Examples with RandomData org.apache.commons.math.random.RandomData used on opensource projects

Example 11 with RandomData

use of org.apache.commons.math.random.RandomData in project knime-core by knime.

the class EqualSizeRowSamplerTest method testCreateRowSampleNoReplacement.

@Test
public void testCreateRowSampleNoReplacement() throws Exception {
    final SubsetSelector<SubsetNoReplacementRowSample> selector = SubsetNoReplacementSelector.getInstance();
    double fraction = 0.5;
    EqualSizeRowSampler<SubsetNoReplacementRowSample> sampler = new EqualSizeRowSampler<SubsetNoReplacementRowSample>(fraction, selector, SamplerTestUtil.TARGET);
    final RandomData rd = TestDataGenerator.createRandomData();
    SubsetNoReplacementRowSample sample = sampler.createRowSample(rd);
    assertEquals(6, sample.getIncludedBitSet().cardinality());
    assertEquals(15, sample.getNrRows());
    fraction = 1.0;
    sampler = new EqualSizeRowSampler<SubsetNoReplacementRowSample>(fraction, selector, SamplerTestUtil.TARGET);
    sample = sampler.createRowSample(rd);
    assertEquals(12, sample.getIncludedBitSet().cardinality());
    assertEquals(15, sample.getNrRows());
    // check if the full minority class is included
    for (int i = 11; i < 15; i++) {
        assertEquals(1, sample.getCountFor(i));
    }
}

Example 12 with RandomData

use of org.apache.commons.math.random.RandomData in project knime-core by knime.

the class SubsetNoReplacementSelectorTest method testSelectNrTotalSmallerZero.

@Test(expected = IllegalArgumentException.class)
public void testSelectNrTotalSmallerZero() throws Exception {
    final RandomData rd = TestDataGenerator.createRandomData();
    SubsetNoReplacementSelector.getInstance().select(rd, -2, 5);
}

Example 13 with RandomData

use of org.apache.commons.math.random.RandomData 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;
}

Example 14 with RandomData

use of org.apache.commons.math.random.RandomData 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()]);
}

Example 15 with RandomData

use of org.apache.commons.math.random.RandomData in project knime-core by knime.

the class MGradientBoostedTreesLearner method learn.

/**
 * {@inheritDoc}
 */
@Override
public AbstractGradientBoostingModel learn(final ExecutionMonitor exec) throws CanceledExecutionException {
    final TreeData actualData = getData();
    final GradientBoostingLearnerConfiguration config = getConfig();
    final int nrModels = config.getNrModels();
    final TreeTargetNumericColumnData actualTarget = getTarget();
    final double initialValue = actualTarget.getMedian();
    final ArrayList<TreeModelRegression> models = new ArrayList<TreeModelRegression>(nrModels);
    final ArrayList<Map<TreeNodeSignature, Double>> coefficientMaps = new ArrayList<Map<TreeNodeSignature, Double>>(nrModels);
    final double[] previousPrediction = new double[actualTarget.getNrRows()];
    Arrays.fill(previousPrediction, initialValue);
    final RandomData rd = config.createRandomData();
    final double alpha = config.getAlpha();
    TreeNodeSignatureFactory signatureFactory = null;
    final int maxLevels = config.getMaxLevels();
    // this should be the default
    if (maxLevels < TreeEnsembleLearnerConfiguration.MAX_LEVEL_INFINITE) {
        final int capacity = IntMath.pow(2, maxLevels - 1);
        signatureFactory = new TreeNodeSignatureFactory(capacity);
    } else {
        signatureFactory = new TreeNodeSignatureFactory();
    }
    exec.setMessage("Learning model");
    TreeData residualData;
    for (int i = 0; i < nrModels; i++) {
        final double[] residuals = new double[actualTarget.getNrRows()];
        for (int j = 0; j < actualTarget.getNrRows(); j++) {
            residuals[j] = actualTarget.getValueFor(j) - previousPrediction[j];
        }
        final double quantile = calculateAlphaQuantile(residuals, alpha);
        final double[] gradients = new double[residuals.length];
        for (int j = 0; j < gradients.length; j++) {
            gradients[j] = Math.abs(residuals[j]) <= quantile ? residuals[j] : quantile * Math.signum(residuals[j]);
        }
        residualData = createResidualDataFromArray(gradients, actualData);
        final RandomData rdSingle = TreeEnsembleLearnerConfiguration.createRandomData(rd.nextLong(Long.MIN_VALUE, Long.MAX_VALUE));
        final RowSample rowSample = getRowSampler().createRowSample(rdSingle);
        final TreeLearnerRegression treeLearner = new TreeLearnerRegression(getConfig(), residualData, getIndexManager(), signatureFactory, rdSingle, rowSample);
        final TreeModelRegression tree = treeLearner.learnSingleTree(exec, rdSingle);
        final Map<TreeNodeSignature, Double> coefficientMap = calcCoefficientMap(residuals, quantile, tree);
        adaptPreviousPrediction(previousPrediction, tree, coefficientMap);
        models.add(tree);
        coefficientMaps.add(coefficientMap);
        exec.setProgress(((double) i) / nrModels, "Finished level " + i + "/" + nrModels);
    }
    return new GradientBoostedTreesModel(getConfig(), actualData.getMetaData(), models.toArray(new TreeModelRegression[models.size()]), actualData.getTreeType(), initialValue, coefficientMaps);
}