Examples with PredictorRecord org.knime.base.node.mine.treeensemble.data.PredictorRecord used on opensource projects

Example 6 with PredictorRecord

use of org.knime.base.node.mine.treeensemble.data.PredictorRecord in project knime-core by knime.

the class TreeEnsembleModel method createBitVectorPredictorRecord.

private PredictorRecord createBitVectorPredictorRecord(final DataRow filterRow) {
    assert filterRow.getNumCells() == 1 : "Expected one cell as bit vector data";
    DataCell c = filterRow.getCell(0);
    if (c.isMissing()) {
        return null;
    }
    BitVectorValue bv = (BitVectorValue) c;
    final long length = bv.length();
    if (length != getMetaData().getNrAttributes()) {
        throw new IllegalArgumentException("The bit-vector in " + filterRow.getKey().getString() + " has the wrong length. (" + length + " instead of " + getMetaData().getNrAttributes() + ")");
    }
    Map<String, Object> valueMap = new LinkedHashMap<String, Object>((int) (length / 0.75 + 1.0));
    for (int i = 0; i < length; i++) {
        valueMap.put(TreeBitColumnMetaData.getAttributeName(i), Boolean.valueOf(bv.get(i)));
    }
    return new PredictorRecord(valueMap);
}

Example 7 with PredictorRecord

use of org.knime.base.node.mine.treeensemble.data.PredictorRecord in project knime-core by knime.

the class TreeEnsembleModel method createNominalNumericPredictorRecord.

private PredictorRecord createNominalNumericPredictorRecord(final DataRow filterRow, final DataTableSpec trainSpec) {
    final int nrCols = trainSpec.getNumColumns();
    Map<String, Object> valueMap = new LinkedHashMap<String, Object>((int) (nrCols / 0.75 + 1.0));
    for (int i = 0; i < nrCols; i++) {
        DataColumnSpec col = trainSpec.getColumnSpec(i);
        String colName = col.getName();
        DataType colType = col.getType();
        DataCell cell = filterRow.getCell(i);
        if (cell.isMissing()) {
            valueMap.put(colName, PredictorRecord.NULL);
        } else if (colType.isCompatible(NominalValue.class)) {
            valueMap.put(colName, cell.toString());
        } else if (colType.isCompatible(DoubleValue.class)) {
            valueMap.put(colName, ((DoubleValue) cell).getDoubleValue());
        } else {
            throw new IllegalStateException("Expected nominal or numeric column type for column \"" + colName + "\" but got \"" + colType + "\"");
        }
    }
    return new PredictorRecord(valueMap);
}

Example 8 with PredictorRecord

use of org.knime.base.node.mine.treeensemble.data.PredictorRecord in project knime-core by knime.

the class TreeEnsembleModel method createByteVectorPredictorRecord.

private PredictorRecord createByteVectorPredictorRecord(final DataRow filterRow) {
    assert filterRow.getNumCells() == 1 : "Expected one cell as byte vector data";
    DataCell c = filterRow.getCell(0);
    if (c.isMissing()) {
        return null;
    }
    ByteVectorValue bv = (ByteVectorValue) c;
    final long length = bv.length();
    if (length != getMetaData().getNrAttributes()) {
        throw new IllegalArgumentException("The byte-vector in " + filterRow.getKey().getString() + " has the wrong length. (" + length + " instead of " + getMetaData().getNrAttributes() + ")");
    }
    Map<String, Object> valueMap = new LinkedHashMap<String, Object>((int) (length / 0.75 + 1.0));
    for (int i = 0; i < length; i++) {
        valueMap.put(TreeNumericColumnMetaData.getAttributeName(i), Integer.valueOf(bv.get(i)));
    }
    return new PredictorRecord(valueMap);
}

Example 9 with PredictorRecord

use of org.knime.base.node.mine.treeensemble.data.PredictorRecord 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;
}