Examples with DoubleCell org.knime.core.data.def.DoubleCell used on opensource projects

Example 21 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class AutoBinner method calcDomainBoundsIfNeccessary.

/**
 * Determines the per column min/max values of the given data if not already present in the domain.
 *
 * @param data the data
 * @param exec the execution context
 * @param recalcValuesFor The columns
 * @return The data with extended domain information
 * @throws InvalidSettingsException ...
 * @throws CanceledExecutionException ...
 */
public BufferedDataTable calcDomainBoundsIfNeccessary(final BufferedDataTable data, final ExecutionContext exec, final List<String> recalcValuesFor) throws InvalidSettingsException, CanceledExecutionException {
    if (null == recalcValuesFor || recalcValuesFor.isEmpty()) {
        return data;
    }
    List<Integer> valuesI = new ArrayList<Integer>();
    for (String colName : recalcValuesFor) {
        DataColumnSpec colSpec = data.getDataTableSpec().getColumnSpec(colName);
        if (!colSpec.getType().isCompatible(DoubleValue.class)) {
            throw new InvalidSettingsException("Can only process numeric " + "data. The column \"" + colSpec.getName() + "\" is not numeric.");
        }
        if (recalcValuesFor.contains(colName) && !colSpec.getDomain().hasBounds()) {
            valuesI.add(data.getDataTableSpec().findColumnIndex(colName));
        }
    }
    if (valuesI.isEmpty()) {
        return data;
    }
    Map<Integer, Double> min = new HashMap<Integer, Double>();
    Map<Integer, Double> max = new HashMap<Integer, Double>();
    for (int col : valuesI) {
        min.put(col, Double.MAX_VALUE);
        max.put(col, Double.MIN_VALUE);
    }
    int c = 0;
    for (DataRow row : data) {
        c++;
        exec.checkCanceled();
        exec.setProgress(c / (double) data.getRowCount());
        for (int col : valuesI) {
            double val = ((DoubleValue) row.getCell(col)).getDoubleValue();
            if (min.get(col) > val) {
                min.put(col, val);
            }
            if (max.get(col) < val) {
                min.put(col, val);
            }
        }
    }
    List<DataColumnSpec> newColSpecList = new ArrayList<DataColumnSpec>();
    int cc = 0;
    for (DataColumnSpec columnSpec : data.getDataTableSpec()) {
        if (recalcValuesFor.contains(columnSpec.getName())) {
            DataColumnSpecCreator specCreator = new DataColumnSpecCreator(columnSpec);
            DataColumnDomainCreator domainCreator = new DataColumnDomainCreator(new DoubleCell(min.get(cc)), new DoubleCell(max.get(cc)));
            specCreator.setDomain(domainCreator.createDomain());
            DataColumnSpec newColSpec = specCreator.createSpec();
            newColSpecList.add(newColSpec);
        } else {
            newColSpecList.add(columnSpec);
        }
        cc++;
    }
    DataTableSpec spec = new DataTableSpec(newColSpecList.toArray(new DataColumnSpec[0]));
    BufferedDataTable newDataTable = exec.createSpecReplacerTable(data, spec);
    return newDataTable;
}

Example 22 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class TreeEnsembleClassificationPredictorCellFactory 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();
    if (appendConfidence) {
        size += 1;
    }
    final boolean appendClassConfidences = cfg.isAppendClassConfidences();
    if (appendClassConfidences) {
        size += m_targetValueMap.size();
    }
    final boolean appendModelCount = cfg.isAppendModelCount();
    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;
    }
    final Voting voting = m_votingFactory.createVoting();
    final int nrModels = ensembleModel.getNrModels();
    int nrValidModels = 0;
    for (int i = 0; i < nrModels; i++) {
        if (hasOutOfBagFilter && m_predictor.isRowPartOfTrainingData(row.getKey(), i)) {
        // ignore, row was used to train the model
        } else {
            TreeModelClassification m = ensembleModel.getTreeModelClassification(i);
            TreeNodeClassification match = m.findMatchingNode(record);
            voting.addVote(match);
            nrValidModels += 1;
        }
    }
    final NominalValueRepresentation[] targetVals = ((TreeTargetNominalColumnMetaData) ensembleModel.getMetaData().getTargetMetaData()).getValues();
    String majorityClass = voting.getMajorityClass();
    int index = 0;
    if (majorityClass == null) {
        assert nrValidModels == 0;
        Arrays.fill(result, DataType.getMissingCell());
        index = size - 1;
    } else {
        result[index++] = m_targetValueMap.get(majorityClass);
        // final float[] distribution = voting.getClassProbabilities();
        if (appendConfidence) {
            result[index++] = new DoubleCell(voting.getClassProbabilityForClass(majorityClass));
        }
        if (appendClassConfidences) {
            for (String targetValue : m_targetValueMap.keySet()) {
                result[index++] = new DoubleCell(voting.getClassProbabilityForClass(targetValue));
            }
        }
    }
    if (appendModelCount) {
        result[index++] = new IntCell(voting.getNrVotes());
    }
    return result;
}

Example 23 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class ProximityMatrix method createTable.

public BufferedDataTable createTable(final ExecutionContext exec) throws CanceledExecutionException {
    int numCols = getNumCols();
    int numRows = getNumRows();
    DataColumnSpec[] colSpecs = new DataColumnSpec[numCols];
    for (int i = 0; i < colSpecs.length; i++) {
        colSpecs[i] = new DataColumnSpecCreator(getRowKeyForTable(1, i).getString(), DoubleCell.TYPE).createSpec();
    }
    DataTableSpec tableSpec = new DataTableSpec(colSpecs);
    BufferedDataContainer container = exec.createDataContainer(tableSpec);
    for (int i = 0; i < numRows; i++) {
        exec.checkCanceled();
        exec.setProgress(((double) i) / numRows, "Row " + i + "/" + numRows);
        DataCell[] cells = new DataCell[numCols];
        for (int j = 0; j < numCols; j++) {
            cells[j] = new DoubleCell(getEntryAt(i, j));
        }
        container.addRowToTable(new DefaultRow(getRowKeyForTable(0, i), cells));
    }
    container.close();
    return container.getTable();
}

Example 24 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class TreeEnsembleStatisticsNodeModel method execute.

@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModel treeEnsemble = ((TreeEnsembleModelPortObject) inObjects[0]).getEnsembleModel();
    EnsembleStatistic ensembleStats = new EnsembleStatistic(treeEnsemble);
    DataContainer containerEnsembleStats = exec.createDataContainer(createEnsembleStatsSpec());
    DataCell[] cells = new DataCell[7];
    cells[0] = new IntCell(treeEnsemble.getNrModels());
    cells[1] = new IntCell(ensembleStats.getMinLevel());
    cells[2] = new IntCell(ensembleStats.getMaxLevel());
    cells[3] = new DoubleCell(ensembleStats.getAvgLevel());
    cells[4] = new IntCell(ensembleStats.getMinNumNodes());
    cells[5] = new IntCell(ensembleStats.getMaxNumNodes());
    cells[6] = new DoubleCell(ensembleStats.getAvgNumNodes());
    containerEnsembleStats.addRowToTable(new DefaultRow(RowKey.createRowKey(0L), cells));
    containerEnsembleStats.close();
    DataContainer containerTreeStats = exec.createDataContainer(createTreeStatsSpec());
    for (int i = 0; i < treeEnsemble.getNrModels(); i++) {
        DataCell[] treeCells = new DataCell[2];
        TreeStatistic treeStat = ensembleStats.getTreeStatistic(i);
        treeCells[0] = new IntCell(treeStat.getNumLevels());
        treeCells[1] = new IntCell(treeStat.getNumNodes());
        containerTreeStats.addRowToTable(new DefaultRow(RowKey.createRowKey((long) i), treeCells));
    }
    containerTreeStats.close();
    return new PortObject[] { (PortObject) containerEnsembleStats.getTable(), (PortObject) containerTreeStats.getTable() };
}

Example 25 with DoubleCell

use of org.knime.core.data.def.DoubleCell in project knime-core by knime.

the class GradientBoostingPredictorCellFactory method getCell.

/**
 * {@inheritDoc}
 */
@Override
public DataCell getCell(final DataRow row) {
    DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
    double prediction = m_model.predict(m_model.createPredictorRecord(filterRow, m_learnSpec));
    return new DoubleCell(prediction);
}