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

Example 6 with TestDataGenerator

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

the class TreeNodeNominalBinaryConditionTest method testTestCondition.

/**
 * This method tests the
 * {@link TreeNodeNominalBinaryCondition#testCondition(org.knime.base.node.mine.treeensemble2.data.PredictorRecord)}
 * method.
 *
 * @throws Exception
 */
@Test
public void testTestCondition() 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);
    final Map<String, Object> map = Maps.newHashMap();
    final String colName = col.getMetaData().getAttributeName();
    map.put(colName, 0);
    PredictorRecord record = new PredictorRecord(map);
    assertTrue("The value A was not accepted but should have been.", cond.testCondition(record));
    map.clear();
    map.put(colName, 1);
    assertFalse("The value B was falsely accepted", cond.testCondition(record));
    map.clear();
    map.put(colName, 2);
    assertFalse("The value C was falsely accepted", cond.testCondition(record));
    map.clear();
    map.put(colName, 3);
    assertFalse("The value D was falsely accepted", cond.testCondition(record));
    map.clear();
    map.put(colName, PredictorRecord.NULL);
    assertFalse("The condition falsely accepted missing values", cond.testCondition(record));
    cond = new TreeNodeNominalBinaryCondition(col.getMetaData(), BigInteger.valueOf(5), true, true);
    map.clear();
    map.put(colName, 0);
    assertTrue("The value A was falsely rejected.", cond.testCondition(record));
    map.clear();
    map.put(colName, 2);
    assertTrue("The value C was falsely rejected.", cond.testCondition(record));
    map.clear();
    map.put(colName, PredictorRecord.NULL);
    assertTrue("Missing values were falsely rejected.", cond.testCondition(record));
    map.clear();
    map.put(colName, 1);
    assertFalse("The value B was falsely accepted.", cond.testCondition(record));
    map.clear();
    map.put(colName, 3);
    assertFalse("The value B was falsely accepted.", cond.testCondition(record));
    cond = new TreeNodeNominalBinaryCondition(col.getMetaData(), BigInteger.valueOf(5), false, true);
    map.clear();
    map.put(colName, 0);
    assertFalse("The value A was falsely accepted.", cond.testCondition(record));
    map.clear();
    map.put(colName, 2);
    assertFalse("The value C was falsely accepted.", cond.testCondition(record));
    map.clear();
    map.put(colName, PredictorRecord.NULL);
    assertTrue("Missing values were falsely rejected.", cond.testCondition(record));
    map.clear();
    map.put(colName, 1);
    assertTrue("The value B was falsely rejected.", cond.testCondition(record));
    map.clear();
    map.put(colName, 3);
    assertTrue("The value D was falsely rejected.", cond.testCondition(record));
    cond = new TreeNodeNominalBinaryCondition(col.getMetaData(), BigInteger.valueOf(5), false, false);
    map.clear();
    map.put(colName, 0);
    assertFalse("The value A was falsely accepted.", cond.testCondition(record));
    map.clear();
    map.put(colName, 2);
    assertFalse("The value C was falsely accepted.", cond.testCondition(record));
    map.clear();
    map.put(colName, PredictorRecord.NULL);
    assertFalse("Missing values were falsely accepted.", cond.testCondition(record));
    map.clear();
    map.put(colName, 1);
    assertTrue("The value B was falsely rejected.", cond.testCondition(record));
    map.clear();
    map.put(colName, 3);
    assertTrue("The value D was falsely rejected.", cond.testCondition(record));
}

Example 7 with TestDataGenerator

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

the class TreeNumericColumnDataTest method testUpdateChildMemberships.

/**
 * Tests the {@link TreeNumericColumnData#updateChildMemberships(TreeNodeCondition, DataMemberships)} methods with
 * different conditions including missing values.
 *
 * @throws Exception
 */
@Test
public void testUpdateChildMemberships() throws Exception {
    final TreeEnsembleLearnerConfiguration config = createConfig();
    final TestDataGenerator dataGen = new TestDataGenerator(config);
    final int[] indices = new int[] { 0, 1, 2, 3, 4, 5, 6 };
    final double[] weights = new double[7];
    Arrays.fill(weights, 1.0);
    final DataMemberships dataMem = new MockDataColMem(indices, indices, weights);
    final String noMissingsCSV = "-50, -3, -2, 2, 25, 100, 101";
    final TreeNumericColumnData col = dataGen.createNumericAttributeColumn(noMissingsCSV, "noMissings-col", 0);
    // less than or equals
    TreeNodeNumericCondition numCond = new TreeNodeNumericCondition(col.getMetaData(), -2, NumericOperator.LessThanOrEqual, false);
    BitSet inChild = col.updateChildMemberships(numCond, dataMem);
    BitSet expected = new BitSet(3);
    expected.set(0, 3);
    assertEquals("The produced BitSet is incorrect.", expected, inChild);
    // greater than
    numCond = new TreeNodeNumericCondition(col.getMetaData(), 10, NumericOperator.LargerThan, false);
    inChild = col.updateChildMemberships(numCond, dataMem);
    expected.clear();
    expected.set(4, 7);
    assertEquals("The produced BitSet is incorrect", expected, inChild);
    // with missing values
    final String missingsCSV = "-2, 0, 1, 43, 61, 66, NaN";
    final TreeNumericColumnData colWithMissings = dataGen.createNumericAttributeColumn(missingsCSV, "missings-col", 0);
    // less than or equal or missing
    numCond = new TreeNodeNumericCondition(colWithMissings.getMetaData(), 12, NumericOperator.LessThanOrEqual, true);
    inChild = colWithMissings.updateChildMemberships(numCond, dataMem);
    expected.clear();
    expected.set(0, 3);
    expected.set(6);
    assertEquals("The produced BitSet is incorrect", expected, inChild);
    // less than or equals not missing
    numCond = new TreeNodeNumericCondition(colWithMissings.getMetaData(), 12, NumericOperator.LessThanOrEqual, false);
    inChild = colWithMissings.updateChildMemberships(numCond, dataMem);
    expected.clear();
    expected.set(0, 3);
    assertEquals("The produced BitSet is incorrect", expected, inChild);
    // larger than or missing
    numCond = new TreeNodeNumericCondition(colWithMissings.getMetaData(), 43, NumericOperator.LargerThan, true);
    inChild = colWithMissings.updateChildMemberships(numCond, dataMem);
    expected.clear();
    expected.set(4, 7);
    assertEquals("The produced BitSet is incorrect", expected, inChild);
    // larger than not missing
    numCond = new TreeNodeNumericCondition(colWithMissings.getMetaData(), 12, NumericOperator.LargerThan, false);
    inChild = colWithMissings.updateChildMemberships(numCond, dataMem);
    expected.clear();
    expected.set(3, 6);
    assertEquals("The produced BitSet is incorrect", expected, inChild);
}

Example 8 with TestDataGenerator

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

the class TreeNumericColumnDataTest method testXGBoostMissingValueHandling.

/**
 * This method tests if the conditions for child nodes are correct in case of XGBoostMissingValueHandling
 *
 * @throws Exception
 */
@Test
public void testXGBoostMissingValueHandling() throws Exception {
    TreeEnsembleLearnerConfiguration config = createConfig();
    config.setMissingValueHandling(MissingValueHandling.XGBoost);
    final TestDataGenerator dataGen = new TestDataGenerator(config);
    final RandomData rd = config.createRandomData();
    final int[] indices = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
    final double[] weights = new double[10];
    Arrays.fill(weights, 1.0);
    final MockDataColMem dataMem = new MockDataColMem(indices, indices, weights);
    final String dataCSV = "1,2,2,3,4,5,6,7,NaN,NaN";
    final String target1CSV = "A,A,A,A,B,B,B,B,A,A";
    final String target2CSV = "A,A,A,A,B,B,B,B,B,B";
    final double expectedGain = 0.48;
    final TreeNumericColumnData col = dataGen.createNumericAttributeColumn(dataCSV, "testCol", 0);
    final TreeTargetNominalColumnData target1 = TestDataGenerator.createNominalTargetColumn(target1CSV);
    final SplitCandidate split1 = col.calcBestSplitClassification(dataMem, target1.getDistribution(weights, config), target1, rd);
    assertEquals("Wrong gain.", expectedGain, split1.getGainValue(), 1e-8);
    final TreeNodeCondition[] childConds1 = split1.getChildConditions();
    final TreeNodeNumericCondition numCondLeft1 = (TreeNodeNumericCondition) childConds1[0];
    assertEquals("Wrong split point.", 3.5, numCondLeft1.getSplitValue(), 1e-8);
    assertTrue("Missings were not sent in the correct direction.", numCondLeft1.acceptsMissings());
    final TreeNodeNumericCondition numCondRight1 = (TreeNodeNumericCondition) childConds1[1];
    assertEquals("Wrong split point.", 3.5, numCondRight1.getSplitValue(), 1e-8);
    assertFalse("Missings were not sent in the correct direction.", numCondRight1.acceptsMissings());
    final TreeTargetNominalColumnData target2 = TestDataGenerator.createNominalTargetColumn(target2CSV);
    final SplitCandidate split2 = col.calcBestSplitClassification(dataMem, target2.getDistribution(weights, config), target2, rd);
    assertEquals("Wrong gain.", expectedGain, split2.getGainValue(), 1e-8);
    final TreeNodeCondition[] childConds2 = split2.getChildConditions();
    final TreeNodeNumericCondition numCondLeft2 = (TreeNodeNumericCondition) childConds2[0];
    assertEquals("Wrong split point.", 3.5, numCondLeft2.getSplitValue(), 1e-8);
    assertFalse("Missings were not sent in the correct direction.", numCondLeft2.acceptsMissings());
    final TreeNodeNumericCondition numCondRight2 = (TreeNodeNumericCondition) childConds2[1];
    assertEquals("Wrong split point.", 3.5, numCondRight2.getSplitValue(), 1e-8);
    assertTrue("Missings were not sent in the correct direction.", numCondRight2.acceptsMissings());
}

Example 9 with TestDataGenerator

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

the class TreeNumericColumnDataTest method testCalcBestSplitRegression.

@Test
public void testCalcBestSplitRegression() throws InvalidSettingsException {
    String dataCSV = "1,2,3,4,5,6,7,8,9,10";
    String targetCSV = "1,5,4,4.3,6.5,6.5,4,3,3,4";
    TreeEnsembleLearnerConfiguration config = new TreeEnsembleLearnerConfiguration(true);
    config.setNrModels(1);
    config.setDataSelectionWithReplacement(false);
    config.setUseDifferentAttributesAtEachNode(false);
    config.setDataFractionPerTree(1.0);
    config.setColumnSamplingMode(ColumnSamplingMode.None);
    TestDataGenerator dataGen = new TestDataGenerator(config);
    RandomData rd = config.createRandomData();
    TreeTargetNumericColumnData target = TestDataGenerator.createNumericTargetColumn(targetCSV);
    TreeNumericColumnData attribute = dataGen.createNumericAttributeColumn(dataCSV, "test-col", 0);
    TreeData data = new TreeData(new TreeAttributeColumnData[] { attribute }, target, TreeType.Ordinary);
    double[] weights = new double[10];
    Arrays.fill(weights, 1.0);
    DataMemberships rootMem = new RootDataMemberships(weights, data, new DefaultDataIndexManager(data));
    SplitCandidate firstSplit = attribute.calcBestSplitRegression(rootMem, target.getPriors(rootMem, config), target, rd);
    // calculated via OpenOffice calc
    assertEquals(10.885444, firstSplit.getGainValue(), 1e-5);
    TreeNodeCondition[] firstConditions = firstSplit.getChildConditions();
    assertEquals(2, firstConditions.length);
    for (int i = 0; i < firstConditions.length; i++) {
        assertThat(firstConditions[i], instanceOf(TreeNodeNumericCondition.class));
        TreeNodeNumericCondition numCond = (TreeNodeNumericCondition) firstConditions[i];
        assertEquals(1.5, numCond.getSplitValue(), 0);
    }
    // left child contains only one row therefore only look at right child
    BitSet expectedInChild = new BitSet(10);
    expectedInChild.set(1, 10);
    BitSet inChild = attribute.updateChildMemberships(firstConditions[1], rootMem);
    assertEquals(expectedInChild, inChild);
    DataMemberships childMem = rootMem.createChildMemberships(inChild);
    SplitCandidate secondSplit = attribute.calcBestSplitRegression(childMem, target.getPriors(childMem, config), target, rd);
    assertEquals(6.883555, secondSplit.getGainValue(), 1e-5);
    TreeNodeCondition[] secondConditions = secondSplit.getChildConditions();
    for (int i = 0; i < secondConditions.length; i++) {
        assertThat(secondConditions[i], instanceOf(TreeNodeNumericCondition.class));
        TreeNodeNumericCondition numCond = (TreeNodeNumericCondition) secondConditions[i];
        assertEquals(6.5, numCond.getSplitValue(), 0);
    }
}

Example 10 with TestDataGenerator

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

the class TreeTargetNumericColumnDataTest method testGetPriors.

/**
 * Tests the {@link TreeTargetNumericColumnData#getPriors(DataMemberships, TreeEnsembleLearnerConfiguration)} and
 * {@link TreeTargetNumericColumnData#getPriors(double[], TreeEnsembleLearnerConfiguration)} methods.
 */
@Test
public void testGetPriors() {
    String targetCSV = "1,4,3,5,6,7,8,12,22,1";
    // irrelevant but necessary to build TreeDataObject
    String someAttributeCSV = "A,B,A,B,A,A,B,A,A,B";
    TreeEnsembleLearnerConfiguration config = new TreeEnsembleLearnerConfiguration(true);
    TestDataGenerator dataGen = new TestDataGenerator(config);
    TreeTargetNumericColumnData target = TestDataGenerator.createNumericTargetColumn(targetCSV);
    TreeNominalColumnData attribute = dataGen.createNominalAttributeColumn(someAttributeCSV, "test-col", 0);
    TreeData data = new TreeData(new TreeAttributeColumnData[] { attribute }, target, TreeType.Ordinary);
    double[] weights = new double[10];
    Arrays.fill(weights, 1.0);
    DataMemberships rootMem = new RootDataMemberships(weights, data, new DefaultDataIndexManager(data));
    RegressionPriors datMemPriors = target.getPriors(rootMem, config);
    assertEquals(6.9, datMemPriors.getMean(), DELTA);
    assertEquals(69, datMemPriors.getYSum(), DELTA);
    assertEquals(352.9, datMemPriors.getSumSquaredDeviation(), DELTA);
}