Package quickml.supervised.classifier.randomForest

Examples of quickml.supervised.classifier.randomForest.RandomForestBuilder

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        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));

            TreeBuilder forestTreeBuilder = new TreeBuilder(scorer).ignoreAttributeAtNodeProbability(0.5).binaryClassification(true);
            RandomForestBuilder randomForestBuilder = new RandomForestBuilder(forestTreeBuilder).numTrees(100).executorThreadCount(8);
            System.out.println(dsName+", random-forest, "+scorer+", "+crossValidator.getCrossValidatedLoss(randomForestBuilder, instances));
        }
    }
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    @Test
    public void irisTest() throws Exception {
        StationaryCrossValidator stationaryCrossValidator = new StationaryCrossValidator(new ClassifierRMSECrossValLossFunction());
        final List<Instance<AttributesMap>> irisDataset = Benchmarks.loadIrisDataset();
        final double crossValidatedLoss = stationaryCrossValidator.getCrossValidatedLoss(new RandomForestBuilder(), irisDataset);
        double previousLoss = 0.673;
        logger.info("Cross Validated Lost: {}", crossValidatedLoss);
        Assert.assertTrue(crossValidatedLoss <= previousLoss, String.format("Current loss is %s, but previous loss was %s, this is a regression", crossValidatedLoss, previousLoss));
        Assert.assertTrue(crossValidatedLoss > previousLoss * 0.95, String.format("Current loss is %s, but previous loss was %s, this is a significant improvement, previousLoss should be updated", crossValidatedLoss, previousLoss));

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        {
       
            TreeBuilder treeBuilder = new TreeBuilder()
                .ignoreAttributeAtNodeProbability(0.7);
            RandomForestBuilder randomForestBuilder = new RandomForestBuilder(treeBuilder)
                .numTrees(50);
            RandomForest randomForest = randomForestBuilder.buildPredictiveModel(instances);

            attributes = AttributesMap.newHashMap() ;
            attributes.put("height",62);
            attributes.put("weight", 201);
            attributes.put("gender", "female");
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    }


    private static RandomForest getRandomForest(List<Instance<AttributesMap>> trainingData, int maxDepth, int numTrees) {
        TreeBuilder treeBuilder = new TreeBuilder().maxDepth(maxDepth).ignoreAttributeAtNodeProbability(.7);
        RandomForestBuilder randomForestBuilder = new RandomForestBuilder(treeBuilder).numTrees(numTrees);
        return randomForestBuilder.buildPredictiveModel(trainingData);
    }
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        return randomForestBuilder.buildPredictiveModel(trainingData);
    }

    private static PredictiveModelWithDataBuilder<AttributesMap, ? extends PredictiveModel<AttributesMap, PredictionMap>> getPredictiveModelWithDataBuilder(int maxDepth, int numTrees) {
        TreeBuilder treeBuilder = new TreeBuilder().maxDepth(maxDepth).ignoreAttributeAtNodeProbability(.7);
        RandomForestBuilder randomForestBuilder = new RandomForestBuilder(treeBuilder).numTrees(numTrees);
        PredictiveModelWithDataBuilder<AttributesMap, ? extends PredictiveModel<AttributesMap, PredictionMap>> builder = new PredictiveModelWithDataBuilder<>(randomForestBuilder);//.rebuildThreshold(4).splitNodeThreshold(2);
        return builder;
    }
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    Optional<? extends Classifier> wrappedPredictiveModel = Optional.absent();
    private List<PoolAdjacentViolatorsModel.Observation> storedObservations = Lists.newArrayList();
    Serializable positiveClassLabel = Double.valueOf(1.0);

    public CalibratedPredictiveModelBuilder() {
        this(new RandomForestBuilder());
    }
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    }

    @Test
    public void simpleBmiTest() throws IOException, ClassNotFoundException {
        final TreeBuilder tb = new TreeBuilder(new SplitDiffScorer());
        final RandomForestBuilder urfb = new RandomForestBuilder(tb);
        final DownsamplingClassifierBuilder dpmb = new DownsamplingClassifierBuilder(urfb, 0.1);

        final List<Instance<AttributesMap>> instances = TreeBuilderTestUtils.getIntegerInstances(1000);
        final PredictiveModelWithDataBuilder<AttributesMap ,DownsamplingClassifier> wb = new PredictiveModelWithDataBuilder<>(dpmb);
        final long startTime = System.currentTimeMillis();
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        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);
        double defaultLoss = crossVal.getCrossValidatedLoss(defaultRFBuilder, instances);
        double optimizedLoss = crossVal.getCrossValidatedLoss(optimalRFBuilder, instances);
        logger.info("Default PM loss: "+defaultLoss+", optimized PM loss: "+optimizedLoss);
        Assert.assertTrue(optimizedLoss <= defaultLoss, "Default PM loss (" + defaultLoss + ") should be higher or equal to optimized PM loss (" + optimizedLoss + ")");
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        Set<String> whiteList = new HashSet<>();
        whiteList.add("weight");
        whiteList.add("height");
        final List<Instance<AttributesMap>> instances = TreeBuilderTestUtils.getInstances(10000);
        final TreeBuilder tb = new TreeBuilder(new SplitDiffScorer()).splitPredictiveModel("gender", whiteList);
        final RandomForestBuilder rfb = new RandomForestBuilder(tb);
        final SplitOnAttributeClassifierBuilder cpmb = new SplitOnAttributeClassifierBuilder("gender", rfb, 10, 0.1, whiteList, 1);
        final long startTime = System.currentTimeMillis();
        final SplitOnAttributeClassifier splitOnAttributeClassifier = cpmb.buildPredictiveModel(instances);
        final RandomForest randomForest = (RandomForest) splitOnAttributeClassifier.getDefaultPM();
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    private PredictiveModelWithDataBuilder<AttributesMap ,SplitOnAttributeClassifier> getWrappedUpdatablePredictiveModelBuilder() {
        Set<String> whiteList = new HashSet<>();
        whiteList.add("weight");
        whiteList.add("height");
        final TreeBuilder tb = new TreeBuilder(new SplitDiffScorer()).splitPredictiveModel("gender", whiteList);
        final RandomForestBuilder urfb = new RandomForestBuilder(tb);
        final SplitOnAttributeClassifierBuilder ucpmb = new SplitOnAttributeClassifierBuilder("gender", urfb, 10, 0.1, whiteList, 1);
        return new PredictiveModelWithDataBuilder<>(ucpmb);
    }
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Related Classes of quickml.supervised.classifier.randomForest.RandomForestBuilder

  • quickml.Benchmarks
  • quickml.PredictiveAccuracyTests
  • quickml.ReadMeExample
  • quickml.supervised.calibratedPredictiveModel.CalibratedPredictiveModelBuilder
  • quickml.supervised.classifier.decisionTree.TreeBuilder
  • quickml.supervised.classifier.downsampling.DownsamplingClassifierBuilderTest
  • quickml.supervised.classifier.splitOnAttribute.SplitOnAttributeClassifierBuilderTest
  • quickml.supervised.crossValidation.OutOfTimeCrossValidatorTests
  • quickml.supervised.predictiveModelOptimizer.PredictiveModelOptimizerTest
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