Examples with Instances weka.core.Instances used on opensource projects

Example 21 with Instances

use of weka.core.Instances in project dkpro-tc by dkpro.

the class WekaDataWriter method writeClassifierFormat.

@Override
public void writeClassifierFormat(Collection<Instance> instances) throws AnalysisEngineProcessException {
    try {
        Instances masterInstance = initalConfiguration(instances);
        for (Instance inst : instances) {
            double[] featureValues = getFeatureValues(attributeStore, inst);
            weka.core.Instance wekaInstance;
            if (useSparse) {
                wekaInstance = new SparseInstance(1.0, featureValues);
            } else {
                wekaInstance = new DenseInstance(1.0, featureValues);
            }
            wekaInstance.setDataset(masterInstance);
            String outcome = inst.getOutcome();
            if (isRegression) {
                wekaInstance.setClassValue(Double.parseDouble(outcome));
            } else {
                wekaInstance.setClassValue(outcome);
            }
            Double instanceWeight = inst.getWeight();
            if (applyWeighting) {
                wekaInstance.setWeight(instanceWeight);
            }
            // preprocessingFilter.input(wekaInstance);
            // saver.writeIncremental(preprocessingFilter.output());
            saver.writeIncremental(wekaInstance);
        }
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }
}

Example 22 with Instances

use of weka.core.Instances in project dkpro-tc by dkpro.

the class WekaArffTest method main.

/**
 * @param args
 * @throws Exception
 */
public static void main(String[] args) throws Exception {
    File train = new File("src/main/resources/arff/manyInstances/train.arff.gz");
    File test = new File("src/main/resources/arff/manyInstances/test.arff.gz");
    Instances trainData = WekaUtils.getInstances(train, false);
    Instances testData = WekaUtils.getInstances(test, false);
    Classifier cl = new NaiveBayes();
    // no problems until here
    Evaluation eval = new Evaluation(trainData);
    eval.evaluateModel(cl, testData);
}

Example 23 with Instances

use of weka.core.Instances in project dkpro-tc by dkpro.

the class WekaResultsTest method testWekaResultsRegression.

@Test
public void testWekaResultsRegression() throws Exception {
    SMOreg cl = new SMOreg();
    Instances trainData = WekaUtils.removeInstanceId(regressionTrainData, false);
    Instances testData = WekaUtils.removeInstanceId(regressionTestData, false);
    cl.buildClassifier(trainData);
    Evaluation eval = WekaUtils.getEvaluationSinglelabel(cl, trainData, testData);
    assertEquals(0.45, eval.correlationCoefficient(), 0.01);
}

Example 24 with Instances

use of weka.core.Instances in project dkpro-tc by dkpro.

the class WekaResultsTest method testWekaResultsMultiLabel.

@Test
public void testWekaResultsMultiLabel() throws Exception {
    BR cl = new BR();
    cl.setOptions(new String[] { "-W", J48.class.getName() });
    Instances testData = WekaUtils.makeOutcomeClassesCompatible(multiLabelTrainData, multiLabelTestData, true);
    Instances trainData = WekaUtils.removeInstanceId(multiLabelTrainData, true);
    testData = WekaUtils.removeInstanceId(testData, true);
    cl.buildClassifier(trainData);
    Result eval = WekaUtils.getEvaluationMultilabel(cl, trainData, testData, "0.2");
    assertEquals(16.0, eval.L, 0.01);
    assertEquals(0.0, (Double) Result.getStats(eval, "1").get("Exact match"), 0.01);
}

Example 25 with Instances

use of weka.core.Instances in project dkpro-tc by dkpro.

the class WekaUtilTest method tcInstanceToMekaInstanceTest.

@Test
public void tcInstanceToMekaInstanceTest() throws Exception {
    List<String> outcomeValues = Arrays.asList(new String[] { "outc_1", "outc_2", "outc_3" });
    Instance i1 = new Instance();
    i1.addFeature(new Feature("feature1", 2, FeatureType.NUMERIC));
    i1.addFeature(new Feature("feature2", 2, FeatureType.NUMERIC));
    i1.addFeature(new Feature("feature3_{{", "a", FeatureType.STRING));
    Instance i2 = new Instance();
    i2.addFeature(new Feature("feature1", 1, FeatureType.NUMERIC));
    i2.addFeature(new Feature("feature4", "val_1", FeatureType.STRING));
    i2.addFeature(new Feature("feature3_{{", "b", FeatureType.STRING));
    ArrayList<Attribute> attributes = new ArrayList<Attribute>();
    attributes.add(new Attribute("outc_1", Arrays.asList(new String[] { "0", "1" })));
    attributes.add(new Attribute("outc_2", Arrays.asList(new String[] { "0", "1" })));
    attributes.add(new Attribute("outc_3", Arrays.asList(new String[] { "0", "1" })));
    attributes.add(new Attribute("feature5"));
    attributes.add(new Attribute("feature2"));
    attributes.add(new Attribute("feature4", Arrays.asList(new String[] { "val_1", "val_2" })));
    attributes.add(new Attribute("feature1"));
    Instances trainingData = new Instances("test", attributes, 0);
    weka.core.Instance wekaInstance1 = WekaUtils.tcInstanceToMekaInstance(i1, trainingData, outcomeValues);
    weka.core.Instance wekaInstance2 = WekaUtils.tcInstanceToMekaInstance(i2, trainingData, outcomeValues);
    assertEquals(true, wekaInstance1.equalHeaders(wekaInstance2));
    assertEquals(7, wekaInstance1.numAttributes());
    wekaInstance1.dataset().add(wekaInstance1);
    wekaInstance2.dataset().add(wekaInstance2);
    System.out.println(wekaInstance1.dataset() + "\n");
    System.out.println(wekaInstance2.dataset() + "\n");
}