Package quickml.supervised.crossValidation.crossValLossFunctions

Examples of quickml.supervised.crossValidation.crossValLossFunctions.ClassifierLogCVLossFunction

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        return determineAttributeImportance(new TreeBuilder(), trainingData);
    }


    public TreeSet<AttributeScore> determineAttributeImportance(PredictiveModelBuilder predictiveModelBuilder, final Iterable<? extends Instance<AttributesMap>> trainingData) {
        return determineAttributeImportance(new StationaryCrossValidator(4, new ClassifierLogCVLossFunction()), predictiveModelBuilder, trainingData);
    }
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    }

    private static void testWithInstances(String dsName, final List<Instance<AttributesMap>> instances) {
        StationaryCrossValidator crossValidator = new StationaryCrossValidator(new ClassifierLogCVLossFunction());

        for (final Scorer scorer : Lists.newArrayList(new SplitDiffScorer(), new MSEScorer(MSEScorer.CrossValidationCorrection.FALSE), new MSEScorer(MSEScorer.CrossValidationCorrection.TRUE))) {
            final TreeBuilder singleTreeBuilder = new TreeBuilder(scorer).binaryClassification(true);
            System.out.println(dsName+", single-tree, "+scorer+", "+crossValidator.getCrossValidatedLoss(singleTreeBuilder, instances));
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    public void testLossBetween0And1() {
        //  List<Instance<AttributesMap>> trainingData = setUp();
        logger.info("trainingDataSize " + trainingData.size());
        PredictiveModelWithDataBuilder<AttributesMap, ? extends PredictiveModel<AttributesMap, PredictionMap>> predictiveModelWithDataBuilder = getPredictiveModelWithDataBuilder(5, 5);

        ClassifierOutOfTimeCrossValidator crossValidator = new ClassifierOutOfTimeCrossValidator(new ClassifierLogCVLossFunction(), 0.25, 30, new TestDateTimeExtractor()); //number of validation time slices
        double totalLoss = crossValidator.getCrossValidatedLoss(predictiveModelWithDataBuilder, trainingData);
        Assert.assertTrue(totalLoss > 0 && totalLoss <= 1.0);
        logger.info("\n\nAUCLoss\n");
        crossValidator = new ClassifierOutOfTimeCrossValidator(new WeightedAUCCrossValLossFunction(1.0), 0.25, 30, new TestDateTimeExtractor()); //number of validation time slices
        totalLoss = crossValidator.getCrossValidatedLoss(predictiveModelWithDataBuilder, trainingData);
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        testWithTrainingSet(instances);
    }

    private void testWithTrainingSet(final List<Instance<AttributesMap>> instances) {
        final PredictiveModelWithDataBuilderFactory predictiveModelBuilderFactory = new PredictiveModelWithDataBuilderFactory(new RandomForestBuilderFactory());
        final ClassifierStationaryCrossValidator crossVal = new ClassifierStationaryCrossValidator(4, 4, new ClassifierLogCVLossFunction());
        PredictiveModelOptimizer predictiveModelOptimizer = new PredictiveModelOptimizer(predictiveModelBuilderFactory, instances, crossVal);
        final Map<String, Object> optimalParameters = predictiveModelOptimizer.determineOptimalConfiguration();
        logger.info("Optimal parameters: " + optimalParameters);
        RandomForestBuilder defaultRFBuilder = new RandomForestBuilder();
        final PredictiveModelWithDataBuilder optimalRFBuilder = predictiveModelBuilderFactory.buildBuilder(optimalParameters);
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Related Classes of quickml.supervised.crossValidation.crossValLossFunctions.ClassifierLogCVLossFunction

  • quickml.Benchmarks
  • quickml.supervised.crossValidation.OutOfTimeCrossValidatorTests
  • quickml.supervised.inspection.AttributeImportanceFinder
  • quickml.supervised.predictiveModelOptimizer.PredictiveModelOptimizerTest
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