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

Example 26 with Pair

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

the class HDF5PCACoveragePoNCreationUtils method subsetReadCountsToUsableTargets.

/**
     * Subsets targets in the input count to the usable ones based on the percentile threshold indicated
     * by the user.
     *
     * <p>
     *     It returns a pair of object, where the left one is the updated read-counts with only the usable
     *     targets, and the right one is the corresponding target factors.
     * </p>
     *
     * @param readCounts the input read-counts.
     * @param targetFactorPercentileThreshold the minimum median count percentile under which targets are not considered useful.
     * @return never {@code null}.
     */
@VisibleForTesting
static Pair<ReadCountCollection, double[]> subsetReadCountsToUsableTargets(final ReadCountCollection readCounts, final double targetFactorPercentileThreshold, final Logger logger) {
    final double[] targetFactors = calculateTargetFactors(readCounts);
    final double threshold = new Percentile(targetFactorPercentileThreshold).evaluate(targetFactors);
    final List<Target> targetByIndex = readCounts.targets();
    final Set<Target> result = IntStream.range(0, targetFactors.length).filter(i -> targetFactors[i] >= threshold).mapToObj(targetByIndex::get).collect(Collectors.toCollection(LinkedHashSet::new));
    if (result.size() == targetByIndex.size()) {
        logger.info(String.format("All %d targets are kept", targetByIndex.size()));
        return new ImmutablePair<>(readCounts, targetFactors);
    } else {
        final int discardedCount = targetFactors.length - result.size();
        logger.info(String.format("Discarded %d target(s) out of %d with factors below %.2g (%.2f percentile)", discardedCount, targetFactors.length, threshold, targetFactorPercentileThreshold));
        final double[] targetFactorSubset = DoubleStream.of(targetFactors).filter(i -> i >= threshold).toArray();
        return new ImmutablePair<>(readCounts.subsetTargets(result), targetFactorSubset);
    }
}

Example 27 with Pair

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

the class CNLOHCaller method calcNewRhos.

private double[] calcNewRhos(final List<ACNVModeledSegment> segments, final List<double[][][]> responsibilitiesBySeg, final double lambda, final double[] rhos, final int[] mVals, final int[] nVals, final JavaSparkContext ctx) {
    // Since, we pass in the entire responsibilities matrix, we need the correct index for each rho.  That, and the
    //  fact that this is a univariate objective function, means we need to create an instance for each rho.  And
    //  then we blast across Spark.
    final List<Pair<? extends Function<Double, Double>, SearchInterval>> objectives = IntStream.range(0, rhos.length).mapToObj(i -> new Pair<>(new Function<Double, Double>() {

        @Override
        public Double apply(Double rho) {
            return calculateESmnObjective(rho, segments, responsibilitiesBySeg, mVals, nVals, lambda, i);
        }
    }, new SearchInterval(0.0, 1.0, rhos[i]))).collect(Collectors.toList());
    final JavaRDD<Pair<? extends Function<Double, Double>, SearchInterval>> objectivesRDD = ctx.parallelize(objectives);
    final List<Double> resultsAsDouble = objectivesRDD.map(objective -> optimizeIt(objective.getFirst(), objective.getSecond())).collect();
    return resultsAsDouble.stream().mapToDouble(Double::doubleValue).toArray();
}

Example 28 with Pair

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

the class ArtifactStatisticsScorer method calculateArtifactPValue.

/** p-value for being an artifact
     *
     * @param totalAltAlleleCount total number of alt reads
     * @param artifactAltAlleleCount alt read counts in a pair orientation that supports an artifact
     * @param biasP believed bias p value for a binomial distribution in artifact variants
     * @return p-value for this being an artifact
     */
public static double calculateArtifactPValue(final int totalAltAlleleCount, final int artifactAltAlleleCount, final double biasP) {
    ParamUtils.isPositiveOrZero(biasP, "bias parameter must be positive or zero.");
    ParamUtils.isPositiveOrZero(totalAltAlleleCount, "total alt allele count must be positive or zero.");
    ParamUtils.isPositiveOrZero(artifactAltAlleleCount, "artifact supporting alt allele count must be positive or zero.");
    ParamUtils.isPositiveOrZero(totalAltAlleleCount - artifactAltAlleleCount, "Total alt count must be same or greater than the artifact alt count.");
    return new BinomialDistribution(null, totalAltAlleleCount, biasP).cumulativeProbability(artifactAltAlleleCount);
}

Example 29 with Pair

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

the class HDF5PCACoveragePoNCreationUtils method subsetReadCountsToUsableTargets.

/**
     * Subsets targets in the input count to the usable ones based on the percentile threshold indicated
     * by the user.
     *
     * <p>
     *     It returns a pair of object, where the left one is the updated read-counts with only the usable
     *     targets, and the right one is the corresponding target factors.
     * </p>
     *
     * @param readCounts the input read-counts.
     * @param targetFactorPercentileThreshold the minimum median count percentile under which targets are not considered useful.
     * @return never {@code null}.
     */
@VisibleForTesting
static Pair<ReadCountCollection, double[]> subsetReadCountsToUsableTargets(final ReadCountCollection readCounts, final double targetFactorPercentileThreshold, final Logger logger) {
    final double[] targetFactors = calculateTargetFactors(readCounts);
    final double threshold = new Percentile(targetFactorPercentileThreshold).evaluate(targetFactors);
    final List<Target> targetByIndex = readCounts.targets();
    final Set<Target> result = IntStream.range(0, targetFactors.length).filter(i -> targetFactors[i] >= threshold).mapToObj(targetByIndex::get).collect(Collectors.toCollection(LinkedHashSet::new));
    if (result.size() == targetByIndex.size()) {
        logger.info(String.format("All %d targets are kept", targetByIndex.size()));
        return new ImmutablePair<>(readCounts, targetFactors);
    } else {
        final int discardedCount = targetFactors.length - result.size();
        logger.info(String.format("Discarded %d target(s) out of %d with factors below %.2g (%.2f percentile)", discardedCount, targetFactors.length, threshold, targetFactorPercentileThreshold));
        final double[] targetFactorSubset = DoubleStream.of(targetFactors).filter(i -> i >= threshold).toArray();
        return new ImmutablePair<>(readCounts.subsetTargets(result), targetFactorSubset);
    }
}

Example 30 with Pair

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

the class CoverageModelEMWorkspace method updateCopyRatioPosteriorExpectationsLocal.

/**
     * Local implementation of the E-step update of copy ratio posteriors
     *
     * @return a {@link SubroutineSignal} containing the update size (key: "error_norm")
     */
public SubroutineSignal updateCopyRatioPosteriorExpectationsLocal(final double admixingRatio) {
    /* step 1. fetch copy ratio emission data */
    final List<List<CoverageModelCopyRatioEmissionData>> copyRatioEmissionData = fetchCopyRatioEmissionDataLocal();
    /* step 2. run the forward-backward algorithm and calculate copy ratio posteriors */
    final INDArray sampleReadDepths = Transforms.exp(sampleMeanLogReadDepths, true);
    final List<CopyRatioExpectations> copyRatioPosteriorResults = sampleIndexStream().parallel().mapToObj(si -> copyRatioExpectationsCalculator.getCopyRatioPosteriorExpectations(CopyRatioCallingMetadata.builder().sampleName(processedSampleNameList.get(si)).sampleSexGenotypeData(processedSampleSexGenotypeData.get(si)).sampleCoverageDepth(sampleReadDepths.getDouble(si)).emissionCalculationStrategy(EmissionCalculationStrategy.HYBRID_POISSON_GAUSSIAN).build(), processedTargetList, copyRatioEmissionData.get(si))).collect(Collectors.toList());
    /* update log chain posterior expectation */
    sampleLogChainPosteriors.assign(Nd4j.create(copyRatioPosteriorResults.stream().mapToDouble(CopyRatioExpectations::getLogChainPosteriorProbability).toArray(), new int[] { numSamples, 1 }));
    /* sent the results back to workers */
    final ImmutablePair<INDArray, INDArray> copyRatioPosteriorDataPair = convertCopyRatioLatentPosteriorExpectationsToNDArray(copyRatioPosteriorResults);
    final INDArray log_c_st = copyRatioPosteriorDataPair.left;
    final INDArray var_log_c_st = copyRatioPosteriorDataPair.right;
    /* partition the pair of (log_c_st, var_log_c_st), sent the result to workers via broadcast-hash-map */
    pushToWorkers(mapINDArrayPairToBlocks(log_c_st.transpose(), var_log_c_st.transpose()), (p, cb) -> cb.cloneWithUpdatedCopyRatioPosteriors(p.get(cb.getTargetSpaceBlock()).left.transpose(), p.get(cb.getTargetSpaceBlock()).right.transpose(), admixingRatio));
    cacheWorkers("after E-step update of copy ratio posteriors");
    /* collect subroutine signals */
    final List<SubroutineSignal> sigs = mapWorkersAndCollect(CoverageModelEMComputeBlock::getLatestMStepSignal);
    final double errorNormInfinity = Collections.max(sigs.stream().map(sig -> sig.<Double>get(StandardSubroutineSignals.RESIDUAL_ERROR_NORM)).collect(Collectors.toList()));
    return SubroutineSignal.builder().put(StandardSubroutineSignals.RESIDUAL_ERROR_NORM, errorNormInfinity).build();
}