Examples with TreeLearnerRegression org.knime.base.node.mine.treeensemble2.learner.TreeLearnerRegression used on opensource projects

Example 1 with TreeLearnerRegression

use of org.knime.base.node.mine.treeensemble2.learner.TreeLearnerRegression in project knime-core by knime.

the class MGradientBoostedTreesLearner method learn.

/**
 * {@inheritDoc}
 */
@Override
public AbstractGradientBoostingModel learn(final ExecutionMonitor exec) throws CanceledExecutionException {
    final TreeData actualData = getData();
    final GradientBoostingLearnerConfiguration config = getConfig();
    final int nrModels = config.getNrModels();
    final TreeTargetNumericColumnData actualTarget = getTarget();
    final double initialValue = actualTarget.getMedian();
    final ArrayList<TreeModelRegression> models = new ArrayList<TreeModelRegression>(nrModels);
    final ArrayList<Map<TreeNodeSignature, Double>> coefficientMaps = new ArrayList<Map<TreeNodeSignature, Double>>(nrModels);
    final double[] previousPrediction = new double[actualTarget.getNrRows()];
    Arrays.fill(previousPrediction, initialValue);
    final RandomData rd = config.createRandomData();
    final double alpha = config.getAlpha();
    TreeNodeSignatureFactory signatureFactory = null;
    final int maxLevels = config.getMaxLevels();
    // this should be the default
    if (maxLevels < TreeEnsembleLearnerConfiguration.MAX_LEVEL_INFINITE) {
        final int capacity = IntMath.pow(2, maxLevels - 1);
        signatureFactory = new TreeNodeSignatureFactory(capacity);
    } else {
        signatureFactory = new TreeNodeSignatureFactory();
    }
    exec.setMessage("Learning model");
    TreeData residualData;
    for (int i = 0; i < nrModels; i++) {
        final double[] residuals = new double[actualTarget.getNrRows()];
        for (int j = 0; j < actualTarget.getNrRows(); j++) {
            residuals[j] = actualTarget.getValueFor(j) - previousPrediction[j];
        }
        final double quantile = calculateAlphaQuantile(residuals, alpha);
        final double[] gradients = new double[residuals.length];
        for (int j = 0; j < gradients.length; j++) {
            gradients[j] = Math.abs(residuals[j]) <= quantile ? residuals[j] : quantile * Math.signum(residuals[j]);
        }
        residualData = createResidualDataFromArray(gradients, actualData);
        final RandomData rdSingle = TreeEnsembleLearnerConfiguration.createRandomData(rd.nextLong(Long.MIN_VALUE, Long.MAX_VALUE));
        final RowSample rowSample = getRowSampler().createRowSample(rdSingle);
        final TreeLearnerRegression treeLearner = new TreeLearnerRegression(getConfig(), residualData, getIndexManager(), signatureFactory, rdSingle, rowSample);
        final TreeModelRegression tree = treeLearner.learnSingleTree(exec, rdSingle);
        final Map<TreeNodeSignature, Double> coefficientMap = calcCoefficientMap(residuals, quantile, tree);
        adaptPreviousPrediction(previousPrediction, tree, coefficientMap);
        models.add(tree);
        coefficientMaps.add(coefficientMap);
        exec.setProgress(((double) i) / nrModels, "Finished level " + i + "/" + nrModels);
    }
    return new GradientBoostedTreesModel(getConfig(), actualData.getMetaData(), models.toArray(new TreeModelRegression[models.size()]), actualData.getTreeType(), initialValue, coefficientMaps);
}

Example 2 with TreeLearnerRegression

use of org.knime.base.node.mine.treeensemble2.learner.TreeLearnerRegression in project knime-core by knime.

the class RegressionTreeLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inObjects[0];
    DataTableSpec spec = t.getDataTableSpec();
    final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(spec);
    String warn = learnRearranger.getWarning();
    BufferedDataTable learnTable = exec.createColumnRearrangeTable(t, learnRearranger, exec.createSubProgress(0.0));
    DataTableSpec learnSpec = learnTable.getDataTableSpec();
    ExecutionMonitor readInExec = exec.createSubProgress(0.1);
    ExecutionMonitor learnExec = exec.createSubProgress(0.9);
    TreeDataCreator dataCreator = new TreeDataCreator(m_configuration, learnSpec, learnTable.getRowCount());
    exec.setProgress("Reading data into memory");
    TreeData data = dataCreator.readData(learnTable, m_configuration, readInExec);
    m_hiliteRowSample = dataCreator.getDataRowsForHilite();
    m_viewMessage = dataCreator.getViewMessage();
    String dataCreationWarning = dataCreator.getAndClearWarningMessage();
    if (dataCreationWarning != null) {
        if (warn == null) {
            warn = dataCreationWarning;
        } else {
            warn = warn + "\n" + dataCreationWarning;
        }
    }
    readInExec.setProgress(1.0);
    exec.setMessage("Learning tree");
    RandomData rd = m_configuration.createRandomData();
    final IDataIndexManager indexManager;
    if (data.getTreeType() == TreeType.BitVector) {
        indexManager = new BitVectorDataIndexManager(data.getNrRows());
    } else {
        indexManager = new DefaultDataIndexManager(data);
    }
    TreeNodeSignatureFactory signatureFactory = null;
    int maxLevels = m_configuration.getMaxLevels();
    if (maxLevels < TreeEnsembleLearnerConfiguration.MAX_LEVEL_INFINITE) {
        int capacity = IntMath.pow(2, maxLevels - 1);
        signatureFactory = new TreeNodeSignatureFactory(capacity);
    } else {
        signatureFactory = new TreeNodeSignatureFactory();
    }
    final RowSample rowSample = m_configuration.createRowSampler(data).createRowSample(rd);
    TreeLearnerRegression treeLearner = new TreeLearnerRegression(m_configuration, data, indexManager, signatureFactory, rd, rowSample);
    TreeModelRegression regTree = treeLearner.learnSingleTree(learnExec, rd);
    RegressionTreeModel model = new RegressionTreeModel(m_configuration, data.getMetaData(), regTree, data.getTreeType());
    RegressionTreeModelPortObjectSpec treePortObjectSpec = new RegressionTreeModelPortObjectSpec(learnSpec);
    RegressionTreeModelPortObject treePortObject = new RegressionTreeModelPortObject(model, treePortObjectSpec);
    learnExec.setProgress(1.0);
    m_treeModelPortObject = treePortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { treePortObject };
}