use of org.knime.base.node.mine.treeensemble2.data.TreeData 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());
}
use of org.knime.base.node.mine.treeensemble2.data.TreeData in project knime-core by knime.
the class TreeNominalColumnDataTest method testCalcBestSplitCassificationBinaryTwoClassXGBoostMissingValue.
/**
* Tests the XGBoost Missing value handling in case of a two class problem <br>
* currently not tested because missing value handling will probably be implemented differently.
*
* @throws Exception
*/
// @Test
public void testCalcBestSplitCassificationBinaryTwoClassXGBoostMissingValue() throws Exception {
final TreeEnsembleLearnerConfiguration config = createConfig(false);
config.setMissingValueHandling(MissingValueHandling.XGBoost);
final TestDataGenerator dataGen = new TestDataGenerator(config);
// check correct behavior if no missing values are encountered during split search
Pair<TreeNominalColumnData, TreeTargetNominalColumnData> twoClassTennisData = twoClassTennisData(config);
TreeData treeData = dataGen.createTreeData(twoClassTennisData.getSecond(), twoClassTennisData.getFirst());
IDataIndexManager indexManager = new DefaultDataIndexManager(treeData);
double[] rowWeights = new double[TWO_CLASS_INDICES.length];
Arrays.fill(rowWeights, 1.0);
// DataMemberships dataMemberships = TestDataGenerator.createMockDataMemberships(TWO_CLASS_INDICES.length);
DataMemberships dataMemberships = new RootDataMemberships(rowWeights, treeData, indexManager);
TreeTargetNominalColumnData targetData = twoClassTennisData.getSecond();
TreeNominalColumnData columnData = twoClassTennisData.getFirst();
ClassificationPriors priors = targetData.getDistribution(rowWeights, config);
RandomData rd = TestDataGenerator.createRandomData();
SplitCandidate splitCandidate = columnData.calcBestSplitClassification(dataMemberships, priors, targetData, rd);
assertNotNull(splitCandidate);
assertThat(splitCandidate, instanceOf(NominalBinarySplitCandidate.class));
NominalBinarySplitCandidate binarySplitCandidate = (NominalBinarySplitCandidate) splitCandidate;
TreeNodeNominalBinaryCondition[] childConditions = binarySplitCandidate.getChildConditions();
assertEquals(2, childConditions.length);
assertArrayEquals(new String[] { "R" }, childConditions[0].getValues());
assertArrayEquals(new String[] { "R" }, childConditions[1].getValues());
assertEquals(SetLogic.IS_NOT_IN, childConditions[0].getSetLogic());
assertEquals(SetLogic.IS_IN, childConditions[1].getSetLogic());
// check if missing values go left
assertTrue(childConditions[0].acceptsMissings());
assertFalse(childConditions[1].acceptsMissings());
// check correct behavior if missing values are encountered during split search
String dataContainingMissingsCSV = "S,?,O,R,S,R,S,O,O,?";
columnData = dataGen.createNominalAttributeColumn(dataContainingMissingsCSV, "column containing missing values", 0);
treeData = dataGen.createTreeData(targetData, columnData);
indexManager = new DefaultDataIndexManager(treeData);
dataMemberships = new RootDataMemberships(rowWeights, treeData, indexManager);
splitCandidate = columnData.calcBestSplitClassification(dataMemberships, priors, targetData, null);
assertNotNull(splitCandidate);
binarySplitCandidate = (NominalBinarySplitCandidate) splitCandidate;
assertEquals("Gain was not as expected", 0.08, binarySplitCandidate.getGainValue(), 1e-8);
childConditions = binarySplitCandidate.getChildConditions();
String[] conditionValues = new String[] { "O", "?" };
assertArrayEquals("Values in nominal condition did not match", conditionValues, childConditions[0].getValues());
assertArrayEquals("Values in nominal condition did not match", conditionValues, childConditions[1].getValues());
assertEquals("Wrong set logic.", SetLogic.IS_NOT_IN, childConditions[0].getSetLogic());
assertEquals("Wrong set logic.", SetLogic.IS_IN, childConditions[1].getSetLogic());
assertFalse("Missig values are not sent to the correct child.", childConditions[0].acceptsMissings());
assertTrue("Missig values are not sent to the correct child.", childConditions[1].acceptsMissings());
}
use of org.knime.base.node.mine.treeensemble2.data.TreeData in project knime-core by knime.
the class SubsetColumnSampleTest method testIteratorRemove.
@Test(expected = UnsupportedOperationException.class)
public void testIteratorRemove() throws Exception {
final TreeData data = createTreeData();
int[] colIndices = new int[] { 1, 3, 5 };
SubsetColumnSample sample = new SubsetColumnSample(data, colIndices);
sample.iterator().remove();
}
use of org.knime.base.node.mine.treeensemble2.data.TreeData in project knime-core by knime.
the class TreeLearnerClassification method findBestSplitClassification.
private SplitCandidate findBestSplitClassification(final int currentDepth, final DataMemberships dataMemberships, final ColumnSample columnSample, final TreeNodeSignature treeNodeSignature, final ClassificationPriors targetPriors, final BitSet forbiddenColumnSet) {
final TreeData data = getData();
final RandomData rd = getRandomData();
// final ColumnSampleStrategy colSamplingStrategy = getColSamplingStrategy();
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 priorImpurity = targetPriors.getPriorImpurity();
if (priorImpurity < TreeColumnData.EPSILON) {
return null;
}
final TreeTargetNominalColumnData targetColumn = (TreeTargetNominalColumnData) data.getTargetColumn();
SplitCandidate splitCandidate = null;
if (currentDepth == 0 && config.getHardCodedRootColumn() != null) {
final TreeAttributeColumnData rootColumn = data.getColumn(config.getHardCodedRootColumn());
// TODO discuss whether this option makes sense with surrogates
return rootColumn.calcBestSplitClassification(dataMemberships, targetPriors, targetColumn, rd);
}
double bestGainValue = 0.0;
for (TreeAttributeColumnData col : columnSample) {
if (forbiddenColumnSet.get(col.getMetaData().getAttributeIndex())) {
continue;
}
final SplitCandidate currentColSplit = col.calcBestSplitClassification(dataMemberships, targetPriors, targetColumn, rd);
if (currentColSplit != null) {
final double currentGain = currentColSplit.getGainValue();
final boolean tiebreaker = currentGain == bestGainValue ? (rd.nextInt(0, 1) == 0) : false;
if (currentColSplit.getGainValue() > bestGainValue || tiebreaker) {
splitCandidate = currentColSplit;
bestGainValue = currentGain;
}
}
}
return splitCandidate;
}
use of org.knime.base.node.mine.treeensemble2.data.TreeData in project knime-core by knime.
the class TreeLearnerClassification method findBestSplitsClassification.
/**
* Returns a list of SplitCandidates sorted (descending) by their gain
*
* @param currentDepth
* @param rowSampleWeights
* @param treeNodeSignature
* @param targetPriors
* @param forbiddenColumnSet
* @param membershipController
* @return
*/
private SplitCandidate[] findBestSplitsClassification(final int currentDepth, final DataMemberships dataMemberships, final ColumnSample columnSample, final TreeNodeSignature treeNodeSignature, final ClassificationPriors targetPriors, final BitSet forbiddenColumnSet) {
final TreeData data = getData();
final RandomData rd = getRandomData();
// final ColumnSampleStrategy colSamplingStrategy = getColSamplingStrategy();
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 priorImpurity = targetPriors.getPriorImpurity();
if (priorImpurity < TreeColumnData.EPSILON) {
return null;
}
final TreeTargetNominalColumnData targetColumn = (TreeTargetNominalColumnData) data.getTargetColumn();
SplitCandidate splitCandidate = null;
if (currentDepth == 0 && config.getHardCodedRootColumn() != null) {
final TreeAttributeColumnData rootColumn = data.getColumn(config.getHardCodedRootColumn());
// TODO discuss whether this option makes sense with surrogates
return new SplitCandidate[] { rootColumn.calcBestSplitClassification(dataMemberships, targetPriors, targetColumn, rd) };
}
double bestGainValue = 0.0;
final Comparator<SplitCandidate> comp = new Comparator<SplitCandidate>() {
@Override
public int compare(final SplitCandidate o1, final SplitCandidate o2) {
int compareDouble = -Double.compare(o1.getGainValue(), o2.getGainValue());
return compareDouble;
}
};
ArrayList<SplitCandidate> candidates = new ArrayList<SplitCandidate>(columnSample.getNumCols());
for (TreeAttributeColumnData col : columnSample) {
if (forbiddenColumnSet.get(col.getMetaData().getAttributeIndex())) {
continue;
}
SplitCandidate currentColSplit = col.calcBestSplitClassification(dataMemberships, targetPriors, targetColumn, rd);
if (currentColSplit != null) {
candidates.add(currentColSplit);
}
}
if (candidates.isEmpty()) {
return null;
}
candidates.sort(comp);
return candidates.toArray(new SplitCandidate[candidates.size()]);
}
Aggregations