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:org.opentox.qsar.processors.predictors.SimplePredictor.java

License:Open Source License

/**
 * Perform the prediction which is based on the serialized model file on the server.
 * @param data//w  ww .j a va  2 s . c  o  m
 *      Input data for with respect to which the predicitons are calculated
 * @return
 *      A dataset containing the compounds submitted along with their predicted values.
 * @throws QSARException
 *      In case the prediction (as a whole) is not feasible. If the prediction is not
 *      feasible for a single instance, the prediction is set to <code>?</code> (unknown/undefined/missing).
 *      If the prediction is not feasible for all instances, an exception (QSARException) is thrown.
 */
@Override
public Instances predict(final Instances data) throws QSARException {

    Instances dataClone = new Instances(data);
    /**
     * IMPORTANT!
     * String attributes have to be removed from the dataset before
     * applying the prediciton
     */
    dataClone = new AttributeCleanup(ATTRIBUTE_TYPE.string).filter(dataClone);

    /**
     * Set the class attribute of the incoming data to any arbitrary attribute
     * (Choose the last for instance).
     */
    dataClone.setClass(dataClone.attribute(model.getDependentFeature().getURI()));

    /**
     *
     * Create the Instances that will host the predictions. This object contains
     * only two attributes: the compound_uri and the target feature of the model.
     */
    Instances predictions = null;
    FastVector attributes = new FastVector();
    final Attribute compoundAttribute = new Attribute("compound_uri", (FastVector) null);
    final Attribute targetAttribute = dataClone.classAttribute();
    attributes.addElement(compoundAttribute);
    attributes.addElement(targetAttribute);

    predictions = new Instances("predictions", attributes, 0);
    predictions.setClassIndex(1);

    Instance predictionInstance = new Instance(2);
    try {
        final Classifier cls = (Classifier) SerializationHelper.read(filePath);

        for (int i = 0; i < data.numInstances(); i++) {
            try {
                String currentCompound = data.instance(i).stringValue(0);
                predictionInstance.setValue(compoundAttribute, currentCompound);

                if (targetAttribute.type() == Attribute.NUMERIC) {
                    double clsLabel = cls.classifyInstance(dataClone.instance(i));
                    predictionInstance.setValue(targetAttribute, clsLabel);
                } else if (targetAttribute.type() == Attribute.NOMINAL) {
                    double[] clsLable = cls.distributionForInstance(dataClone.instance(i));
                    int indexForNominalElement = maxInArray(clsLable).getPosition();
                    Enumeration nominalValues = targetAttribute.enumerateValues();
                    int counter = 0;
                    String nomValue = "";
                    while (nominalValues.hasMoreElements()) {
                        if (counter == indexForNominalElement) {
                            nomValue = nominalValues.nextElement().toString();
                            break;
                        }
                        counter++;
                    }
                    predictionInstance.setValue(targetAttribute, nomValue);

                    predictionInstance.setValue(targetAttribute, cls.classifyInstance(dataClone.instance(i)));
                }

                predictions.add(predictionInstance);
            } catch (Exception ex) {
                System.out.println(ex);
            }
        }

    } catch (Exception ex) {
    }

    return predictions;
}

---

From source file:org.processmining.analysis.clusteranalysis.ClusterDecisionAnalyzer.java

License:Open Source License

public Instances getDataInfo() {
    // create attribute information
    FastVector attributeInfo = new FastVector();
    // make attribute
    // clean the relevant attribute list and re-fill based on new selection
    // scope//w ww  . j  a v  a  2  s  .  c om
    for (int i = 0; i < agProfiles.numberOfItems(); i++) {
        if (checks[i].isSelected()) {
            String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(i));
            Attribute wekaAtt = new Attribute(name);
            attributeInfo.addElement(wekaAtt);
        }
    }
    // for target concept
    FastVector my_nominal_values = new FastVector(clusters.getClusters().size());
    Attribute targetConcept = null;
    for (Cluster aCluster : clusters.getClusters()) {
        my_nominal_values.addElement(aCluster.getName());
    }
    targetConcept = new Attribute("Cluster", my_nominal_values);
    attributeInfo.addElement(targetConcept);
    attributeInfo.trimToSize();

    // learning
    Instances data = new Instances("Clustering", attributeInfo, 0);
    data.setClassIndex(data.numAttributes() - 1);

    for (Cluster aCluster : clusters.getClusters()) {
        String clusterName = aCluster.getName();
        for (Integer i : aCluster.getTraceIndices()) {
            Instance instance0 = new Instance(attributeInfo.size());
            for (int j = 0; j < agProfiles.numberOfItems(); j++) {
                if (checks[j].isSelected()) {
                    String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(j));
                    Attribute wekaAtt = data.attribute(name);
                    if (wekaAtt != null) {
                        double doubleAttValue = (new Double(agProfiles.getValue(i, j))).doubleValue();
                        instance0.setValue(wekaAtt, doubleAttValue);
                    } else {
                        System.out.println("fail to add");
                    }
                }
            }
            instance0.setDataset(data);
            instance0.setClassValue(clusterName);
            data.add(instance0);
        }
    }
    return data;
}

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From source file:org.processmining.analysis.decisionmining.DecisionMiningLogTrace.java

License:Open Source License

/**
 * Helper method for reading the mapp of data attributes (key,value) pairs,
 * and adding them to the current learning instance.
 * /*ww w .jav a 2  s . c om*/
 * @param data
 *            the map of data attribues
 * @param instance
 *            the current learning instance
 * @param dataset
 *            the current learning problem (contains attribute info)
 */
private void evaluateDataForInstance(Map<String, String> data, Instance instance, Instances dataset) {
    Iterator<Entry<String, String>> dataIterator = data.entrySet().iterator();
    while (dataIterator.hasNext()) {
        Entry<String, String> currentDataEntry = dataIterator.next();
        String attName = currentDataEntry.getKey();
        String attValue = currentDataEntry.getValue();

        // replace white spaces and special characters by underscores both
        // in name and value
        attName = CpnUtils.replaceSpecialCharacters(attName);
        attValue = CpnUtils.replaceSpecialCharacters(attValue);

        // check whether current attribute is contained in relevant
        // attributes
        Attribute wekaAtt = dataset.attribute(attName);
        if (wekaAtt != null) {
            if (wekaAtt.isNominal() == true) {
                // corresponding string can be directly provided
                instance.setValue(wekaAtt, attValue);
            } else if (wekaAtt.isNumeric() == true) {
                // value must be converted into double first
                try {
                    double doubleAttValue = (new Double(attValue)).doubleValue();
                    instance.setValue(wekaAtt, doubleAttValue);
                } catch (NumberFormatException ex) {
                    // the attribute did not contain a parsable number
                    Message.add("attribute " + attName + " appears not to be a numeric attribute!", 2);
                }
            }
        }
    }
}

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From source file:org.processmining.analysis.decisionmining.DecisionMiningLogTraceForAuLdg.java

License:Open Source License

/**
 * Helper method for reading the mapp of data attributes (key,value) pairs,
 * and adding them to the current learning instance.
 * /*w  w w  .java  2  s  . co m*/
 * @param data
 *          the map of data attribues
 * @param instance
 *          the current learning instance
 * @param dataset
 *          the current learning problem (contains attribute info)
 */
private void evaluateDataForInstance(Map<String, String> data, Instance instance, Instances dataset) {
    Iterator<Entry<String, String>> dataIterator = data.entrySet().iterator();
    while (dataIterator.hasNext()) {
        Entry<String, String> currentDataEntry = dataIterator.next();
        String attName = currentDataEntry.getKey();
        String attValue = currentDataEntry.getValue();

        // replace white spaces and special characters by underscores both in name
        // and value
        attName = CpnUtils.replaceSpecialCharacters(attName);
        attValue = CpnUtils.replaceSpecialCharacters(attValue);

        // check whether current attribute is contained in relevant attributes
        Attribute wekaAtt = dataset.attribute(attName);
        if (wekaAtt != null) {
            if (wekaAtt.isNominal() == true) {
                // corresponding string can be directly provided
                instance.setValue(wekaAtt, attValue);
            } else if (wekaAtt.isNumeric() == true) {
                // value must be converted into double first
                try {
                    double doubleAttValue = (new Double(attValue)).doubleValue();
                    instance.setValue(wekaAtt, doubleAttValue);
                } catch (NumberFormatException ex) {
                    // the attribute did not contain a parsable number
                    Message.add("attribute " + attName + " appears not to be a numeric attribute!", 2);
                }
            }
        }
    }
}

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From source file:org.processmining.analysis.traceclustering.profile.AggregateProfile.java

License:Open Source License

public Instances getWekaData() {
    Instances data = null;/* ww  w  . j  a  v a2  s  .  c  o  m*/

    // create attribute information
    FastVector attributeInfo = new FastVector();
    // make attribute
    // clean the relevant attribute list and re-fill based on new selection
    // scope
    for (int i = 0; i < numberOfItems(); i++) {
        String name = CpnUtils.replaceSpecialCharacters(getItemKey(i));
        Attribute wekaAtt = new Attribute(name);
        attributeInfo.addElement(wekaAtt);
    }
    attributeInfo.trimToSize();
    data = new Instances("Clustering", attributeInfo, 0);
    try {
        for (int i = 0; i < getLog().numberOfInstances(); i++) {
            Instance instance0 = new Instance(attributeInfo.size());
            for (int j = 0; j < numberOfItems(); j++) {
                String name = CpnUtils.replaceSpecialCharacters(getItemKey(j));
                Attribute wekaAtt = data.attribute(name);
                if (wekaAtt != null) {
                    double doubleAttValue = (new Double(getValue(i, j))).doubleValue();
                    instance0.setValue(wekaAtt, doubleAttValue);
                } else {
                    Message.add("Weka Error: fail to add", Message.ERROR);
                }
            }
            instance0.setDataset(data);
            data.add(instance0);
        }
    } catch (Exception c) {
        Message.add("Weka Error: " + c.toString(), Message.ERROR);
    }

    return data;
}

---

From source file:org.prom5.analysis.clusteranalysis.ClusterDecisionAnalyzer.java

License:Open Source License

public Instances getDataInfo() {
    // create attribute information
    FastVector attributeInfo = new FastVector();
    // make attribute
    // clean the relevant attribute list and re-fill based on new selection scope
    for (int i = 0; i < agProfiles.numberOfItems(); i++) {
        if (checks[i].isSelected()) {
            String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(i));
            Attribute wekaAtt = new Attribute(name);
            attributeInfo.addElement(wekaAtt);
        }//from  www. j a va 2  s . c o m
    }
    // for target concept
    FastVector my_nominal_values = new FastVector(clusters.getClusters().size());
    Attribute targetConcept = null;
    for (Cluster aCluster : clusters.getClusters()) {
        my_nominal_values.addElement(aCluster.getName());
    }
    targetConcept = new Attribute("Cluster", my_nominal_values);
    attributeInfo.addElement(targetConcept);
    attributeInfo.trimToSize();

    // learning
    Instances data = new Instances("Clustering", attributeInfo, 0);
    data.setClassIndex(data.numAttributes() - 1);

    for (Cluster aCluster : clusters.getClusters()) {
        String clusterName = aCluster.getName();
        for (Integer i : aCluster.getTraceIndices()) {
            Instance instance0 = new Instance(attributeInfo.size());
            for (int j = 0; j < agProfiles.numberOfItems(); j++) {
                if (checks[j].isSelected()) {
                    String name = CpnUtils.replaceSpecialCharacters(agProfiles.getItemKey(j));
                    Attribute wekaAtt = data.attribute(name);
                    if (wekaAtt != null) {
                        double doubleAttValue = (new Double(agProfiles.getValue(i, j))).doubleValue();
                        instance0.setValue(wekaAtt, doubleAttValue);
                    } else {
                        System.out.println("fail to add");
                    }
                }
            }
            instance0.setDataset(data);
            instance0.setClassValue(clusterName);
            data.add(instance0);
        }
    }
    return data;
}

---

From source file:org.prom5.analysis.decisionmining.DecisionMiningLogTrace.java

License:Open Source License

/**
 * Helper method for reading the mapp of data attributes (key,value) pairs, 
 * and adding them to the current learning instance.
 * @param data the map of data attribues
 * @param instance the current learning instance
 * @param dataset the current learning problem (contains attribute info)
 *//*w  w w.j  ava2  s .  co m*/
private void evaluateDataForInstance(Map<String, String> data, Instance instance, Instances dataset) {
    Iterator<Entry<String, String>> dataIterator = data.entrySet().iterator();
    while (dataIterator.hasNext()) {
        Entry<String, String> currentDataEntry = dataIterator.next();
        String attName = currentDataEntry.getKey();
        String attValue = currentDataEntry.getValue();

        // replace white spaces and special characters by underscores both in name and value
        attName = CpnUtils.replaceSpecialCharacters(attName);
        attValue = CpnUtils.replaceSpecialCharacters(attValue);

        // check whether current attribute is contained in relevant attributes
        Attribute wekaAtt = dataset.attribute(attName);
        if (wekaAtt != null) {
            if (wekaAtt.isNominal() == true) {
                // corresponding string can be directly provided
                instance.setValue(wekaAtt, attValue);
            } else if (wekaAtt.isNumeric() == true) {
                // value must be converted into double first
                try {
                    double doubleAttValue = (new Double(attValue)).doubleValue();
                    instance.setValue(wekaAtt, doubleAttValue);
                } catch (NumberFormatException ex) {
                    // the attribute did not contain a parsable number
                    Message.add("attribute " + attName + " appears not to be a numeric attribute!", 2);
                }
            }
        }
    }
}

---

From source file:org.prom5.analysis.traceclustering.profile.AggregateProfile.java

License:Open Source License

public Instances getWekaData() {
    Instances data = null;//from www .  j a v a2s .  co m

    // create attribute information
    FastVector attributeInfo = new FastVector();
    // make attribute
    // clean the relevant attribute list and re-fill based on new selection scope
    for (int i = 0; i < numberOfItems(); i++) {
        String name = CpnUtils.replaceSpecialCharacters(getItemKey(i));
        Attribute wekaAtt = new Attribute(name);
        attributeInfo.addElement(wekaAtt);
    }
    attributeInfo.trimToSize();
    data = new Instances("Clustering", attributeInfo, 0);
    try {
        for (int i = 0; i < getLog().numberOfInstances(); i++) {
            Instance instance0 = new Instance(attributeInfo.size());
            for (int j = 0; j < numberOfItems(); j++) {
                String name = CpnUtils.replaceSpecialCharacters(getItemKey(j));
                Attribute wekaAtt = data.attribute(name);
                if (wekaAtt != null) {
                    double doubleAttValue = (new Double(getValue(i, j))).doubleValue();
                    instance0.setValue(wekaAtt, doubleAttValue);
                } else {
                    Message.add("Weka Error: fail to add", Message.ERROR);
                }
            }
            instance0.setDataset(data);
            data.add(instance0);
        }
    } catch (Exception c) {
        Message.add("Weka Error: " + c.toString(), Message.ERROR);
    }

    return data;
}

---

From source file:org.wkwk.classifier.MyC45.java

@Override
public void buildClassifier(Instances data) throws Exception {
    getCapabilities().testWithFail(data);

    for (int i = 0; i < data.numAttributes(); i++) {
        Attribute attr = data.attribute(i);
        for (int j = 0; j < 10; j++) {
            Instance instance = data.instance(j);
            if (instance.isMissing(attr)) {
                instance.setValue(attr, fillMissingValue(data, attr));
            }/*from  w w  w  .  jav a 2  s . c  o m*/
        }
    }

    data.deleteWithMissingClass();
    makeTree(data);
}

---

From source file:oxis.yologp.YOLogPDescriptor.java

License:Open Source License

/**
 * Predict the LogP.//from   w  w  w .j a va  2  s.c o m
 *
 */
private void predict() throws Exception {

    Instances instances = buildDataset();

    Map<Object, Object> properties;
    for (DrugStruct drugStruct : listDrug) {

        if (drugStruct.drug.getProperty("flag")) {
            properties = drugStruct.drug.getProperties();
            Instance instance = new DenseInstance(instances.numAttributes()); //28 + 1024
            instance.setDataset(instances);
            for (Object propKey : properties.keySet()) {
                if (!(propKey.equals("hash") || propKey.equals("flag") || propKey.equals("smiles"))) {
                    try {
                        instance.setValue(instances.attribute(propKey.toString()),
                                Double.parseDouble(properties.get(propKey).toString()));
                    } catch (NullPointerException ex) {
                        Logger.getLogger(YOLogPDescriptor.class.getName()).log(Level.WARNING,
                                "Property not used: {0}", propKey.toString());
                    }
                }
            }

            double predicted = model.classifyInstance(instance);
            predicted = Math.round(predicted * 100) / 100.0d;
            instance.setClassValue(predicted);
            instances.add(instance);
            drugStruct.drug.setProperty("predicted", predicted);
        }
    }
}

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