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[ML] Guard against the case there are insufficient populated values to estimate variance #13

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Mar 14, 2018
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[ML] Guard against the case there are insufficient populated values to estimate variance #13

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160 changes: 83 additions & 77 deletions lib/maths/CPeriodicityHypothesisTests.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1301,7 +1301,7 @@ bool CPeriodicityHypothesisTests::testStatisticsFor(const TFloatMeanAccumulatorC
return false;
}

LOG_TRACE("populated = " << 100.0 * populated << "%");
LOG_TRACE("populated = " << 100.0 * populated / static_cast<double>(buckets.size()) << "%");

stats.s_Range = range.max() - range.min();
stats.s_B = populated;
Expand Down Expand Up @@ -1475,65 +1475,68 @@ bool CPeriodicityHypothesisTests::testPeriod(const TTimeTimePr2Vec &windows,
LOG_TRACE(" populated = " << b);

double df1{B - b};
double v1{varianceAtPercentile(residualVariance<double>(trend, scale), df1,
50.0 + CONFIDENCE_INTERVAL / 2.0)};
LOG_TRACE(" variance = " << v1);
LOG_TRACE(" varianceThreshold = " << vt);
LOG_TRACE(" significance = " << CStatisticalTests::leftTailFTest(v1 / v0, df1, df0));

double Rt{stats.s_Rt * CTools::truncate(1.0 - 0.5 * (vt - v1) / vt, 0.9, 1.0)};
if (v1 < vt && CStatisticalTests::leftTailFTest(v1 / v0, df1, df0) <= MAXIMUM_SIGNIFICANCE)
{
double R{CSignal::autocorrelation(period, values)};
R = autocorrelationAtPercentile(R, B, 50.0 - CONFIDENCE_INTERVAL / 2.0);
LOG_TRACE(" autocorrelation = " << R);
LOG_TRACE(" autocorrelationThreshold = " << Rt);
if (R > Rt)
if (df1 > 0.0)
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@tveasey tveasey Mar 13, 2018 •
edited
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Aside from this check, the following differences are all due to whitespace.

{
double v1{varianceAtPercentile(residualVariance<double>(trend, scale), df1,
50.0 + CONFIDENCE_INTERVAL / 2.0)};
LOG_TRACE(" variance = " << v1);
LOG_TRACE(" varianceThreshold = " << vt);
LOG_TRACE(" significance = " << CStatisticalTests::leftTailFTest(v1 / v0, df1, df0));

double Rt{stats.s_Rt * CTools::truncate(1.0 - 0.5 * (vt - v1) / vt, 0.9, 1.0)};
if (v1 < vt && CStatisticalTests::leftTailFTest(v1 / v0, df1, df0) <= MAXIMUM_SIGNIFICANCE)
{
return true;
double R{CSignal::autocorrelation(period, values)};
R = autocorrelationAtPercentile(R, B, 50.0 - CONFIDENCE_INTERVAL / 2.0);
LOG_TRACE(" autocorrelation = " << R);
LOG_TRACE(" autocorrelationThreshold = " << Rt);
if (R > Rt)
{
return true;
}
}
}

// The amplitude test.
// The amplitude test.

double F1{1.0};
if (v1 > 0.0)
{
try
double F1{1.0};
if (v1 > 0.0)
{
std::size_t n{static_cast<std::size_t>(
std::ceil(Rt * static_cast<double>(length(window) / period_)))};
TMeanAccumulator level;
for (const auto &value : values)
try
{
if (CBasicStatistics::count(value) > 0.0)
std::size_t n{static_cast<std::size_t>(
std::ceil(Rt * static_cast<double>(length(window) / period_)))};
TMeanAccumulator level;
for (const auto &value : values)
{
level.add(CBasicStatistics::mean(value));
if (CBasicStatistics::count(value) > 0.0)
{
level.add(CBasicStatistics::mean(value));
}
}
}
TMinAmplitudeVec amplitudes(period, {n, CBasicStatistics::mean(level)});
periodicTrend(values, window, m_BucketLength, amplitudes);
boost::math::normal normal(0.0, std::sqrt(v1));
std::for_each(amplitudes.begin(), amplitudes.end(),
[&F1, &normal, at](CMinAmplitude &x)
{
if (x.amplitude() >= at)
TMinAmplitudeVec amplitudes(period, {n, CBasicStatistics::mean(level)});
periodicTrend(values, window, m_BucketLength, amplitudes);
boost::math::normal normal(0.0, std::sqrt(v1));
std::for_each(amplitudes.begin(), amplitudes.end(),
[&F1, &normal, at](CMinAmplitude &x)
{
F1 = std::min(F1, x.significance(normal));
}
});
if (x.amplitude() >= at)
{
F1 = std::min(F1, x.significance(normal));
}
});
}
catch (const std::exception &e)
{
LOG_ERROR("Unable to compute significance of amplitude: " << e.what());
}
}
catch (const std::exception &e)
LOG_TRACE(" F(amplitude) = " << F1);

if (1.0 - std::pow(1.0 - F1, b) <= MAXIMUM_SIGNIFICANCE)
{
LOG_ERROR("Unable to compute significance of amplitude: " << e.what());
return true;
}
}
LOG_TRACE(" F(amplitude) = " << F1);

if (1.0 - std::pow(1.0 - F1, b) <= MAXIMUM_SIGNIFICANCE)
{
return true;
}
return false;
}

Expand Down Expand Up @@ -1627,7 +1630,7 @@ bool CPeriodicityHypothesisTests::testPartition(const TTimeTimePr2Vec &partition
{
for (std::size_t i = 0u; i < 2; ++i)
{
for (auto &&delta : deltas[i])
for (auto &delta : deltas[i])
{
delta = (delta + m_BucketLength) % windowLength;
}
Expand Down Expand Up @@ -1692,39 +1695,42 @@ bool CPeriodicityHypothesisTests::testPartition(const TTimeTimePr2Vec &partition
}

double df1{B - b};
double variance{correction * minimum[0].first};
double v1{varianceAtPercentile(variance, df1, 50.0 + CONFIDENCE_INTERVAL / 2.0)};
LOG_TRACE(" variance = " << v1);
LOG_TRACE(" varianceThreshold = " << vt);
LOG_TRACE(" significance = " << CStatisticalTests::leftTailFTest(v1 / v0, df1, df0));

if (v1 <= vt && CStatisticalTests::leftTailFTest(v1 / v0, df1, df0) <= MAXIMUM_SIGNIFICANCE)
if (df1 > 0.0)
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@tveasey tveasey Mar 13, 2018

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Aside from this check, the following differences are all due to whitespace.

{
double R{-1.0};
double Rt{stats.s_Rt * CTools::truncate(1.0 - 0.5 * (vt - v1) / vt, 0.9, 1.0)};
double variance{correction * minimum[0].first};
double v1{varianceAtPercentile(variance, df1, 50.0 + CONFIDENCE_INTERVAL / 2.0)};
LOG_TRACE(" variance = " << v1);
LOG_TRACE(" varianceThreshold = " << vt);
LOG_TRACE(" significance = " << CStatisticalTests::leftTailFTest(v1 / v0, df1, df0));

startOfPartition = best[0].second;
windows[0] = calculateWindows(startOfPartition, windowLength, repeat, partition[0]);
windows[1] = calculateWindows(startOfPartition, windowLength, repeat, partition[1]);
for (const auto &windows_ : windows)
if (v1 <= vt && CStatisticalTests::leftTailFTest(v1 / v0, df1, df0) <= MAXIMUM_SIGNIFICANCE)
{
TFloatMeanAccumulatorVec partitionValues;
project(values, windows_, m_BucketLength, partitionValues);
std::size_t windowLength_(length(windows_[0]) / m_BucketLength);
double BW{std::accumulate(partitionValues.begin(), partitionValues.end(), 0.0,
[](double n, const TFloatMeanAccumulator &value)
{ return n + (CBasicStatistics::count(value) > 0.0 ? 1.0 : 0.0); })};
R = std::max(R, autocorrelationAtPercentile(CSignal::autocorrelation(
windowLength_ + period, partitionValues),
BW, 50.0 - CONFIDENCE_INTERVAL / 2.0));
LOG_TRACE(" autocorrelation = " << R);
LOG_TRACE(" autocorrelationThreshold = " << Rt);
}
double R{-1.0};
double Rt{stats.s_Rt * CTools::truncate(1.0 - 0.5 * (vt - v1) / vt, 0.9, 1.0)};

if (R > Rt)
{
stats.s_StartOfPartition = startOfPartition;
return true;
startOfPartition = best[0].second;
windows[0] = calculateWindows(startOfPartition, windowLength, repeat, partition[0]);
windows[1] = calculateWindows(startOfPartition, windowLength, repeat, partition[1]);
for (const auto &windows_ : windows)
{
TFloatMeanAccumulatorVec partitionValues;
project(values, windows_, m_BucketLength, partitionValues);
std::size_t windowLength_(length(windows_[0]) / m_BucketLength);
double BW{std::accumulate(partitionValues.begin(), partitionValues.end(), 0.0,
[](double n, const TFloatMeanAccumulator &value)
{ return n + (CBasicStatistics::count(value) > 0.0 ? 1.0 : 0.0); })};
R = std::max(R, autocorrelationAtPercentile(CSignal::autocorrelation(
windowLength_ + period, partitionValues),
BW, 50.0 - CONFIDENCE_INTERVAL / 2.0));
LOG_TRACE(" autocorrelation = " << R);
LOG_TRACE(" autocorrelationThreshold = " << Rt);
}

if (R > Rt)
{
stats.s_StartOfPartition = startOfPartition;
return true;
}
}
}
return false;
Expand Down