Package quickml.supervised.classifier.decisionTree

Examples of quickml.supervised.classifier.decisionTree.TreeBuilder$AttributeCharacteristics

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        Assert.assertEquals(treeSize, newRandomForest.trees.size(), "Expected same trees");
        Assert.assertEquals(firstTreeNodeSize, newRandomForest.trees.get(0).node.size(), "Expected same nodes");
    }

    private PredictiveModelWithDataBuilder<AttributesMap ,RandomForest> getWrappedUpdatablePredictiveModelBuilder() {
        final TreeBuilder tb = new TreeBuilder(new SplitDiffScorer()).updatable(true);
        final RandomForestBuilder urfb = new RandomForestBuilder(tb);
        return new PredictiveModelWithDataBuilder<>(urfb);
    }
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    }

    @Test
    public void twoDeterministicRandomForestsAreEqual() throws IOException, ClassNotFoundException {
        final List<Instance<AttributesMap>> instancesTrain = TreeBuilderTestUtils.getInstances(10000);
        final RandomForestBuilder urfb = new RandomForestBuilder(new TreeBuilder(new SplitDiffScorer()).updatable(true));
        MapUtils.random.setSeed(1l);
        final RandomForest randomForest1 = urfb.executorThreadCount(1).buildPredictiveModel(instancesTrain);
        MapUtils.random.setSeed(1l);
        final RandomForest randomForest2 = urfb.executorThreadCount(1).buildPredictiveModel(instancesTrain);
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    public void testCrossValidator() {
        CrossValLossFunction<PredictionMap> crossValLossFunction = Mockito.mock(CrossValLossFunction.class);

        int folds = 4;
        ClassifierStationaryCrossValidator crossValidator = new ClassifierStationaryCrossValidator(folds, folds, crossValLossFunction);
        TreeBuilder treeBuilder = new TreeBuilder();
        List<Instance<AttributesMap>> instances = getInstances();
        crossValidator.getCrossValidatedLoss(treeBuilder, instances);

        Mockito.verify(crossValLossFunction, Mockito.times(folds)).getLoss(Mockito.<List<LabelPredictionWeight<PredictionMap>>>any());
    }
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        Assert.assertTrue(String.format("Error should be < 0.1 but was %s (prob=%s, desired=0.05)", error, correctedMinorityInstanceOccurance), error < 0.01);
    }

    @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);
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    public void simpleBmiTest() throws Exception {
        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.decisionTree.TreeBuilder$AttributeCharacteristics

  • org.apache.commons.lang.mutable.MutableInt
  • quickml.Benchmarks
  • quickml.ReadMeExample
  • quickml.supervised.classifier.decisionTree.scorers.MSEScorer
  • quickml.supervised.classifier.downsampling.DownsamplingClassifierBuilderTest
  • quickml.supervised.classifier.randomForest.RandomForestBuilder
  • quickml.supervised.classifier.randomForest.RandomForestBuilderFactory
  • quickml.supervised.classifier.randomForest.RandomForestBuilderTest
  • quickml.supervised.classifier.splitOnAttribute.SplitOnAttributeClassifierBuilderTest
  • quickml.supervised.classifier.temporallyWeightClassifier.TemporallyReweightedClassifierBuilderTest
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