Package etc.aloe.data

Examples of etc.aloe.data.ExampleSet

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        WekaModel instance = new WekaModel(classifier);

        Boolean[] expResult = new Boolean[]{true, true, false, false};
        Double[] expConfidence = new Double[]{1.0, 1.0, 0.0, 0.0};

        ExampleSet examples = new ExampleSet(testInstances);

        Predictions predictions = instance.getPredictions(examples);

        assertEquals(expResult.length, predictions.size());
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        System.out.println("== Labeling and Testing ==");

        //First extract features
        FeatureExtraction extraction = getFeatureExtractionImpl();
        ExampleSet examples = extraction.extractFeatures(segmentSet.getBasicExamples(), featureSpecification);

        this.featureValues = examples.getInstances();
       
        //Predict the labels
        Predictions predictions = this.model.getPredictions(examples);

        //Map back onto messages
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        SegmentSet trainingSegments = segmentSet.onlyLabeled();
        if (getBalancingImpl() != null) {
            trainingSegments = getBalancingImpl().balance(trainingSegments);
        }

        ExampleSet basicExamples = trainingSegments.getBasicExamples();

        //Generate the features
        FeatureGeneration generation = getFeatureGenerationImpl();
        this.featureSpecification = generation.generateFeatures(basicExamples);

        //Extract features
        FeatureExtraction extraction = getFeatureExtractionImpl();
        ExampleSet examples = extraction.extractFeatures(basicExamples, this.featureSpecification);

        //Train the model
        Training training = getTrainingImpl();
        this.featureValues = examples.getInstances();
        this.model = training.train(examples);

        //Get the top features
        this.topFeatures = getFeatureWeightingImpl().getTopFeatures(examples, this.model, NUM_TOP_FEATURES);
        this.featureWeights = getFeatureWeightingImpl().getFeatureWeights(examples, this.model);
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                if (getBalancingImpl() != null) {
                    trainingSegments = getBalancingImpl().balance(trainingSegments);
                }

                System.out.println("- Extracting basic features from training set");
                ExampleSet basicTrainingExamples = trainingSegments.getBasicExamples();
                trainingSegments = null;

                FeatureGeneration generation = getFeatureGenerationImpl();
                System.out.println("- Generating features");
                FeatureSpecification spec = generation.generateFeatures(basicTrainingExamples);

                FeatureExtraction extraction = getFeatureExtractionImpl();
                System.out.println("- Extracting features from training set");
                ExampleSet trainingSet = extraction.extractFeatures(basicTrainingExamples, spec);
                basicTrainingExamples = null;

                Training training = getTrainingImpl();
                Model model = training.train(trainingSet);
                trainingSet = null;
               
                System.out.println("- Splitting out test set");
                SegmentSet testingSegments = new SegmentSet();
                testingSegments.setSegments(split.getTestingForFold(segmentSet.getSegments(), foldIndex, this.folds));
                if (getBalancingImpl() != null && balanceTestSet) {
                    testingSegments = getBalancingImpl().balance(testingSegments);
                }

                System.out.println("- Extracting basic features from test set");
                ExampleSet basicTestingExamples = testingSegments.getBasicExamples();

                System.out.println("- Extracting features from test set");
                ExampleSet testingSet = extraction.extractFeatures(basicTestingExamples, spec);
                basicTestingExamples = null;

                Predictions predictions = model.getPredictions(testingSet);
                EvaluationReport report = new EvaluationReport("Fold " + (foldIndex + 1), falsePositiveCost, falseNegativeCost);
                report.addPredictions(predictions);
               
                LabelMapping mapping = getMappingImpl();
                mapping.map(predictions, testingSegments);
                report.addLabeledTestData(testingSegments);
               
                evaluationReport.addPartial(report);
                int numCorrect = report.getTrueNegativeCount() + report.getTruePositiveCount();
                System.out.println("- Fold " + (foldIndex + 1) + " completed (" + numCorrect + "/" + testingSet.size() + " correct).");
                System.out.println();
            }
        } else {
            System.out.println("== Skipping Cross Validation ==");
        }
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        FeatureSpecification spec = new FeatureSpecification();
        spec.addFilter(addFilter);
        spec.addFilter(removeFilter);

        FeatureExtractionImpl instance = new FeatureExtractionImpl();
        ExampleSet examples = instance.extractFeatures(segments.getBasicExamples(), spec);
        assertNotNull(examples);
        assertNotNull(examples.getInstances());

        Instances instances = examples.getInstances();
        //3 base attrs + 4 basic features + 1 label
        assertEquals(8, instances.numAttributes());
        //Contains the added attribute in the right place
        assertEquals(attrName, instances.attribute(basicInstances.numAttributes()).name());
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            //First extract features
            FeatureExtraction extraction = getFeatureExtractionImpl();
            extraction.setVerbosity(Loggable.Verbosity.Quiet);

            ExampleSet examples = extraction.extractFeatures(segmentSet.getBasicExamples(), featureSpecification);

            //Predict the labels
            Predictions prediction = this.model.getPredictions(examples);

            //Map back onto messages
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    }
   
    @Override
    public FeatureSpecification generateFeatures(ExampleSet basicExamples) {

        ExampleSet examples = basicExamples.copy();
        FeatureSpecification spec = new FeatureSpecification();

        System.out.print("Configuring features over " + examples.size() + " examples... ");

        try {
            spec.addFilter(getPronounsFilter(examples));
            spec.addFilter(getPunctuationFilter(examples));
            spec.addFilter(getSpecialWordsFilter(examples));
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    @Test
    public void testTrain() {
        System.out.println("train");

        TrainingImpl instance = new TrainingImpl();
        WekaModel model = instance.train(new ExampleSet(instances));

        //The test here is whether the model works
        Boolean[] expResult = new Boolean[]{true, true, false, false};
        Double[] expConfidence = new Double[]{1.0, 1.0, 0.0, 0.0};

        ExampleSet examples = new ExampleSet(testInstances);
        Predictions predictions = model.getPredictions(examples);

        assertEquals(expResult.length, predictions.size());

        for (int i = 0; i < expResult.length; i++) {
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    private Verbosity verbosity = Verbosity.Normal;

    @Override
    public ExampleSet extractFeatures(ExampleSet basicExamples, FeatureSpecification spec) {
        ExampleSet examples = basicExamples;

        if (this.verbosity.ordinal() > Verbosity.Quiet.ordinal()) {
            System.out.print("Extracting features for " + examples.size() + " examples... ");
        }

        for (Filter filter : spec.getFilters()) {
            try {
                Instances instances = Filter.useFilter(examples.getInstances(), filter);
                examples = new ExampleSet(instances);
            } catch (Exception e) {
                System.err.println("Unable to apply filter: " + filter.toString());
                System.err.println("\t" + e.getMessage());
                return null;
            }
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    }
   
    @Override
    public FeatureSpecification generateFeatures(ExampleSet basicExamples) {

        ExampleSet examples = basicExamples.copy();
        FeatureSpecification spec = new FeatureSpecification();

        System.out.print("Configuring features over " + examples.size() + " examples... ");

        try {
            spec.addFilter(getPronounsFilter(examples));
            spec.addFilter(getPunctuationFilter(examples));
            spec.addFilter(getSpecialWordsFilter(examples));
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Related Classes of etc.aloe.data.ExampleSet

  • etc.aloe.controllers.CrossValidationController
  • etc.aloe.controllers.InteractiveController
  • etc.aloe.controllers.LabelingController
  • etc.aloe.controllers.TrainingController
  • etc.aloe.cscw2013.FeatureExtractionImpl
  • etc.aloe.cscw2013.FeatureExtractionImplTest
  • etc.aloe.cscw2013.FeatureGenerationImpl
  • etc.aloe.cscw2013.TrainingImplTest
  • etc.aloe.cscw2013.WekaModelTest
  • etc.aloe.oilspill2010.BigramFeatureGenerationImpl
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