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[ML] Correct the multimodal prior confidence interval calculation #176

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Merged
merged 4 commits into from
Aug 2, 2018
Merged

[ML] Correct the multimodal prior confidence interval calculation #176

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81c84f6
Correct multimodal prior confidence interval calculation
tveasey Aug 1, 2018
7790058
Documentation
tveasey Aug 1, 2018
36b4463
Add unit test
tveasey Aug 2, 2018
f1d6a05
Formatting fixes
tveasey Aug 2, 2018
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3 changes: 3 additions & 0 deletions docs/CHANGELOG.asciidoc
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Expand Up @@ -62,6 +62,9 @@ Fix corner case failing to calculate lgamma values and the correspoinding log er
Influence count per bucket for metric population analyses was wrong and lead to wrong influencer scoring ({pull}#150[#150])
Fix a possible SIGSEGV for jobs with multivariate by fields enabled which would lead to the job failing ({pull}#170[#170])

Correct the model bounds and typical value calculation for time series models which use a multimodal distribution.
This issue could cause "Unable to bracket left percentile =..." errors to appear in the logs. ({pull}#176[#176])

=== Regressions

=== Known Issues
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4 changes: 2 additions & 2 deletions include/maths/CMultimodalPriorUtils.h
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Expand Up @@ -206,8 +206,8 @@ class MATHS_EXPORT CMultimodalPriorUtils : private core::CNonInstantiatable {
return support;
}

double p1 = std::log((1.0 - percentage) / 2.0);
double p2 = std::log((1.0 + percentage) / 2.0);
double p1 = maths_t::count(weights) * std::log((1.0 - percentage) / 2.0);
double p2 = maths_t::count(weights) * std::log((1.0 + percentage) / 2.0);

CLogCdf<PRIOR> fl(CLogCdf<PRIOR>::E_Lower, prior, weights);
CLogCdf<PRIOR> fu(CLogCdf<PRIOR>::E_Upper, prior, weights);
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31 changes: 29 additions & 2 deletions lib/maths/unittest/CMultimodalPriorTest.cc
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Expand Up @@ -941,10 +941,10 @@ void CMultimodalPriorTest::testMarginalLikelihoodConfidenceInterval() {

using TMeanAccumulator = maths::CBasicStatistics::SSampleMean<double>::TAccumulator;

test::CRandomNumbers rng;

LOG_DEBUG(<< "Synthetic");
{
test::CRandomNumbers rng;

double w1 = 0.2;
double location1 = 0.1;
double squareScale1 = 0.2;
Expand Down Expand Up @@ -1047,6 +1047,33 @@ void CMultimodalPriorTest::testMarginalLikelihoodConfidenceInterval() {
CPPUNIT_ASSERT_DOUBLES_EQUAL(-111.0, i90.first, 0.5);
CPPUNIT_ASSERT_DOUBLES_EQUAL(158952.0, i90.second, 0.5);
}

LOG_DEBUG(<< "Non-unit count weight");
{
// The confidence interval should be independent of the sample
// count weight.

TDoubleVec modes[2];
rng.generateNormalSamples(10.0, 2.0, 50, modes[0]);
rng.generateNormalSamples(20.0, 2.0, 50, modes[1]);
TDoubleVec samples(modes[0].begin(), modes[0].end());
samples.insert(samples.end(), modes[1].begin(), modes[1].end());

CMultimodalPrior filter(makePrior());
filter.addSamples(samples);

CPPUNIT_ASSERT_EQUAL(std::size_t(2), filter.numberModes());

TDoubleDoublePr interval{filter.marginalLikelihoodConfidenceInterval(
90.0, maths_t::countWeight(1.0))};
TDoubleDoublePr weightedInterval{filter.marginalLikelihoodConfidenceInterval(
90.0, maths_t::countWeight(0.3))};
LOG_DEBUG(<< "interval = " << core::CContainerPrinter::print(interval));
LOG_DEBUG(<< "weightedInterval = "
<< core::CContainerPrinter::print(weightedInterval));
CPPUNIT_ASSERT_EQUAL(core::CContainerPrinter::print(interval),
core::CContainerPrinter::print(weightedInterval));
}
}

void CMultimodalPriorTest::testSampleMarginalLikelihood() {
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