Examples with MultiClassGradientBoostedTreesModel org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel used on opensource projects

Example 1 with MultiClassGradientBoostedTreesModel

use of org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel in project knime-core by knime.

the class GBTPMMLExporterNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    GradientBoostingModelPortObject gbtPO = (GradientBoostingModelPortObject) inObjects[0];
    AbstractGBTModelPMMLTranslator<?> translator;
    AbstractGradientBoostingModel gbtModel = gbtPO.getEnsembleModel();
    if (gbtModel instanceof GradientBoostedTreesModel) {
        translator = new RegressionGBTModelPMMLTranslator((GradientBoostedTreesModel) gbtModel, gbtPO.getSpec().getLearnTableSpec());
    } else if (gbtModel instanceof MultiClassGradientBoostedTreesModel) {
        translator = new ClassificationGBTModelPMMLTranslator((MultiClassGradientBoostedTreesModel) gbtModel, gbtPO.getSpec().getLearnTableSpec());
    } else {
        throw new IllegalArgumentException("Unknown gradient boosted trees model type '" + gbtModel.getClass().getSimpleName() + "'.");
    }
    PMMLPortObjectSpec pmmlSpec = createPMMLSpec(gbtPO.getSpec(), gbtModel);
    PMMLPortObject pmmlPO = new PMMLPortObject(pmmlSpec);
    pmmlPO.addModelTranslater(translator);
    return new PortObject[] { pmmlPO };
}

Example 2 with MultiClassGradientBoostedTreesModel

use of org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel 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 3 with MultiClassGradientBoostedTreesModel

use of org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel in project knime-core by knime.

the class GradientBoostingClassificationPredictorNodeModel method execute.

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

Example 4 with MultiClassGradientBoostedTreesModel

use of org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel in project knime-core by knime.

the class GradientBoostingPredictor method createPredictionRearranger.

/**
 * {@inheritDoc}
 */
@Override
protected ColumnRearranger createPredictionRearranger() throws InvalidSettingsException {
    TreeEnsembleModelPortObjectSpec modelSpec = getModelSpec();
    DataTableSpec dataSpec = getDataSpec();
    ColumnRearranger predictionRearranger;
    boolean hasPossibleValues = modelSpec.getTargetColumnPossibleValueMap() != null;
    if (modelSpec.getTargetColumn().getType().isCompatible(DoubleValue.class)) {
        predictionRearranger = new ColumnRearranger(dataSpec);
        @SuppressWarnings("unchecked") GradientBoostingPredictor<GradientBoostedTreesModel> pred = (GradientBoostingPredictor<GradientBoostedTreesModel>) this;
        predictionRearranger.append(GradientBoostingPredictorCellFactory.createFactory(pred));
    } else if (getConfiguration().isAppendClassConfidences() && !hasPossibleValues) {
        // can't add confidence columns (possible values unknown)
        predictionRearranger = null;
    } else {
        predictionRearranger = new ColumnRearranger(dataSpec);
        @SuppressWarnings("unchecked") GradientBoostingPredictor<MultiClassGradientBoostedTreesModel> pred = (GradientBoostingPredictor<MultiClassGradientBoostedTreesModel>) this;
        predictionRearranger.append(LKGradientBoostingPredictorCellFactory.createFactory(pred));
    }
    return predictionRearranger;
}

Example 5 with MultiClassGradientBoostedTreesModel

use of org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel in project knime-core by knime.

the class ClassificationGBTModelExporter method doWrite.

/**
 * {@inheritDoc}
 */
@Override
protected void doWrite(final MiningModel model) {
    Segmentation modelChain = model.addNewSegmentation();
    modelChain.setMultipleModelMethod(MULTIPLEMODELMETHOD.MODEL_CHAIN);
    MultiClassGradientBoostedTreesModel gbt = getGBTModel();
    // write one segment per class
    for (int i = 0; i < gbt.getNrClasses(); i++) {
        addClassSegment(modelChain, i);
    }
    // combine class predictions
    addAggregationSegment(modelChain);
}