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

Example 6 with GradientBoostingModelPortObject

use of org.knime.base.node.mine.treeensemble2.model.GradientBoostingModelPortObject 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);
        }
    };
}

Example 7 with GradientBoostingModelPortObject

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

the class GradientBoostingPredictorNodeModel 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();
    final GradientBoostingPredictor<GradientBoostedTreesModel> pred = new GradientBoostingPredictor<>((GradientBoostedTreesModel) model.getEnsembleModel(), modelSpec, dataSpec, m_configuration);
    ColumnRearranger rearranger = pred.getPredictionRearranger();
    BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
    return new BufferedDataTable[] { outTable };
}

Example 8 with GradientBoostingModelPortObject

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

the class GradientBoostingClassificationLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inData[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);
    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");
    AbstractGradientBoostingLearner learner = new LKGradientBoostedTreesLearner(m_configuration, data);
    AbstractGradientBoostingModel model;
    // m_configuration.setMissingValueHandling(MissingValueHandling.XGBoost);
    // try {
    model = learner.learn(learnExec);
    // } catch (ExecutionException e) {
    // Throwable cause = e.getCause();
    // if (cause instanceof Exception) {
    // throw (Exception)cause;
    // }
    // throw e;
    // }
    GradientBoostingModelPortObject modelPortObject = new GradientBoostingModelPortObject(ensembleSpec, model);
    learnExec.setProgress(1.0);
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { modelPortObject };
}

Example 9 with GradientBoostingModelPortObject

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

the class GradientBoostingRegressionLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inData[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");
    AbstractGradientBoostingLearner learner = new MGradientBoostedTreesLearner(m_configuration, data);
    AbstractGradientBoostingModel model;
    // try {
    model = learner.learn(learnExec);
    // } catch (ExecutionException e) {
    // Throwable cause = e.getCause();
    // if (cause instanceof Exception) {
    // throw (Exception)cause;
    // }
    // throw e;
    // }
    GradientBoostingModelPortObject modelPortObject = new GradientBoostingModelPortObject(ensembleSpec, model);
    learnExec.setProgress(1.0);
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { modelPortObject };
}

Example 10 with GradientBoostingModelPortObject

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

the class GradientBoostingPMMLPredictorNodeModel 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];
            GradientBoostingModelPortObject gbt = importModel(model);
            final GradientBoostingPredictor<?> pred = new GradientBoostingPredictor<>(gbt.getEnsembleModel(), gbt.getSpec(), dataSpec, m_configuration);
            ColumnRearranger rearranger = pred.getPredictionRearranger();
            StreamableFunction func = rearranger.createStreamableFunction(1, 0);
            func.runFinal(inputs, outputs, exec);
        }
    };
}