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

Example 11 with TreeEnsembleModel

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

the class TreeEnsembleLearnerNodeView method newModel.

private void newModel(final int index) {
    assert SwingUtilities.isEventDispatchThread();
    final MODEL nodeModel = getNodeModel();
    TreeEnsembleModel model = nodeModel.getEnsembleModel();
    DataTable hiliteRowSample = nodeModel.getHiliteRowSample();
    UpdateTreeWorker updateWorker = new UpdateTreeWorker(hiliteRowSample, model, index);
    UpdateTreeWorker old = m_updateWorkerRef.getAndSet(updateWorker);
    if (old != null) {
        old.cancel(true);
    }
    updateWorker.execute();
}

Example 12 with TreeEnsembleModel

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

the class TreeEnsembleRegressionLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inObjects[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");
    TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
    TreeEnsembleModel model;
    try {
        model = learner.learnEnsemble(learnExec);
    } catch (ExecutionException e) {
        Throwable cause = e.getCause();
        if (cause instanceof Exception) {
            throw (Exception) cause;
        }
        throw e;
    }
    TreeEnsembleModelPortObject modelPortObject = TreeEnsembleModelPortObject.createPortObject(ensembleSpec, model, exec.createFileStore(UUID.randomUUID().toString() + ""));
    learnExec.setProgress(1.0);
    exec.setMessage("Out of bag prediction");
    TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, modelPortObject, spec);
    outOfBagPredictor.setOutofBagFilter(learner.getRowSamples(), data.getTargetColumn());
    ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
    BufferedDataTable outOfBagTable = exec.createColumnRearrangeTable(t, outOfBagRearranger, outOfBagExec);
    BufferedDataTable colStatsTable = learner.createColumnStatisticTable(exec.createSubExecutionContext(0.0));
    m_ensembleModelPortObject = modelPortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { outOfBagTable, colStatsTable, modelPortObject };
}

Example 13 with TreeEnsembleModel

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

the class TreeEnsembleRegressionPredictorCellFactory method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    TreeEnsembleModelPortObject modelObject = m_predictor.getModelObject();
    TreeEnsemblePredictorConfiguration cfg = m_predictor.getConfiguration();
    final TreeEnsembleModel ensembleModel = modelObject.getEnsembleModel();
    int size = 1;
    final boolean appendConfidence = cfg.isAppendPredictionConfidence();
    final boolean appendModelCount = cfg.isAppendModelCount();
    if (appendConfidence) {
        size += 1;
    }
    if (appendModelCount) {
        size += 1;
    }
    final boolean hasOutOfBagFilter = m_predictor.hasOutOfBagFilter();
    DataCell[] result = new DataCell[size];
    DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
    PredictorRecord record = ensembleModel.createPredictorRecord(filterRow, m_learnSpec);
    if (record == null) {
        // missing value
        Arrays.fill(result, DataType.getMissingCell());
        return result;
    }
    Mean mean = new Mean();
    Variance variance = new Variance();
    final int nrModels = ensembleModel.getNrModels();
    for (int i = 0; i < nrModels; i++) {
        if (hasOutOfBagFilter && m_predictor.isRowPartOfTrainingData(row.getKey(), i)) {
        // ignore, row was used to train the model
        } else {
            TreeModelRegression m = ensembleModel.getTreeModelRegression(i);
            TreeNodeRegression match = m.findMatchingNode(record);
            double nodeMean = match.getMean();
            mean.increment(nodeMean);
            variance.increment(nodeMean);
        }
    }
    int nrValidModels = (int) mean.getN();
    int index = 0;
    result[index++] = nrValidModels == 0 ? DataType.getMissingCell() : new DoubleCell(mean.getResult());
    if (appendConfidence) {
        result[index++] = nrValidModels == 0 ? DataType.getMissingCell() : new DoubleCell(variance.getResult());
    }
    if (appendModelCount) {
        result[index++] = new IntCell(nrValidModels);
    }
    return result;
}

Example 14 with TreeEnsembleModel

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

the class Proximity method calcProximities.

public static ProximityMatrix calcProximities(final BufferedDataTable[] tables, final TreeEnsembleModelPortObject modelPortObject, final ExecutionContext exec) throws InvalidSettingsException, InterruptedException, ExecutionException, CanceledExecutionException {
    ProximityMatrix proximityMatrix = null;
    boolean optionalTable = false;
    switch(tables.length) {
        case 1:
            if (tables[0].size() <= 65500) {
                proximityMatrix = new SingleTableProximityMatrix(tables[0]);
            } else {
                // this is unfortunate and we should maybe think of a different solution
                proximityMatrix = new TwoTablesProximityMatrix(tables[0], tables[0]);
            }
            break;
        case 2:
            optionalTable = true;
            proximityMatrix = new TwoTablesProximityMatrix(tables[0], tables[1]);
            break;
        default:
            throw new IllegalArgumentException("Currently only up to two tables are supported.");
    }
    final TreeEnsembleModelPortObjectSpec modelSpec = modelPortObject.getSpec();
    final TreeEnsembleModel ensembleModel = modelPortObject.getEnsembleModel();
    int[][] learnColIndicesInTables = null;
    if (optionalTable) {
        learnColIndicesInTables = new int[][] { modelSpec.calculateFilterIndices(tables[0].getDataTableSpec()), modelSpec.calculateFilterIndices(tables[1].getDataTableSpec()) };
    } else {
        learnColIndicesInTables = new int[][] { modelSpec.calculateFilterIndices(tables[0].getDataTableSpec()) };
    }
    final ThreadPool tp = KNIMEConstants.GLOBAL_THREAD_POOL;
    final int procCount = 3 * Runtime.getRuntime().availableProcessors() / 2;
    final Semaphore semaphore = new Semaphore(procCount);
    final AtomicReference<Throwable> proxThrowableRef = new AtomicReference<Throwable>();
    final int nrTrees = ensembleModel.getNrModels();
    final Future<?>[] calcFutures = new Future<?>[nrTrees];
    exec.setProgress(0, "Starting proximity calculation per tree.");
    for (int i = 0; i < nrTrees; i++) {
        semaphore.acquire();
        finishedTree(i, exec, nrTrees);
        checkThrowable(proxThrowableRef);
        AbstractTreeModel treeModel = ensembleModel.getTreeModel(i);
        ExecutionMonitor subExec = exec.createSubProgress(0.0);
        if (optionalTable) {
            calcFutures[i] = tp.enqueue(new TwoTablesProximityCalcRunnable(proximityMatrix, tables, learnColIndicesInTables, treeModel, modelPortObject, semaphore, proxThrowableRef, subExec));
        } else {
            calcFutures[i] = tp.enqueue(new SingleTableProximityCalcRunnable(proximityMatrix, tables, learnColIndicesInTables, treeModel, modelPortObject, semaphore, proxThrowableRef, subExec));
        }
    }
    for (int i = 0; i < procCount; i++) {
        semaphore.acquire();
        finishedTree(nrTrees - procCount + i, exec, nrTrees);
    }
    for (Future<?> future : calcFutures) {
        try {
            future.get();
        } catch (Exception e) {
            proxThrowableRef.compareAndSet(null, e);
        }
    }
    checkThrowable(proxThrowableRef);
    proximityMatrix.normalize(1.0 / nrTrees);
    return proximityMatrix;
}

Example 15 with TreeEnsembleModel

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

the class RandomForestClassificationLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inObjects[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);
    Map<String, DataCell> targetValueMap = ensembleSpec.getTargetColumnPossibleValueMap();
    if (targetValueMap == null) {
        throw new InvalidSettingsException("The target column does not " + "have possible values assigned. Most likely it " + "has too many different distinct values (learning an ID " + "column?) Fix it by preprocessing the table using " + "a \"Domain Calculator\".");
    }
    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");
    // Use xgboost missing value handling
    m_configuration.setMissingValueHandling(MissingValueHandling.XGBoost);
    TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
    TreeEnsembleModel model;
    try {
        model = learner.learnEnsemble(learnExec);
    } catch (ExecutionException e) {
        Throwable cause = e.getCause();
        if (cause instanceof Exception) {
            throw (Exception) cause;
        }
        throw e;
    }
    TreeEnsembleModelPortObject modelPortObject = TreeEnsembleModelPortObject.createPortObject(ensembleSpec, model, exec.createFileStore("TreeEnsemble"));
    learnExec.setProgress(1.0);
    exec.setMessage("Out of bag prediction");
    TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, modelPortObject, spec);
    outOfBagPredictor.setOutofBagFilter(learner.getRowSamples(), data.getTargetColumn());
    ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
    BufferedDataTable outOfBagTable = exec.createColumnRearrangeTable(t, outOfBagRearranger, outOfBagExec);
    BufferedDataTable colStatsTable = learner.createColumnStatisticTable(exec.createSubExecutionContext(0.0));
    m_ensembleModelPortObject = modelPortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { outOfBagTable, colStatsTable, modelPortObject };
}