Examples with ExecutionContext org.knime.core.node.ExecutionContext used on opensource projects

Example 11 with ExecutionContext

use of org.knime.core.node.ExecutionContext in project knime-core by knime.

the class RandomForestProximityNodeModel method execute.

@Override
protected BufferedDataTable[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    TreeEnsembleModelPortObject model = (TreeEnsembleModelPortObject) inObjects[0];
    BufferedDataTable table1 = (BufferedDataTable) inObjects[1];
    BufferedDataTable table2 = (BufferedDataTable) inObjects[2];
    BufferedDataTable[] tables;
    if (table2 != null) {
        tables = new BufferedDataTable[] { table1, table2 };
    } else {
        tables = new BufferedDataTable[] { table1 };
    }
    ExecutionContext calcExec = exec.createSubExecutionContext(0.7);
    ExecutionContext writeExec = exec.createSubExecutionContext(0.3);
    exec.setMessage("Calculating Proximity");
    ProximityMatrix pm = null;
    ProximityMeasure proximityMeasure = ProximityMeasure.valueOf(m_proximityMeasure.getStringValue());
    switch(proximityMeasure) {
        case PathProximity:
            pm = new PathProximity(tables, model).calculatePathProximities(calcExec);
            break;
        case Proximity:
            pm = Proximity.calcProximities(tables, model, calcExec);
            break;
        default:
            throw new IllegalStateException("Illegal proximity measure encountered.");
    }
    exec.setMessage("Writing");
    return new BufferedDataTable[] { pm.createTable(writeExec) };
}

Example 12 with ExecutionContext

use of org.knime.core.node.ExecutionContext in project knime-core by knime.

the class RegressionTreePMMLPredictorNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            PMMLPortObject model = (PMMLPortObject) ((PortObjectInput) inputs[0]).getPortObject();
            DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
            Pair<RegressionTreeModel, RegressionTreeModelPortObjectSpec> treeSpecPair = importModel(model);
            final RegressionTreePredictor pred = new RegressionTreePredictor(treeSpecPair.getFirst(), treeSpecPair.getSecond(), dataSpec, m_configuration);
            ColumnRearranger rearranger = pred.getPredictionRearranger();
            StreamableFunction func = rearranger.createStreamableFunction(1, 0);
            func.runFinal(inputs, outputs, exec);
        }
    };
}

Example 13 with ExecutionContext

use of org.knime.core.node.ExecutionContext in project knime-core by knime.

the class RegressionTreePredictorNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            RegressionTreeModelPortObject model = (RegressionTreeModelPortObject) ((PortObjectInput) inputs[0]).getPortObject();
            DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
            final RegressionTreePredictor pred = new RegressionTreePredictor(model.getModel(), model.getSpec(), dataSpec, m_configuration);
            ColumnRearranger rearranger = pred.getPredictionRearranger();
            StreamableFunction func = rearranger.createStreamableFunction(1, 0);
            func.runFinal(inputs, outputs, exec);
        }
    };
}

Example 14 with ExecutionContext

use of org.knime.core.node.ExecutionContext in project knime-core by knime.

the class GradientBoostingClassificationPredictorNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            GradientBoostingModelPortObject model = (GradientBoostingModelPortObject) ((PortObjectInput) inputs[0]).getPortObject();
            TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
            DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
            final GradientBoostingPredictor<MultiClassGradientBoostedTreesModel> pred = new GradientBoostingPredictor<>((MultiClassGradientBoostedTreesModel) model.getEnsembleModel(), modelSpec, dataSpec, m_configuration);
            ColumnRearranger rearranger = pred.getPredictionRearranger();
            StreamableFunction func = rearranger.createStreamableFunction(1, 0);
            func.runFinal(inputs, outputs, exec);
        }
    };
}

Example 15 with ExecutionContext

use of org.knime.core.node.ExecutionContext in project knime-core by knime.

the class GradientBoostingPredictorNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            GradientBoostingModelPortObject model = (GradientBoostingModelPortObject) ((PortObjectInput) inputs[0]).getPortObject();
            TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
            DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
            final GradientBoostingPredictor<GradientBoostedTreesModel> pred = new GradientBoostingPredictor<>((GradientBoostedTreesModel) model.getEnsembleModel(), modelSpec, dataSpec, m_configuration);
            ColumnRearranger rearranger = pred.getPredictionRearranger();
            StreamableFunction func = rearranger.createStreamableFunction(1, 0);
            func.runFinal(inputs, outputs, exec);
        }
    };
}