Example usage for weka.core Instances insertAttributeAt

Introduction

In this page you can find the example usage for weka.core Instances insertAttributeAt.

Prototype

public void insertAttributeAt(Attribute att, int position) 

Source Link

Document

Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.

Usage

From source file:moa.streams.generators.multilabel.MetaMultilabelGenerator.java

License:Open Source License

/**
 * GenerateMultilabelHeader./*from   www .  j  a  va  2s . co m*/
 *
 * @param   si   single-label Instances
 */
protected MultilabelInstancesHeader generateMultilabelHeader(Instances si) {
    Instances mi = new Instances(si, 0, 0);
    mi.setClassIndex(-1);
    mi.deleteAttributeAt(mi.numAttributes() - 1);
    FastVector bfv = new FastVector();
    bfv.addElement("0");
    bfv.addElement("1");
    for (int i = 0; i < this.m_L; i++) {
        mi.insertAttributeAt(new Attribute("class" + i, bfv), i);
    }
    this.multilabelStreamTemplate = mi;
    this.multilabelStreamTemplate.setRelationName("SYN_Z" + this.labelCardinalityOption.getValue() + "L"
            + this.m_L + "X" + m_A + "S" + metaRandomSeedOption.getValue() + ": -C " + this.m_L);
    this.multilabelStreamTemplate.setClassIndex(this.m_L);
    return new MultilabelInstancesHeader(multilabelStreamTemplate, m_L);
}

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From source file:mulan.transformations.IncludeLabelsTransformation.java

License:Open Source License

/**
 *
 * @param mlData multi-label data//from   w ww .  jav  a2 s.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;
}

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From source file:mulan.transformations.LabelPowersetTransformation.java

License:Open Source License

public Instances transformInstances(MultiLabelInstances mlData) throws Exception {
    Instances data = mlData.getDataSet();
    int numLabels = mlData.getNumLabels();
    int[] labelIndices = mlData.getLabelIndices();

    Instances newData = null;

    // gather distinct label combinations
    HashSet<LabelSet> labelSets = new HashSet<LabelSet>();
    int numInstances = data.numInstances();
    for (int i = 0; i < numInstances; i++) {
        // construct labelset
        double[] dblLabels = new double[numLabels];
        for (int j = 0; j < numLabels; j++) {
            int index = labelIndices[j];
            dblLabels[j] = Double.parseDouble(data.attribute(index).value((int) data.instance(i).value(index)));
        }/*from  w  w w .j av  a 2 s  . c  o  m*/
        LabelSet labelSet = new LabelSet(dblLabels);

        // add labelset if not already present
        labelSets.add(labelSet);
    }

    // create class attribute
    ArrayList<String> classValues = new ArrayList<String>(labelSets.size());
    for (LabelSet subset : labelSets) {
        classValues.add(subset.toBitString());
    }
    Attribute newClass = new Attribute("class", classValues);

    // remove all labels
    newData = RemoveAllLabels.transformInstances(data, labelIndices);

    // add new class attribute
    newData.insertAttributeAt(newClass, newData.numAttributes());
    newData.setClassIndex(newData.numAttributes() - 1);

    // add class values
    for (int i = 0; i < newData.numInstances(); i++) {
        //System.out.println(newData.instance(i).toString());
        String strClass = "";
        for (int j = 0; j < numLabels; j++) {
            int index = labelIndices[j];
            strClass = strClass + data.attribute(index).value((int) data.instance(i).value(index));
        }
        //System.out.println(strClass);
        newData.instance(i).setClassValue(strClass);
    }
    transformedFormat = new Instances(newData, 0);
    return newData;
}

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

License:Open Source License

public Instances transformInstances(MultiLabelInstances mlData) throws Exception {
    labelIndices = mlData.getLabelIndices();
    numOfLabels = mlData.getNumLabels();
    Instances data = mlData.getDataSet();

    Instances transformed = new Instances(mlData.getDataSet(), 0);

    // delete all labels
    transformed = RemoveAllLabels.transformInstances(transformed, labelIndices);

    // add single label attribute
    ArrayList<String> classValues = new ArrayList<String>(numOfLabels);
    for (int x = 0; x < numOfLabels; x++) {
        classValues.add("Class" + (x + 1));
    }//w  w  w  .  j  av  a2  s. c  o m
    Attribute newClass = new Attribute("Class", classValues);
    transformed.insertAttributeAt(newClass, transformed.numAttributes());
    transformed.setClassIndex(transformed.numAttributes() - 1);

    for (int instanceIndex = 0; instanceIndex < data.numInstances(); instanceIndex++) {
        //System.out.println(data.instance(instanceIndex).toString());
        List<Instance> result = transformInstance(data.instance(instanceIndex));
        for (Instance instance : result) {
            //System.out.println(instance.toString());
            transformed.add(instance);
            //System.out.println(transformed.instance(transformed.numInstances()-1));
        }
    }
    return transformed;
}

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From source file:mulan.transformations.PT6Transformation.java

License:Open Source License

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());
    }/*www  .  ja va 2s. c  o m*/
    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;
}

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From source file:myJ48.MyJ48.java

public Instances NumericToNominalByThreshold(Instances numericSet, int idx_attribute, double threshold)
        throws Exception {
    double[] values;
    Instances NominalizedSet = new Instances(numericSet);
    //System.out.println("number of instances: " + NominalizedSet.numInstances());
    values = numericSet.attributeToDoubleArray(idx_attribute);
    List<String> nominalValue = new ArrayList<String>();
    nominalValue.add("low");
    nominalValue.add("high");
    Attribute nominalAttrib = new Attribute(numericSet.attribute(idx_attribute).name() + "_nominal",
            nominalValue);// ww  w .  j  a  v a2 s. c  o m
    NominalizedSet.insertAttributeAt(nominalAttrib, idx_attribute);
    for (int i = 0; i < values.length; i++) {
        if (values[i] <= threshold) {
            NominalizedSet.instance(i).setValue(idx_attribute, "low");
        } else {
            NominalizedSet.instance(i).setValue(idx_attribute, "high");
        }
    }
    String[] options = { "-R", String.valueOf(idx_attribute + 2) };
    Filter remove = (Filter) Class.forName("weka.filters.unsupervised.attribute.Remove").newInstance();
    ((OptionHandler) remove).setOptions(options);
    remove.setInputFormat(NominalizedSet);
    NominalizedSet = Filter.useFilter(NominalizedSet, remove);

    return NominalizedSet;
}

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From source file:org.jaqpot.algorithm.resource.WekaMLR.java

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {

    try {//from   w  w  w  .  j av  a2s.c  o  m
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST).entity(
                    ErrorReportFactory.badRequest("Dataset is empty", "Cannot train model on empty dataset"))
                    .build();
        }

        String base64Model = (String) request.getRawModel();
        byte[] modelBytes = Base64.getDecoder().decode(base64Model);
        ByteArrayInputStream bais = new ByteArrayInputStream(modelBytes);
        ObjectInput in = new ObjectInputStream(bais);
        WekaModel model = (WekaModel) in.readObject();

        Classifier classifier = model.getClassifier();
        Instances data = InstanceUtils.createFromDataset(request.getDataset());
        List<String> additionalInfo = (List) request.getAdditionalInfo();
        String dependentFeature = additionalInfo.get(0);
        String dependentFeatureName = additionalInfo.get(1);
        data.insertAttributeAt(new Attribute(dependentFeature), data.numAttributes());
        List<LinkedHashMap<String, Object>> predictions = new ArrayList<>();
        //            data.stream().forEach(instance -> {
        //                try {
        //                    double prediction = classifier.classifyInstance(instance);
        //                    Map<String, Object> predictionMap = new HashMap<>();
        //                    predictionMap.put("Weka MLR prediction of " + dependentFeature, prediction);
        //                    predictions.add(predictionMap);
        //                } catch (Exception ex) {
        //                    Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
        //                }
        //            });

        for (int i = 0; i < data.numInstances(); i++) {
            Instance instance = data.instance(i);
            try {
                double prediction = classifier.classifyInstance(instance);
                LinkedHashMap<String, Object> predictionMap = new LinkedHashMap<>();
                predictionMap.put("Weka MLR prediction of " + dependentFeatureName, prediction);
                predictions.add(predictionMap);
            } catch (Exception ex) {
                Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
                return Response.status(Response.Status.BAD_REQUEST).entity(
                        ErrorReportFactory.badRequest("Error while gettting predictions.", ex.getMessage()))
                        .build();
            }
        }

        PredictionResponse response = new PredictionResponse();
        response.setPredictions(predictions);
        return Response.ok(response).build();
    } catch (Exception ex) {
        Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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From source file:org.jaqpot.algorithm.resource.WekaPLS.java

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {
    try {/*w w w.  j av  a2s . co m*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response
                    .status(Response.Status.BAD_REQUEST).entity(ErrorReportFactory
                            .badRequest("Dataset is empty", "Cannot make predictions on empty dataset"))
                    .build();
        }

        String base64Model = (String) request.getRawModel();
        byte[] modelBytes = Base64.getDecoder().decode(base64Model);
        ByteArrayInputStream bais = new ByteArrayInputStream(modelBytes);
        ObjectInput in = new ObjectInputStream(bais);
        WekaModel model = (WekaModel) in.readObject();

        Classifier classifier = model.getClassifier();
        Instances data = InstanceUtils.createFromDataset(request.getDataset());
        String dependentFeature = (String) request.getAdditionalInfo();
        data.insertAttributeAt(new Attribute(dependentFeature), data.numAttributes());
        data.setClass(data.attribute(dependentFeature));
        List<LinkedHashMap<String, Object>> predictions = new ArrayList<>();
        //            data.stream().forEach(instance -> {
        //                try {
        //                    double prediction = classifier.classifyInstance(instance);
        //                    Map<String, Object> predictionMap = new HashMap<>();
        //                    predictionMap.put("Weka PLS prediction of " + dependentFeature, prediction);
        //                    predictions.add(predictionMap);
        //                } catch (Exception ex) {
        //                    Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        //                }
        //            });

        for (int i = 0; i < data.numInstances(); i++) {
            Instance instance = data.instance(i);
            try {
                double prediction = classifier.classifyInstance(instance);
                LinkedHashMap<String, Object> predictionMap = new LinkedHashMap<>();
                predictionMap.put("Weka PLS prediction of " + dependentFeature, prediction);
                predictions.add(predictionMap);
            } catch (Exception ex) {
                Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
                return Response.status(Response.Status.BAD_REQUEST).entity(
                        ErrorReportFactory.badRequest("Error while gettting predictions.", ex.getMessage()))
                        .build();
            }
        }

        PredictionResponse response = new PredictionResponse();
        response.setPredictions(predictions);
        return Response.ok(response).build();
    } catch (IOException | ClassNotFoundException ex) {
        Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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From source file:org.jaqpot.algorithm.resource.WekaRBF.java

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {

    try {/*from   w w w .java2 s .  c  o m*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response
                    .status(Response.Status.BAD_REQUEST).entity(ErrorReportFactory
                            .badRequest("Dataset is empty", "Cannot make predictions on empty dataset"))
                    .build();
        }

        String base64Model = (String) request.getRawModel();
        byte[] modelBytes = Base64.getDecoder().decode(base64Model);
        ByteArrayInputStream bais = new ByteArrayInputStream(modelBytes);
        ObjectInput in = new ObjectInputStream(bais);
        WekaModel model = (WekaModel) in.readObject();

        Classifier classifier = model.getClassifier();
        Instances data = InstanceUtils.createFromDataset(request.getDataset());
        String dependentFeature = (String) request.getAdditionalInfo();
        data.insertAttributeAt(new Attribute(dependentFeature), data.numAttributes());
        data.setClass(data.attribute(dependentFeature));

        List<LinkedHashMap<String, Object>> predictions = new ArrayList<>();
        //            data.stream().forEach(instance -> {
        //                try {
        //                    double prediction = classifier.classifyInstance(instance);
        //                    Map<String, Object> predictionMap = new HashMap<>();
        //                    predictionMap.put("Weka MLR prediction of " + dependentFeature, prediction);
        //                    predictions.add(predictionMap);
        //                } catch (Exception ex) {
        //                    Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
        //                }
        //            });

        for (int i = 0; i < data.numInstances(); i++) {
            Instance instance = data.instance(i);
            try {
                double prediction = classifier.classifyInstance(instance);
                LinkedHashMap<String, Object> predictionMap = new LinkedHashMap<>();
                predictionMap.put("Weka RBF prediction of " + dependentFeature, prediction);
                predictions.add(predictionMap);
            } catch (Exception ex) {
                Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
                return Response.status(Response.Status.BAD_REQUEST).entity(
                        ErrorReportFactory.badRequest("Error while gettting predictions.", ex.getMessage()))
                        .build();
            }
        }

        PredictionResponse response = new PredictionResponse();
        response.setPredictions(predictions);
        return Response.ok(response).build();
    } catch (IOException | ClassNotFoundException ex) {
        Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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From source file:org.jaqpot.algorithm.resource.WekaSVM.java

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {
    try {//from   w  ww  .j a v  a2 s  .  c  o m
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response
                    .status(Response.Status.BAD_REQUEST).entity(ErrorReportFactory
                            .badRequest("Dataset is empty", "Cannot make predictions on empty dataset"))
                    .build();
        }

        String base64Model = (String) request.getRawModel();
        byte[] modelBytes = Base64.getDecoder().decode(base64Model);
        ByteArrayInputStream bais = new ByteArrayInputStream(modelBytes);
        ObjectInput in = new ObjectInputStream(bais);
        WekaModel model = (WekaModel) in.readObject();

        Classifier classifier = model.getClassifier();
        Instances data = InstanceUtils.createFromDataset(request.getDataset());
        String dependentFeature = (String) request.getAdditionalInfo();
        data.insertAttributeAt(new Attribute(dependentFeature), data.numAttributes());
        data.setClass(data.attribute(dependentFeature));
        List<LinkedHashMap<String, Object>> predictions = new ArrayList<>();
        //            data.stream().forEach(instance -> {
        //                try {
        //                    double prediction = classifier.classifyInstance(instance);
        //                    Map<String, Object> predictionMap = new HashMap<>();
        //                    predictionMap.put("Weka SVM prediction of " + dependentFeature, prediction);
        //                    predictions.add(predictionMap);
        //                } catch (Exception ex) {
        //                    Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        //                }
        //            });

        for (int i = 0; i < data.numInstances(); i++) {
            Instance instance = data.instance(i);
            try {
                double prediction = classifier.classifyInstance(instance);
                LinkedHashMap<String, Object> predictionMap = new LinkedHashMap<>();
                predictionMap.put("Weka SVM prediction of " + dependentFeature, prediction);
                predictions.add(predictionMap);
            } catch (Exception ex) {
                Logger.getLogger(WekaMLR.class.getName()).log(Level.SEVERE, null, ex);
                return Response.status(Response.Status.BAD_REQUEST).entity(
                        ErrorReportFactory.badRequest("Error while gettting predictions.", ex.getMessage()))
                        .build();
            }
        }

        PredictionResponse response = new PredictionResponse();
        response.setPredictions(predictions);
        return Response.ok(response).build();
    } catch (Exception ex) {
        Logger.getLogger(WekaSVM.class.getName()).log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

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