Examples with SplitCandidate org.knime.base.node.mine.treeensemble2.learner.SplitCandidate used on opensource projects

Example 1 with SplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.SplitCandidate in project knime-core by knime.

the class TreeNominalColumnData method calcBestSplitRegression.

/**
 * {@inheritDoc}
 */
@Override
public SplitCandidate calcBestSplitRegression(final DataMemberships dataMemberships, final RegressionPriors targetPriors, final TreeTargetNumericColumnData targetColumn, final RandomData rd) {
    final NominalValueRepresentation[] nomVals = getMetaData().getValues();
    final ColumnMemberships columnMemberships = dataMemberships.getColumnMemberships(getMetaData().getAttributeIndex());
    final boolean useBinaryNominalSplits = getConfiguration().isUseBinaryNominalSplits();
    if (useBinaryNominalSplits) {
        return calcBestSplitRegressionBinaryBreiman(columnMemberships, targetPriors, targetColumn, nomVals, rd);
    } else {
        return calcBestSplitRegressionMultiway(columnMemberships, targetPriors, targetColumn, nomVals, rd);
    }
}

Example 2 with SplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.SplitCandidate in project knime-core by knime.

the class TreeNominalColumnData method calcBestSplitClassification.

/**
 * {@inheritDoc}
 */
@Override
public SplitCandidate calcBestSplitClassification(final DataMemberships dataMemberships, final ClassificationPriors targetPriors, final TreeTargetNominalColumnData targetColumn, final RandomData rd) {
    final NominalValueRepresentation[] targetVals = targetColumn.getMetaData().getValues();
    IImpurity impCriterion = targetPriors.getImpurityCriterion();
    // distribution of target for each attribute value
    final NominalValueRepresentation[] nomVals = getMetaData().getValues();
    final boolean useBinaryNominalSplits = getConfiguration().isUseBinaryNominalSplits();
    final ColumnMemberships columnMemberships = dataMemberships.getColumnMemberships(getMetaData().getAttributeIndex());
    if (useBinaryNominalSplits) {
        if (targetVals.length == 2) {
            return calcBestSplitClassificationBinaryTwoClass(columnMemberships, targetPriors, targetColumn, impCriterion, nomVals, targetVals, rd);
        } else {
            return calcBestSplitClassificationBinaryPCA(columnMemberships, targetPriors, targetColumn, impCriterion, nomVals, targetVals, rd);
        // return calcBestSplitClassificationBinary(membershipController, rowWeights, targetPriors, targetColumn,
        // impCriterion, nomVals, targetVals, originalIndexInColumnList, rd);
        }
    } else {
        return calcBestSplitClassificationMultiway(columnMemberships, targetPriors, targetColumn, impCriterion, nomVals, targetVals, rd);
    }
}

Example 3 with SplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.SplitCandidate in project knime-core by knime.

the class TreeLearnerRegression method findBestSplitsRegression.

private SplitCandidate[] findBestSplitsRegression(final int currentDepth, final DataMemberships dataMemberships, final ColumnSample columnSample, final RegressionPriors targetPriors, final BitSet forbiddenColumnSet) {
    final TreeData data = getData();
    final RandomData rd = getRandomData();
    final TreeEnsembleLearnerConfiguration config = getConfig();
    final int maxLevels = config.getMaxLevels();
    if (maxLevels != TreeEnsembleLearnerConfiguration.MAX_LEVEL_INFINITE && currentDepth >= maxLevels) {
        return null;
    }
    final int minNodeSize = config.getMinNodeSize();
    if (minNodeSize != TreeEnsembleLearnerConfiguration.MIN_NODE_SIZE_UNDEFINED) {
        if (targetPriors.getNrRecords() < minNodeSize) {
            return null;
        }
    }
    final double priorSquaredDeviation = targetPriors.getSumSquaredDeviation();
    if (priorSquaredDeviation < TreeColumnData.EPSILON) {
        return null;
    }
    final TreeTargetNumericColumnData targetColumn = getTargetData();
    ArrayList<SplitCandidate> splitCandidates = null;
    if (currentDepth == 0 && config.getHardCodedRootColumn() != null) {
        final TreeAttributeColumnData rootColumn = data.getColumn(config.getHardCodedRootColumn());
        return new SplitCandidate[] { rootColumn.calcBestSplitRegression(dataMemberships, targetPriors, targetColumn, rd) };
    } else {
        splitCandidates = new ArrayList<SplitCandidate>(columnSample.getNumCols());
        for (TreeAttributeColumnData col : columnSample) {
            if (forbiddenColumnSet.get(col.getMetaData().getAttributeIndex())) {
                continue;
            }
            SplitCandidate currentColSplit = col.calcBestSplitRegression(dataMemberships, targetPriors, targetColumn, rd);
            if (currentColSplit != null) {
                splitCandidates.add(currentColSplit);
            }
        }
    }
    Comparator<SplitCandidate> comp = new Comparator<SplitCandidate>() {

        @Override
        public int compare(final SplitCandidate arg0, final SplitCandidate arg1) {
            int compareDouble = -Double.compare(arg0.getGainValue(), arg1.getGainValue());
            return compareDouble;
        }
    };
    if (splitCandidates.isEmpty()) {
        return null;
    }
    splitCandidates.sort(comp);
    return splitCandidates.toArray(new SplitCandidate[splitCandidates.size()]);
}

Example 4 with SplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.SplitCandidate 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 5 with SplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.SplitCandidate in project knime-core by knime.

the class TreeNominalColumnDataTest method testCalcBestSplitRegressionBinaryXGBoostMissingValueHandling.

/**
 * Tests the XGBoost missing value handling in case of a regression with binary splits.
 *
 * @throws Exception
 */
@Test
public void testCalcBestSplitRegressionBinaryXGBoostMissingValueHandling() throws Exception {
    final TreeEnsembleLearnerConfiguration config = createConfig(true);
    config.setMissingValueHandling(MissingValueHandling.XGBoost);
    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(NominalBinarySplitCandidate.class));
    assertEquals("Wrong gain.", 22.755555, split.getGainValue(), 1e-5);
    assertTrue("No missing values in dataCol therefore the missedRows BitSet must be empty.", split.getMissedRows().isEmpty());
    NominalBinarySplitCandidate nomSplit = (NominalBinarySplitCandidate) split;
    TreeNodeNominalBinaryCondition[] conditions = nomSplit.getChildConditions();
    assertEquals("Binary split candidate must have two children.", 2, conditions.length);
    final String[] values = new String[] { "A", "C" };
    assertArrayEquals("Wrong values in split condition.", values, conditions[0].getValues());
    assertArrayEquals("Wrong values in split condition.", values, conditions[1].getValues());
    assertFalse("Missings should go with majority", conditions[0].acceptsMissings());
    assertTrue("Missings should go with majority", conditions[1].acceptsMissings());
    assertEquals("Wrong set logic.", SetLogic.IS_NOT_IN, conditions[0].getSetLogic());
    assertEquals("Wrong set logic.", SetLogic.IS_IN, conditions[1].getSetLogic());
    // 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(NominalBinarySplitCandidate.class));
    assertEquals("Wrong gain.", 36.1, split.getGainValue(), 1e-5);
    assertTrue("Conditions should handle missing values therefore the missedRows BitSet must be empty.", split.getMissedRows().isEmpty());
    nomSplit = (NominalBinarySplitCandidate) split;
    conditions = nomSplit.getChildConditions();
    assertEquals("Binary split candidate must have two children.", 2, conditions.length);
    assertArrayEquals("Wrong values in split condition.", values, conditions[0].getValues());
    assertArrayEquals("Wrong values in split condition.", values, conditions[1].getValues());
    assertTrue("Missings should go with B (because there target values are similar)", conditions[0].acceptsMissings());
    assertFalse("Missings should go with B (because there target values are similar)", conditions[1].acceptsMissings());
    assertEquals("Wrong set logic.", SetLogic.IS_NOT_IN, conditions[0].getSetLogic());
    assertEquals("Wrong set logic.", SetLogic.IS_IN, conditions[1].getSetLogic());
}