Examples with TreeEnsembleModelPortObject org.knime.base.node.mine.treeensemble.model.TreeEnsembleModelPortObject used on opensource projects

Example 6 with TreeEnsembleModelPortObject

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModelPortObject in project knime-core by knime.

the class TreeEnsembleModelExtractorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModelPortObject treeEnsembleModel = (TreeEnsembleModelPortObject) inObjects[0];
    DataTableSpec outSpec = createOutSpec();
    BufferedDataContainer container = exec.createDataContainer(outSpec, false, 0);
    int nrModels = treeEnsembleModel.getEnsembleModel().getNrModels();
    for (int i = 0; i < nrModels; i++) {
        PMMLPortObject pmmlObject = treeEnsembleModel.createDecisionTreePMMLPortObject(i);
        DataCell cell = PMMLCellFactory.create(pmmlObject.getPMMLValue().toString());
        RowKey key = RowKey.createRowKey(i);
        container.addRowToTable(new DefaultRow(key, cell));
        exec.checkCanceled();
        exec.setProgress(i / (double) nrModels, "Exported model " + (i + 1) + "/" + nrModels);
    }
    container.close();
    return new BufferedDataTable[] { container.getTable() };
}

Example 7 with TreeEnsembleModelPortObject

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModelPortObject 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 8 with TreeEnsembleModelPortObject

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModelPortObject in project knime-core by knime.

the class RandomForestRegressionLearnerNodeModel 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();
    TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
    ExecutionMonitor readInExec = exec.createSubProgress(0.1);
    ExecutionMonitor learnExec = exec.createSubProgress(0.8);
    ExecutionMonitor outOfBagExec = exec.createSubProgress(0.1);
    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 trees");
    TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
    TreeEnsembleModel model;
    try {
        model = learner.learnEnsemble(learnExec);
    } catch (ExecutionException e) {
        Throwable cause = e.getCause();
        if (cause instanceof Exception) {
            throw (Exception) cause;
        }
        throw e;
    }
    TreeEnsembleModelPortObject modelPortObject = new TreeEnsembleModelPortObject(ensembleSpec, model);
    learnExec.setProgress(1.0);
    exec.setMessage("Out of bag prediction");
    TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, modelPortObject, spec);
    outOfBagPredictor.setOutofBagFilter(learner.getRowSamples(), data.getTargetColumn());
    ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
    BufferedDataTable outOfBagTable = exec.createColumnRearrangeTable(t, outOfBagRearranger, outOfBagExec);
    BufferedDataTable colStatsTable = learner.createColumnStatisticTable(exec.createSubExecutionContext(0.0));
    m_ensembleModelPortObject = modelPortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { outOfBagTable, colStatsTable, modelPortObject };
}

Example 9 with TreeEnsembleModelPortObject

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModelPortObject in project knime-core by knime.

the class TreeEnsembleClassificationPredictorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModelPortObject model = (TreeEnsembleModelPortObject) inObjects[0];
    TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
    BufferedDataTable data = (BufferedDataTable) inObjects[1];
    DataTableSpec dataSpec = data.getDataTableSpec();
    final TreeEnsemblePredictor pred = new TreeEnsemblePredictor(modelSpec, model, dataSpec, m_configuration);
    ColumnRearranger rearranger = pred.getPredictionRearranger();
    BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
    return new BufferedDataTable[] { outTable };
}

Example 10 with TreeEnsembleModelPortObject

use of org.knime.base.node.mine.treeensemble.model.TreeEnsembleModelPortObject in project knime-core by knime.

the class TreeEnsembleShrinkerNodeModel method execute.

@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModel treeEnsemble = ((TreeEnsembleModelPortObject) inObjects[0]).getEnsembleModel();
    TreeEnsembleModelPortObject resultEnsemble;
    int resultSize = m_config.getResultSize(treeEnsemble.getNrModels());
    boolean shrink = true;
    if (!m_config.isResultSizeAutomatic()) {
        // Check if result size is valid
        if (resultSize < 1) {
            // Result size is to small, use 1
            setWarningMessage("The configured result size is smaller than 1, defaulting to 1");
            resultSize = 1;
        } else if (resultSize > treeEnsemble.getNrModels()) {
            // Result size is to big, just keep current ensemble
            setWarningMessage("The configured result size is bigger than the size of the input ensemble, defaulting to the input ensembles size");
            shrink = false;
        } else if (resultSize == treeEnsemble.getNrModels()) {
            // Result size is ensemble size -> we don't need to shrink
            shrink = false;
        }
    }
    // If our result size is not smaller than the current ensemble we don't have to do the following and therefore can save time
    if (shrink) {
        BufferedDataTable inData = (BufferedDataTable) inObjects[1];
        // Create shrinker
        TreeEnsembleShrinker shrinker = new TreeEnsembleShrinker(treeEnsemble, inData, m_config.getTargetColumn(), exec);
        // Shrink ensemble
        if (m_config.isResultSizeAutomatic()) {
            shrinker.autoShrink();
        } else {
            shrinker.shrinkTo(resultSize);
        }
        // Get shrunk ensemble
        TreeEnsembleModel newEnsemble = shrinker.getModel();
        // Push flow variable with archived accuracy
        pushFlowVariableDouble("Tree Ensemble Shrinker Prediction Accuracy", shrinker.getAccuracy());
        // Create port object for tree ensemble
        resultEnsemble = new TreeEnsembleModelPortObject(((TreeEnsembleModelPortObject) inObjects[0]).getSpec(), newEnsemble);
    } else {
        // We did not need to shrink just use input tree ensemble port object
        resultEnsemble = (TreeEnsembleModelPortObject) inObjects[0];
    }
    // Convert tree ensemble port object to PMML
    PMMLPortObject pmmlEnsemble = convertToPmmlEnsemble(resultEnsemble, exec);
    return new PortObject[] { pmmlEnsemble };
}