Package quickml.supervised.classifier.decisionTree

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

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  private ExecutorService executorService;
  private int baggingSampleSize = 0;
  private Serializable id;

    public RandomForestBuilder() {
    this(new TreeBuilder().ignoreAttributeAtNodeProbability(0.5));
  }
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        return parametersToOptimize;
    }

    @Override
    public RandomForestBuilder buildBuilder(Map<String, Object> predictiveModelParameters) {
        TreeBuilder treeBuilder = treeBuilderBuilder.buildBuilder(predictiveModelParameters);
        final int numTrees = (Integer) predictiveModelParameters.get(NUM_TREES);
        final int bagSize = (Integer) predictiveModelParameters.get(BAG_SIZE);
        return new RandomForestBuilder(treeBuilder)
                .numTrees(numTrees)
                .withBagging(bagSize);
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    public AttributeImportanceFinder() {

    }

    public TreeSet<AttributeScore> determineAttributeImportance(final Iterable<? extends Instance<AttributesMap>> trainingData) {
        return determineAttributeImportance(new TreeBuilder(), 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));

            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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        attributes.put("gender", "male");
        instances.add(new InstanceImpl<AttributesMap>(attributes, "healthy"));
       
        {
           
            TreeBuilder treeBuilder = new TreeBuilder();
            Tree tree = treeBuilder.buildPredictiveModel(instances);

            attributes = AttributesMap.newHashMap() ;
            attributes.put("height",62);
            attributes.put("weight", 201);
            attributes.put("gender", "female");
            Serializable classification = tree.getClassificationByMaxProb(attributes);
            if (classification.equals("healthy")) {
                System.out.println("They are healthy!");
            } else if (classification.equals("underweight")) {
                System.out.println("They are underweight!");
            } else {
                System.out.println("They are overweight!");
            }
           
            tree.node.dump(System.out);
           
        }
       
        {
       
            TreeBuilder treeBuilder = new TreeBuilder()
                .ignoreAttributeAtNodeProbability(0.7);
            RandomForestBuilder randomForestBuilder = new RandomForestBuilder(treeBuilder)
                .numTrees(50);
            RandomForest randomForest = randomForestBuilder.buildPredictiveModel(instances);
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        AttributesMap  map = AttributesMap.newHashMap() ;
        map.put("2", "2");
        instances.add(new InstanceImpl<AttributesMap>(map, "1"));
        instances.add(new InstanceImpl<AttributesMap>(map, "2"));
        instances.add(new InstanceImpl<AttributesMap>(map, "3"));
        PredictiveModelBuilder predictiveModelBuilder = new TreeBuilder();
        final TemporallyReweightedClassifierBuilder cpmb = new TemporallyReweightedClassifierBuilder(predictiveModelBuilder, new MapDateTimeExtractor());
        cpmb.buildPredictiveModel(instances);
    }
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    }

    @Test
    public void simpleBmiTest() throws Exception {
        final List<Instance<AttributesMap>> instances = TreeBuilderTestUtils.getIntegerInstances(10000);
        final PredictiveModelBuilder tb = new TreeBuilder(new SplitDiffScorer());
        final TemporallyReweightedClassifierBuilder builder = new TemporallyReweightedClassifierBuilder(tb, new MapDateTimeExtractor());
        final long startTime = System.currentTimeMillis();
        final TemporallyReweightedClassifier model = builder.buildPredictiveModel(instances);

        TreeBuilderTestUtils.serializeDeserialize(model);
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        // Assert.assertTrue(totalLoss > 0 && totalLoss <=1.0);
    }


    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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        RandomForestBuilder randomForestBuilder = new RandomForestBuilder(treeBuilder).numTrees(numTrees);
        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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*/
public class RandomForestBuilderTest {
    @Test
    public void simpleBmiTest() throws Exception {
        final List<Instance<AttributesMap>> instances = TreeBuilderTestUtils.getInstances(10000);
        final TreeBuilder tb = new TreeBuilder(new SplitDiffScorer());
        final RandomForestBuilder rfb = new RandomForestBuilder(tb);
        final long startTime = System.currentTimeMillis();
        final RandomForest randomForest = rfb.buildPredictiveModel(instances);

        TreeBuilderTestUtils.serializeDeserialize(randomForest);
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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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