Examples with DataColumnSpec org.knime.core.data.DataColumnSpec used on opensource projects

Example 61 with DataColumnSpec

use of org.knime.core.data.DataColumnSpec 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 62 with DataColumnSpec

use of org.knime.core.data.DataColumnSpec in project knime-core by knime.

the class LinRegLearnerNodeDialogPane method loadSettingsFrom.

/**
 * {@inheritDoc}
 */
@Override
protected void loadSettingsFrom(final NodeSettingsRO settings, final PortObjectSpec[] specs) throws NotConfigurableException {
    // must check if there are at least two numeric columns
    int numColsCount = 0;
    DataTableSpec dts = (DataTableSpec) specs[0];
    for (DataColumnSpec c : dts) {
        if (c.getType().isCompatible(DoubleValue.class)) {
            numColsCount++;
            if (numColsCount >= 2) {
                break;
            }
        }
    }
    if (numColsCount < 2) {
        throw new NotConfigurableException("Too few numeric columns " + "(need at least 2): " + numColsCount);
    }
    boolean includeAll = settings.getBoolean(LinRegLearnerNodeModel.CFG_VARIATES_USE_ALL, false);
    String[] includes = settings.getStringArray(LinRegLearnerNodeModel.CFG_VARIATES, new String[0]);
    String target = settings.getString(LinRegLearnerNodeModel.CFG_TARGET, null);
    boolean isCalcError = settings.getBoolean(LinRegLearnerNodeModel.CFG_CALC_ERROR, true);
    int first = settings.getInt(LinRegLearnerNodeModel.CFG_FROMROW, 1);
    int count = settings.getInt(LinRegLearnerNodeModel.CFG_ROWCNT, 10000);
    m_selectionPanel.update(dts, target);
    m_filterPanel.setKeepAllSelected(includeAll);
    // if includes list is empty, put everything into the include list
    m_filterPanel.update(dts, includes.length == 0, includes);
    // must hide the target from filter panel
    // updating m_filterPanel first does not work as the first
    // element in the spec will always be in the exclude list.
    String selected = m_selectionPanel.getSelectedColumn();
    if (selected != null) {
        DataColumnSpec colSpec = dts.getColumnSpec(selected);
        m_filterPanel.hideColumns(colSpec);
    }
    m_isCalcErrorChecker.setSelected(isCalcError);
    m_firstSpinner.setValue(first);
    m_countSpinner.setValue(count);
}

Example 63 with DataColumnSpec

use of org.knime.core.data.DataColumnSpec in project knime-core by knime.

the class LinRegLearnerNodeModel method computeIncludes.

/**
 * Determines the list of variate columns (learning columns). This is
 * either the m_includes[] field or, if m_includeAll is set, the list
 * of double-compatible columns in the input table spec (excluding the
 * response column).
 * @param in Spec contributing the column list
 * @return A new array containg the variates
 * @throws InvalidSettingsException If no double-compatible learning columns
 * exist in the input table.
 */
private String[] computeIncludes(final DataTableSpec in) throws InvalidSettingsException {
    String[] includes;
    if (m_includeAll) {
        List<String> includeList = new ArrayList<String>();
        for (DataColumnSpec s : in) {
            if (s.getType().isCompatible(DoubleValue.class)) {
                String name = s.getName();
                if (!name.equals(m_target)) {
                    includeList.add(name);
                }
            }
        }
        includes = includeList.toArray(new String[includeList.size()]);
        if (includes.length == 0) {
            throw new InvalidSettingsException("No double-compatible " + "variables (learning columns) in input table");
        }
    } else {
        if (m_includes == null) {
            throw new InvalidSettingsException("No settings available");
        }
        includes = m_includes.clone();
    }
    return includes;
}

Example 64 with DataColumnSpec

use of org.knime.core.data.DataColumnSpec in project knime-core by knime.

the class RegressionPredictorNodeDialogPane method loadSettingsFrom.

/**
 * {@inheritDoc}
 */
@Override
protected void loadSettingsFrom(final NodeSettingsRO settings, final PortObjectSpec[] specs) throws NotConfigurableException {
    RegressionPredictorSettings s = new RegressionPredictorSettings();
    s.loadSettingsForDialog(settings);
    m_hasCustomPredictionName.setSelected(s.getHasCustomPredictionName());
    PMMLPortObjectSpec portSpec = (PMMLPortObjectSpec) specs[0];
    DataTableSpec tableSpec = (DataTableSpec) specs[1];
    if (s.getCustomPredictionName() != null) {
        m_customPredictionName.setText(s.getCustomPredictionName());
    } else {
        try {
            DataColumnSpec[] outSpec = RegressionPredictorCellFactory.createColumnSpec(portSpec, tableSpec, new RegressionPredictorSettings());
            m_customPredictionName.setText(outSpec[outSpec.length - 1].getName());
        } catch (InvalidSettingsException e) {
        // Open dialog and give a chance define settings
        }
    }
    m_includeProbs.setSelected(s.getIncludeProbabilities());
    m_probColumnSuffix.setText(s.getPropColumnSuffix());
    updateEnableState();
}

Example 65 with DataColumnSpec

use of org.knime.core.data.DataColumnSpec in project knime-core by knime.

the class RegressionPredictorNodeModel method createRearranger.

private ColumnRearranger createRearranger(final PMMLGeneralRegressionContent content, final PMMLPortObjectSpec pmmlSpec, final DataTableSpec inDataSpec) throws InvalidSettingsException {
    if (content == null) {
        throw new InvalidSettingsException("No input");
    }
    // the predictor can only predict linear regression models
    if (!(content.getModelType().equals(ModelType.multinomialLogistic) || content.getModelType().equals(ModelType.generalLinear))) {
        throw new InvalidSettingsException("Model Type: " + content.getModelType() + " is not supported.");
    }
    if (content.getModelType().equals(ModelType.generalLinear) && !content.getFunctionName().equals(FunctionName.regression)) {
        throw new InvalidSettingsException("Function Name: " + content.getFunctionName() + " is not supported for linear regression.");
    }
    if (content.getModelType().equals(ModelType.multinomialLogistic) && !content.getFunctionName().equals(FunctionName.classification)) {
        throw new InvalidSettingsException("Function Name: " + content.getFunctionName() + " is not supported for logistic regression.");
    }
    // are nominal values
    for (PMMLPredictor factor : content.getFactorList()) {
        DataColumnSpec columnSpec = inDataSpec.getColumnSpec(factor.getName());
        if (null == columnSpec) {
            throw new InvalidSettingsException("The column \"" + factor.getName() + "\" is in the model but not in given table.");
        }
        if (!columnSpec.getType().isCompatible(NominalValue.class)) {
            throw new InvalidSettingsException("The column \"" + factor.getName() + "\" is supposed to be nominal.");
        }
    }
    // check if all covariates are in the given data table and that they
    // are numeric values
    Pattern pattern = Pattern.compile("(.*)\\[\\d+\\]");
    for (PMMLPredictor covariate : content.getCovariateList()) {
        DataColumnSpec columnSpec = inDataSpec.getColumnSpec(covariate.getName());
        if (null == columnSpec) {
            Matcher matcher = pattern.matcher(covariate.getName());
            boolean found = matcher.matches();
            columnSpec = inDataSpec.getColumnSpec(matcher.group(1));
            found = found && null != columnSpec;
            if (!found) {
                throw new InvalidSettingsException("The column \"" + covariate.getName() + "\" is in the model but not in given table.");
            }
        }
        if (columnSpec != null && !columnSpec.getType().isCompatible(DoubleValue.class) && !(content.getVectorLengths().containsKey(columnSpec.getName()) && ((columnSpec.getType().isCollectionType() && columnSpec.getType().getCollectionElementType().isCompatible(DoubleValue.class)) || columnSpec.getType().isCompatible(BitVectorValue.class) || columnSpec.getType().isCompatible(ByteVectorValue.class)))) {
            throw new InvalidSettingsException("The column \"" + covariate.getName() + "\" is supposed to be numeric.");
        }
    }
    ColumnRearranger c = new ColumnRearranger(inDataSpec);
    if (content.getModelType().equals(ModelType.generalLinear)) {
        c.append(new LinReg2Predictor(content, inDataSpec, pmmlSpec, pmmlSpec.getTargetFields().get(0), m_settings));
    } else {
        c.append(new LogRegPredictor(content, inDataSpec, pmmlSpec, pmmlSpec.getTargetFields().get(0), m_settings));
    }
    return c;
}