Example usage for weka.core Instances setClass

List of usage examples for weka.core Instances setClass

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Introduction

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

Prototype

public void setClass(Attribute att)

Source Link

Document

Sets the class attribute.

Usage

From source file:lu.lippmann.cdb.datasetview.tasks.UnsupervisedFeatureSelectionTask.java

License:Open Source License

/**
 * {@inheritDoc}/*from w  w  w .  ja va 2  s .  com*/
 */
@Override
Instances process0(final Instances dataSet) throws Exception {
    final int k;
    if (this.ratio == -1)
        k = getFeaturesCountFromInput(null, dataSet.numAttributes());
    else
        k = (int) Math.round(this.ratio * dataSet.numAttributes());

    final List<Integer> attrToKeep = WekaMachineLearningUtil.computeUnsupervisedFeaturesSelection(dataSet, k);
    if (!attrToKeep.contains(dataSet.classIndex()))
        attrToKeep.add(dataSet.classIndex());
    final int[] array = ArraysUtil.transform(attrToKeep);

    System.out.println("unsupervised fs -> before=" + dataSet.numAttributes() + " after=" + array.length);

    final Instances newds = WekaDataProcessingUtil.buildFilteredByAttributesDataSet(dataSet, array);
    final Attribute clsAttr = newds.attribute(dataSet.classAttribute().name());
    System.out.println(clsAttr + " " + dataSet.classAttribute().name());
    newds.setClass(clsAttr);
    return newds;
}

From source file:mlflex.learners.WekaLearner.java

License:Open Source License

/** Creates Weka instances from ML-Flex collections.
 *
 * @param dependentVariableInstances ML-Flex collection of dataInstances
 * @return Weka instances/*from  www  .  ja va2 s.  c  o  m*/
 * @throws Exception
 */
private static Instances GetEvaluationInstances(Predictions predictions) throws Exception {
    FastVector wekaAttributeVector = GetAttributeVector(predictions);

    Instances wekaInstances = new Instances("DataSet", wekaAttributeVector, predictions.Size());
    wekaInstances.setClass((Attribute) wekaAttributeVector.elementAt(1));

    for (Prediction prediction : predictions.GetAll())
        wekaInstances.add(GetInstance(wekaInstances, wekaAttributeVector, prediction));

    return wekaInstances;
}

From source file:mlflex.WekaInMemoryLearner.java

License:Open Source License

/** Creates Weka instances from ML-Flex collections.
 *
 *
 * @param dependentVariableInstances Dependent variable data instances
 * @param attVector Vector of Weka attributes
 * @param instances ML-Flex collection of instances
 * @return Weka instances/*  w  w w  . j a v a 2 s.c  o  m*/
 * @throws Exception
 */
public static Instances GetInstances(DataInstanceCollection dependentVariableInstances, FastVector attVector,
        DataInstanceCollection instances) throws Exception {
    Instances wekaInstances = new Instances("DataSet", attVector, instances.Size());

    if (dependentVariableInstances != null)
        wekaInstances.setClass((Attribute) attVector.elementAt(attVector.size() - 1));

    for (DataValues instance : instances)
        wekaInstances.add(GetInstance(wekaInstances, attVector, instance, dependentVariableInstances));

    return wekaInstances;
}

From source file:mulan.transformations.regression.ChainTransformation.java

License:Open Source License

/**
 * Deletes all target attributes that appear after the first targetsToKeep in the chain. The
 * target attribute at position targetsToKeep in the chain is set as the class attribute.
 * //from ww w . ja v a2 s  .c  o  m
 * @param data the input data set
 * @param chain a chain (permutation) of the indices of the target attributes
 * @param numTargetsToKeep the number of target attributes from the beginning of the chain that
 *            should be kept, 1&lt;=numTargetsToKeep&lt;=numOfTargets
 * @return the transformed Instances object. The input object is not modified.
 * @throws Exception Potential exception thrown. To be handled in an upper level.
 */
public static Instances transformInstances(Instances data, int[] chain, int numTargetsToKeep) throws Exception {
    int numOfTargets = chain.length;
    if (numTargetsToKeep < 1 || numTargetsToKeep > numOfTargets) {
        throw new Exception("keepFirstKTargets should be between 1 and numOfTargets");
    }
    // Indices of attributes to remove
    int[] indicesToRemove = new int[numOfTargets - numTargetsToKeep];
    // the indices of the target attributes whose position in the chain is
    // after the first keepFirstKTargets attributes are marked for removal
    for (int i = 0; i < numOfTargets - numTargetsToKeep; i++) {
        indicesToRemove[i] = chain[numTargetsToKeep + i];
    }

    Remove remove = new Remove();
    remove.setAttributeIndicesArray(indicesToRemove);
    remove.setInputFormat(data);
    // get the class attribute name, the name of the target attribute which is placed in the
    // targetsToKeep position of the chain
    String classAttributeName = data.attribute(chain[numTargetsToKeep - 1]).name();
    Instances transformed = Filter.useFilter(data, remove);
    transformed.setClass(transformed.attribute(classAttributeName));
    return transformed;
}

From source file:old.CFS.java

/**
 * takes a dataset as first argument/*from  w w w.  j a v a2s.  c o m*/
 *
 * @param args        the commandline arguments
 * @throws Exception  if something goes wrong
 */
public static void main(String[] args) throws Exception {
    // load data
    System.out.println("\n0. Loading data");
    DataSource source = new DataSource("D:\\ALL\\imdb_grid_size=1000_MIN=50_genres=5.arff");
    Instances data = source.getDataSet();
    data.setClass(data.attribute("Horror"));

    if (data.classIndex() == -1)
        data.setClassIndex(data.numAttributes() - 1);

    //    // 1. meta-classifier
    //    useClassifier(data);
    //
    //    // 2. filter
    //    useFilter(data);

    // 3. low-level
    useLowLevel(data);
}

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

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {
    try {/*from   w  ww . j  a va 2s  .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 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();
    }
}

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

License:Open Source License

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

    try {/*  w ww .  jav  a  2 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();
    }
}

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

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {
    try {//from   ww  w .j a  v a 2 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();
    }
}

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

License:Open Source License

@POST
@Path("prediction")
public Response prediction(PredictionRequest request) {
    try {//from w  w  w.  j  a v a  2s .c  o m
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST)
                    .entity("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("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();
    }
}

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

License:Open Source License

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

    try {//from   w w w  . j a  v  a 2  s.  c om
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST)
                    .entity("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("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();
    }
}