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

Example 41 with TreeEnsembleLearnerConfiguration

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

the class EnsembleOptionsPanel method loadSettings.

/**
 * Load settings from config <b>cfg</b>
 *
 * @param cfg
 */
public void loadSettings(final TreeEnsembleLearnerConfiguration cfg) {
    m_nrModelsSpinner.setValue(cfg.getNrModels());
    double dataFrac = cfg.getDataFractionPerTree();
    boolean isDataWithReplacement = cfg.isDataSelectionWithReplacement();
    boolean isEqualSizeSampling = cfg.getRowSamplingMode() == RowSamplingMode.EqualSize;
    boolean doesSampling = dataFrac < 1.0 || isDataWithReplacement || isEqualSizeSampling;
    m_dataFractionPerTreeSpinner.setValue(dataFrac);
    if (m_dataFractionPerTreeChecker.isSelected() != doesSampling) {
        m_dataFractionPerTreeChecker.doClick();
    }
    if (isDataWithReplacement) {
        m_dataSamplingWithReplacementChecker.doClick();
    } else {
        m_dataSamplingWithOutReplacementChecker.doClick();
    }
    m_dataSamplingModeComboBox.setSelectedItem(cfg.getRowSamplingMode());
    double colFrac = cfg.getColumnFractionLinearValue();
    int colAbsolute = cfg.getColumnAbsoluteValue();
    boolean useDifferentAttributesAtEachNode = cfg.isUseDifferentAttributesAtEachNode();
    ColumnSamplingMode columnFraction = cfg.getColumnSamplingMode();
    switch(columnFraction) {
        case None:
            m_columnFractionNoneButton.doClick();
            useDifferentAttributesAtEachNode = false;
            colFrac = 1.0;
            break;
        case Linear:
            m_columnFractionLinearButton.doClick();
            break;
        case Absolute:
            m_columnFractionAbsoluteButton.doClick();
            break;
        case SquareRoot:
            m_columnFractionSqrtButton.doClick();
            colFrac = 1.0;
            break;
    }
    m_columnFractionLinearTreeSpinner.setValue(colFrac);
    m_columnFractionAbsoluteTreeSpinner.setValue(colAbsolute);
    if (useDifferentAttributesAtEachNode) {
        m_columnUseDifferentSetOfAttributesForNodes.doClick();
    } else {
        m_columnUseSameSetOfAttributesForNodes.doClick();
    }
    Long seed = cfg.getSeed();
    if (m_seedChecker.isSelected() != (seed != null)) {
        m_seedChecker.doClick();
    }
    m_seedTextField.setText(Long.toString(seed != null ? seed : System.currentTimeMillis()));
}

Example 42 with TreeEnsembleLearnerConfiguration

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

the class EnsembleOptionsPanel method saveSettings.

/**
 * Save settings to config <b>cfg</b>
 *
 * @param cfg
 * @throws InvalidSettingsException
 */
public void saveSettings(final TreeEnsembleLearnerConfiguration cfg) throws InvalidSettingsException {
    cfg.setNrModels((Integer) m_nrModelsSpinner.getValue());
    double dataFrac;
    boolean isSamplingWithReplacement;
    if (m_dataFractionPerTreeChecker.isSelected()) {
        dataFrac = (Double) m_dataFractionPerTreeSpinner.getValue();
        isSamplingWithReplacement = m_dataSamplingWithReplacementChecker.isSelected();
    } else {
        dataFrac = 1.0;
        isSamplingWithReplacement = false;
    }
    cfg.setDataFractionPerTree(dataFrac);
    cfg.setDataSelectionWithReplacement(isSamplingWithReplacement);
    cfg.setRowSamplingMode((RowSamplingMode) m_dataSamplingModeComboBox.getSelectedItem());
    ColumnSamplingMode cf;
    double columnFrac = 1.0;
    int columnAbsolute = TreeEnsembleLearnerConfiguration.DEF_COLUMN_ABSOLUTE;
    boolean isUseDifferentAttributesAtEachNode = m_columnUseDifferentSetOfAttributesForNodes.isSelected();
    if (m_columnFractionNoneButton.isSelected()) {
        cf = ColumnSamplingMode.None;
        isUseDifferentAttributesAtEachNode = false;
    } else if (m_columnFractionLinearButton.isSelected()) {
        cf = ColumnSamplingMode.Linear;
        columnFrac = (Double) m_columnFractionLinearTreeSpinner.getValue();
    } else if (m_columnFractionAbsoluteButton.isSelected()) {
        cf = ColumnSamplingMode.Absolute;
        columnAbsolute = (Integer) m_columnFractionAbsoluteTreeSpinner.getValue();
    } else if (m_columnFractionSqrtButton.isSelected()) {
        cf = ColumnSamplingMode.SquareRoot;
    } else {
        throw new InvalidSettingsException("No column selection policy selected");
    }
    cfg.setColumnSamplingMode(cf);
    cfg.setColumnFractionLinearValue(columnFrac);
    cfg.setColumnAbsoluteValue(columnAbsolute);
    cfg.setUseDifferentAttributesAtEachNode(isUseDifferentAttributesAtEachNode);
    Long seed;
    if (m_seedChecker.isSelected()) {
        final String seedText = m_seedTextField.getText();
        try {
            seed = Long.valueOf(seedText);
        } catch (Exception e) {
            throw new InvalidSettingsException("Unable to parse seed \"" + seedText + "\"", e);
        }
    } else {
        seed = null;
    }
    cfg.setSeed(seed);
}

Example 43 with TreeEnsembleLearnerConfiguration

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

the class TreeEnsembleRegressionLearnerNodeDialogPane method saveSettingsTo.

/**
 * {@inheritDoc}
 */
@Override
protected void saveSettingsTo(final NodeSettingsWO settings) throws InvalidSettingsException {
    TreeEnsembleLearnerConfiguration cfg = new TreeEnsembleLearnerConfiguration(true);
    m_attributeSelectionPanel.saveSettings(cfg);
    m_treeOptionsPanel.saveSettings(cfg);
    m_ensembleOptionsPanel.saveSettings(cfg);
    cfg.save(settings);
}

Example 44 with TreeEnsembleLearnerConfiguration

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

the class TreeNominalColumnDataTest method createConfig.

private static TreeEnsembleLearnerConfiguration createConfig(final boolean isRegression) throws InvalidSettingsException {
    final TreeEnsembleLearnerConfiguration config = new TreeEnsembleLearnerConfiguration(isRegression);
    config.setColumnSamplingMode(ColumnSamplingMode.None);
    config.setDataSelectionWithReplacement(false);
    config.setNrModels(1);
    config.setUseDifferentAttributesAtEachNode(false);
    config.setDataFractionPerTree(1.0);
    config.setUseBinaryNominalSplits(true);
    if (!isRegression) {
        config.setSplitCriterion(SplitCriterion.Gini);
    }
    return config;
}

Example 45 with TreeEnsembleLearnerConfiguration

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

the class TreeNominalColumnDataTest method testCalcBestSplitCassificationBinaryTwoClassXGBoostMissingValue1.

/**
 * Tests the XGBoost missing value handling variant, where for each split it is tried which direction for missing
 * values provides the better gain.
 *
 * @throws Exception
 */
@Test
public void testCalcBestSplitCassificationBinaryTwoClassXGBoostMissingValue1() 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);
    String dataContainingMissingsCSV = "S,?,O,R,S,R,S,?,O,?";
    final TreeNominalColumnData columnData = dataGen.createNominalAttributeColumn(dataContainingMissingsCSV, "column containing missing values", 0);
    final TreeTargetNominalColumnData target = twoClassTennisData.getSecond();
    double[] rowWeights = new double[TWO_CLASS_INDICES.length];
    Arrays.fill(rowWeights, 1.0);
    // based on the ordering in the columnData
    final int[] originalIndex = new int[] { 0, 4, 6, 2, 8, 3, 5, 1, 7, 9 };
    final int[] columnIndex = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
    final DataMemberships dataMem = new MockDataColMem(originalIndex, columnIndex, rowWeights);
    final SplitCandidate split = columnData.calcBestSplitClassification(dataMem, target.getDistribution(rowWeights, config), target, TestDataGenerator.createRandomData());
    assertThat(split, instanceOf(NominalBinarySplitCandidate.class));
    final NominalBinarySplitCandidate nomSplit = (NominalBinarySplitCandidate) split;
    TreeNodeNominalBinaryCondition[] childConditions = nomSplit.getChildConditions();
    assertEquals("Wrong gain value.", 0.18, nomSplit.getGainValue(), 1e-8);
    final String[] conditionValues = new String[] { "S", "R" };
    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());
    assertTrue("Missing values are not sent to the correct child.", childConditions[0].acceptsMissings());
    assertFalse("Missing values are not sent to the correct child.", childConditions[1].acceptsMissings());
}