Examples with PMMLSimplePredicate org.knime.base.node.mine.decisiontree2.PMMLSimplePredicate used on opensource projects

Example 11 with PMMLSimplePredicate

use of org.knime.base.node.mine.decisiontree2.PMMLSimplePredicate in project knime-core by knime.

the class DecisionTreeLearnerNodeModel method buildTree.

/**
 * Recursively induces the decision tree.
 *
 * @param table the {@link InMemoryTable} representing the data for this
 *            node to determine the split and after that perform
 *            partitioning
 * @param exec the execution context for progress information
 * @param depth the current recursion depth
 */
private DecisionTreeNode buildTree(final InMemoryTable table, final ExecutionContext exec, final int depth, final SplitQualityMeasure splitQualityMeasure, final ParallelProcessing parallelProcessing) throws CanceledExecutionException, IllegalAccessException {
    exec.checkCanceled();
    // derive this node's id from the counter
    int nodeId = m_counter.getAndIncrement();
    DataCell majorityClass = table.getMajorityClassAsCell();
    LinkedHashMap<DataCell, Double> frequencies = table.getClassFrequencies();
    // if the distribution allows for a leaf
    if (table.isPureEnough()) {
        // free memory
        table.freeUnderlyingDataRows();
        double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
        exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
        return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
    } else {
        // find the best splits for all attributes
        SplitFinder splittFinder = new SplitFinder(table, splitQualityMeasure, m_averageSplitpoint.getBooleanValue(), m_minNumberRecordsPerNode.getIntValue(), m_binaryNominalSplitMode.getBooleanValue(), m_maxNumNominalsForCompleteComputation.getIntValue());
        // check for enough memory
        checkMemory();
        // get the best split among the best attribute splits
        Split split = splittFinder.getSplit();
        // if no best split could be evaluated, create a leaf node
        if (split == null || !split.isValidSplit()) {
            table.freeUnderlyingDataRows();
            double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
            exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
            return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
        }
        // partition the attribute lists according to this split
        Partitioner partitioner = new Partitioner(table, split, m_minNumberRecordsPerNode.getIntValue());
        if (!partitioner.couldBeUsefulPartitioned()) {
            table.freeUnderlyingDataRows();
            double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
            exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
            return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
        }
        // get the just created partitions
        InMemoryTable[] partitionTables = partitioner.getPartitionTables();
        // recursively build the  child nodes
        DecisionTreeNode[] children = new DecisionTreeNode[partitionTables.length];
        ArrayList<ParallelBuilding> threads = new ArrayList<ParallelBuilding>();
        int i = 0;
        for (InMemoryTable partitionTable : partitionTables) {
            exec.checkCanceled();
            if (partitionTable.getNumberDataRows() * m_numberAttributes < 10000 || !parallelProcessing.isThreadAvailable()) {
                children[i] = buildTree(partitionTable, exec, depth + 1, splitQualityMeasure, parallelProcessing);
            } else {
                String threadName = "Build thread, node: " + nodeId + "." + i;
                ParallelBuilding buildThread = new ParallelBuilding(threadName, partitionTable, exec, depth + 1, i, splitQualityMeasure, parallelProcessing);
                LOGGER.debug("Start new parallel building thread: " + threadName);
                threads.add(buildThread);
                buildThread.start();
            }
            i++;
        }
        // already assigned to the child array
        for (ParallelBuilding buildThread : threads) {
            children[buildThread.getThreadIndex()] = buildThread.getResultNode();
            exec.checkCanceled();
            if (buildThread.getException() != null) {
                for (ParallelBuilding buildThread2 : threads) {
                    buildThread2.stop();
                }
                throw new RuntimeException(buildThread.getException().getMessage());
            }
        }
        threads.clear();
        if (split instanceof SplitContinuous) {
            double splitValue = ((SplitContinuous) split).getBestSplitValue();
            // return new DecisionTreeNodeSplitContinuous(nodeId,
            // majorityClass, frequencies, split
            // .getSplitAttributeName(), children, splitValue);
            String splitAttribute = split.getSplitAttributeName();
            PMMLPredicate[] splitPredicates = new PMMLPredicate[] { new PMMLSimplePredicate(splitAttribute, PMMLOperator.LESS_OR_EQUAL, Double.toString(splitValue)), new PMMLSimplePredicate(splitAttribute, PMMLOperator.GREATER_THAN, Double.toString(splitValue)) };
            return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
        } else if (split instanceof SplitNominalNormal) {
            // else the attribute is nominal
            DataCell[] splitValues = ((SplitNominalNormal) split).getSplitValues();
            // return new DecisionTreeNodeSplitNominal(nodeId, majorityClass,
            // frequencies, split.getSplitAttributeName(),
            // splitValues, children);
            int num = children.length;
            PMMLPredicate[] splitPredicates = new PMMLPredicate[num];
            String splitAttribute = split.getSplitAttributeName();
            for (int j = 0; j < num; j++) {
                splitPredicates[j] = new PMMLSimplePredicate(splitAttribute, PMMLOperator.EQUAL, splitValues[j].toString());
            }
            return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
        } else {
            // binary nominal
            SplitNominalBinary splitNominalBinary = (SplitNominalBinary) split;
            DataCell[] splitValues = splitNominalBinary.getSplitValues();
            // return new DecisionTreeNodeSplitNominalBinary(nodeId,
            // majorityClass, frequencies, split
            // .getSplitAttributeName(), splitValues,
            // splitNominalBinary.getIntMappingsLeftPartition(),
            // splitNominalBinary.getIntMappingsRightPartition(),
            // children/* children[0]=left, ..[1] right */);
            String splitAttribute = split.getSplitAttributeName();
            int[][] indices = new int[][] { splitNominalBinary.getIntMappingsLeftPartition(), splitNominalBinary.getIntMappingsRightPartition() };
            PMMLPredicate[] splitPredicates = new PMMLPredicate[2];
            for (int j = 0; j < splitPredicates.length; j++) {
                PMMLSimpleSetPredicate pred = null;
                pred = new PMMLSimpleSetPredicate(splitAttribute, PMMLSetOperator.IS_IN);
                pred.setArrayType(PMMLArrayType.STRING);
                LinkedHashSet<String> values = new LinkedHashSet<String>();
                for (int index : indices[j]) {
                    values.add(splitValues[index].toString());
                }
                pred.setValues(values);
                splitPredicates[j] = pred;
            }
            return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
        }
    }
}

Example 12 with PMMLSimplePredicate

use of org.knime.base.node.mine.decisiontree2.PMMLSimplePredicate in project knime-core by knime.

the class DecisionTreeLearnerNodeModel2 method buildTree.

/**
 * Recursively induces the decision tree.
 *
 * @param table the {@link InMemoryTable} representing the data for this
 *            node to determine the split and after that perform
 *            partitioning
 * @param exec the execution context for progress information
 * @param depth the current recursion depth
 */
private DecisionTreeNode buildTree(final InMemoryTable table, final ExecutionContext exec, final int depth, final SplitQualityMeasure splitQualityMeasure, final ParallelProcessing parallelProcessing, final int firstSplitCol) throws CanceledExecutionException, IllegalAccessException {
    exec.checkCanceled();
    // derive this node's id from the counter
    int nodeId = m_counter.getAndIncrement();
    DataCell majorityClass = table.getMajorityClassAsCell();
    LinkedHashMap<DataCell, Double> frequencies = table.getClassFrequencies();
    // if the distribution allows for a leaf
    if (table.isPureEnough()) {
        // free memory
        table.freeUnderlyingDataRows();
        double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
        exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
        return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
    } else {
        Split split = null;
        // find best split in specified column for first split
        if (depth == 0 && m_useFirstSplitCol.getBooleanValue()) {
            if (table.isNominal(firstSplitCol)) {
                if (m_binaryNominalSplitMode.getBooleanValue()) {
                    split = new SplitNominalBinary(table, firstSplitCol, splitQualityMeasure, m_minNumberRecordsPerNode.getIntValue(), m_maxNumNominalsForCompleteComputation.getIntValue());
                } else {
                    split = new SplitNominalNormal(table, firstSplitCol, splitQualityMeasure, m_minNumberRecordsPerNode.getIntValue());
                }
            } else {
                split = new SplitContinuous(table, firstSplitCol, splitQualityMeasure, m_averageSplitpoint.getBooleanValue(), m_minNumberRecordsPerNode.getIntValue());
            }
            if (Double.isNaN(split.getBestQualityMeasure()) || split.getBestQualityMeasure() == 0.0) {
                m_warningMessageSb.append("The specified root split column \"").append(split.getSplitAttributeName()).append("\" does not contain a valid split.");
            }
        }
        if (split == null) {
            // no root split column found or selected
            // find the best splits for all attributes
            SplitFinder splittFinder = new SplitFinder(table, splitQualityMeasure, m_averageSplitpoint.getBooleanValue(), m_minNumberRecordsPerNode.getIntValue(), m_binaryNominalSplitMode.getBooleanValue(), m_maxNumNominalsForCompleteComputation.getIntValue());
            // check for enough memory
            checkMemory();
            // get the best split among the best attribute splits
            split = splittFinder.getSplit();
        }
        // if no best split could be evaluated, create a leaf node
        if (split == null || !split.isValidSplit()) {
            table.freeUnderlyingDataRows();
            double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
            exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
            return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
        }
        // partition the attribute lists according to this split
        Partitioner partitioner = new Partitioner(table, split, m_minNumberRecordsPerNode.getIntValue());
        if (!partitioner.couldBeUsefulPartitioned()) {
            table.freeUnderlyingDataRows();
            double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
            exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
            return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
        }
        // get the just created partitions
        InMemoryTable[] partitionTables = partitioner.getPartitionTables();
        // recursively build the  child nodes
        DecisionTreeNode[] children = new DecisionTreeNode[partitionTables.length];
        ArrayList<ParallelBuilding> threads = new ArrayList<ParallelBuilding>();
        int i = 0;
        for (InMemoryTable partitionTable : partitionTables) {
            exec.checkCanceled();
            if (partitionTable.getNumberDataRows() * m_numberAttributes < 10000 || !parallelProcessing.isThreadAvailable()) {
                children[i] = buildTree(partitionTable, exec, depth + 1, splitQualityMeasure, parallelProcessing, firstSplitCol);
            } else {
                String threadName = "Build thread, node: " + nodeId + "." + i;
                ParallelBuilding buildThread = new ParallelBuilding(threadName, partitionTable, exec, depth + 1, i, splitQualityMeasure, parallelProcessing);
                LOGGER.debug("Start new parallel building thread: " + threadName);
                threads.add(buildThread);
                buildThread.start();
            }
            i++;
        }
        // already assigned to the child array
        for (ParallelBuilding buildThread : threads) {
            children[buildThread.getThreadIndex()] = buildThread.getResultNode();
            exec.checkCanceled();
            if (buildThread.getException() != null) {
                for (ParallelBuilding buildThread2 : threads) {
                    buildThread2.stop();
                }
                throw new RuntimeException(buildThread.getException().getMessage());
            }
        }
        threads.clear();
        if (split instanceof SplitContinuous) {
            double splitValue = ((SplitContinuous) split).getBestSplitValue();
            // return new DecisionTreeNodeSplitContinuous(nodeId,
            // majorityClass, frequencies, split
            // .getSplitAttributeName(), children, splitValue);
            String splitAttribute = split.getSplitAttributeName();
            PMMLPredicate[] splitPredicates = new PMMLPredicate[] { new PMMLSimplePredicate(splitAttribute, PMMLOperator.LESS_OR_EQUAL, Double.toString(splitValue)), new PMMLSimplePredicate(splitAttribute, PMMLOperator.GREATER_THAN, Double.toString(splitValue)) };
            return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
        } else if (split instanceof SplitNominalNormal) {
            // else the attribute is nominal
            DataCell[] splitValues = ((SplitNominalNormal) split).getSplitValues();
            // return new DecisionTreeNodeSplitNominal(nodeId, majorityClass,
            // frequencies, split.getSplitAttributeName(),
            // splitValues, children);
            int num = children.length;
            PMMLPredicate[] splitPredicates = new PMMLPredicate[num];
            String splitAttribute = split.getSplitAttributeName();
            for (int j = 0; j < num; j++) {
                splitPredicates[j] = new PMMLSimplePredicate(splitAttribute, PMMLOperator.EQUAL, splitValues[j].toString());
            }
            return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
        } else {
            // binary nominal
            SplitNominalBinary splitNominalBinary = (SplitNominalBinary) split;
            DataCell[] splitValues = splitNominalBinary.getSplitValues();
            // return new DecisionTreeNodeSplitNominalBinary(nodeId,
            // majorityClass, frequencies, split
            // .getSplitAttributeName(), splitValues,
            // splitNominalBinary.getIntMappingsLeftPartition(),
            // splitNominalBinary.getIntMappingsRightPartition(),
            // children/* children[0]=left, ..[1] right */);
            String splitAttribute = split.getSplitAttributeName();
            int[][] indices = new int[][] { splitNominalBinary.getIntMappingsLeftPartition(), splitNominalBinary.getIntMappingsRightPartition() };
            PMMLPredicate[] splitPredicates = new PMMLPredicate[2];
            for (int j = 0; j < splitPredicates.length; j++) {
                PMMLSimpleSetPredicate pred = null;
                pred = new PMMLSimpleSetPredicate(splitAttribute, PMMLSetOperator.IS_IN);
                pred.setArrayType(PMMLArrayType.STRING);
                LinkedHashSet<String> values = new LinkedHashSet<String>();
                for (int index : indices[j]) {
                    values.add(splitValues[index].toString());
                }
                pred.setValues(values);
                splitPredicates[j] = pred;
            }
            return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
        }
    }
}

Example 13 with PMMLSimplePredicate

use of org.knime.base.node.mine.decisiontree2.PMMLSimplePredicate in project knime-core by knime.

the class TreeNodeNominalConditionTest method testToPMMLPredicate.

/**
 * This method tests the {@link TreeNodeNominalCondition#toPMMLPredicate()} method.
 *
 * @throws Exception
 */
@Test
public void testToPMMLPredicate() throws Exception {
    final TreeEnsembleLearnerConfiguration config = new TreeEnsembleLearnerConfiguration(false);
    final TestDataGenerator dataGen = new TestDataGenerator(config);
    final TreeNominalColumnData col = dataGen.createNominalAttributeColumn("A,A,B,C,C,D", "testcol", 0);
    TreeNodeNominalCondition cond = new TreeNodeNominalCondition(col.getMetaData(), 3, false);
    PMMLPredicate predicate = cond.toPMMLPredicate();
    assertThat(predicate, instanceOf(PMMLSimplePredicate.class));
    PMMLSimplePredicate simplePredicate = (PMMLSimplePredicate) predicate;
    assertEquals("Wrong operator", PMMLOperator.EQUAL, simplePredicate.getOperator());
    assertEquals("Wrong split value", "D", simplePredicate.getThreshold());
    cond = new TreeNodeNominalCondition(col.getMetaData(), 0, true);
    predicate = cond.toPMMLPredicate();
    assertThat(predicate, instanceOf(PMMLCompoundPredicate.class));
    PMMLCompoundPredicate compound = (PMMLCompoundPredicate) predicate;
    assertEquals("Wrong boolean operator.", PMMLBooleanOperator.OR, compound.getBooleanOperator());
    List<PMMLPredicate> preds;
    preds = compound.getPredicates();
    assertEquals("Wrong number of predicates in compound predicate.", 2, preds.size());
    assertThat(preds.get(0), instanceOf(PMMLSimplePredicate.class));
    simplePredicate = (PMMLSimplePredicate) preds.get(0);
    assertEquals("Wrong operator", PMMLOperator.EQUAL, simplePredicate.getOperator());
    assertEquals("Wrong split value", "A", simplePredicate.getThreshold());
    assertEquals("Wrong attribute.", col.getMetaData().getAttributeName(), simplePredicate.getSplitAttribute());
    assertThat(preds.get(1), instanceOf(PMMLSimplePredicate.class));
    simplePredicate = (PMMLSimplePredicate) preds.get(1);
    assertEquals("Should be isMissing", PMMLOperator.IS_MISSING, simplePredicate.getOperator());
}

Example 14 with PMMLSimplePredicate

use of org.knime.base.node.mine.decisiontree2.PMMLSimplePredicate in project knime-core by knime.

the class TreeNodeNominalCondition method toPMMLPredicate.

/**
 * {@inheritDoc}
 */
@Override
public PMMLPredicate toPMMLPredicate() {
    final PMMLSimplePredicate simplePredicate = new PMMLSimplePredicate(getAttributeName(), PMMLOperator.EQUAL, getValue());
    if (!acceptsMissings()) {
        // return simple predicate if condition rejects missing values
        return simplePredicate;
    }
    // add compound predicate to allow for missing values
    final PMMLCompoundPredicate compPredicate = new PMMLCompoundPredicate(PMMLBooleanOperator.OR);
    compPredicate.addPredicate(simplePredicate);
    final PMMLSimplePredicate missing = new PMMLSimplePredicate();
    missing.setSplitAttribute(getAttributeName());
    missing.setOperator(PMMLOperator.IS_MISSING);
    compPredicate.addPredicate(missing);
    return compPredicate;
}

Example 15 with PMMLSimplePredicate

use of org.knime.base.node.mine.decisiontree2.PMMLSimplePredicate in project knime-core by knime.

the class TreeNodeNumericCondition method toPMMLPredicate.

/**
 * {@inheritDoc}
 */
@Override
public PMMLPredicate toPMMLPredicate() {
    PMMLCompoundPredicate compound = new PMMLCompoundPredicate(PMMLBooleanOperator.OR);
    switch(m_numericOperator) {
        case LargerThanOrMissing:
            compound.addPredicate(new PMMLSimplePredicate(getAttributeName(), PMMLOperator.GREATER_THAN, Double.toString(m_splitValue)));
            compound.addPredicate(new PMMLSimplePredicate(getAttributeName(), PMMLOperator.IS_MISSING, Double.toString(m_splitValue)));
            return compound;
        case LessThanOrEqualOrMissing:
            compound.addPredicate(new PMMLSimplePredicate(getAttributeName(), PMMLOperator.LESS_OR_EQUAL, Double.toString(m_splitValue)));
            compound.addPredicate(new PMMLSimplePredicate(getAttributeName(), PMMLOperator.IS_MISSING, Double.toString(m_splitValue)));
            return compound;
    }
    final PMMLOperator pmmlOperator = m_numericOperator.m_pmmlOperator;
    if (pmmlOperator == null) {
        throw new IllegalStateException("There is no equivalent PMMLOperator for this NumericOperator.");
    }
    final PMMLSimplePredicate simplePredicate = new PMMLSimplePredicate(getAttributeName(), pmmlOperator, Double.toString(m_splitValue));
    if (!acceptsMissings()) {
        // return simple predicate that rejects missing values
        return simplePredicate;
    }
    // create compound to allow for missing values
    compound.addPredicate(simplePredicate);
    final PMMLSimplePredicate missing = new PMMLSimplePredicate();
    missing.setSplitAttribute(getAttributeName());
    missing.setOperator(PMMLOperator.IS_MISSING);
    compound.addPredicate(missing);
    return compound;
}