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

Example 36 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;
    }
    OccurrenceCounter<String> counter = new OccurrenceCounter<String>();
    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);
            String majorityClassName = match.getMajorityClassName();
            counter.add(majorityClassName);
            nrValidModels += 1;
        }
    }
    String bestValue = counter.getMostFrequent();
    int index = 0;
    if (bestValue == null) {
        assert nrValidModels == 0;
        Arrays.fill(result, DataType.getMissingCell());
        index = size - 1;
    } else {
        result[index++] = m_targetValueMap.get(bestValue);
        if (appendConfidence) {
            final int freqValue = counter.getFrequency(bestValue);
            result[index++] = new DoubleCell(freqValue / (double) nrValidModels);
        }
        if (appendClassConfidences) {
            for (String key : m_targetValueMap.keySet()) {
                int frequency = counter.getFrequency(key);
                double ratio = frequency / (double) nrValidModels;
                result[index++] = new DoubleCell(ratio);
            }
        }
    }
    if (appendModelCount) {
        result[index++] = new IntCell(nrValidModels);
    }
    return result;
}

Example 37 with DoubleCell

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

the class RegressionTreePredictorCellFactory method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    RegressionTreeModelPortObject modelObject = m_predictor.getModelObject();
    final RegressionTreeModel treeModel = modelObject.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;
}

Example 38 with DoubleCell

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

the class TestDataGenerator method createNumericTargetColumn.

public static TreeTargetNumericColumnData createNumericTargetColumn(final String dataCSV) {
    double[] values = asDataArray(dataCSV);
    DataColumnSpec targetSpec = new DataColumnSpecCreator("test-target", DoubleCell.TYPE).createSpec();
    TreeTargetNumericColumnDataCreator targetCreator = new TreeTargetNumericColumnDataCreator(targetSpec);
    for (int i = 0; i < values.length; i++) {
        RowKey rowKey = RowKey.createRowKey((long) i);
        targetCreator.add(rowKey, new DoubleCell(values[i]));
    }
    return targetCreator.createColumnData();
}

Example 39 with DoubleCell

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

the class TreeNumericColumnDataTest method createNumericColumnData.

public static TreeOrdinaryNumericColumnData createNumericColumnData(final TreeEnsembleLearnerConfiguration config, final double[] data, final String name, final int attributeIndex) {
    DataColumnSpec colSpec = new DataColumnSpecCreator(name, DoubleCell.TYPE).createSpec();
    TreeOrdinaryNumericColumnDataCreator colCreator = new TreeOrdinaryNumericColumnDataCreator(colSpec);
    for (int i = 0; i < data.length; i++) {
        final RowKey key = RowKey.createRowKey(i);
        if (Double.isNaN(data[i])) {
            colCreator.add(key, new MissingCell(null));
        } else {
            colCreator.add(key, new DoubleCell(data[i]));
        }
    }
    return colCreator.createColumnData(attributeIndex, config);
}

Example 40 with DoubleCell

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

the class JoinedTableTest method getRandomRow.

private static final DataRow getRandomRow(final String id) {
    DataCell[] cells = new DataCell[3];
    cells[0] = new StringCell(id + "-" + RAND.nextInt(100));
    cells[1] = new IntCell(RAND.nextInt());
    cells[2] = new DoubleCell(RAND.nextDouble());
    return new DefaultRow(id, cells);
}