Examples with TreeModelRegression org.knime.base.node.mine.treeensemble2.model.TreeModelRegression used on opensource projects

Example 16 with TreeModelRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeModelRegression in project knime-core by knime.

the class RegressionGBTModelExporter method doWrite.

/**
 * {@inheritDoc}
 */
@Override
protected void doWrite(final MiningModel model) {
    // write the initial value
    Targets targets = model.addNewTargets();
    Target target = targets.addNewTarget();
    GradientBoostedTreesModel gbtModel = getGBTModel();
    target.setField(gbtModel.getMetaData().getTargetMetaData().getAttributeName());
    target.setRescaleConstant(gbtModel.getInitialValue());
    // write the model
    Segmentation segmentation = model.addNewSegmentation();
    List<TreeModelRegression> trees = IntStream.range(0, gbtModel.getNrModels()).mapToObj(gbtModel::getTreeModelRegression).collect(Collectors.toList());
    writeSumSegmentation(segmentation, trees, gbtModel.getCoeffientMaps());
}

Example 17 with TreeModelRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeModelRegression in project knime-core by knime.

the class TreeEnsembleRegressionPredictorCellFactory method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    TreeEnsembleModelPortObject modelObject = m_predictor.getModelObject();
    TreeEnsemblePredictorConfiguration cfg = m_predictor.getConfiguration();
    final TreeEnsembleModel ensembleModel = modelObject.getEnsembleModel();
    int size = 1;
    final boolean appendConfidence = cfg.isAppendPredictionConfidence();
    final boolean appendModelCount = cfg.isAppendModelCount();
    if (appendConfidence) {
        size += 1;
    }
    if (appendModelCount) {
        size += 1;
    }
    final boolean hasOutOfBagFilter = m_predictor.hasOutOfBagFilter();
    DataCell[] result = new DataCell[size];
    DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
    PredictorRecord record = ensembleModel.createPredictorRecord(filterRow, m_learnSpec);
    if (record == null) {
        // missing value
        Arrays.fill(result, DataType.getMissingCell());
        return result;
    }
    Mean mean = new Mean();
    Variance variance = new Variance();
    final int nrModels = ensembleModel.getNrModels();
    for (int i = 0; i < nrModels; i++) {
        if (hasOutOfBagFilter && m_predictor.isRowPartOfTrainingData(row.getKey(), i)) {
        // ignore, row was used to train the model
        } else {
            TreeModelRegression m = ensembleModel.getTreeModelRegression(i);
            TreeNodeRegression match = m.findMatchingNode(record);
            double nodeMean = match.getMean();
            mean.increment(nodeMean);
            variance.increment(nodeMean);
        }
    }
    int nrValidModels = (int) mean.getN();
    int index = 0;
    result[index++] = nrValidModels == 0 ? DataType.getMissingCell() : new DoubleCell(mean.getResult());
    if (appendConfidence) {
        result[index++] = nrValidModels == 0 ? DataType.getMissingCell() : new DoubleCell(variance.getResult());
    }
    if (appendModelCount) {
        result[index++] = new IntCell(nrValidModels);
    }
    return result;
}

Example 18 with TreeModelRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeModelRegression in project knime-core by knime.

the class MGradientBoostedTreesLearner method calcCoefficientMap.

private Map<TreeNodeSignature, Double> calcCoefficientMap(final double[] residuals, final double quantile, final TreeModelRegression tree) {
    final List<TreeNodeRegression> leafs = tree.getLeafs();
    final Map<TreeNodeSignature, Double> coefficientMap = new HashMap<TreeNodeSignature, Double>((int) (leafs.size() / 0.75 + 1));
    final double learningRate = getConfig().getLearningRate();
    for (TreeNodeRegression leaf : leafs) {
        final int[] indices = leaf.getRowIndicesInTreeData();
        final double[] values = new double[indices.length];
        for (int i = 0; i < indices.length; i++) {
            values[i] = residuals[indices[i]];
        }
        final double median = calcMedian(values);
        double sum = 0;
        for (int i = 0; i < values.length; i++) {
            sum += Math.signum(values[i] - median) * Math.min(quantile, Math.abs(values[i] - median));
        }
        final double coefficient = median + (1.0 / values.length) * sum;
        coefficientMap.put(leaf.getSignature(), coefficient * learningRate);
    }
    return coefficientMap;
}

Example 19 with TreeModelRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeModelRegression 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 };
}

Example 20 with TreeModelRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeModelRegression in project knime-core by knime.

the class RegressionTreePredictorCellFactory method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    final RegressionTreeModel treeModel = m_predictor.getModel();
    int size = 1;
    DataCell[] result = new DataCell[size];
    DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
    PredictorRecord record = treeModel.createPredictorRecord(filterRow, m_learnSpec);
    if (record == null) {
        // missing value
        Arrays.fill(result, DataType.getMissingCell());
        return result;
    }
    TreeModelRegression tree = treeModel.getTreeModel();
    TreeNodeRegression match = tree.findMatchingNode(record);
    double nodeMean = match.getMean();
    result[0] = new DoubleCell(nodeMean);
    return result;
}