Examples with PMMLPortObject org.knime.core.node.port.pmml.PMMLPortObject used on opensource projects

Example 6 with PMMLPortObject

use of org.knime.core.node.port.pmml.PMMLPortObject in project knime-core by knime.

the class DecTreePredictorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
public PortObject[] execute(final PortObject[] inPorts, final ExecutionContext exec) throws CanceledExecutionException, Exception {
    exec.setMessage("Decision Tree Predictor: Loading predictor...");
    PMMLPortObject port = (PMMLPortObject) inPorts[INMODELPORT];
    List<Node> models = port.getPMMLValue().getModels(PMMLModelType.TreeModel);
    if (models.isEmpty()) {
        String msg = "Decision Tree evaluation failed: " + "No tree model found.";
        LOGGER.error(msg);
        throw new RuntimeException(msg);
    }
    PMMLDecisionTreeTranslator trans = new PMMLDecisionTreeTranslator();
    port.initializeModelTranslator(trans);
    DecisionTree decTree = trans.getDecisionTree();
    decTree.resetColorInformation();
    BufferedDataTable inData = (BufferedDataTable) inPorts[INDATAPORT];
    // get column with color information
    String colorColumn = null;
    for (DataColumnSpec s : inData.getDataTableSpec()) {
        if (s.getColorHandler() != null) {
            colorColumn = s.getName();
            break;
        }
    }
    decTree.setColorColumn(colorColumn);
    exec.setMessage("Decision Tree Predictor: start execution.");
    PortObjectSpec[] inSpecs = new PortObjectSpec[] { inPorts[0].getSpec(), inPorts[1].getSpec() };
    DataTableSpec outSpec = createOutTableSpec(inSpecs);
    BufferedDataContainer outData = exec.createDataContainer(outSpec);
    long coveredPattern = 0;
    long nrPattern = 0;
    long rowCount = 0;
    long numberRows = inData.size();
    exec.setMessage("Classifying...");
    for (DataRow thisRow : inData) {
        DataCell cl = null;
        LinkedHashMap<String, Double> classDistrib = null;
        try {
            Pair<DataCell, LinkedHashMap<DataCell, Double>> pair = decTree.getWinnerAndClasscounts(thisRow, inData.getDataTableSpec());
            cl = pair.getFirst();
            LinkedHashMap<DataCell, Double> classCounts = pair.getSecond();
            classDistrib = getDistribution(classCounts);
            if (coveredPattern < m_maxNumCoveredPattern.getIntValue()) {
                // remember this one for HiLite support
                decTree.addCoveredPattern(thisRow, inData.getDataTableSpec());
                coveredPattern++;
            } else {
                // too many patterns for HiLite - at least remember color
                decTree.addCoveredColor(thisRow, inData.getDataTableSpec());
            }
            nrPattern++;
        } catch (Exception e) {
            LOGGER.error("Decision Tree evaluation failed: " + e.getMessage());
            throw e;
        }
        if (cl == null) {
            LOGGER.error("Decision Tree evaluation failed: result empty");
            throw new Exception("Decision Tree evaluation failed.");
        }
        DataCell[] newCells = new DataCell[outSpec.getNumColumns()];
        int numInCells = thisRow.getNumCells();
        for (int i = 0; i < numInCells; i++) {
            newCells[i] = thisRow.getCell(i);
        }
        if (m_showDistribution.getBooleanValue()) {
            for (int i = numInCells; i < newCells.length - 1; i++) {
                String predClass = outSpec.getColumnSpec(i).getName();
                if (classDistrib != null && classDistrib.get(predClass) != null) {
                    newCells[i] = new DoubleCell(classDistrib.get(predClass));
                } else {
                    newCells[i] = new DoubleCell(0.0);
                }
            }
        }
        newCells[newCells.length - 1] = cl;
        outData.addRowToTable(new DefaultRow(thisRow.getKey(), newCells));
        rowCount++;
        if (rowCount % 100 == 0) {
            exec.setProgress(rowCount / (double) numberRows, "Classifying... Row " + rowCount + " of " + numberRows);
        }
        exec.checkCanceled();
    }
    if (coveredPattern < nrPattern) {
        // let the user know that we did not store all available pattern
        // for HiLiting.
        this.setWarningMessage("Tree only stored first " + m_maxNumCoveredPattern.getIntValue() + " (of " + nrPattern + ") rows for HiLiting!");
    }
    outData.close();
    m_decTree = decTree;
    exec.setMessage("Decision Tree Predictor: end execution.");
    return new BufferedDataTable[] { outData.getTable() };
}

Example 7 with PMMLPortObject

use of org.knime.core.node.port.pmml.PMMLPortObject in project knime-core by knime.

the class MLPPredictorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
public PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable testdata = (BufferedDataTable) inData[1];
    PMMLPortObject pmmlPort = (PMMLPortObject) inData[0];
    List<Node> models = pmmlPort.getPMMLValue().getModels(PMMLModelType.NeuralNetwork);
    if (models.isEmpty()) {
        String msg = "Neural network evaluation failed: " + "No neural network model found.";
        LOGGER.error(msg);
        throw new RuntimeException(msg);
    }
    PMMLNeuralNetworkTranslator trans = new PMMLNeuralNetworkTranslator();
    pmmlPort.initializeModelTranslator(trans);
    m_mlp = trans.getMLP();
    m_columns = getLearningColumnIndices(testdata.getDataTableSpec(), pmmlPort.getSpec());
    DataColumnSpec targetCol = pmmlPort.getSpec().getTargetCols().iterator().next();
    MLPClassificationFactory mymlp;
    /*
         * Regression
         */
    if (m_mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
        mymlp = new MLPClassificationFactory(true, m_columns, targetCol);
    } else if (m_mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
        /*
             * Classification
             */
        mymlp = new MLPClassificationFactory(false, m_columns, targetCol);
    } else {
        throw new Exception("Unsupported Mode: " + m_mlp.getMode());
    }
    ColumnRearranger colre = new ColumnRearranger(testdata.getDataTableSpec());
    colre.append(mymlp);
    BufferedDataTable bdt = exec.createColumnRearrangeTable(testdata, colre, exec);
    return new BufferedDataTable[] { bdt };
}

Example 8 with PMMLPortObject

use of org.knime.core.node.port.pmml.PMMLPortObject in project knime-core by knime.

the class RegressionPredictorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
public PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    PMMLPortObject port = (PMMLPortObject) inData[0];
    List<Node> models = port.getPMMLValue().getModels(PMMLModelType.GeneralRegressionModel);
    if (models.isEmpty()) {
        LOGGER.warn("No regression models in the input PMML.");
        @SuppressWarnings("deprecation") org.knime.base.node.mine.regression.predict.RegressionPredictorNodeModel regrPredictor = new org.knime.base.node.mine.regression.predict.RegressionPredictorNodeModel();
        @SuppressWarnings("deprecation") PortObject[] regrPredOut = regrPredictor.execute(inData, exec);
        if (regrPredOut.length > 0 && regrPredOut[0] instanceof BufferedDataTable) {
            BufferedDataTable regrPredOutTable = (BufferedDataTable) regrPredOut[0];
            // replace name of prediction column (the last column of regrPredOutTable)
            return new PortObject[] { adjustSpecOfRegressionPredictorTable(regrPredOutTable, inData, exec) };
        } else {
            return regrPredOut;
        }
    }
    PMMLGeneralRegressionTranslator trans = new PMMLGeneralRegressionTranslator();
    port.initializeModelTranslator(trans);
    BufferedDataTable data = (BufferedDataTable) inData[1];
    DataTableSpec spec = data.getDataTableSpec();
    ColumnRearranger c = createRearranger(trans.getContent(), port.getSpec(), spec);
    BufferedDataTable out = exec.createColumnRearrangeTable(data, c, exec);
    return new BufferedDataTable[] { out };
}

Example 9 with PMMLPortObject

use of org.knime.core.node.port.pmml.PMMLPortObject in project knime-core by knime.

the class CategoryToNumberNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    if (m_settings.getIncludedColumns().length == 0) {
        // nothing to convert, let's return the input table.
        setWarningMessage("No columns selected," + " returning input.");
    }
    BufferedDataTable inData = (BufferedDataTable) inObjects[0];
    DataTableSpec inSpec = (DataTableSpec) inObjects[0].getSpec();
    ColumnRearranger rearranger = createRearranger(inSpec);
    BufferedDataTable outTable = exec.createColumnRearrangeTable(inData, rearranger, exec);
    // the optional PMML in port (can be null)
    PMMLPortObject inPMMLPort = (PMMLPortObject) inObjects[1];
    PMMLPortObjectSpecCreator creator = new PMMLPortObjectSpecCreator(inPMMLPort, rearranger.createSpec());
    PMMLPortObject outPMMLPort = new PMMLPortObject(creator.createSpec(), inPMMLPort);
    for (CategoryToNumberCellFactory factory : m_factories) {
        PMMLMapValuesTranslator trans = new PMMLMapValuesTranslator(factory.getConfig(), new DerivedFieldMapper(inPMMLPort));
        outPMMLPort.addGlobalTransformations(trans.exportToTransDict());
    }
    return new PortObject[] { outTable, outPMMLPort };
}

Example 10 with PMMLPortObject

use of org.knime.core.node.port.pmml.PMMLPortObject in project knime-core by knime.

the class RegressionTreePMMLTranslatorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    final RegressionTreeModelPortObject treePO = (RegressionTreeModelPortObject) inObjects[0];
    final RegressionTreeModel model = treePO.getModel();
    final RegressionTreeModelPortObjectSpec treeSpec = treePO.getSpec();
    PMMLPortObjectSpec pmmlSpec = createPMMLSpec(treeSpec, model);
    PMMLPortObject portObject = new PMMLPortObject(pmmlSpec);
    final TreeModelRegression tree = model.getTreeModel();
    final RegressionTreeModelPMMLTranslator translator = new RegressionTreeModelPMMLTranslator(tree, model.getMetaData(), treeSpec.getLearnTableSpec());
    portObject.addModelTranslater(translator);
    if (translator.hasWarning()) {
        setWarningMessage(translator.getWarning());
    }
    return new PortObject[] { portObject };
}