Examples with Pair org.apache.commons.math3.util.Pair used on opensource projects

Example 61 with Pair

use of org.apache.commons.math3.util.Pair in project gatk by broadinstitute.

the class TargetCoverageSexGenotypeCalculator method processReadCountsAndTargets.

/**
     * Processes raw read counts and targets:
     * <dl>
     *     <dt> If more than one sample is present in the collection, filters out fully uncovered targets
     *     from read counts and removes the uncovered targets from the target list</dt>
     *
     *     <dt> Otherwise, does nothing and warns the user
     *     </dt>
     * </dl>
     *
     * @param rawReadCounts raw read count collection
     * @param targetList user provided target list
     * @return pair of processed read counts and targets
     */
private ImmutablePair<ReadCountCollection, List<Target>> processReadCountsAndTargets(@Nonnull final ReadCountCollection rawReadCounts, @Nonnull final List<Target> targetList) {
    final ReadCountCollection finalReadCounts;
    final List<Target> finalTargetList;
    /* remove totally uncovered targets */
    if (rawReadCounts.columnNames().size() > 1) {
        finalReadCounts = ReadCountCollectionUtils.removeTotallyUncoveredTargets(rawReadCounts, logger);
        final Set<Target> targetSetFromProcessedReadCounts = new HashSet<>(finalReadCounts.targets());
        finalTargetList = targetList.stream().filter(targetSetFromProcessedReadCounts::contains).collect(Collectors.toList());
    } else {
        final long numUncoveredTargets = rawReadCounts.records().stream().filter(rec -> (int) rec.getDouble(0) == 0).count();
        final long numAllTargets = rawReadCounts.targets().size();
        logger.info("Since only one sample is given for genotyping, the user is responsible for asserting" + " the aptitude of targets. Fully uncovered (irrelevant) targets can not be automatically" + " identified (total targets: " + numAllTargets + ", uncovered targets: " + numUncoveredTargets + ")");
        finalReadCounts = rawReadCounts;
        finalTargetList = targetList;
    }
    return ImmutablePair.of(finalReadCounts, finalTargetList);
}

Example 62 with Pair

use of org.apache.commons.math3.util.Pair in project gatk-protected by broadinstitute.

the class CoverageModelParameters method adaptModelToReadCountCollection.

/**
     * This method "adapts" a model to a read count collection in the following sense:
     *
     *     - removes targets that are not included in the model from the read counts collection
     *     - removes targets that are in the read count collection from the model
     *     - rearranges model targets in the same order as read count collection targets
     *
     * The modifications are not done in-place and the original input parameters remain intact.
     *
     * @param model a model
     * @param readCounts a read count collection
     * @return a pair of model and read count collection
     */
public static ImmutablePair<CoverageModelParameters, ReadCountCollection> adaptModelToReadCountCollection(@Nonnull final CoverageModelParameters model, @Nonnull final ReadCountCollection readCounts, @Nonnull final Logger logger) {
    logger.info("Adapting model to read counts...");
    Utils.nonNull(model, "The model parameters must be non-null");
    Utils.nonNull(readCounts, "The read count collection must be non-null");
    Utils.nonNull(logger, "The logger must be non-null");
    final List<Target> modelTargetList = model.getTargetList();
    final List<Target> readCountsTargetList = readCounts.targets();
    final Set<Target> mutualTargetSet = Sets.intersection(new HashSet<>(modelTargetList), new HashSet<>(readCountsTargetList));
    final List<Target> mutualTargetList = readCountsTargetList.stream().filter(mutualTargetSet::contains).collect(Collectors.toList());
    logger.info("Number of mutual targets: " + mutualTargetList.size());
    Utils.validateArg(mutualTargetList.size() > 0, "The intersection between model targets and targets from read count" + " collection is empty. Please check there the model is compatible with the given read count" + " collection.");
    if (modelTargetList.size() > mutualTargetList.size()) {
        logger.info("The following targets dropped from the model: " + Sets.difference(new HashSet<>(modelTargetList), mutualTargetSet).stream().map(Target::getName).collect(Collectors.joining(", ", "[", "]")));
    }
    if (readCountsTargetList.size() > mutualTargetList.size()) {
        logger.info("The following targets dropped from read counts: " + Sets.difference(new HashSet<>(readCountsTargetList), mutualTargetSet).stream().map(Target::getName).collect(Collectors.joining(", ", "[", "]")));
    }
    /* the targets in {@code subsetReadCounts} follow the original order of targets in {@code readCounts} */
    final ReadCountCollection subsetReadCounts = readCounts.subsetTargets(mutualTargetSet);
    /* fetch original model parameters */
    final INDArray originalModelTargetMeanBias = model.getTargetMeanLogBias();
    final INDArray originalModelTargetUnexplainedVariance = model.getTargetUnexplainedVariance();
    final INDArray originalModelMeanBiasCovariates = model.getMeanBiasCovariates();
    /* re-arrange targets, mean log bias, and target-specific unexplained variance */
    final Map<Target, Integer> modelTargetsToIndexMap = IntStream.range(0, modelTargetList.size()).mapToObj(ti -> ImmutablePair.of(modelTargetList.get(ti), ti)).collect(Collectors.toMap(Pair<Target, Integer>::getLeft, Pair<Target, Integer>::getRight));
    final int[] newTargetIndicesInOriginalModel = mutualTargetList.stream().mapToInt(modelTargetsToIndexMap::get).toArray();
    final INDArray newModelTargetMeanBias = Nd4j.create(new int[] { 1, mutualTargetList.size() });
    final INDArray newModelTargetUnexplainedVariance = Nd4j.create(new int[] { 1, mutualTargetList.size() });
    IntStream.range(0, mutualTargetList.size()).forEach(ti -> {
        newModelTargetMeanBias.put(0, ti, originalModelTargetMeanBias.getDouble(0, newTargetIndicesInOriginalModel[ti]));
        newModelTargetUnexplainedVariance.put(0, ti, originalModelTargetUnexplainedVariance.getDouble(0, newTargetIndicesInOriginalModel[ti]));
    });
    /* if model has bias covariates and/or ARD, re-arrange mean/var of bias covariates as well */
    final INDArray newModelMeanBiasCovariates;
    if (model.isBiasCovariatesEnabled()) {
        newModelMeanBiasCovariates = Nd4j.create(new int[] { mutualTargetList.size(), model.getNumLatents() });
        IntStream.range(0, mutualTargetList.size()).forEach(ti -> {
            newModelMeanBiasCovariates.get(NDArrayIndex.point(ti), NDArrayIndex.all()).assign(originalModelMeanBiasCovariates.get(NDArrayIndex.point(newTargetIndicesInOriginalModel[ti]), NDArrayIndex.all()));
        });
    } else {
        newModelMeanBiasCovariates = null;
    }
    return ImmutablePair.of(new CoverageModelParameters(mutualTargetList, newModelTargetMeanBias, newModelTargetUnexplainedVariance, newModelMeanBiasCovariates, model.getBiasCovariateARDCoefficients()), subsetReadCounts);
}

Example 63 with Pair

use of org.apache.commons.math3.util.Pair in project gatk by broadinstitute.

the class CoverageModelParameters method adaptModelToReadCountCollection.

/**
     * This method "adapts" a model to a read count collection in the following sense:
     *
     *     - removes targets that are not included in the model from the read counts collection
     *     - removes targets that are in the read count collection from the model
     *     - rearranges model targets in the same order as read count collection targets
     *
     * The modifications are not done in-place and the original input parameters remain intact.
     *
     * @param model a model
     * @param readCounts a read count collection
     * @return a pair of model and read count collection
     */
public static ImmutablePair<CoverageModelParameters, ReadCountCollection> adaptModelToReadCountCollection(@Nonnull final CoverageModelParameters model, @Nonnull final ReadCountCollection readCounts, @Nonnull final Logger logger) {
    logger.info("Adapting model to read counts...");
    Utils.nonNull(model, "The model parameters must be non-null");
    Utils.nonNull(readCounts, "The read count collection must be non-null");
    Utils.nonNull(logger, "The logger must be non-null");
    final List<Target> modelTargetList = model.getTargetList();
    final List<Target> readCountsTargetList = readCounts.targets();
    final Set<Target> mutualTargetSet = Sets.intersection(new HashSet<>(modelTargetList), new HashSet<>(readCountsTargetList));
    final List<Target> mutualTargetList = readCountsTargetList.stream().filter(mutualTargetSet::contains).collect(Collectors.toList());
    logger.info("Number of mutual targets: " + mutualTargetList.size());
    Utils.validateArg(mutualTargetList.size() > 0, "The intersection between model targets and targets from read count" + " collection is empty. Please check there the model is compatible with the given read count" + " collection.");
    if (modelTargetList.size() > mutualTargetList.size()) {
        logger.info("The following targets dropped from the model: " + Sets.difference(new HashSet<>(modelTargetList), mutualTargetSet).stream().map(Target::getName).collect(Collectors.joining(", ", "[", "]")));
    }
    if (readCountsTargetList.size() > mutualTargetList.size()) {
        logger.info("The following targets dropped from read counts: " + Sets.difference(new HashSet<>(readCountsTargetList), mutualTargetSet).stream().map(Target::getName).collect(Collectors.joining(", ", "[", "]")));
    }
    /* the targets in {@code subsetReadCounts} follow the original order of targets in {@code readCounts} */
    final ReadCountCollection subsetReadCounts = readCounts.subsetTargets(mutualTargetSet);
    /* fetch original model parameters */
    final INDArray originalModelTargetMeanBias = model.getTargetMeanLogBias();
    final INDArray originalModelTargetUnexplainedVariance = model.getTargetUnexplainedVariance();
    final INDArray originalModelMeanBiasCovariates = model.getMeanBiasCovariates();
    /* re-arrange targets, mean log bias, and target-specific unexplained variance */
    final Map<Target, Integer> modelTargetsToIndexMap = IntStream.range(0, modelTargetList.size()).mapToObj(ti -> ImmutablePair.of(modelTargetList.get(ti), ti)).collect(Collectors.toMap(Pair<Target, Integer>::getLeft, Pair<Target, Integer>::getRight));
    final int[] newTargetIndicesInOriginalModel = mutualTargetList.stream().mapToInt(modelTargetsToIndexMap::get).toArray();
    final INDArray newModelTargetMeanBias = Nd4j.create(new int[] { 1, mutualTargetList.size() });
    final INDArray newModelTargetUnexplainedVariance = Nd4j.create(new int[] { 1, mutualTargetList.size() });
    IntStream.range(0, mutualTargetList.size()).forEach(ti -> {
        newModelTargetMeanBias.put(0, ti, originalModelTargetMeanBias.getDouble(0, newTargetIndicesInOriginalModel[ti]));
        newModelTargetUnexplainedVariance.put(0, ti, originalModelTargetUnexplainedVariance.getDouble(0, newTargetIndicesInOriginalModel[ti]));
    });
    /* if model has bias covariates and/or ARD, re-arrange mean/var of bias covariates as well */
    final INDArray newModelMeanBiasCovariates;
    if (model.isBiasCovariatesEnabled()) {
        newModelMeanBiasCovariates = Nd4j.create(new int[] { mutualTargetList.size(), model.getNumLatents() });
        IntStream.range(0, mutualTargetList.size()).forEach(ti -> {
            newModelMeanBiasCovariates.get(NDArrayIndex.point(ti), NDArrayIndex.all()).assign(originalModelMeanBiasCovariates.get(NDArrayIndex.point(newTargetIndicesInOriginalModel[ti]), NDArrayIndex.all()));
        });
    } else {
        newModelMeanBiasCovariates = null;
    }
    return ImmutablePair.of(new CoverageModelParameters(mutualTargetList, newModelTargetMeanBias, newModelTargetUnexplainedVariance, newModelMeanBiasCovariates, model.getBiasCovariateARDCoefficients()), subsetReadCounts);
}

Example 64 with Pair

use of org.apache.commons.math3.util.Pair in project gatk by broadinstitute.

the class HDF5PCACoveragePoNCreationUtilsUnitTest method testSubsetTargetToUsableOnes.

@Test(dataProvider = "readCountAndPercentileData")
public void testSubsetTargetToUsableOnes(final ReadCountCollection readCount, final double percentile) {
    final Median median = new Median();
    final RealMatrix counts = readCount.counts();
    final double[] targetMedians = IntStream.range(0, counts.getRowDimension()).mapToDouble(i -> median.evaluate(counts.getRow(i))).toArray();
    final double threshold = new Percentile(percentile).evaluate(targetMedians);
    final Boolean[] toBeKept = DoubleStream.of(targetMedians).mapToObj(d -> d >= threshold).toArray(Boolean[]::new);
    final int toBeKeptCount = (int) Stream.of(toBeKept).filter(b -> b).count();
    final Pair<ReadCountCollection, double[]> result = HDF5PCACoveragePoNCreationUtils.subsetReadCountsToUsableTargets(readCount, percentile, NULL_LOGGER);
    Assert.assertEquals(result.getLeft().targets().size(), toBeKeptCount);
    Assert.assertEquals(result.getRight().length, toBeKeptCount);
    int nextIndex = 0;
    for (int i = 0; i < toBeKept.length; i++) {
        if (toBeKept[i]) {
            int index = result.getLeft().targets().indexOf(readCount.targets().get(i));
            Assert.assertEquals(index, nextIndex++);
            Assert.assertEquals(counts.getRow(i), result.getLeft().counts().getRow(index));
            Assert.assertEquals(result.getRight()[index], targetMedians[i]);
        } else {
            Assert.assertEquals(result.getLeft().targets().indexOf(readCount.targets().get(i)), -1);
        }
    }
}