstractClassifier.makeCopies(getBaseClassifier(), numModels);
    nodata = new boolean[numModels];
    metaDataTest = new Instances[numModels];

    Instances trainingData = tempTrainingSet.getDataSet();

    int counter = 0;
    // Creation of one-vs-one models
    for (int label1 = 0; label1 < numLabels - 1; label1++) {
        // Attribute of label 1
        Attribute attrLabel1 = trainingData.attribute(labelIndices[label1]);
        for (int label2 = label1 + 1; label2 < numLabels; label2++) {
            debug("Building one-vs-one model " + (counter + 1) + "/" + numModels);
            // Attribute of label 2
            Attribute attrLabel2 = trainingData.attribute(labelIndices[label2]);

            // initialize training set
            Instances dataOneVsOne = new Instances(trainingData, 0);
            // filter out examples with no preference
            for (int i = 0; i < trainingData.numInstances(); i++) {
                Instance tempInstance;/*  www .j  a  v  a  2 s  . c om*/
                if (trainingData.instance(i) instanceof SparseInstance) {
                    tempInstance = new SparseInstance(trainingData.instance(i));
                } else {
                    tempInstance = new DenseInstance(trainingData.instance(i));
                }

                int nominalValueIndex;
                nominalValueIndex = (int) tempInstance.value(labelIndices[label1]);
                String value1 = attrLabel1.value(nominalValueIndex);
                nominalValueIndex = (int) tempInstance.value(labelIndices[label2]);
                String value2 = attrLabel2.value(nominalValueIndex);

                if (!value1.equals(value2)) {
                    tempInstance.setValue(attrLabel1, value1);
                    dataOneVsOne.add(tempInstance);
                }
            }

            // remove all labels apart from label1 and place it at the end
            Reorder filter = new Reorder();
            int numPredictors = trainingData.numAttributes() - numLabels;
            int[] reorderedIndices = new int[numPredictors + 1];

            System.arraycopy(featureIndices, 0, reorderedIndices, 0, numPredictors);
            reorderedIndices[numPredictors] = labelIndices[label1];
            filter.setAttributeIndicesArray(reorderedIndices);
            filter.setInputFormat(dataOneVsOne);
            dataOneVsOne = Filter.useFilter(dataOneVsOne, filter);
            //System.out.println(dataOneVsOne.toString());
            dataOneVsOne.setClassIndex(numPredictors);

            // build model label1 vs label2
            if (dataOneVsOne.size() > 0) {
                oneVsOneModels[counter].buildClassifier(dataOneVsOne);
            } else {
                nodata[counter] = true;
            }
            dataOneVsOne.delete();
            metaDataTest[counter] = dataOneVsOne;
            counter++;
        }
    }
}

From source file:mulan.classifier.transformation.TwoStagePrunedClassifierChainArchitecture.java

License:Open Source License

@Override
protected void buildInternal(MultiLabelInstances trainingSet) throws Exception {
    // Virtual label models
    debug("Building calibration label models");
    virtualLabelModels = new BinaryRelevance(getBaseClassifier());
    virtualLabelModels.setDebug(getDebug());
    virtualLabelModels.build(trainingSet);

    // One-vs-one models
    numModels = ((numLabels) * (numLabels - 1)) / 2;
    oneVsOneModels = AbstractClassifier.makeCopies(getBaseClassifier(), numModels);
    nodata = new boolean[numModels];
    metaDataTest = new Instances[numModels];

    ArrayList<MultiLabelOutput> predictions;
    predictions = predictLabels(trainingSet);

    int counter = 0;
    // Creation of one-vs-one models
    for (int label1 = 0; label1 < numLabels - 1; label1++) {
        for (int label2 = label1 + 1; label2 < numLabels; label2++) {
            //Generate the chain: Test the same dataset
            MultiLabelInstances tempTrainingSet = GenerateChain(trainingSet, label1, label2, predictions);

            Instances trainingData = tempTrainingSet.getDataSet();

            labelIndices = tempTrainingSet.getLabelIndices();
            featureIndices = tempTrainingSet.getFeatureIndices();

            // Attribute of label 1
            Attribute attrLabel1 = trainingData.attribute(labelIndices[label1]);

            debug("Building one-vs-one model " + (counter + 1) + "/" + numModels);
            // Attribute of label 2
            Attribute attrLabel2 = trainingData.attribute(labelIndices[label2]);

            // initialize training set
            Instances dataOneVsOne = new Instances(trainingData, 0);
            // filter out examples with no preference
            for (int i = 0; i < trainingData.numInstances(); i++) {
                Instance tempInstance;//from   w w  w . j  a  va  2 s. c o m
                if (trainingData.instance(i) instanceof SparseInstance) {
                    tempInstance = new SparseInstance(trainingData.instance(i));
                } else {
                    tempInstance = new DenseInstance(trainingData.instance(i));
                }

                int nominalValueIndex;
                nominalValueIndex = (int) tempInstance.value(labelIndices[label1]);
                String value1 = attrLabel1.value(nominalValueIndex);
                nominalValueIndex = (int) tempInstance.value(labelIndices[label2]);
                String value2 = attrLabel2.value(nominalValueIndex);

                if (!value1.equals(value2)) {
                    tempInstance.setValue(attrLabel1, value1);
                    dataOneVsOne.add(tempInstance);
                }
            }

            // remove all labels apart from label1 and place it at the end
            Reorder filter = new Reorder();
            int numPredictors = trainingData.numAttributes() - numLabels;
            int[] reorderedIndices = new int[numPredictors + 1];
            System.arraycopy(featureIndices, 0, reorderedIndices, 0, numPredictors);
            reorderedIndices[numPredictors] = labelIndices[label1];
            filter.setAttributeIndicesArray(reorderedIndices);
            filter.setInputFormat(dataOneVsOne);
            dataOneVsOne = Filter.useFilter(dataOneVsOne, filter);
            //System.out.println(dataOneVsOne.toString());
            dataOneVsOne.setClassIndex(numPredictors);

            // build model label1 vs label2
            if (dataOneVsOne.size() > 0) {
                oneVsOneModels[counter].buildClassifier(dataOneVsOne);
            } else {
                nodata[counter] = true;
            }
            dataOneVsOne.delete();
            metaDataTest[counter] = dataOneVsOne;
            counter++;
        }
    }
}