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

Example 1 with TreeNodeNominalCondition

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

the class TreeNominalColumnData method updateChildMembershipsMultiway.

private BitSet updateChildMembershipsMultiway(final TreeNodeNominalCondition nomCondition, final DataMemberships parentMemberships) {
    String value = nomCondition.getValue();
    int att = -1;
    final NominalValueRepresentation[] reps = getMetaData().getValues();
    for (final NominalValueRepresentation rep : reps) {
        if (rep.getNominalValue().equals(value)) {
            att = rep.getAssignedInteger();
            break;
        }
    }
    if (att == -1) {
        throw new IllegalStateException("Unknown value: " + value);
    }
    ColumnMemberships columnMemberships = parentMemberships.getColumnMemberships(getMetaData().getAttributeIndex());
    BitSet inChild = new BitSet(columnMemberships.size());
    columnMemberships.reset();
    int start = 0;
    for (int a = 0; a < att; a++) {
        start += m_nominalValueCounts[a];
    }
    // Make sure that we are using an index >= start
    if (!columnMemberships.nextIndexFrom(start)) {
        return inChild;
    }
    boolean reachedEnd = false;
    int end = start + m_nominalValueCounts[att];
    for (int index = columnMemberships.getIndexInColumn(); index < end; index = columnMemberships.getIndexInColumn()) {
        inChild.set(columnMemberships.getIndexInDataMemberships());
        if (!columnMemberships.next()) {
            reachedEnd = true;
            break;
        }
    }
    if (!reachedEnd && containsMissingValues() && nomCondition.acceptsMissings()) {
        // move to missing values
        for (int i = att; i < reps.length - 1; i++) {
            start += m_nominalValueCounts[i];
        }
        if (columnMemberships.nextIndexFrom(start)) {
            do {
                inChild.set(columnMemberships.getIndexInDataMemberships());
            } while (columnMemberships.next());
        }
    }
    return inChild;
}

Example 2 with TreeNodeNominalCondition

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

the class NominalMultiwaySplitCandidate method getChildConditions.

/**
 * {@inheritDoc}
 */
@Override
public TreeNodeNominalCondition[] getChildConditions() {
    TreeNominalColumnMetaData columnMeta = getColumnData().getMetaData();
    NominalValueRepresentation[] values = columnMeta.getValues();
    final int lengthNonMissing = values[values.length - 1].getNominalValue().equals(NominalValueRepresentation.MISSING_VALUE) ? values.length - 1 : values.length;
    List<TreeNodeCondition> resultList = new ArrayList<TreeNodeCondition>(lengthNonMissing);
    for (int i = 0; i < lengthNonMissing; i++) {
        if (m_sumWeightsAttributes[i] >= TreeColumnData.EPSILON) {
            resultList.add(new TreeNodeNominalCondition(columnMeta, i, i == m_missingsGoToChildIdx));
        }
    }
    return resultList.toArray(new TreeNodeNominalCondition[resultList.size()]);
}

Example 3 with TreeNodeNominalCondition

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

the class TreeNominalColumnDataTest method testCalcBestSplitClassificationMultiWay.

/**
 * Tests the method
 * {@link TreeNominalColumnData#calcBestSplitClassification(DataMemberships, ClassificationPriors, TreeTargetNominalColumnData, RandomData)}
 * using multiway splits
 *
 * @throws Exception
 */
@Test
public void testCalcBestSplitClassificationMultiWay() throws Exception {
    TreeEnsembleLearnerConfiguration config = createConfig(false);
    config.setUseBinaryNominalSplits(false);
    Pair<TreeNominalColumnData, TreeTargetNominalColumnData> tennisData = tennisData(config);
    TreeNominalColumnData columnData = tennisData.getFirst();
    TreeTargetNominalColumnData targetData = tennisData.getSecond();
    TreeData treeData = createTreeData(tennisData);
    assertEquals(SplitCriterion.Gini, config.getSplitCriterion());
    double[] rowWeights = new double[SMALL_COLUMN_DATA.length];
    Arrays.fill(rowWeights, 1.0);
    IDataIndexManager indexManager = new DefaultDataIndexManager(treeData);
    DataMemberships dataMemberships = new RootDataMemberships(rowWeights, treeData, indexManager);
    ClassificationPriors priors = targetData.getDistribution(rowWeights, config);
    SplitCandidate splitCandidate = columnData.calcBestSplitClassification(dataMemberships, priors, targetData, null);
    assertNotNull(splitCandidate);
    assertThat(splitCandidate, instanceOf(NominalMultiwaySplitCandidate.class));
    assertFalse(splitCandidate.canColumnBeSplitFurther());
    // manually via libre office calc
    assertEquals(0.0744897959, splitCandidate.getGainValue(), 0.00001);
    NominalMultiwaySplitCandidate multiWaySplitCandidate = (NominalMultiwaySplitCandidate) splitCandidate;
    TreeNodeNominalCondition[] childConditions = multiWaySplitCandidate.getChildConditions();
    assertEquals(3, childConditions.length);
    assertEquals("S", childConditions[0].getValue());
    assertEquals("O", childConditions[1].getValue());
    assertEquals("R", childConditions[2].getValue());
}

Example 4 with TreeNodeNominalCondition

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

the class TreeNominalColumnDataTest method testCalcBestSplitRegressionMultiwayXGBoostMissingValueHandling.

/**
 * This method tests the XGBoost missing value handling in case of a regression task and multiway splits.
 *
 * @throws Exception
 */
@Test
public void testCalcBestSplitRegressionMultiwayXGBoostMissingValueHandling() throws Exception {
    final TreeEnsembleLearnerConfiguration config = createConfig(true);
    config.setMissingValueHandling(MissingValueHandling.XGBoost);
    config.setUseBinaryNominalSplits(false);
    final TestDataGenerator dataGen = new TestDataGenerator(config);
    final String noMissingCSV = "A, A, A, B, B, B, B, C, C";
    final String noMissingsTarget = "1, 2, 2, 7, 6, 5, 2, 3, 1";
    TreeNominalColumnData dataCol = dataGen.createNominalAttributeColumn(noMissingCSV, "noMissings", 0);
    TreeTargetNumericColumnData targetCol = TestDataGenerator.createNumericTargetColumn(noMissingsTarget);
    double[] weights = new double[9];
    Arrays.fill(weights, 1.0);
    int[] indices = new int[9];
    for (int i = 0; i < indices.length; i++) {
        indices[i] = i;
    }
    final RandomData rd = config.createRandomData();
    DataMemberships dataMemberships = new MockDataColMem(indices, indices, weights);
    // first test the case that there are no missing values during training (we still need to provide a missing value direction for prediction)
    SplitCandidate split = dataCol.calcBestSplitRegression(dataMemberships, targetCol.getPriors(weights, config), targetCol, rd);
    assertNotNull("SplitCandidate may not be null", split);
    assertThat(split, instanceOf(NominalMultiwaySplitCandidate.class));
    assertEquals("Wrong gain.", 22.888888, split.getGainValue(), 1e-5);
    assertTrue("No missing values in dataCol therefore the missedRows BitSet must be empty.", split.getMissedRows().isEmpty());
    NominalMultiwaySplitCandidate nomSplit = (NominalMultiwaySplitCandidate) split;
    TreeNodeNominalCondition[] conditions = nomSplit.getChildConditions();
    assertEquals("3 nominal values therefore there must be 3 children.", 3, conditions.length);
    assertEquals("Wrong value.", "A", conditions[0].getValue());
    assertEquals("Wrong value.", "B", conditions[1].getValue());
    assertEquals("Wrong value.", "C", conditions[2].getValue());
    assertFalse("Missings should go with majority", conditions[0].acceptsMissings());
    assertTrue("Missings should go with majority", conditions[1].acceptsMissings());
    assertFalse("Missings should go with majority", conditions[2].acceptsMissings());
    // test the case that there are missing values during training
    final String missingCSV = "A, A, A, B, B, B, B, C, C, ?";
    final String missingTarget = "1, 2, 2, 7, 6, 5, 2, 3, 1, 8";
    dataCol = dataGen.createNominalAttributeColumn(missingCSV, "missing", 0);
    targetCol = TestDataGenerator.createNumericTargetColumn(missingTarget);
    weights = new double[10];
    Arrays.fill(weights, 1.0);
    indices = new int[10];
    for (int i = 0; i < indices.length; i++) {
        indices[i] = i;
    }
    dataMemberships = new MockDataColMem(indices, indices, weights);
    split = dataCol.calcBestSplitRegression(dataMemberships, targetCol.getPriors(weights, config), targetCol, rd);
    assertNotNull("SplitCandidate may not be null.", split);
    assertThat(split, instanceOf(NominalMultiwaySplitCandidate.class));
    // assertEquals("Wrong gain.", 36.233333333, split.getGainValue(), 1e-5);
    assertTrue("Conditions should handle missing values therefore the missedRows BitSet must be empty.", split.getMissedRows().isEmpty());
    nomSplit = (NominalMultiwaySplitCandidate) split;
    conditions = nomSplit.getChildConditions();
    assertEquals("3 values (not counting missing values) therefore there must be 3 children.", 3, conditions.length);
    assertEquals("Wrong value.", "A", conditions[0].getValue());
    assertEquals("Wrong value.", "B", conditions[1].getValue());
    assertEquals("Wrong value.", "C", conditions[2].getValue());
    assertFalse("Missings should go with majority", conditions[0].acceptsMissings());
    assertTrue("Missings should go with majority", conditions[1].acceptsMissings());
    assertFalse("Missings should go with majority", conditions[2].acceptsMissings());
}

Example 5 with TreeNodeNominalCondition

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