Example usage for weka.core Instance setValue

Introduction

In this page you can find the example usage for weka.core Instance setValue.

Prototype

public void setValue(Attribute att, String value);

Source Link

Document

Sets a value of an nominal or string attribute to the given value.

Usage

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

License:Open Source License

protected MultiLabelOutput makePredictionInternal(Instance instance) throws Exception {
    double[] confidences = new double[numLabels];
    boolean[] bipartition = new boolean[numLabels];

    Instance newInstance = pt6Trans.transformInstance(instance);
    //calculate confidences
    //debug(instance.toString());
    for (int i = 0; i < numLabels; i++) {
        newInstance.setDataset(transformed);
        newInstance.setValue(newInstance.numAttributes() - 2,
                instance.dataset().attribute(labelIndices[i]).name());
        //debug(newInstance.toString());
        double[] temp = baseClassifier.distributionForInstance(newInstance);
        //debug(temp.toString());
        confidences[i] = temp[transformed.classAttribute().indexOfValue("1")];
        //debug("" + confidences[i]);
        bipartition[i] = temp[transformed.classAttribute().indexOfValue("1")] >= temp[transformed
                .classAttribute().indexOfValue("0")] ? true : false;
        //debug("" + bipartition[i]);
    }/*  ww  w .  ja v a  2s  .co  m*/

    MultiLabelOutput mlo = new MultiLabelOutput(bipartition, confidences);
    return mlo;
}

---

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

License:Open Source License

/**
 * Attaches an index attribute at the beginning of each instance
 *
 * @param original//  www .  j av a2  s  .c  om
 * @return
 */
protected Instances attachIndexes(Instances original) {

    ArrayList<Attribute> attributes = new ArrayList<Attribute>(original.numAttributes() + 1);

    for (int i = 0; i < original.numAttributes(); i++) {
        attributes.add(original.attribute(i));
    }
    // Add attribute for holding the index at the beginning.
    attributes.add(0, new Attribute("Index"));
    Instances transformed = new Instances("Meta format", attributes, 0);
    for (int i = 0; i < original.numInstances(); i++) {
        Instance newInstance;
        newInstance = (Instance) original.instance(i).copy();
        newInstance.setDataset(null);
        newInstance.insertAttributeAt(0);
        newInstance.setValue(0, i);

        transformed.add(newInstance);
    }

    transformed.setClassIndex(original.classIndex() + 1);
    return transformed;
}

---

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;
                if (trainingData.instance(i) instanceof SparseInstance) {
                    tempInstance = new SparseInstance(trainingData.instance(i));
                } else {
                    tempInstance = new DenseInstance(trainingData.instance(i));
                }/*from www  . j ava2s  .c  om*/

                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.TwoStageClassifierChainArchitecture.java

License:Open Source License

private Instance modifyDenseInstance(Instance instance, double[] confidences) {
    Instance modifiedIns = new DenseInstance(instance);
    for (int i = confidences.length - 1; i >= 0; i--) {
        modifiedIns.insertAttributeAt(0);
        modifiedIns.setValue(0, confidences[i]);
    }/*  w  w w .  j  av a2s .c o m*/
    return modifiedIns;
}

---

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;
                if (trainingData.instance(i) instanceof SparseInstance) {
                    tempInstance = new SparseInstance(trainingData.instance(i));
                } else {
                    tempInstance = new DenseInstance(trainingData.instance(i));
                }//from  w w  w.jav a2s  .c  o  m

                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

public Instance modifyDenseInstance(Instance ins, double value1, double value2) {
    Instance modifiedIns = new DenseInstance(ins);
    modifiedIns.insertAttributeAt(0);//from  ww w  .j  av  a2  s. c  o  m
    modifiedIns.setValue(0, value1);
    modifiedIns.insertAttributeAt(0);
    modifiedIns.setValue(0, value2);
    return modifiedIns;
}

---

From source file:mulan.data.ConverterLibSVM.java

License:Open Source License

/**
 * Converts a multi-label dataset from LibSVM format to the format
 * that is compatible with Mulan. It constructs one ARFF and one XML file. 
 *
 * @param path the directory that contains the source file and will contain 
 * the target files// w  ww . ja  v a2 s . c  om
 * @param sourceFilename the name of the source file
 * @param relationName the relation name of the arff file that will be 
 * constructed
 * @param targetFilestem the filestem for the target files (.arff and .xml)
 */
public static void convertFromLibSVM(String path, String sourceFilename, String targetFilestem,
        String relationName) {
    BufferedReader aReader = null;
    BufferedWriter aWriter = null;

    int numLabels = 0;
    int numAttributes = 0;
    int numInstances = 0;
    double meanParsedAttributes = 0;

    // Calculate number of labels and attributes

    String Line = null;
    try {
        aReader = new BufferedReader(new FileReader(path + sourceFilename));

        while ((Line = aReader.readLine()) != null) {
            numInstances++;

            StringTokenizer strTok = new StringTokenizer(Line, " ");
            while (strTok.hasMoreTokens()) {
                String token = strTok.nextToken();

                if (token.indexOf(":") == -1) {
                    // parse label info
                    StringTokenizer labelTok = new StringTokenizer(token, ",");
                    while (labelTok.hasMoreTokens()) {
                        String strLabel = labelTok.nextToken();
                        int intLabel = Integer.parseInt(strLabel);
                        if (intLabel > numLabels) {
                            numLabels = intLabel;
                        }
                    }
                } else {
                    // parse attribute info
                    meanParsedAttributes++;
                    StringTokenizer attrTok = new StringTokenizer(token, ":");
                    String strAttrIndex = attrTok.nextToken();
                    int intAttrIndex = Integer.parseInt(strAttrIndex);
                    if (intAttrIndex > numAttributes) {
                        numAttributes = intAttrIndex;
                    }
                }
            }
        }

        numLabels++;

        System.out.println("Number of attributes: " + numAttributes);
        System.out.println("Number of instances: " + numInstances);
        System.out.println("Number of classes: " + numLabels);

        System.out.println("Constructing XML file... ");
        LabelsMetaDataImpl meta = new LabelsMetaDataImpl();
        for (int label = 0; label < numLabels; label++) {
            meta.addRootNode(new LabelNodeImpl("Label" + (label + 1)));
        }

        String labelsFilePath = path + targetFilestem + ".xml";
        try {
            LabelsBuilder.dumpLabels(meta, labelsFilePath);
            System.out.println("Done!");
        } catch (LabelsBuilderException e) {
            File labelsFile = new File(labelsFilePath);
            if (labelsFile.exists()) {
                labelsFile.delete();
            }
            System.out.println("Construction of labels XML failed!");
        }

        meanParsedAttributes /= numInstances;
        boolean Sparse = false;
        if (meanParsedAttributes < numAttributes) {
            Sparse = true;
            System.out.println("Dataset is sparse.");
        }

        // Define Instances class to hold data
        ArrayList<Attribute> attInfo = new ArrayList<Attribute>(numAttributes + numLabels);
        Attribute[] att = new Attribute[numAttributes + numLabels];

        for (int i = 0; i < numAttributes; i++) {
            att[i] = new Attribute("Att" + (i + 1));
            attInfo.add(att[i]);
        }
        ArrayList<String> ClassValues = new ArrayList<String>(2);
        ClassValues.add("0");
        ClassValues.add("1");
        for (int i = 0; i < numLabels; i++) {
            att[numAttributes + i] = new Attribute("Label" + (i + 1), ClassValues);
            attInfo.add(att[numAttributes + i]);
        }

        // Re-read file and convert into multi-label arff
        int countInstances = 0;

        aWriter = new BufferedWriter(new FileWriter(path + targetFilestem + ".arff"));
        Instances data = new Instances(relationName, attInfo, 0);
        aWriter.write(data.toString());

        aReader = new BufferedReader(new FileReader(path + sourceFilename));

        while ((Line = aReader.readLine()) != null) {
            countInstances++;

            // set all  values to 0
            double[] attValues = new double[numAttributes + numLabels];
            Arrays.fill(attValues, 0);

            Instance tempInstance = new DenseInstance(1, attValues);
            tempInstance.setDataset(data);

            // separate class info from attribute info
            // ensure class info exists
            StringTokenizer strTok = new StringTokenizer(Line, " ");

            while (strTok.hasMoreTokens()) {
                String token = strTok.nextToken();

                if (token.indexOf(":") == -1) {
                    // parse label info
                    StringTokenizer labelTok = new StringTokenizer(token, ",");
                    while (labelTok.hasMoreTokens()) {
                        String strLabel = labelTok.nextToken();
                        int intLabel = Integer.parseInt(strLabel);
                        tempInstance.setValue(numAttributes + intLabel, 1);
                    }
                } else {
                    // parse attribute info
                    StringTokenizer AttrTok = new StringTokenizer(token, ":");
                    String strAttrIndex = AttrTok.nextToken();
                    String strAttrValue = AttrTok.nextToken();
                    tempInstance.setValue(Integer.parseInt(strAttrIndex) - 1, Double.parseDouble(strAttrValue));
                }
            }

            if (Sparse) {
                SparseInstance tempSparseInstance = new SparseInstance(tempInstance);
                aWriter.write(tempSparseInstance.toString() + "\n");
            } else {
                aWriter.write(tempInstance.toString() + "\n");
            }

        }
    } catch (IOException e) {
        e.printStackTrace();
    } finally {
        try {
            if (aReader != null) {
                aReader.close();
            }
            if (aWriter != null) {
                aWriter.close();
            }
        } catch (IOException ex) {
            ex.printStackTrace();
        }
    }
}

---

From source file:mulan.regressor.transformation.RegressorChainSimple.java

License:Open Source License

protected MultiLabelOutput makePredictionInternal(Instance instance) throws Exception {
    double[] scores = new double[numLabels];

    // create a new temporary instance so that the passed instance is not altered
    Instances dataset = instance.dataset();
    Instance tempInstance = DataUtils.createInstance(instance, instance.weight(), instance.toDoubleArray());

    for (int counter = 0; counter < numLabels; counter++) {
        dataset.setClassIndex(chain[counter]);
        tempInstance.setDataset(dataset);
        // find the appropriate position for that score in the scores array
        // i.e. which is the corresponding target
        int pos = 0;
        for (int i = 0; i < numLabels; i++) {
            if (chain[counter] == labelIndices[i]) {
                pos = i;//  w ww  .ja va2 s. c  o m
                break;
            }
        }
        scores[pos] = chainRegressors[counter].classifyInstance(tempInstance);
        tempInstance.setValue(chain[counter], scores[pos]);
    }

    MultiLabelOutput mlo = new MultiLabelOutput(scores, true);
    return mlo;
}

---

From source file:mulan.transformations.IncludeLabelsTransformation.java

License:Open Source License

/**
 *
 * @param mlData multi-label data/*from   w ww.j a  va  2s.  c  o  m*/
 * @return transformed instances
 * @throws Exception Potential exception thrown. To be handled in an upper level.
 */
public Instances transformInstances(MultiLabelInstances mlData) throws Exception {
    int numLabels = mlData.getNumLabels();
    labelIndices = mlData.getLabelIndices();

    // remove all labels
    Instances transformed = RemoveAllLabels.transformInstances(mlData);

    // add at the end an attribute with values the label names
    ArrayList<String> labelNames = new ArrayList<String>(numLabels);
    for (int counter = 0; counter < numLabels; counter++) {
        labelNames.add(mlData.getDataSet().attribute(labelIndices[counter]).name());
    }
    Attribute attrLabel = new Attribute("Label", labelNames);
    transformed.insertAttributeAt(attrLabel, transformed.numAttributes());

    // and at the end a binary attribute
    ArrayList<String> binaryValues = new ArrayList<String>(2);
    binaryValues.add("0");
    binaryValues.add("1");
    Attribute classAttr = new Attribute("Class", binaryValues);
    transformed.insertAttributeAt(classAttr, transformed.numAttributes());

    // add instances
    transformed = new Instances(transformed, 0);
    transformed.setClassIndex(transformed.numAttributes() - 1);
    Instances data = mlData.getDataSet();
    for (int instanceIndex = 0; instanceIndex < data.numInstances(); instanceIndex++) {
        for (int labelCounter = 0; labelCounter < numLabels; labelCounter++) {
            Instance temp;
            temp = RemoveAllLabels.transformInstance(data.instance(instanceIndex), labelIndices);
            temp.setDataset(null);
            temp.insertAttributeAt(temp.numAttributes());
            temp.insertAttributeAt(temp.numAttributes());
            temp.setDataset(transformed);
            temp.setValue(temp.numAttributes() - 2, (String) labelNames.get(labelCounter));
            if (data.attribute(labelIndices[labelCounter])
                    .value((int) data.instance(instanceIndex).value(labelIndices[labelCounter])).equals("1")) {
                temp.setValue(temp.numAttributes() - 1, "1");
            } else {
                temp.setValue(temp.numAttributes() - 1, "0");
            }
            transformed.add(temp);
        }
    }

    return transformed;
}

---

From source file:mulan.transformations.multiclass.Copy.java

License:Open Source License

/**
 * Transforms a multi-label instance to a list of single-label instances,
 * one for each of the labels that annotate the instance, by copying the
 * feature vector//w  w  w  .j  a  v  a  2  s. c om
 *
 * @param instance a multi-label instance
 * @return a list with the transformed single-label instances
 */
List<Instance> transformInstance(Instance instance) {
    List<Instance> result = new ArrayList<Instance>();
    for (int counter = 0; counter < numOfLabels; counter++) {
        if (instance.attribute(labelIndices[counter]).value((int) instance.value(labelIndices[counter]))
                .equals("1")) {
            Instance transformed = null;
            try {
                transformed = RemoveAllLabels.transformInstance(instance, labelIndices);
                transformed.setDataset(null);
                transformed.insertAttributeAt(transformed.numAttributes());
                transformed.setValue(transformed.numAttributes() - 1, counter);
            } catch (Exception ex) {
                Logger.getLogger(Copy.class.getName()).log(Level.SEVERE, null, ex);
            }
            result.add(transformed);
        }
    }
    return result;
}

---

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