Examples with TestDataGenerator org.knime.base.node.mine.treeensemble2.data.TestDataGenerator used on opensource projects

Example 1 with TestDataGenerator

use of org.knime.base.node.mine.treeensemble2.data.TestDataGenerator in project knime-core by knime.

the class AbstractColumnSampleTest method createTreeData.

/**
 * @return TreeData object for testing purposes
 */
protected static TreeData createTreeData() {
    final TestDataGenerator dataGen = new TestDataGenerator(new TreeEnsembleLearnerConfiguration(false));
    final TreeAttributeColumnData[] cols = new TreeAttributeColumnData[10];
    for (int i = 0; i < TREE_DATA_SIZE; i++) {
        if (i % 2 == 0) {
            cols[i] = dataGen.createNominalAttributeColumn("a, b", "nom" + i, i);
        } else {
            cols[i] = dataGen.createNumericAttributeColumn("1, 2", "num1", i);
        }
    }
    TreeTargetColumnData target = TestDataGenerator.createNominalTargetColumn("A, B");
    return dataGen.createTreeData(target, cols);
}

Example 2 with TestDataGenerator

use of org.knime.base.node.mine.treeensemble2.data.TestDataGenerator 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());
}

Example 3 with TestDataGenerator

use of org.knime.base.node.mine.treeensemble2.data.TestDataGenerator 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 4 with TestDataGenerator

use of org.knime.base.node.mine.treeensemble2.data.TestDataGenerator 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());
}

Example 5 with TestDataGenerator

use of org.knime.base.node.mine.treeensemble2.data.TestDataGenerator in project knime-core by knime.

the class TreeNodeNominalBinaryConditionTest method testToPMMLPredicate.

/**
 * This method tests the
 * {@link TreeNodeNominalBinaryCondition#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);
    TreeNodeNominalBinaryCondition cond = new TreeNodeNominalBinaryCondition(col.getMetaData(), BigInteger.valueOf(1), true, false);
    PMMLPredicate predicate = cond.toPMMLPredicate();
    assertThat(predicate, instanceOf(PMMLSimpleSetPredicate.class));
    PMMLSimpleSetPredicate setPredicate = (PMMLSimpleSetPredicate) predicate;
    assertEquals("Wrong attribute", col.getMetaData().getAttributeName(), setPredicate.getSplitAttribute());
    assertEquals("Wrong set predicate", PMMLSetOperator.IS_IN, setPredicate.getSetOperator());
    assertArrayEquals("Wrong values", new String[] { "A" }, setPredicate.getValues().toArray(new String[1]));
    cond = new TreeNodeNominalBinaryCondition(col.getMetaData(), BigInteger.valueOf(2), false, true);
    predicate = cond.toPMMLPredicate();
    assertEquals("Wrong attribute", col.getMetaData().getAttributeName(), predicate.getSplitAttribute());
    assertThat(predicate, instanceOf(PMMLCompoundPredicate.class));
    PMMLCompoundPredicate compoundPredicate = (PMMLCompoundPredicate) predicate;
    assertEquals("Wrong boolean operator", PMMLBooleanOperator.OR, compoundPredicate.getBooleanOperator());
    LinkedList<PMMLPredicate> preds = compoundPredicate.getPredicates();
    assertEquals("Number of predicates did not match.", 2, preds.size());
    assertThat(preds.get(0), instanceOf(PMMLSimpleSetPredicate.class));
    setPredicate = (PMMLSimpleSetPredicate) preds.get(0);
    assertEquals("Wrong attribute", col.getMetaData().getAttributeName(), setPredicate.getSplitAttribute());
    assertEquals("Wrong set predicate", PMMLSetOperator.IS_NOT_IN, setPredicate.getSetOperator());
    assertArrayEquals("Wrong values", new String[] { "B" }, setPredicate.getValues().toArray(new String[1]));
    assertThat(preds.get(1), instanceOf(PMMLSimplePredicate.class));
    PMMLSimplePredicate simplePredicate = (PMMLSimplePredicate) preds.get(1);
    assertEquals("Should be isMissing", PMMLOperator.IS_MISSING, simplePredicate.getOperator());
}