Examples with NominalValueRepresentation org.knime.base.node.mine.treeensemble2.data.NominalValueRepresentation used on opensource projects

Example 1 with NominalValueRepresentation

use of org.knime.base.node.mine.treeensemble2.data.NominalValueRepresentation 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 2 with NominalValueRepresentation

use of org.knime.base.node.mine.treeensemble2.data.NominalValueRepresentation in project knime-core by knime.

the class TreeEnsembleClassificationPredictorCellFactory2 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();
    TreeTargetNominalColumnMetaData targetMeta = (TreeTargetNominalColumnMetaData) ensembleModel.getMetaData().getTargetMetaData();
    final double[] classProbabilities = new double[targetMeta.getValues().length];
    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();
            final float[] nodeClassProbs = match.getTargetDistribution();
            double instancesInNode = 0;
            for (int c = 0; c < nodeClassProbs.length; c++) {
                instancesInNode += nodeClassProbs[c];
            }
            for (int c = 0; c < classProbabilities.length; c++) {
                classProbabilities[c] += nodeClassProbs[c] / instancesInNode;
            }
            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);
        int indexBest = -1;
        double probBest = -1;
        for (int c = 0; c < classProbabilities.length; c++) {
            double prob = classProbabilities[c];
            if (prob > probBest) {
                probBest = prob;
                indexBest = c;
            }
        }
        result[index++] = new StringCell(targetMeta.getValues()[indexBest].getNominalValue());
        if (appendConfidence) {
            // final int freqValue = counter.getFrequency(bestValue);
            // result[index++] = new DoubleCell(freqValue / (double)nrValidModels);
            result[index++] = new DoubleCell(probBest);
        }
        if (appendClassConfidences) {
            for (NominalValueRepresentation nomVal : targetMeta.getValues()) {
                double prob = classProbabilities[nomVal.getAssignedInteger()] / nrValidModels;
                result[index++] = new DoubleCell(prob);
            }
        }
    }
    if (appendModelCount) {
        result[index++] = new IntCell(nrValidModels);
    }
    return result;
}

Example 3 with NominalValueRepresentation

use of org.knime.base.node.mine.treeensemble2.data.NominalValueRepresentation in project knime-core by knime.

the class AbstractTreeEnsembleModel 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)) {
            TreeNominalColumnMetaData nomColMeta = (TreeNominalColumnMetaData) m_metaData.getAttributeMetaData(i);
            NominalValueRepresentation[] nomVals = nomColMeta.getValues();
            int assignedInteger = -1;
            String val = cell.toString();
            // find assignedInteger of value
            for (NominalValueRepresentation nomVal : nomVals) {
                if (nomVal.getNominalValue().equals(val)) {
                    assignedInteger = nomVal.getAssignedInteger();
                    break;
                }
            }
            // the value is not known to the model
            if (assignedInteger == -1) {
                // treat as missing value
                valueMap.put(colName, PredictorRecord.NULL);
            } else {
                valueMap.put(colName, Integer.valueOf(assignedInteger));
            }
        } else if (colType.isCompatible(DoubleValue.class)) {
            double val = ((DoubleValue) cell).getDoubleValue();
            if (Double.isNaN(val)) {
                // make sure that NaNs are treated as missing values
                // bug AP-7169
                valueMap.put(colName, PredictorRecord.NULL);
            } else {
                valueMap.put(colName, val);
            }
        } else {
            throw new IllegalStateException("Expected nominal or numeric column type for column \"" + colName + "\" but got \"" + colType + "\"");
        }
    }
    return new PredictorRecord(valueMap);
}

Example 4 with NominalValueRepresentation

use of org.knime.base.node.mine.treeensemble2.data.NominalValueRepresentation in project knime-core by knime.

the class NominalColumnHelperUtil method getRepOrFail.

static NominalValueRepresentation getRepOrFail(final Map<String, NominalValueRepresentation> val2Rep, final String value) {
    NominalValueRepresentation rep = val2Rep.get(value);
    CheckUtils.checkNotNull(rep, "There is no representation for value \"%s\".", value);
    return rep;
}

Example 5 with NominalValueRepresentation

use of org.knime.base.node.mine.treeensemble2.data.NominalValueRepresentation in project knime-core by knime.

the class ClassificationTreeModelExporter method addNodeContent.

/**
 * {@inheritDoc}
 */
@Override
protected void addNodeContent(final int nodeId, final Node pmmlNode, final TreeNodeClassification node) {
    pmmlNode.setScore(node.getMajorityClassName());
    float[] targetDistribution = node.getTargetDistribution();
    NominalValueRepresentation[] targetVals = node.getTargetMetaData().getValues();
    double sum = 0.0;
    for (float v : targetDistribution) {
        sum += v;
    }
    pmmlNode.setRecordCount(sum);
    // adding score distribution (class counts)
    for (int i = 0; i < targetDistribution.length; i++) {
        String className = targetVals[i].getNominalValue();
        double freq = targetDistribution[i];
        ScoreDistribution pmmlScoreDist = pmmlNode.addNewScoreDistribution();
        pmmlScoreDist.setValue(className);
        pmmlScoreDist.setRecordCount(freq);
    }
}